Masaryk University
Faculty of Economics and Administration
Department of Finance
and
Institute for Financial Market
European Financial Systems 2013
Proceedings
of the 10th
International
Scientific Conference
10-11 June 2013
Telč, Czech Republic
Suggested citation:
AUTHOR, A. Tittle of the paper. In: European Financial Systems 2013.
Proceedings of the 10th International Scientific Conference, Brno: Masaryk
University, 2013, pp. xx-xx. ISBN 978-80-210-6294-8.
Editors:
Oleg Deev
Veronika  Kajurová
Jan  Krajíček
©  2013  Masarykova  univerzita
ISBN 978-80-210-6294-8
All published papers have been reviewed before publishing. The proceedings have not
been amended or proofread. Editors are not responsible for the language used in the
papers.
Program Committee of the Conference
Chair: Ing. Petr Valouch, Ph.D. (Masaryk University)
Members:
 prof.  Dr.  Ing.  Dana  Dluhošová  (VŠB  – Technical University of Ostrava)
 prof.  Ing.  Eva  Horvátová,  CSc.  (University  of  Economics  in  Bratislava)
 prof.  Ing.  Eva  Kislingerová,  CSc.  (University  of  Economics,  Prague)
 prof.  Ing.  Oldřich  Rejnuš,  CSc.  (Masaryk  University)
 doc.  Ing.  Jaroslav  Sedláček,  CSc.  (Masaryk  University)
 prof.  Ing.  Viktória  Čejková,  Ph.D. (Masaryk University)
 prof.  Ing.  Anna  Majtánová,  Ph.D. (University of Economics in Bratislava)
 doc. Ing. Eva Kafková,  Ph.D. (University of Economics in Bratislava)
 doc.  Ing.  Peter  Krištofík,  Ph.D. (Matej Bel University, Banska Bystrica)
Organizing Committee of the Conference
Chair: Ing.  Jan  Krajíček,  Ph.D.
Members:
 Ing.  Veronika  Kajurová
 Oleg Deev
Conference website: www.efs.econ.muni.cz
PREFACE
Dear readers,
It is my pleasure to introduce you a collection of papers from the 10th
International
Scientific Conference European Financial Systems 2013, organized annually by the
Department of Finance at the Faculty of Economics and Administration, Masaryk
University. We can now say that our conference has a long history starting in 2003 in
Brno, Czech Republic and thereafter continuing as an annual event. To celebrate our first
anniversary the conference was organized at a very distinctive and special place – the
city of Telč, a UNESCO World Heritage Site.
This year's conference was focused on the current issues related to the financial crisis in
the European Union, specifically in financial markets, banking and insurance industries
and corporate finance. Because the collection of papers presents the latest scientific
knowledge in this area, I believe you will get a number of new insights useful for your
scientific enquires as well as your educational or practical activities.
I would also like to express my conviction that we meet each other again in occasion of
the 11th year of the conference to be held in 2014.
I wish you a pleasant reading!
Petr Valouch
Chairman of the Program Committee
CONTENTS
Hana Bártová
Karel  Hanzlík
The System Solution of Uninsurable Risks with Focus on
Floods in the Czech Republic
8
Luděk  Benada
Juraj  Hruška
Verification of a linear dependence between the risk premium
and the systematic risk: Empirical testing based on data from
PSE
15
Martin  Boďa
Emília  Zimková
Efficiency in the Slovak banking industry: a DEA application
of the profit approach
22
Hana  Bohušová
Patrik Svoboda
Is the asset-liability approach a significant change to revenue
recognition?
31
Gábor  Bóta
Mihály  Ormos
Performance of Hungarian Mutual Funds and the Financial
Crisis
40
Viktória  Čejková
Svatopluk  Nečas
Insurance of Risks of Small and Medium-sized Enterprises 47
Božena  Chovancová
Peter  Árendáš
Country Risk as a Phenomenon of the Current European Debt
Crisis
55
Romana  Čižinská
Tomáš  Krabec
Rate of Investments as a Parameter of the VIM Model for
Appraising Trademarks of Unlisted Companies
61
Oleg Deev
Co-movements   of   bank   stocks’   prices   as   signs   of  
interdependence and contagion
70
Eva  Ducháčková
Resolving the consequences of work-related injuries in the
conditions of the Czech Republic
79
Jiří  Dvořák
Martina  Janková
Trends in Integration of the Electronic Banking 87
Ewa Dziwok
Implied forward rates as a source of market expectations –
implications for monetary policy in Poland
91
Martin  Geško
Factors influencing the microeconomic and fiscal effects of
the reduced VAT on books
99
Ján  Gogola Value at Risk estimation using a Monte Carlo simulation 107
Andrius Guzavicius
Vytas Barkauskas
Vidas Tamulis
Nowcasting business cycles using transportation index 114
Tana Hajdikova
Lenka Komarkova
Petr Pirozek
Financial and operational performance of hospitals in the
Czech Republic
120
Eva  Horvátová
Analysis of the problem of pro-cyclicality in the Eurozone and
pro-cyclicality solutions in Basel III
126
Jana Hvozdenska
Veronika Kajurova
The yield curve as a predictor of economic activity – the case
of the Visegrad group
134
Eva  Hýblová
Zuzana  Křížová
Jaroslav  Sedláček
Mergers in Czech Agricultural Companies 140
Eva  Kafková
Igor  Hudák
The calculation of the output gap in the machinery sector of
the Slovak Republic
147
Silvie  Kafková Credibility Theory and Its Using In Insurance Practice 153
Veronika  Kajurová
Jana  Hvozdenská
Linkages between bonds and credit default swaps of the
European financial institutions
158
František  Kalouda
Roman  Vaníček
Alternative bankruptcy models – first results 164
Maria Klimikova
Martin Vovk
Crisis Management of Banks in the Context of Financial Crisis 169
Jan  Krajíček Cash Management in Practice 176
Pavla  Kubová
Karina  Mužáková
Polluter Pays Principle in the Czech Republic in the Context of
Public Environmental Protection Expenditures
181
Oleksandra Lemeshko The fundamentals of accounting for business combinations 189
Dagmar  Linnertová
Oleg Deev
Martin Cupal
Investigation of factors affecting short interest in the
Australian Stock Exchange
196
Alena  Maaytová
Stanislav Klazar
Public Contracts in Czech Teaching Hospitals - efficiency
issues
201
Jan  Matoušek
Miroslav  Mareš
Contemporary crisis and strategic management of the private
banking sector in the Czech Republic
207
Grzegorz Michalski
Financial consequences linked with investments in current
assets: Polish firms case
213
Peter  Mokrička
Influence of volatility on the value of the modern structured
products
221
Vladimir Novotny
History of Financial Crises and its impact to current regulatory
development of Basel Committee
226
Mihály  Ormos
Dusán  Timotity
Asset Pricing with Conditional Value at Risk 232
Mihály  Ormos
Dávid  Zibriczky
Asset Pricing and Entropy 241
Gabriela  Oškrdalová
Analysis of phishing in the e-banking field and protection
techniques against this type of fraud
249
Dalibor Panek
Single supervisory mechanism bank as a first step to the
banking union
255
Michal Plaček
Utilization   of   Benford’s   Law   by   Testing   Government  
Macroeconomics Data
258
Lenka  Přečková
Availability of Information about the Company Insurance
against Natural Hazards on the Czech Insurance Market
264
Oldřich  Rejnuš
Kamil  Smolík
Commodity Markets Financialization and its Impact on the
World Economy
272
Svend Reuse
Martin Svoboda
The Impacts of Basel III onto the European Banking Sector 279
František  Řezáč
Martin  Řezáč
Outsourcing in Insurance 285
Jaroslav  Sedláček
Petr Valouch
Zuzana  Křížová
Eva  Hýblová
Alois  Konečný
Restructuring of the Czech Banking Sector – Influence of
Mergers on Selected Financial Indicators
292
Miroslav Sponer Perspectives of the Monetary Development of EU 299
Anna  Sroczyńska-
Baron
The choice of portfolio based on the theory of cooperative
games
305
Daniel  Stavárek Lessons Learned from the 2013 Banking Crisis in Cyprus 312
Boris  Šturc
Natália  Žoldáková
Microeconomic factors influencing impaired loans and credit
derivatives prices
320
Irena  Szarowská Can tax policy co-cause the crisis? 323
Zuzana  Töröková
Boris  Šturc
Stress testing in relation to the financial crisis 331
Petr Valouch
Alois  Konečný
Maria  Králová
Influence of a merger on the EBIT of merged companies in
the Czech Republic
336
Eva  Vávrová
Assessing the progress of the implementation of the Solvency
II regulation
343
Jitka  Veselá Some specifics of the valuation of bank stocks 349
Tomasz  Węgrzyn
Stock selection based on financial ratios on the Warsaw Stock
Exchange. Analysis between 2001 and 2010
356
Joanna Wyrobek
Zbigniew  Stańczyk
Decoupling hypothesis and the financial crisis 362
8
The System Solution of Uninsurable Risks
with Focus on Floods in the Czech Republic
Hana  Bártová,  Karel  Hanzlík
University of Economics, Prague
Faculty of Finance and Accounting
W. Churchill Sq. 4, 130 67 Prague 3, Czech Republic
E-mail: hana.bartova@vse.cz
karel.hanzlik@vse.cz
Abstract: Uninsurable risks influence negatively lives and value of property. Flood risk
and risk realization have broad impact on public and private finance with the most
significant consequences in the riskiest flood zones. Current trend in the insurance
industry is to make exceptions from insurance protection. Our research is focused on
multi-source system and on a definition of the role of each participant in the fund. This
solution is based on insurance pool creation. Gross premium calculation constitutes an
integral part of paper and supports increased interest in extension of insurance
protection due to profitable solution of uninsurable risks. Our approach is based on real
data of the Czech insurance market and our estimations and calculations.
Keywords: flood, gross premium calculation, insurance pool, uninsurable risk
JEL codes: G22, G28, P11, R11
1 Introduction
Insurance services play an important role within a meaning of branch of economy and a
part of financial market. Insurance protects our lives, property and ensure sustainable
economic processes. The role of insurance is strengthened by changing economic
conditions, globalization of financial markets, modernization, intensive industrial
production, technological improvement and last but not least by rational society with
different social fabric. Effective insurance protection is based on needs satisfaction and
new desires in order to provide commercial services in high quality. Significance of
insurance is enhanced by ensuring constant security, economic support and finally by
enterprise promotion.
Contemporary insurance trends are influenced by economic conditions in the light of
actual financial and debt crises, which have impact on development of total premiums
written. Global economy recovering influences positively demand for commercial
insurance products, especially for life insurance products. Non - life insurance segment is
influenced negatively by increasing number of natural catastrophes and enormous losses.
High costs of non - life risk realization have significant impact on value of equity,
requirements of the state interventions and lead to extra public expenses.
Non - life risks and especially natural catastrophes cause high amount of private and
public expenditures. Generally the highest losses caused by natural catastrophes in the
Czech Republic are consequences of floods, deluges and overflows. Flood risks influence
significantly value of property in flood zones. Recent flood occurrence in the Czech
Republic highlighted insufficiency of prevention, protection and loss solution of
commercial insurance. Flood claims adjustment means at present high losses of
insurance industry, which weaken the role of risk protecting institution and its risk taking
authority. All these reasons participate in trend of uninsurable risks definition.
Uninsurable risks and avoidance of insurance protection influence negatively importance
of commercial insurance solution such as an effective and efficient tool, which is possible
to reduce costs and eliminate consequences of flood risks. Our research is focused on
uninsurable risks. We pay attention to flood risks and suggest a solution, which should be
possible to cover uninsurable flood risk in the Czech Republic. Our solution is based on
commercial insurance pool and on multi-source funding system.
9
The first part of this paper is focused on system description with putting emphasis on
major roles of each fund participant. The second part is dealt with gross premium
calculation, which is mainly based on our calculations and estimations due to lack of flood
data.
2 The System Solution of Uninsurable Flood Risks
2.1 Insurance Pool
Membership in the insurance pool becomes automatically in case of providing domestic
property and household contents insurance.
Participation in the pool is based on compulsory entry fee, which is defined in the level of
10 million CZK. These costs represent a kind of fee, which ensures to insurer to take a
part in a new market segment - flood zones. About 30 thousand real estates were built in
high risk zone in according to last research. We estimate that number of uninsurable
buildings is higher (approximately 100 thousand addresses) because of extension of flood
zones.
2.2 Members of Multi-Fund System
Intermediary Network
Our   system   uses   internal   and   external   network   of   intermediaries.   Using   insurers´   own  
distribution channels has neutral impact on premiums, because provision and cost
schemes are common and strictly predetermined. Calculated premium includes also
administrative costs, which are specified as a percentage limit.
Claims Adjustment
System is based on using a service of own network of claims adjusters. This arrangement
contributes to costs reduction and simultaneously to prevent from insurance payments
manipulation and to make loss settlement faster. Each of insurers also shares risk costs
of his own insurance policy. A compulsory financial participation in loss removing is
estimated on the level of 10 % of each insured event, because of quick loss removal.
Adjustment service is provided by insurer, who manages insurance policy. Staff adjusters
settle insurance claims of estates, which risks are covered by insurer. Settlement costs
are paid by the Fund.
State Authority
The state position is designed as independent without interventions into pool operations.
Opposed  to  classic  approach  of  reinsurance  markets  the  state  doesn´t  participate  in  risk  
settlement of any loss. The state reinsurance approach corresponds to the surplus, but
all claims developments seem as one claim. Debit account is formed up to 100 % of
current status of reserves. The main role of the state is concentrated in covering of
claims development and simultaneously   authority   doesn´t   participate   in   each   risk   and  
individual loss.
Our suggested system is closed to reinsurance model of loss excessiveness CATXL
(CATastrophe eXcess of Loss cover), which is devoted to reinsure cumulative claims.
A ceding insurer   doesn´t   pay   any   premiums   to   the   state   authority,   because   these  
payments are hidden in cash flows into the reserve fund. Excessive damages are
substituted by direct deposits on flood fund account. Instead of deposits it is possible to
issue flood obligations, which are ready for the same risk development. Emissions of
flood obligations play a major role in case of flood fund reserves overdrafts.
Currently institutions of the insurance markets use a mix of incremental and radical
innovations with focus on consistency of public and private solutions and on the
insurance markets positive stimulation by investment instruments (e.g. insurance-linked
securities).   Traditional   flood   obligations   should   be   substituted   by   “weather   bonds”  
10
(known as catastrophe bonds, which interest payments depend on risk realization),
insurance futures or event-linked derivatives.
Pool Supervisor
Model  doesn´t  expect  a  risk  transfer  to  the  reinsurance  markets.  A  participation  of  the  
Czech National Bank in conditions of the insurance fund is fundamental and this authority
supervises over the fund. The Czech National Bank as the integrated financial authority
takes a responsibility for smooth fund running, creation of reserves, entrance and
controlled fund leaving of pool members.
The Czech financial system authority also determines fluctuations and flood loading,
declares amounts of calculated costs connected with an insurance product and especially
controls and takes part in profit redistribution into pool members and fund reserves in
case of positive claims development. Redistribution of fund profit shares should be very
sporadic. We expect that free reserves and profit will be used in process of anti-flood
barriers building to avoid of future repeated catastrophic losses. One of system´s  aims  is  
a prevention and to eliminate compensation payments. Profit should mitigate negative
effects caused by floods, deluges and overflows and expenditures on prevention should
decrease future fund and pool requirements for policy coverage. Redistributed profit
flows   to   insurers´   accounts   in   according   to   their   pool   participation,   concretely   to   risk  
premiums paid to the fund. These profit flows are supposed to support regions with the
highest flood, deluge and overflow risks. Profit transfer is closed to the fact that insurers
are participated in costs coverage and it is in their interest to build anti-flood barriers and
to finance anti-flood protection in concrete regions.
3 Coverage of Uninsurable Risks and Premium
Calculation
Assumed flood risk is implemented at least in annual frequency of 1 % of realties.
Degree of loss (in according to Table 1) is 10.05 % taking into account distribution of
loss frequency and loss amount. Average loss corresponds to claims development of last
16 years in the Czech Republic (it is considered average insurance penetration and
personal property and no business property).
Determination of net risk premium is based on specified degree of loss. Loss participation
is set as percentage in order to improve motivation to quick loss adjustment.
Nevertheless loss adjustment is limited by the lowest amount of 10 thousand CZK, which
should decrease a number of administrative difficult minor losses.
Our main aim is to focus on contingency losses. Supposed function of franchise is to
influence positively adjustment of claims.
Table 1 Insurance event - 1 % of realties is affected by loss in 1 year (in CZK)
Average
sum
insured
Individual
insurance
payment
Amount
of
losses
Total
insurance
payment
Insurance
payment after
participation of 10
% (min. 10 K)
2 000 000 50 000 800 40 000 000 32 000 000
400 000 110 44 000 000 39 600 000
600 000 20 12 000 000 10 800 000
1 000 000 35 35 000 000 31 500 000
2 000 000 35 70 000 000 63 000 000
Total 1000 201 000 000 176 900 000
Source: Cipra (2006) and our calculations
11
Specified Degree of Loss
Evaluation of specified degree of loss (q2) respects relative frequency of individual degree
of loss or partial loss. Final specified degree of loss is described in Table 2.
Table 2 Specified degree of loss
z Tz tz Yz bz Gz
0.10 800 0.80 0.04 0.80 0.0400
0.25 110 0.11 0.01 0.91 0.0538
0.50 20 0.02 0.01 0.93 0.0613
0.75 35 0.04 0.02 0.97 0.0831
1.00 35 0.04 0.03 1.00 0.1138
1 000 1.00 0.11
Source: Cipra (2006) and our calculations
Calculation of specified degree of loss is determined by real data and by hypothetical 1 %
of flood affected real estates.
 z - interval degree of loss
 Tz - amount of loss in loss interval
 tz - relative frequency in loss interval
 Yz - wage degree of loss in loss interval
 bz - cumulative and relative frequency of loss in loss interval
 Gz - wage amount of losses in loss interval
 i - middle interval
q2 = Σ  tz * (z -i)  =  Σ  Yz = G1.00 (1)
Gz = Y0.10 + Y0.25 + Y0.50 + Y0.75 + Y1.00 (2)
Expression of specified degree of loss is summed by interval degree of loss. Specified
degree of loss (q2) within available information is determined on the level of 11.38 %.
Net Premium Calculation
We search for an amount of premium that is enough to all claims adjustment generated
during a calendar year. This process is based on principle of equivalence and full value
insurance.
Non-Participating Net Premium
Calculation of non-participating net premium in our model is based on technical rate of
interest in amount of 2 %, which expresses estimated current interest level in market
conditions of the Czech Republic. Amount of non-participating net premium is 2 253 CZK.
S
P(h)
H
= v * q1 * q2 * S (3)
 P - net premium
 S - sum insured
 H - insurance value (We expect, that h = H for the purpose of full value
insurance.)
 h - ratio of insurance value and actual value
 v - discount rate (0.990)
 q1 - loss frequency
Excess Participation
In case of excess participation on the level of 10 % (at least 10 thousand CZK) net
premium is equalled to 1 422 CZK (S
EPP(h)
H
). It follows rate calculation of net premium
with excess participation in amount of 0.0711 ‰. Calculation is shortened due to
assumption of correct sum insured adjustment in according to insured value.
12
S
EPP(h)
H
= v * q1 * [G1 + (1 – b1) * s – G0,1 - (1– b0,1)* x0] * H (4)
 s - full value insurance
 x0 - ratio of contracted sum insured and insurance value
Gross Premium Calculation
Net premium with excess participation in amount of 1 422 CZK is correct in case of
steady and calm claims development. In case of claims deterioration it is necessary to
calculate fluctuation reserve, which serves to unexpected increase in insurance events (in
the Czech conditions it means fifty-year and hundred-year floods). Fluctuation reserve
and net premium create risk premium rate, which takes into account fluctuation in loss
distribution and sudden events.
Gross premium consists of net premium, safety loading, administration costs and profit
calculation of insurer.
Safety Loading
Assessment of safety loading as a part of gross premium calculation is mentioned in
Table 3.
Table 3 Calculation of safety loading
z2
z Tz z2
* Tz
0.0025 0.0500 800.00 2.00
0.0156 0.1250 110.00 1.72
0.1406 0.3750 20.00 2.81
0.3906 0.6250 35.00 13.67
0.7656 0.8750 35.00 26.80
1.3150 1 000.00 47.00
Source: Cipra (2006) and our calculations
Risk Premium Rate
Essential risk premium is extended by fluctuation loading in according to cover
fluctuations in long term. Risk loading calculation considers 30 thousand uninsurable
units and models real fluctuations in recent years in the Czech Republic. In order to high
probability of risk realization out of current loss interval, we complemented risk premium
rate additionally by safety loading of the fourth flood zone.
Calculation of risk loading is essentially stricter, because model is based on the highest
risk units. We benefit from the presumption that 10 % of estates are affected by
insurance event of total loss (2 million CZK) once per fifty years. In order to cover these
fluctuations it is necessary to collect net premium in the amount of 5.4 billion CZK during
50 years (after deduction of participation amounting to 10 %, which means 5 400 CZK of
loading for estate in value of 2 million CZK).
RP  =  (1+λ1) * P +  λ *  s  +  λ3 * s2
(5)
 RP - risk premium
 λ1, 3 - non-negative coefficients which equal to 0 due to principle of standard
deviation  (RP  =  λ * s)
 s - estimated standard deviation
s =
1
N
∗ (z ∗ S − p ∗ S)^2
(6)
 N - estimated amount of insurance contracts
 p - annual net premium calculated by sum insured per unit
13
RP = P + 4/N * H * zi
2
(7)
RP = 1 421.64  +  [(4/30  000  *  √1.3150  +  0.0018)  *  2 000 000] = 5 327.436 (8)
Importance of risk loading is overvalued through integration of higher than average sum
insured. Risk premium is amounting to 5 327 CZK, which is accorded with selected value
of estates (2 million CZK). Risk premium included margin of safety in the form of flood
loading, which is estimated on the level of 73.3 %.
Final amount of premium is 5 327 CZK and insurance risk premium rate is calculated in
the amount of 2.66 ‰.
Administration costs
Our model defines administration costs purposely as a minimum of gross premium. We
estimate, that the highest administration costs should be acquisition. Other kinds of
these costs are mostly joined with claims adjustment. Administrative burden should be
minimized due to the fact, that our insurance product is designed as additional insurance
to existing products.
We can distinguish independent and dependent administration costs. Independent costs
are indifferent to the amount of sum insured. We estimate these costs at the level of
0.5 %. Dependent costs are described in the Table 4.
Table 4 Dependent administrative costs
Type of cost Space of time
Amount and origin
of cost
Loading for
management
expenses
Acquisition 1 year 5.0 % of rate 0.193 ‰
Own administrative permanent 0.1 % of rate 0.004 ‰
Organization permanent 0.025 % of rate 0.010 ‰
Collection permanent 0.75 % of rate 0.029 ‰
Cancellation 1 year 0.10 % of rate 0.004 ‰
Adjustment 1 year 2.00 % of the fund 0.077 ‰
Other permanent 1.00 % of rate 0.039 ‰
Calculation of profit permanent 0.50 % of rate 0.019 ‰
Source: Cipra (2006) and our calculations
It is necessary to limit administration and other costs in order to ensure that insurer
doesn´t   hide   profit   into   increased   administrative   costs.   Fixed   amount   of   administrative  
costs in primary rate is reliable protection against increasing insurance costs.
B =
∗ ,
( ,       ,       ,       ,       ,    , ) ,                 
(9)
 b - gross  premium  rate  at  risk  premium  rate  “rp”  per  unit  sum  insured
Model’s gross premium rate is calculated in the amount of 3.016 ‰.  Finally  annual  
total gross premium equals to 6 032 CZK in case of sum insured of 2 000 000 CZK.
4 Conclusions
Commercial solution of uninsurable risks with focus on floods, deluges and overflows
especially in the conditions of the Czech insurance market is based on pool approach,
which should ensure full insurance protection to all participating members and insurance
users. Non - life flood risks have significant impact on private and public finance and
claims development influences negatively thousands of the Czech residents and value of
their property. Our research highlights the importance of uninsurable risks solution in
order to protect final consumers of commercial insurance and also to prevent from high
losses and devastating consequences of flood risk realization.
14
Model of multi-source system solution is used on our assumptions and available data.
Relevant data access is the main factor, which participates in final version of our model.
Gross premium calculations are based on public data, which are published by the interest
association  of  commercial  insurance  companies  ČAP.  All  data  are  also  conformed  to  the  
real market development in the Czech Republic. Our calculations and presumptions are
related to the lack of high quality input information of the Czech insurance market, which
are followed by our estimations of risk development. Final model version depends on a
high extent to factors mentioned above.
System solution of uninsurable flood risks meets requirement to solve exceptions of
insurance protection made by commercial insurance companies. Key parts of our model
are represented by commercial insurance companies joined to the flood pool, the state
authority, fund supervisor, insurance intermediary network and claims adjusters. All
functions of system members are strictly predetermined. System is supposed to be
profitable. This aim is used to increasing demand for proper commercial insurance
product.
Our solution is able to cover enormous flood risk development and total losses caused at
least by fifty-year flood. In case of excessive risk development the fund reserves cover
10 % of total losses. Calculated risk premium is based on percentage participation of
particular insurer. We assume involvement in claims adjustment and immediate loss
compensation. Each consumer of flood insurance pays calculated annual gross premium
per unit of his property. Insurer payments are provided by pool reserves, which
subsequently flow to policyholders in the form of insurance payments in case of claims
development. Due to this purpose the insurance pool consists of main parts - risk
premium, reserves creation, insurance payments, preventive measures and participation
in profits. In case of moderate claims development pool reserves support areas
negatively influenced by floods in the past. It should be in insurers own interest to
finance anti-flood protection in concrete regions and to avoid of next enormous flood
losses. System also makes conditions to quick claims adjustment with minimum extra
costs. We assumes own network of claims adjusters as well as own insurance
intermediary network with contributions of external intermediaries. On the top of system
there is the independent state authority with supervisor who represents the integrated
financial authority supervising over the fund.
Total gross premium is set at the level of 6 032 CZK in case of sum insured of 2 million
CZK, which is closed to the fact of current value of particular unit insured. System
solution is designed to conditions of the Czech insurance market and takes into account
special domestic factors. In according to our presumptions this solution is possible to be
adopted within any insurance market in case of using relevant data and particular risk
factors.
Acknowledgments
The system solution of uninsurable risks could be developed thanks for financial support
of the University of Economics, Prague through the Internal Grant Agency (project
registration number F1/8/2013) and scholarship budget of the Faculty of Finance and
Accounting.
References
Cipra, T. (2006). Pojistná  matematika - teorie a praxe, 2nd Ed. Prague: EKOPRESS.
Čamrová,  L.  et  al.  (2006).  Povodňové  škody  a  nástroje  k  jejich  snížení.  Prague:  IEEP.
Association of commercial insurance companies. Statistiky - ostatní.   Available   from  
<http://www.cap.cz/statistics.aspx?t=1>.
Swiss Reinsurance Company Ltd. (2011). World insurance in 2010. Premiums back to
growth - capital increases, issue 2/2011. Switzerland: Swiss Re.
Swiss Reinsurance Company Ltd. (2011). Product innovation in non-life insurance
markets.  Where  little  “i”  meets  big  “I”,  issue  4/2011.  Switzerland:  Swiss  Re.
15
Verification of a linear dependence between the risk premium
and the systematic risk: Empirical testing based on data from PSE
Luděk  Benada,  Juraj  Hruška
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  602  00  Brno,  Czech  Republic
E-mail: benada.esf@gmail.com
206887@mail.muni.cz
Abstract: In our article we focus on an empirical research of the Capital Asset Pricing
Model. The equilibrium model is tested on the data from the Prague Stock Exchange. Our
research is focused on the relationship of a systematic risk with an individual market
premium. We verify, whether it is possible to identify an explanatory power of the market
beta on an excessive return. We are dealing with the hypothesis whether it is possible to
use a non-diversifiable risk as a crucial part of the explanatory factor for investment
decisions. For the study the Black-Jensen-Scholes methodology will be used. We apply
only the one factor model and its modified version. The conditions for the modification
will depend on the nature of the market risk premium. Our analysis is applied to the
period before the financial crisis, during the crisis and post-crisis. We believe that the
analysis across the turbulent development enhances the credibility of our results.
Although it is necessary to take into account the nature of the investigated market,
especially its size and liquidity, the findings of our research confirm the explanatory
power of the conditional CAPM to the return on investigated risk assets. In contrast, the
results of the unconditional model could not be verified. While there are numerous
studies dealing with the empirical testing of unconditional and conditional CAPM,
according to our findings, there is no similar study with respect to the processing and
application. The results of our research can be practically applied for active traders on
the Prague Stock Exchange and similar markets.
Keywords: Risk premium, systematic risk, linearity, CAPM
JEL codes: G11, G12, C12
1 Introduction
On the basis of Mean-Variance model (Markowitz, 1952) formed mutually independent
study investigating the Capital Asset Pricing Model - CAPM (Treynor, 1961, Sharpe, 1964,
Lintner, 1965, Mossing, 1966), which is widely used in making an investment decision or
in estimating the cost of capital. Despite that, the validity of the model is difficult to
prove empirically. Some authors, such as Roll (1977), this model completely repudiate
because of the fact that this model can only test the efficiency of market portfolio and
the market portfolio is unknown. Because of that, only estimates are used for testing.
Thus, the model is generating inaccuracies. The model is also criticized for unrealistic
assumptions (Goetzmann & Kumar, 2008, Chae & Yang, 2008). The confirmation of
CAPM validity is difficult on established capital markets, where a certain level of efficiency
exists. The situation is even worse on emerging markets that are typical of a higher
degree of excess yield in comparison to developed capital markets. But there is also
present a greater likelihood of achieving loss. Considerable growth phases are
accompanied by more rapid declines. Just in the period of decline it is difficult to prove
the validity of the model, since it contradicts the theory. In graphical illustration the
Security Market Line will be declining. With cyclical phases of growth and decline deals
the modified version of CAPM, introduced by Pettengill et al. (1995). His approach
assumes distinguish phases of growth and correction. This conditional model should
therefore be particularly suitable for testing in emerging markets which also include the
Czech Republic.
16
The Czech Republic in accordance to the classification of Standard & Poor's is one of the
emerging markets. These markets are characterized by its rapid economic growth and
industrialization. This was characterized in the past for the Czech Republic as well.
Today, however, recession of EU strongly reflects on the Czech economy. In the
emerging markets investors can reach higher returns than they would have been able to
reach in developed countries. The possibility of greater expected return is accompanied
by higher risk (Chiaku Chukwuogor, 2008). The Czech Republic belongs to transitive
economies, which is typical for the transition from a centrally planned economy to a
market economy. That could be one of the reasons why the capital market in the Czech
Republic is not sufficiently developed in comparison with advanced financial markets. The
emergence of modern Prague Stock Exchange dates back to the 1992. Currently there
are fifteen companies traded on the main market (SPAD), which is the most liquid. The
market capitalization of the Prague Stock Exchange is 53.244 million USD and trading
volumes for the year 2011 were 19,526 million USD. The market is characterized by
lower liquidity relative to developed markets.
2 Methodology and Data
Our paper is focused on the testing of the Capital Asset Pricing Model. The analyzed data
are obtained from the Prague Stock Exchange (PSE). The investigated period was from
2005 to 2013. In total we analyzed eight companies (CETV, CEZ, ERSTE Group Bank,
Komercni banka, ORCO, Philip Morris CR, Telefonica O2 C.R., Unipetrol). The selection of
stocks was motivated by the trading continuity throughout the whole period. However
these companies do not constitute the entire market of the PSE we believe that the
nature of the investigated market will be through those stocks authentically displayed.
The weights of all stock in the index are shown in the following table.
Table 1 The Composition of PX
Source: Fact Book 2012 Prague Stock Exchange
The table 1 shows that the researched stock represents 80 % of weight in the index. In
addition, these stocks have the largest trading volumes.
The investigated period is characterized by a turbulent development. It is caused by the
financial crisis that hits the PSE in 2008. The development of the market shows the
following chart.
The index PX indicates a volatile market and shows a significant drop in stock prices in
the wake of the financial crisis. To better describe the market condition and capture the
dynamic evaluation of beta, we divided the whole examined period in three sub periods
(2005-2007, 2007-2009, 2009-2013).
17
Figure 1. Development of the PX (2005 – 2013)
Source: Database of PatriaPlus
The tests are based on the day to day return and by the stock prices we reflect the
number of trading days. The return of the whole market is represented by the index PX
on the next figure.
Figure 2 Return of PX (2005 – 2013)
Table 2 Summary statistic of PX (1928 observations)
Source: Database of PatriaPlus
Model Specification
Rate of return
For the day to day return on the stock and market the following formula will be used:
𝑅 =
   ( )
( )
(1)
𝑅    ……………...…is  the  return  of  a  stock,
𝑃 ………………...is  the  closing  price  of  a  stock,
𝑃( ) ……………..is  the  closing  price of a stock in the previous day.
18
No dividend payment will be taken in to the account.
Risk free interest rate
As the risk-free asset we have used the government bonds of the Czech Republic. Twoyears
bonds issued in 2005 and 2007 were chosen for the first two periods. For the third
period we use an interest rate of a three-year bond, which was issued in 2009. The
interest rates of considered bonds are 2.599%, 3.368% and 4.149%. Since the interest
rates are expressed as an annual percentage, we will make appropriate corrections,
which give the rate per day.
Individual risk premium and market premium
𝑟 = Ri – Rf (2)
𝑟 ……………………is  the  individual  risk  premium,
Rf .............................is the risk-free interest rate.
𝑟 = Rm – Rf (3)
𝑟 ……………………is  the  market  premium,
Rm  ………………..is  the  market  return.
Unconditional CAPM model
Step 1 – estimating the beta coefficient from OLS, for the estimation we used rolling
window and lag three months.
𝑟 = 𝛼 + 𝛽 *𝑟 + εit (4)
𝛼 , 𝛽 …………….estimated  parameters,
εit…………………..is random error.
Step 2 – in the second regression beta is used as an parameter.
𝑟 =  +  * 𝛽 + µμ (5)
 ,  ……………estimated  parameters,
µμ ………………….  is random error.
Verifying the hypothesis:
H0:  = 0
H1:   0
H0:  = 𝑟
H1:   𝑟
Conditional CAPM model
1. Step – the first step is identical to the previous model
2. Step – the beta is again used as an parameter, in addition to the first model a
dummy variable will be incorporated to distinguish between the situation of
positive (up-market) and negative (down-market) market premium
𝑟 =     +  ** 𝛽 +  *(1-)* 𝛽 + ǫt, (6)
δ  = 1 if (Rmt − Rft) > 0 ("up-market"),
δ  = 0 if (Rmt − Rft) < 0 ("down-market"),
ǫt    …………….is  random  error.
To meet the requirements of the conditional CAPM, the following must be true
H0: 0 = 0,  > 0 and  < 0.
H1: otherwise
3 Findings
As seen in the first model with the unconditional CAPM, which is the situation where no
distinction between positive and negative market premium is provided, it was not
possible to confirm the validity of the model in any of the observed periods.
19
Table 3 Results of the second regression in each period – unconditional model
Table 4 Results of the second regression – first period, conditional model
Table 5 Results of the second regression – second period, conditional model
Table 6 Results of the second regression – third period, conditional model
On the other hand, in the second model, the results were much more encouraging. In the
first period, only two stocks could not confirmed the validity of the model in the upmarket
situation. In the case of negative market premium the result of all stocks were
significant. This confirmed the hypothesis of leverage effect (Porras, 2012). In the next
period, which was characterized by the crisis, the validity of the model was lower and
only by five stocks the results are significant. In the last period the model demonstrated
again a greater force. In six cases, the up-market at the significant level of 99 %
confirmed the hypothesis about the significance of the parameter gamma_2. Except,
Orco, the results wer also significant for the negative market premium. The conditional
model shows considerable improvement in the indicator of R^2.
20
4 Conclusions
In our paper we investigated the equilibrium model CAPM and its modified version the
conditional CAPM first time introduced by Pettengil et. all. (1995). In our analyses we
focused on the Prague Stock Exchange. This market belongs to emerging markets. Since,
the market showed turbulent development, we divided the examined data into three
periods (2005-2007, 2007-2009, 2009-2013). The first period was not influenced by the
financial crisis and growth in the market can be observed in contrast to the next period,
where it was possible to notice more instability in the market and downturn. Moreover, in
our investigation we confirmed that the beta, which reflects the systematic risk on an
asset, exhibits a considerably dynamic development. We tried to verify the CAPM using
empirical tests.
We tested first the unconditional CAPM. This model is constantly used in practical
investment decisions, although it was developed nearly half a century ago. To verify the
Capital Asset Pricing Model, we employed the data from the Prague Stock Exchange,
especially Czech government bond prices and the index PX.
The validation of the model consisted of two parts. First we created a regression analysis
using the Ordinary Least Squares (OLS). From the regression we estimated the betas,
which we used in the second regression as a parameter. The results from our analyses
showed that the null hypothesis of a direct proportion between the individual risk
premium and the market risk premium could not be confirmed.
Assumptions were not met as measured by the results of the model. According to the tstatistic
we came to the conclusion that the unconditional CAPM cannot be used to
explain the behavior of investors in the PSE. In addition, the explanatory power of the
regression was very low.
Subsequently, we conducted a test of the conditional model. The procedure coincided
with the initial test, with the difference being that we incorporated the different market
conditions. Data were processed with regard to whether the market premium was
positive or negative. The results of the conditional model were significantly better in the
sense of validation the model. The explanatory power of the regression was stronger as
well.
The empirical analysis showed that the chosen methodology and the period have an
impact on the validity of the model. However we divided the whole period into three
parts, we could not validate the unconditional CAPM.
The result of our study confirms the validity of a linear dependence of an individual risk
premium and a systematic risk only if the conditional CAPM model is applied. The finding
could be eminent in respect of the extensive use of the CAPM in an investment decisions
or by capital valuation.
Acknowledgments
The article is part of the output of the specific research project MUNI/A/0753/2012 European
financial systems 2013, solved at the Department of Finance, Faculty of
Economics and Administration, Masaryk University 2013
References
Black, F., Jensen, M. C., & Scholes, M. S. (1972). The Capital Asset Pricing Model: Some
Empirical Tests. Studies in the Theory of Capital Markets, New York: Praeger Publishers.
Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2011). Modern Portfolio
Theory and Investment Analysis, 8th ed., New Jersey: John Wiley&Sons.
Lintner, J. (1965). The Valuation of Risk Assets and the Selection of Risky Investments in
Stock Portfolios and Capital Budgets, The Review of Economics and Statistics, 47, 1, pp.
13-37.
21
Markowitz, H. (1952). Portfolio Selection, Journal of Finance, 7, 1, pp. 77-91.
Mossin, J. (1966). Equilibrium in a Capital Asset Market, Econometrica, 34, 4, pp. 768-
783.
Pettengill, G., Sundaram, S., and Mathur, I. (1995). The Conditional Relation Between
Beta and Returns. Journal of Financial and Quantitative Analysis, 30, 1, pp. 607-636.
Roll, R. (1977). A  critique  of  the  asset  pricing  theory’s  tests  Part I: On past and potential
testability of the theory, Journal of Financial Economics, 4, 2, pp. 129-176.
Ruiz-Porras, A., Lozenzo-Valdés,   A.   (2011). Modelación   de   los   rendimientos   bursátiles  
mexicanos  mediante  los  modelos  TGARCH  y  EGARCH:  Un  estudio  econométrico  para  30  
acciones  y  el  Índice  de  Precios  y  Cotizaciones, MPRA Working Paper.
Sharpe, W. F. (1963). A Simplified Model for Portfolio Analysis. Management Science, 9,
2, pp. 277-293.
Treynor, Jack L. (1961). Market Value, Time, and Risk. Unpublished manuscript dated
8/8/61, No. 95-209.
22
Efficiency in the Slovak banking industry:
a DEA application of the profit approach
Martin  Boďa1
,  Emília  Zimková2
1
Matej  Bel  University  in  Banská  Bystrica
Faculty of Economics, Department of Quantitative Methods and Information Systems
Tajovského  10,  975  90  Banská  Bystrica,  Slovak Republic
E-mail: martin.boda@umb.sk
2
Matej Bel University  in  Banská  Bystrica
Faculty of Economics, Department of Finance and Accounting
Tajovského  10,  975  90  Banská  Bystrica,  Slovak Republic
E-mail: emilia.zimkova@umb.sk
Abstract: The paper investigates into the efficiency of the Slovak banking industry over
the years 2000 – 2011 through the prism of the profit approach to the perception of
efficiency of commercial banks. The aim of the paper is to benchmark individual
commercial banks with respect to their efficiency status under the profit definition of
efficiency. To this end, the non-parametric method of evaluation is employed based on
the slack-based measure (SBM) model of data envelopment analysis. During the period
of  12  years  examined,  Slovenská  sporiteľňa  maintained  comparatively  high  SBM  technical  
efficiency and other Slovak banks were subject to positive or negative changes in their
comparative efficiency profile.
Keywords: efficiency, the Slovak banking industry, SBM model, the profit approach.
JEL codes:G21, C44.
1 Introduction
Over recent years, the topic of measuring efficiency of commercial banks has
experienced massive upheaval. Three principal approaches have been developed (to say
nothing of their modifications) and are used intensively in evaluating efficiency of
commercial banks. Out of these approaches, viz. the service-oriented approach, the
intermediation approach and the profit approach, exclusive attention in the paper is
given   to   the   profit   approach   as   the   basal   “commercial”   standpoint   under   which   Slovak  
banks operate striving after a high profit margin for their owners. The task of efficiency
evaluation necessitates the assumption of the existence of a production function in the
banking industry (which links the inputs and outputs of banks and determines the shape
of the production frontier) and efficiency calculations then often base on measuring the
distance of individual commercial banks from the production frontier embodied in the
production function. The framework under which the production function is specified can
be either parametric, non-parametric or semi-parametric. Whereas the parametric
method restricts the production function to a certain parametric form which is then
estimated, the non-parametric method avoids the formulation of a parametric production
function and constructs on some well-understood economic principles the production
possibility set. One of these principles is expressed in the technical scalability of
operations, to wit, in the specification of the character of returns to scale under which the
banking industry operates.
In the paper, the non-parametric slack-based measure (SBM) model is applied with the
profit   approach   of   interpreting   bank’s   efficiency   to   a data set of eleven organisational
units1
of the Slovak banking industry for the period of the years 2000 – 2011. During this
period, the Slovak banking sector has gone through some changes as to its structure,
competitiveness and regulation. Nonetheless, three periodization landmarks were
identified and catalogued, between which it is assumed that the production function did
1
Note   that   the   term   „organisational   unit“   is   more   appropriate   here   as   some   of   the   banks   fused   or   merged
during the evaluated period.
23
not change and remained intact, i.e. was invariant with respect to a time shift. These
landmarks divided the entire period of twelve years into three sub-periods, over which
the data on individual organizational units of the Slovak banking industry were pooled for
their joint evaluation. In each sub-period, individual organisational units were evaluated
and their efficiency was determined by the slack-based measure. This procedure permits
to fulfil the aim of the paper, which is to benchmark individual Slovak commercial banks
with respect to their efficiency status under the profit approach.
The paper is organized in 5 sections, the first of which is introductory and the last is
concluding. The second section explains the profit approach for measuring efficiency of
commercial banks and provides, in a condensed way, an overview of some relevant
studies on the topic. The third methodological section is accompanied by the fourth
section which presents the results and includes their interpretation.
2 Conceptual issues and an overview of the literature
Conceptual views of efficiency of commercial banks differ. Individual theoretical concepts
concur that commercial banks are agents of a transformation process and that during
this process they transmute a set of inputs into a set of outputs. These inputs and
outputs are linked in operations of commercial banks through a production function,
which specifies the maximum attainable outputs at the given level of inputs. On the one
hand, there is an explicit or implicit agreement that such a functional relationship exists;
on the other hand, there is no general agreement on the production function of
commercial banks which would reflect the aims that commercial banks seek to pursue.
So far three main approaches have been developed and used both in theory and practice,
and these are the intermediation approach, the service-oriented approach and the profit
approach. The primary source of difference between them is the treatment of deposits,
which have both input and output characteristics. Only the newest of these approaches,
the profit approach, is clarified in the next paragraph since only this approach is topical
for the paper.
The profit approach was introduced by Berger and Mester (2003). They investigated cost
and profit productivity of the US banking industry. They found out that during the period
from 1991 to 1997 cost productivity worsened whereas profit productivity improved
substantially. They concluded that banks had been offering wider range of financial
services providing additional convenience, which may have raised costs but also raised
revenues by more than the cost increased. Since this approach is relatively new, there is
no consensus as to whether these positions hold. Studies which would account for
possible impact of the financial crisis on efficiency of commercial banks prepared under
this approach were not published yet. The foregoing research of the profit approach was
the value-added approach, which considers that both liability and assets categories
possess some output characteristics. Only those categories which contribute to a
substantial value added are treated as outputs whilst the others are treated as either
inputs or intermediate products depending on the specific attributes of each category.
This approach was promoted by Berger, Hanweck, and Humphrey (1987), and Berger
and Humphrey (1997).
The most commonly used approach in the banking industry is probably the
intermediation approach but this approach does not account for the strong profit
orientation that is present and perceptible in commercial operations of the Slovak
banking sector. The negligence of other approaches in empirical research leaves some
room for a thorough application of the profit approach and even calls for it.
There has been intensive research on measuring efficiency of commercial banks and their
benchmarking. It is probably not possible to give an exhaustive overview of the relevant
literature on this topic. In consequence of this, the scope of the presentation here is
limited only to the research that focused on the so-called Visegrad Group countries (the
Czech Republic, Poland, Hungary and the Slovak Republic, in the alphabetical order) and
utilized the non-parametric approach of the data envelopment analysis (DEA). Table 1
24
summarizes empirical studies that are germane to this topic and lists especially those
studies that have applied the non-parametric approach based on a DEA model. The list of
research works is structured into two categories: cross-country studies (which
investigated the efficiency of commercial banks across several countries for the purpose
of comparison) and single market studies (which focused on the efficiency of commercial
banks in one country only).
Table 1 Empirical studies grouped by the Visegrad Group coverage
(in the alphabetical order)
Cross-country studies DEA methodology
Grigorian and Manole (2002) CCR, BCC
Kenjegalieva et al (2009) BCC
Stavárek  (2006) CCR, BCC
Tomova (2006) BCC
Vincova (2006) CCR, BCC
Single market studies DEA methodology
Stavárek,  Řepková  (2012) CCR, BCC
Wozniewska (2008) CCR, BCC
Zemanová  (2007) CCR
Although the majority of the studies (amongst the cited authors: Grigorian and Manole,
2002;;  Tomova,  2006;;  Řepková  and  Stavárek,  2012; Kenjegalieva et al., 2009) fell back
upon both nonparametric and parametric techniques to evaluate the bank performance in
terms of efficiency, all of the studies that are listed in Table 1 utilized in their quest only
basic and rudimentary DEA models, the model by Charnes, Cooper and Rhodes
addressed conventionally as the CCR model and the model by Banker, Charnes and
Cooper addressed conventionally as the BCC model. In this paper, the model by Tone, a
more advanced DEA model, is utilized and this model is usually called the SBM (slackbased
measure) model.
3 Methodology
In order to determine the technical efficiency of organizational units of the Slovak
banking sector under the profit approach, the SBM analysis was applied on the panel
data of eleven organizational units of the Slovak banking sector during the period from
2000 to 2011. (This data span is chosen in regard to the data availability and currency
for the needs of this analysis.) The methodological procedure stands on several
characteristic points or assumptions that underlie and shape the line of research and they
are summarized and explained in brief in the following text.
The assumption of the production frontier time invariance during three
identified phases of the Slovak banking sector development
Every sensible and interpretable application of the DEA rests on the explicit assumption
of the existence of a production frontier that delimits the set of all disposable inputs and
attainable outputs. The optimization task of a chosen DEA model then constructs in a
non-parametric fashion the production frontier and measures in an appropriate way the
distance of individual production units from the production frontier and captures it in the
form of a score (used in assessing the relative efficiency of a given production unit and in
the benchmarking). However, it often happens that the production frontier changes over
time (even this change is required for the theory of economic growth to be valid and
hold). However, in some consecutive periods the production frontier due to the inertia of
the economic environment may remain invariant, that is invariant with respect to a time
shift. This view is applied in the paper and it is believed that there are some phases in
the evolution of the Slovak banking sector during which its production frontier remained
25
intact and free of shift. During the period from 2000 to 2011, three sub-periods can be
identified and justified: 2000 – 2003, 2004 – 2008, 2009 – 2011.
– The first phase (2000 to 2003) reflects the last quivers of the restructuralization of
major banks and of the privatization of selected banks in Slovakia that commenced in
the 1990s. Whereas political decisions on the transfer of illiquid assets into a
specialized state institution were taken in the second   half   of   the   1990’s   and   the  
transfer itself took place especially in the years 1998 and 1999, the cleaning-up of
balance sheets of the Slovak major banks ended only at the beginning of the 2000s.
The privatization of banks in Slovakia that was started by an entry of the Austrian
group  Erste  Bank  into  Slovenská  sporiteľňa  in  January  2001  and  continued  by  other  
acquisitions resulted in the integration of the Slovak banking sector into the global
banking system due to its property relations with the European largest banking
groups. This phase was also accompanied by final stages of the transformation
process of the Slovak economy.
– The second phase (2004 – 2008) begins by the accession of Slovakia into the
European Union and terminates by the entry of Slovakia into the euro area. Slovak
banks during this period implemented a new system of corporate governance, moved
to the communication with targeted client segments and towards electronic banking
services. There were significant changes in balance sheet and off-balance sheet
operations, in the structure of services and in the orientation on investment banking,
mortgage banking as well as asset management. In the years 2006 – 2008 Slovak
banks were intensively engaged in the preparations for the entry of Slovakia into the
euro area as of 1 January 2009.
– The third phase (2009 – 2011) is marked by the successful adaptation of the Slovak
banking sector to the euro environment and by the manifestations of the economic
crisis in the Slovak economy. Thanks to commercial banks as well, the process of the
euro-conversion was smooth and trouble-free. On the other hand, of impact on the
Slovak banking sector was the global economic crises that brought about a dramatic
fall in the economic growth of Slovakia. Small Slovak banks faced existence
problems.
The selection of organizational units of the Slovak banking sector and the
selection of a particular set of inputs and outputs with each approach
The dataset comprises the data on 11 organizational units (commercial banks) operating
in the Slovak Republic and it covers the great majority of Slovak banking structures (as
the total of included banks exceeds 90 percent of the Slovak banking assets). The
organizational units considered in the paper are listed in Table 2. In order to assure
consistency of the analysis, building societies and special financial institutions are subject
to exclusion. The source of the data is TREND Holding, s.r.o., Bratislava.
Table 2 Organizational units of the Slovak banking sector subjected to the analysis
Organizational unit
Citibank Europe plc, foreign bank subsidiary (before 2009 Citibank (Slovakia), a. s.)
Československá  obchodná  banka,  a.  s.  (in  2009  merged  with  Istrobanka,  a.  s.)
Prima banka Slovensko, a. s. (before 2011: Dexia banka Slovensko, a. s.)
Privatbanka, a.s. (before 2005 Banka Slovakia, a. s.)
OTP Banka Slovensko, a. s. Poštová  banka,  a.s.
Slovenská  sporiteľňa,  a.  s. Tatra banka, a.s.
VOLKSBANK Slovensko, a. s. (since 2013 Sberbank Slovensko, a. s.)
Všeobecná  úverová  banka,  a.  s.
UniCredit Bank Slovakia, a. s. (a 2007 merger of UniBanka, a. s. & HVB Bank Slovakia, a. s.)
The data used in the empirical analysis are the yearly data of balance-sheet items
disclosed by the eleven organizational units of the Slovak banking sector during the
period 2000 – 2011. During the period some of the organizational units underwent a
26
merger or a takeover, it was therefore necessary to operate with totals on the banks
which changed its legal and economic status. The data on these banks were aggregated
as a total and only the merger or the acquisitor is considered. The hypothesis on the
time-invariance of the production function permitted the pooling of individual
organizational  units  in  the  identified  phases  and  resulted  in  “bank-years”.  Only  the  data  
for 4 bank-years   were   not   complete   (ČSOB   /   Istrobanka   2007   &   Citibank   2009,   2010,  
2011). In consequence, the first phase was represented by 114 = 44 bank-years, the
second phase included 115 – 1 = 54 bank-years, and finally, the third phase was
formed by 114 – 3 = 41 available bank-years. The data are evaluated within the
framework of the DEA separately; in other words, the DEA was conducted for each of the
three phases independently.
Only one input and only one output are recognized in the study. The input selected under
the profit approach is total operating expenses and the output to be maximized is
represented by net interest income.
The pooling of the data from different years necessitated their deflation for a proper
analysis. Individual data were deflated to the prices of 2000 by the GDP deflator provided
by Eurostat.
The employment of the SBM model
In this paper, the assumption of variable returns to scale is formed (which, of course
includes a specific case of constant returns to scale) and combined with a non-oriented
SBM model in evaluating the organizational units of the Slovak banking sector on a
comparative basis. Note that the studies enumerated earlier used in their analysis the
rudimentary CCR or BCC model. This study thus goes farther.
In the exact formulation of the SBM model, it is assumed that the data on n production
units are available, where any production unit o, o  {1, ..., n}, produces s desirable
outputs out of m inputs. The values of inputs of production unit o are represented by
vector xo = (xo1, ..., xom)′   and   the   values   of   outputs   by   vector   yo = (yo1, ..., yos)′.   The  
elements of both vectors are positive. Individual inputs and outputs have corresponding
vectors of potential slacks sx
o = (sx
o1, ..., sx
om)′   and   sy
o = (sy
o1, ..., sy
os)′ , which states
how individual inputs and outputs must be improved in order that production unit
o become efficient (whereas vector of inputs xo need be reduced by sx
o and vector of
outputs yo need be increased by sy
o). These slacks are to be identified by the DEA and
serve as an exclusive basis of efficiency calculation for respective production unit o.
For each production unit o, o  {1, ..., n}, it is necessary to solve the following task of
linear programming of the non-oriented SBM model under the assumption of variable
returns to scale,
  1
1
1
1
1 /
, , !min
1 /
i m
oi oii
o j s
oj ojj
m
s
s x
s y






 



x
x y
y
λ s s
1
1
1
{ }
with respect to { }
{ } 1,
i n
o i ii
i n
o i ii
i n
ii






  
  
 



x
y
s x λ x 0
s y λ y 0
λ λ 0
(1)
The symbol „“   denotes at a vector that respective elements of this vector are nonnegative
and at least one element is non-zero.
The restrictions of the optimization task constructs the production possibility set with
respect to n production units and their observed inputs x1, ..., xn and outputs y1, ..., yn
as well as their convex linear combinations in m
and s
respectively. The coefficient ρ
takes values at interval [0, 1] and it is the SBM score of technical efficiency (in this case
of production unit o whose task (1) is subject to optimization. If for some production unit
ρ = 1 happens to be the case, this production unit is called SBM-efficient, which means
that it is technically efficient in the sample of n production units to be evaluated.
In the paper, the role of production units is undertook by individual organizational units
in the Slovak banking sector in the three phases of its development. Three runs of the
27
SBM analysis were made with (the number of inputs) m = 1 and (the number of outputs)
s = 1, one for each phase. In the first phase, the number of decision making units was
n = 44, in the second phase, n = 54, whilst in the third phase, n = 30.
4 Results
All computations were performed in program R (R Development Core Team, 2013) by
means of functionalities included in package nonparaeff (Oh and Suh, 2013). The results
are displayed compactly in Table 3; which presents the information on the achieved SBM
score for each organizational unit of the Slovak banking sector in each phase. The
(simplifying if understandable) designations of individual organizational units indicated in
the table are also used in the interpretations.
Table 3 Results of the application of the SBM model in the individual phases
FIRST PHASE SECOND PHASE THIRD PHASE FIRST PHASE SECOND PHASE THIRD PHASE
YEAR SCORE YEAR SCORE YEAR SCORE YEAR SCORE YEAR SCORE YEAR SCORE
Citibank ČSOB  /  Istrobanka
2000 0.694 2004 0.778 2009 NA 2000 0.929 2004 0.744 2009 0.602
2001 0.848 2005 0.596 2010 NA 2001 0.755 2005 0.452 2010 0.512
2002 0.907 2006 0.552 2011 NA 2002 0.655 2006 0.623 2011 0.467
2003 0.687 2007 0.669 --- --- 2003 0.886 2007 0.769 --- ---
--- --- 2008 0.856 --- --- --- --- 2008 NA --- ---
OTP Banka Slovensko Poštová  banka
2000 0.151 2004 0.594 2009 0.483 2000 0.357 2004 0.656 2009 0.628
2001 0.475 2005 0.644 2010 0.486 2001 0.379 2005 0.462 2010 0.74
2002 0.41 2006 0.727 2011 0.468 2002 0.665 2006 0.671 2011 1
2003 0.496 2007 0.723 --- --- 2003 0.533 2007 0.776 --- ---
--- --- 2008 0.768 --- --- --- --- 2008 0.841 --- ---
Prima banka Privat banka
2000 0.889 2004 0.808 2009 0.341 2000 0.931 2004 1 2009 1
2001 0.836 2005 0.626 2010 0.357 2001 0.767 2005 0.752 2010 0.674
2002 1 2006 0.882 2011 0.384 2002 1 2006 0.966 2011 0.727
2003 0.85 2007 0.853 --- --- 2003 0.918 2007 0.869 --- ---
--- --- 2008 0.817 --- --- --- --- 2008 1 --- ---
Slovenská  sporiteľňa Tatra banka
2000 0.826 2004 0.798 2009 0.797 2000 1 2004 0.817 2009 0.573
2001 0.829 2005 0.77 2010 0.988 2001 0.79 2005 0.754 2010 0.617
2002 1 2006 0.871 2011 1 2002 1 2006 0.744 2011 0.673
2003 0.939 2007 0.979 --- --- 2003 0.735 2007 0.681 --- ---
--- --- 2008 1 --- --- --- --- 2008 0.708 --- ---
UniCredit Bank VOLKSBANK Slovensko
2000 0.744 2004 0.749 2009 0.401 2000 0.705 2004 0.649 2009 0.351
2001 0.7 2005 0.622 2010 0.401 2001 0.633 2005 0.654 2010 0.398
2002 0.967 2006 0.878 2011 0.452 2002 0.687 2006 0.711 2011 0.388
2003 0.657 2007 0.873 --- --- 2003 0.685 2007 0.653 --- ---
--- --- 2008 0.894 --- --- --- --- 2008 0.714 --- ---
Všeobecná  úverová  banka
2000 0.704 2004 0.878 2009 0.853
2001 0.831 2005 0.849 2010 0.901
2002 0.799 2006 0.848 2011 0.94
2003 0.855 2007 0.931 --- ---
--- --- 2008 1 --- ---
Each sub-period subjected to the analysis represents a structurally different quality of the
economic environment, in which commercial banks pursue their goals and undertake
their business activities. This fact is entertained in the subsequent economic
interpretations of the results obtained. Whilst the first sub-period (2000 to 2003) is the
ending phase in the restructuralization and privatization of the Slovak banking sector and
is marked with the introduction of the foreign corporate governance in the area of
28
banking, the second sub-period (2004 – 2008) is heavily influenced with the accession of
the Slovak Republic to the European Union in 2004 and is characteristic of a dynamic
economic growth as well as an increasing offer of banking services. Eventually, the third
sub-period (2009 – 2011) delimits itself with the onset of the financial crisis and goes
into an economic downturn.
The results of SBM technical efficiency scores for individual sub-periods are reproduced
for convenience graphically in Figure 1. Individual scores are marked with red asterisks
and their arithmetic average for each sub-period and for each of eleven organizational
units of the Slovak banking sector are displayed by the black triangle.
Figure 1 SBM technical efficiency scores for individual banks in the three sub-periods
Source: the authors
The application of the SBM model in league with the profit concept of banking efficiency
resulted in the knowledge that the number of full technically efficient organizational units
of the Slovak banking sector relatively oscilated over the identified three phases. Whilst
in the first sub-period, a total of four banks were found technically efficient in any year of
the given sub-period   (i.   e.   Prima   banka,   Privat   banka,   Slovenská   sporiteľňa   and   twice  
Tatra banka), only three banks were found technically efficient for the second sub-period
(twice  Privat  banka,  Slovenská  sporiteľňa  and  Všeobecná  úverová  banka)  as  well  as  for  
the third sub-period  (Poštová  banka,  Privat  banka  and  Slovenská  sporiteľňa).  The  pattern  
of technical efficiency in individual sub-periods is aptly captured by Figure 1.
– In the first sub-period of 2000 – 2003,  Poštová  banka  and  OTP  banka  Slovensko  were  
clearly least technically efficient in the Slovak banking sector, of course, on a
comparative basis and with respect to the profit approach of efficiency. Both of these
banks are retail banks. On the other hand, comparatively most efficient banks are
obviously  two  wholesale  banks  Slovenská  sporiteľňa  and  Tatra  banka,  and  two  retail  
banks  Prima  banka  and  Privat  banka.  Another  wholesale  banka,  Všeobecná  úverová  
banka, in the first sub-period can be classified as a mediocre banks with respect to its
profit  efficiency.  The  largest  Slovak  commercial  bank,  Slovenská  sporiteľňa,  attained  
technical efficiency in 2002, a year after the government cleared the last remains of
illiquid assets off its portfolio. Tatra banka manifested technical efficiency in two
years, 2000 and 2002, even without governmental assistance. The comparative
efficiency of privatized commercial banks may be attributed to new corporate
governance and to the promising economic growth of the Slovak economy.
– The second sub-period of 2004 – 2008 brought about some changes in the efficiency
pattern of the Slovak banking sector. SBM efficiency scores are seemingly somewhat
higher than in the previous sub-period (nonetheless, this can by no means be
interpreted as an improvement in the overall efficiency). Three organizational units
Citibank
ČSOB  /  Istrobanka
OTPBanka Slovensko
Poštová  banka
Prima banka
Privat banka
Slovenská  sporiteľňa
Tatra banka
UniCredit Bank
VOLKSBANK Slovensko
Všeobecná  úverová  banka
0.2 0.4 0.6 0.8 1.0
FIRST PHASE
SBM efficiency scores
* * **
*** *
* ** *
** **
** **
** **
** **
** **
** **
** **
* ** *
Citibank
ČSOB  /  Istrobanka
OTPBanka Slovensko
Poštová  banka
Prima banka
Privat banka
Slovenská  sporiteľňa
Tatra banka
UniCredit Bank
VOLKSBANK Slovensko
Všeobecná  úverová  banka
0.2 0.4 0.6 0.8 1.0
SECOND PHASE
SBM efficiency scores
*** * *
** * *
* * ** *
** * * *
** ***
** ** *
** * **
*****
** ***
** ** *
*** * *
Citibank
ČSOB  /  Istrobanka
OTPBanka Slovensko
Poštová  banka
Prima banka
Privat banka
Slovenská  sporiteľňa
Tatra banka
UniCredit Bank
VOLKSBANK Slovensko
Všeobecná  úverová  banka
0.2 0.4 0.6 0.8 1.0
THIRD PHASE
SBM efficiency scores
***
***
* * *
***
** *
* **
* * *
** *
* **
* * *
29
are comparatively more efficient during this second phase of the development of the
Slovak  banking  sector,  two  of  them  are  wholesale  banks  (Všeobecná  úverová  banka,  
Slovenská  sporiteľňa)  and  one  of  them  is  a  retail  bank  (Privat  banka).  In  plain  words,  
Slovenská  sporiteľňa  and  Privat  banka  retained  in  the  second  sub-period the status of
benchmark in technical efficiency. None of the organizational units manifested
considerably lower SBM efficiency scores and individual banks form a coherent group
in terms when comparing their operating expenses and interest income. This can be
possibly attributed to the fact that – during this sub-period – the real Slovak economy
underwent a dynamic growth path and commercial banks started to extend the
supply of their services especially with asset management, leasing, structural
products. This is consistent with their ambitions of profit maximization in the good
times of the Slovak economy.
– Obviously, a most heterogeneous distribution of efficiency in the Slovak banking
sector is found with the third sub-period of 2009 – 2011. During this stage, two
organizational  units  (i.e.,  Privat  banka  and  Poštová  banka)  may  have  had  attained  full  
technical efficiency in one year, but their average efficiency performance is ordinary
on a comparative footing. A high efficiency profile was maintained by two wholesale
banks  (Slovenská  sporiteľňa  and  Všeobecná  úverová  banka)  and  this  contrasts  with  
other three small banks (VOLKSBANK Slovensko, Prima banka, UniCredit bank)
whose efficiency scores were uniformly low during the sub-period under the
evaluation. All of these three organizational units were, during this sub-period or
later, subject to changes in ownership relationships. Another aspect of the changes
between the two consecutive sub-periods is that the comparative efficiency status of
Privat banka slightly worsened and this bank no longer exhibited a high efficiency
profile held in the previous two sub-periods. Similar is true e. g. for UniCredit Bank,
and  the  reverse  holds  e.  g.  for  Poštová  banka.  It  is  also  of  interest  that  the  technical  
efficiency of Prima banka rapidly declined during the identified three sub-periods, and
Tatra banka is characteristic of a gradual decline in its efficiency status.
5 Conclusions
This paper represents a case study, in which organizational units of the Slovak banking
sector over the years 2000 – 2011 are evaluated in terms of technical efficiency that
they exhibit in their operations from the standpoint of the profit approach. In the
evaluation, a non-parametric SBM model was applied under two crucial assumptions. The
assumption of variable returns to scale is one of classical assumptions made in such
analyses, and this assumption is supplemented here by the requirement that the
production frontier remain constant in three identified sub-periods (phases) in the
evolution of the Slovak banking sector: 2000 – 2003, 2004 – 2008 and 2009 – 2011. In
each of these sub-periods, organizational units of the Slovak banking sector were pooled
in one data frame, from which the information on the shape of the production frontier
(constant in individual sub-periods and common to all organizational units in the given
sub-period) was extracted by the SBM model in the form of SBM technical efficiency
scores.   During   the   entire   period   of   12   years,   on   a   comparative   basis,   Slovenská  
sporiteľňa   was   able   to   preserve   high   SBM   technical   efficiency   scores.   Some   banks  
indicated  improvements   (Poštová   banka) and others manifested gradual declines (such
as UniCredit Bank, Tatra banka) during the entire period. Privat banka worsened
comparatively in the final sub-period.
Acknowledgments
The   paper   is   prepared   under   the   ægis   of   the   grant   scheme   VEGA   No.   1/0967/11
Innovative approaches to the system of corporate performance evaluation and under the
project ITMS 26110230082 Mobility – Support of Science, Research and Education at
Matej  Bel  University  in  Banská  Bystrica (Mobility – podpora  vedy,  výskumu  a  vzdelávania
na UMB) within the bounds of financial subsidy contract No. 018/2012/1.2/OPV. Also the
30
financial support of the grant scheme VEGA No. 1/1276/12 Dynamics of macroeconomic
processes in open economies is kindly appreciated.
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31
Is the asset-liability approach a significant change
to revenue recognition?
Hana  Bohušová,  Patrik  Svoboda
Mendel University in Brno
Department of Accounting and Taxes,
Zemědělská  1,  613  00  Brno, Czech Republic
E-mail: hana.bohusova@mendelu.cz
patrik.svoboda@mendelu.cz
Abstract: In November 2011, the exposure draft of a new common standard regarding
the revenue recognition from contracts with customers was published. The main
objective is to unify the principles for revenue recognition and the possibility of its
consistent application across sectors. This paper is focused on the identification of areas
where we can assume that there will be the most significant differences in reported
revenue compared to the current treatment. The aim of the research is the evaluation of
its impact on the reported amounts of revenues and their course over time. The greatest
difference was considered in accounting for performance obligations at the time of
contract inception. Some differences arise in determining transaction price, it can lead to
earlier recognition of revenue compared to the current treatment under U.S. GAAP and
IFRS. Research conclusions may contribute to the preparers or users of financial
statements. It is intended especially for the purpose of introducing them into practice
while respecting the information needs of users of financial statements of those
companies that are affected by change in methodology.
Keywords: revenues, asset-liability approach, contracts with customers
JEL code: M41
1 Introduction
Difference in national accounting systems imposes additional costs on companies that
prepare financial statements based on multiple reporting models in order to raise capital
in  different  capital  markets.    According  to  Svoboda,  Bohušová  (2013)  financial  reporting  
as a result of application of accounting treatments should become a comprehensible
source of information for users from different countries. The way out of this situation is a
global harmonization of financial reporting. Over 100 countries have recently moved to
IFRS reporting or decided to require the use of these standards. On the other hand, there
are US Generally Accepted Accounting Principles (US GAAP) which were the only
reporting system, which was accepted by financial and capital markets in the USA. These
two systems could be suitable to be a global system of financial reporting. Convergence
of U.S. GAAP and IFRS is an important stepping stone on the journey to a global, high
quality set of financial reporting standards development.
The two most significant organizations in the field of financial reporting regulation setters
in the world - The Financial Accounting Standard Board (FASB) and International
Accounting Standards Board (IASB) begun significantly cooperate in the development of
common principles based standards in 2002. In September 2002 in American Norwalk
was published an agreement Memorandum of Understanding (MoU). In this agreement
FASB and IASB have committed to the approach of convergence of accounting standards
(IAS/IFRS and US GAAP), so as to be acceptable by the world's capital markets. The
process of convergence of US GAAP and IFRS has been realized through a series of subprojects
aimed at short-term or long-term period. These are the projects where the
unification of accounting rules was expected no later than at the end of 2008. Defined
target had not been fulfilled and the supposed completion date was postponed to 2014.
In 2012 the IASB and FASB published a joint progress report. The IASB and FASB remain
committed to completing the remaining three convergence projects - financial
32
instruments, revenue recognition and leases. Currently, these projects represent priority
areas of convergence.
2 Methodology and Data
The paper focuses on the convergence of financial reporting systems (IFRS and U.S.
GAAP) in the area of revenue reporting. The main aim of this paper is to analyse and
evaluate effect of application of the published Exposure Draft and Re-exposure Draft of
Revenue from Contracts with Customers on financial statements of reporting entity. The
designed approach to revenue recognition is based on the requirement that this area
should be consistently regulated by a single universal standard. There are identified
areas in which we can assume the most important differences from the current approach
to revenue recognition and reporting in the paper. A prerequisite for fulfilling the main
aim is comparison of current approach to revenue recognition under IFRS and US GAAP
and the Draft of the new standard. The result of the comparison is used for identification
of areas where the most significant differences are supposed to arise. The overall impact
on the reported amounts of revenues and their progress over time is evaluated.
There are used standard methods of scientific work in this paper. The method of
description is used to describe the development in the area of IAS/IFRS and US GAAP
convergence. Then, the comparative analysis is used to discuss the differences in
revenue recognition and measurement under IAS/IFRS and US GAAP. The OIBDA
(operating income before depreciation and amortization) indicator is used for
demonstration of effects of a new methodology. At the end, the methods of synthesis,
deduction and induction are used.
3 Results
Revenue is a crucial number to users of financial statements in assessing a company
performance and prospects. However, revenue recognition requirements in US GAAP
differ from IFRS revenue recognition principles, and both sets of requirements are
considered to be need of improvement (Preliminary Views, 2008). FASB and IASB
initiated their joint project on revenue recognition in 2002. The preparation of a new
common standard on revenue recognition is the main aim of this project. The publication
of Discussion Paper - Preliminary Views on Revenue Recognition in Contracts with
Customers was the first joint project output. The boards received more than 220
comment letters on their discussion paper. The majority comments were from preparers
and some from users or user representative organizations. The idea of a single revenue
recognition model was strongly supported. The Exposure Draft of Revenue from
Contracts with Customers was issued on June, 24th, 2010. It was significantly affected
by the reaction of the professional public in the form of Comment Letters. The proposal
provoked an extensive public consultation and Boards received 986 letters (comment
letters) from the business entities, users of financial statements, auditors, regulators,
professional associations, consultants and others. Most of the responses came from areas
that were supposed to be significantly affected by a new methodology – engineering,
construction, auditing, insurance and technology companies. On the basis of these
reactions IASB and FASB have made the conclusion – to postpone publication of the final
standard and re-issue a revised exposure draft for a new public discussion. The reexposure
draft – ED/2011/6 "Revenue from contracts with customers" was issued on 24
November 2011. In addition to ED/2011/6 Basis for Conclusions (BC) was issued. The
process of the ED development and alternative views on selected issues were described
in BC. By March 2012 stakeholders were given the opportunity to comment the newly
issued draft. During this period the Boards received more than 300 letters. Application of
standard in its current form would, in the opinion of Boards, improve comparability,
reduce the need for interpretative guidance for specific types of transactions and
improved disclosure. Issue of a final standard is expected for the first half of 2013.
33
Revenue Recognition and Recording under US GAAP
Before the FASB Accounting Standards Codification took place, there were over 100
pieces of treatment concerning revenue recognition in U.S. GAAP. Although the revenue
recognition accounting literature, except for industry special guidance, is generally
arranged and organized into a single topic in the Codification (Topic 600 Revenue, ASC
605, Revenue Recognition), the volume of the accounting literature and its complexity
has not changed.
Two criteria must be met for revenue recognition. Revenue recognition criteria are
defined in the FASB Concepts Statement No. 5 Recognition and Measurement in Financial
Statements of Business Enterprises. Criteria require that revenue must be realized or
realizable and must be earned. Revenues are not recognized until earned. Revenues are
considered to have been earned when the entity has substantially accomplished what it
must to do to be entitled to the benefits represented by the revenues (CON5). The
application of the earning process for revenue recognition seems not to be consistent for
application in different industries.
There is the SEC Staff Accounting Bulletin (SAB) No. 104 for revenue recognition (Topic
13) and more than 100 industry-specific pieces of guidance which can serve conflicting
results for economically similar transactions because an earning process is not precisely
defined. The existence of different requirements for economically similar transaction
reduces the comparability of revenues across entities and industries. The detailed rules
often contain exceptions for particular types of transactions.
Revenue Recognition under IAS/IFRS
The incomes and expenses are defined in the conceptual framework of IAS/IFRS. The
definition of income encompasses both revenues and gains. There are two IAS standards
for revenue recording (IAS 18 – Revenue, IAS 11 – Construction Contracts). Revenue is
recognized when it is probable that future economic benefits will flow to the enterprise
and they can be measured reliably. Revenue is recorded in fair value. The revenue
relating to long-time contracts recording is the special area of revenue recording in
IAS/IFRS. Revenue and costs associated with construction contracts are determined in
IAS 11 Construction Contracts.
Table 1 Problems with recognition under US GAAP and IFRS
US GAAP IFRS
 More than 100 standards on revenue
could produce conflicting results for
economically similar transaction,
 There is not any general approach for all
kinds of revenue recognition,
 Application of earning process could
lead   to   misrepresentation   of   an   entity´s  
contractual rights and obligation in
financial statements.
 The criteria for revenue recognition
under IAS 18 and IAS 11 are not
compatible.
 Entities could recognize amount of
revenue in the financial statements that do
not faithfully represent economic
phenomena (revenue recognition for sale
of goods depends on when the risk and
rewards of ownership are transferred),
 Lack of guidance for multiple-elements
arrangement.
Source: based on IASB and FASB information
Based on the comparative analysis, the authors came to the conclusion that although the
definitions of the moment of revenue recognition in the two systems differ, revenue
recognition can be expected at the same time in the case of most ordinary transactions.
An exception may be represented by transactions where the price is contingent for the
future and it is uncertain  enforcement  of  the  claim  (Boček,  2012).  The  fixed  price  U.S.  
GAAP requirement may cause delay of recognition of revenues in comparison with the
IFRS requirement for reliable measurement.
34
There are no significant differences in revenues from the sale of goods recording and
reporting in the IFRS and U.S. GAAP. Recognition of revenues is deferred until the
moment when substantially all the risks and rewards of ownership are transferred to the
customer for a defined price. Some differences in accounting for revenue associated with
the sale of goods can be seen in the details of specific transactions in U.S. GAAP, while
such detailed regulations do not exist in IAS/IFRS.
The approach to revenue from the rendering of services recognition is different in the two
existing models. Recognition of revenue from services rendering is based on to the
condition that the outcome from the transaction must be reliably measurable, U.S. GAAP
requires extra reliable evidence that the services were provided. Significantly different
rules relate to the recognition of revenue arising from long-term contracts. It does not
exist any authoritative literature for the choice between the proportional and the finished
performance method, so the choice is up to the entity in the U.S. GAAP. When using the
proportional performance method, the entity should use methods based on output or
recognize the revenue evenly over the service period. Using the percentage of
completion method is not permitted other than for long-term construction contracts and
software development. U.S. GAAP, however, offers a similar proportional performance
method for other services. IFRS does not allow the use of method of completed
performance under U.S. GAAP on the other hand, there is no analogy to the zero-profit
method used in IFRS.
Possible approaches to Revenue Recognition
There are two possible approaches to revenue recognition. The earning process model is
used in US GAAP. According to FASB CON 5 the revenue is recognized when payment is
realized or realizable and the earning process is complete. This model attempts to
account for revenue directly without considering how assets and liabilities arise and
change throughout the exchange with the customer. Because assets and liabilities are
ignored, deferred debits and credits sometimes arise that do not meet the definition of
assets and liabilities. An asset and liability model is the second possible approach to the
revenue recognition. This model is consistent with the existing definitions of revenue
under the IFRS and US GAAP. The model focuses on the changes in assets and liabilities
themselves to determine how much revenue to be recognized, because the revenue itself
is not measured directly. Under this model deferred debits and credits that do not meet
the definitions of assets and liabilities are not recognized. In opinions of IASB (2008) and
FASB this model leads to more faithful and more consistent depiction of the underlying
economics of transactions than the earning process model. There is a problem in the
asset and liability model application – which assets and liabilities affect revenue. The
existing definitions of revenue provide few clues in this area. There is no conceptually
right or wrong answer about which assets or liabilities should affect revenue. It depends
on the point of view and on the industry.
ED/2011/6 uses the asset and liability approach when revenue arises through
recognizing and measuring increases in specified assets and decreases in specified
liabilities. It means that all contracts with customers (with the possible exception of
contracts for financial instruments, insurance and leases) would be analyzed into
contract´s  assets  (the  right  to  receive  payment)  and  contract´s    liabilities  (the  obligation  
to perform under the contract) and the amount of revenue to be recognized is
determined by considering how much specified assets and liabilities change in a period.
A contract with customers is defined by the IASB and FASB (2008) as an agreement
between two or more parties that creates enforceable obligations. The combination of the
rights and obligations in the contract gives rise to a net contract position. The net
contract position could be contract asset, contract liability or nil. In this approach,
revenue is recognized when a contract asset increases or contract liability decreases by
an   entity’s   performance.   The   key   change   from   the   earning   process   model   is   that  
revenues  are  based  on  the  changes  in  contract´s  assets  and  liabilities.
In   Hepp´s   and   Brady´s   (2011)   opinion,   in   using   this approach a two-step process is
applied. It starts with effective recognition of the contract assets and performance
35
obligations arising from an executory contract and ends with derecognition of the
performance obligation. Differences between the current approach to revenue recognition
and the approach in the ED may lead to a number of differences from existing accounting
method and may result in incorrect presentation which could cause complications in the
capital markets (Mesa Maziano, 2005). On the other hand, the new approach is fully
supported by the FASB and IASB(2008). The use of it leads to the unification of
approaches to the recognition of revenue for all types of transactions. In the opinion of
the IASB (2008) and FASB, application of this model leads to more faithful presentation
of nature of operations than the earnings process model.
The main difference compared to the current treatment is the fact that the revenue from
the sale of goods and rendering services are viewed in the same way. The ED is based on
the revenue recognition by a reporting entity in the way to depict the transfer of
promised goods or services to customers in an amount that reflects the consideration to
which entity expects to be entitled in exchange for the provided goods or services (ED,
paragraph 3 (2011)). To achieve this principle can be reached by the realization of the
following steps:
(a) identify the contract with a customer;
(b) identify the separate performance obligations in the contract;
(c) determine the transaction price;
(d) allocate the transaction price to the separate performance obligations in the
contract;
(e) recognize revenue when (or as) the entity satisfies a performance obligation.
Identification of the contract with customer
The contract must have commercial substance for the purposes of recognizing revenue in
accordance with the ED, moreover the parties have approved the contract and are
committed   to   perform   their   respective   obligations,   the   entity   can   identify   each   party’s  
rights regarding the goods or services to be transferred, and the entity can identify the
payment terms for the goods or services. Requirements must be applied to individual
contracts with customers. There are specifically defined requirements for contracts
combination and vice versa for the segmentation of the contract showing independent of
each transaction - the sale of goods and rendering services into separate sub-units,
which can lead to different results than using existing methods in the ED. According to
Boček   (2012)   the current IFRS and U.S. GAAP is interested in details of contractual
modifications in construction contracts, both systems provide guidance on the special
kinds of long-term contracts. The substance of construction contracts leads to
conclusions that the contracting parties are aware of the fact that the contract will not be
carried out exactly according to the original plan, estimate and budget (CL # 47,
Engineering & Construction Companies). It is necessary to clearly specify contractual
modifications. The ED is not as specific as the current treatment and according to authors
it may result in inconsistent practice. Compared to current practice, it is also expected
delay of the moment of accounting for modifications, which arises from the difference in
treatment - while the existing arrangements (IAS 11 and ASC605) make the recognition
of contractual modifications when its approval is likely, the ED requires recognition only
at the moment of its approval.
Separate performance obligations identification
A reporting entity that contractually commits to provide more than one item of goods or
services should consider each such an item as a separate performance obligation only if
they are different, which, according to the ED occurs when:
- unit or other units selling the same or similar product or service separately,
- the unit may sell a product or service separately because the product or service has a
distinct function and distinct profit margin.
This approach represents a significant change for entities that sell goods and services
together under the bundled transaction, which is the case of mobile operators and
36
providers of IT services. In these cases, it is necessary to establish the extent to which it
are currently recorded individual elements of contracts and what extent it would be by
using reporting according to the ED.
Transaction price determination
According to the ED (2011) the transaction price is the amount of consideration to which
an entity expects to be entitled in exchange for transferring promised goods or services
to a customer, excluding amounts collected on behalf of third parties (e.g. sales taxes,
VAT   or   excises).   The   transaction   price   does   not   include   the   effects   of   the   customer’s  
credit risk. On the other hand, the transaction price is adjusted for the effects of variable
performance, the time value of money and non-monetary performance payable to
customers.
The transaction price is estimated by the reporting entity at each reporting date due to
discounts, bonuses or performance incentives for customers in variable amount. In this
context, the ED uses the concept of a reasonable estimate of the performance. If the
entity cannot use a reasonable estimate of the performance, there is no recognition of
revenues or transaction prices and revenues are recognized only at the amount in which
revenue can be reasonably estimated. This is the difference between the current
approach and the ED. According to the current approach, the entity recognizes revenue
only in case that the amount of revenue can be reliably measured. The ED requires
revenue recognition only to the portion of the goods or service for which there is a
transfer of control over the performance to the customer and it might be reasonably
estimated. If an entity is not reasonably assured to be entitled to the amount of the
transaction price allocated to satisfied performance obligations, the cumulative amount of
revenue recognized as of the reporting date is limited to the amount of the transaction
price to which the entity is reasonably assured to be entitled.
According to the ED entity has an obligation to assess the customer's ability to meet its
obligations. The entity shall adjust the amount of performance with respect to credit risk
of the customer in determining the transaction price. Subsequent changes no longer
affect the amount of revenue. There is not requirement of fixed or ascertainable price
paid by customer in comparison to the current approach. It could lead to earlier
recognition of revenue. If the variable compensation is likely, but not sufficiently secured,
the new model can result in earlier recognition of revenue in the initial phase of the
transaction. Possibility of revenue measurement in expected value in connection with the
reporting of revenue from long-term contracts over time, however, makes an
opportunity for transaction designed to premature recognition of revenue and thereby
improvement of the performance of the reporting unit, which could according to the
American Accounting Association (CL 94) lead to similar problems, as was the Enron
case, where the future profits were recognized early on the basis of discounted expected
present values. It is similar case like the use of the revaluation model with artificial
overvaluation  of  assets,  even  though  according  to  Valouch,  Sedláček  (2009)  their  proper  
use should lead to faithfully representation of reality.
The ED differs from the existing arrangements in U.S. GAAP - SEC Topic 13 significantly
and allows earlier recognition of revenue. Any difference between the valuation of an
contract receivable and revenue reported according to the ED would be presented in the
income statement in a separate line adjacent to the line of reported revenue. Unlike the
current practice it will probably lead to earlier recognition of revenue with an uncertain
enforceability, and uncollectible receivables will be recognized in the income statement
close to revenues. Entity, in accordance with the ED should adjust the value of income to
reflect the time value of money in cases where there is a significant difference between
the moment of the transfer of goods or services and the moment of payment. To adjust
the promised amount of consideration to reflect the time value of money, an entity shall
use the discount rate that would be reflected in a separate financing transaction between
the entity and its customer at the contract inception. That rate would reflect the credit
characteristics of the party receiving financing in the contract as well as any collateral or
security provided by the customer or the entity, which might include assets transferred in
37
the contract. To take into account the time value of money revenue is reported in the
amount that would match the selling price that would be paid in cash at the time of
transfer of goods or services.
Effect of financing would be in accordance with ED reported separately from revenues
from the sale of goods and services. In the case that the customer promises
consideration in a form other than cash, it must be measured at fair value. Compared to
the current approach the ED does not contain specific cases of non-cash considerations,
such as fees for the classification of goods in the supply of chain store and advertising
barter transactions.
Transaction price allocation
For a contract that has more than one separate performance obligation, the reporting
entity shall allocate the transaction price to each separate performance obligation in an
amount that depicts the amount of consideration to which the entity expects to be
entitled in exchange for satisfying each separate performance obligation. If there is not a
separate selling price directly observable, the entity should make estimation.
Recognition of revenue when the entity satisfies a performance obligation
Revenue is recognized when the performance obligation has been satisfied and the goods
or services have been transferred to the customer and the customer obtains control of
that asset. If all conditions for revenue recognition are met, the entity shall recognize
revenue in an amount corresponding to the transaction price appropriate for the
commitment   to   provide   goods   or   services.   According   to   Boček   (2012),   the   concept of
control differs from the current approach. The transfer of significant risks and rewards
may cause that revenue from some transactions may be recognized in a different time.
The main differences arise for transactions such as the sale of goods by the
distributor/dealer who has the right to return the goods, if it does not sell (e.g.
consigned). In the current approach the seller retains significant risks associated with the
sale, the revenue from sale can be deferred until the moment the dealer sells the goods
to the next customer. Under the ED, the transaction should be split into two performance
obligations – sale of goods to dealer and the subsequent right to return the goods.
Revenue from the first performance obligation (sales less cost of goods expected returns)
may be recognized at the time of the initial sale, it means earlier than at present. The
OIBDA indicator is appropriate to use for demonstration of the impact of introducing a
new model of revenue recognition in the field of telecommunications. It is the case of one
of the most common transactions - the sale of discounted device as an incentive to sign
long-term contracts. Providing discounted device can no longer be regarded as a
marketing strategy and must be considered in the context of the entire contract as a
single performance obligation, if there are no technical or other barriers to its use. In
accordance with current practices in IFRS and U.S. GAAP there are no differences in the
values of this indicator in the case of direct or indirect sale of bundled device and
services. In both cases, at the time of signing the contract the loss from the sale of
device is recognized and profits are recognized over the following months during the
contract. The impacts of both kinds of sale on OIBDA indicator are different. Commission
is considered as incremental costs in this case and it must be capitalized and amortized
over the contract period. In case of indirect sales no initial loss is recognized at the time
of contract inception. Using the new methodology leads to revenue recognition
acceleration - this effect is called front-loading.
4 Conclusions
The main aim of the ED development is preparation of a uniform approach to the
recognition, measurement, accounting and reporting of revenue from contracts with
customers. It can be said that the ED fulfills this aim. On the other hand, it is too general
in a current form. The authors believe that if it were adopted in its current form, it would
be necessary to specify methodological procedures such as interpretations in certain
areas. The greatest difference from the current approach can be considered accounting
38
for performance obligations at the time of contract inception. Also, it can be assumed
some difference of determining transaction prices, which can lead in many cases to
earlier recognition of revenue compared to the current treatment under U.S. GAAP and
IFRS. Revenues could be recognized in different values due to tighter requirements
respecting the time value of money.
On the basis of responses to the revised ED it can be assumed the most significant
impact in the field of telecommunications and IT services, compared with the original ED
issued in 2009, where the most critical reactions came from the construction industry
due to the original ED was revised to its current form. In the affected areas can be
expected to deepen the differences between the information requirements of users of
financial statements and the information provided by financial statements. Despite the
fact it can be said that the ED corresponds to the specified aim, because according to
Procházka  (2011)  the  ED  defines  the  principles  for  revenue  recognition  on  a  general  basis  
and will ensure consistency of accounting policies and comparability of financial
statements of different enterprises.
Acknowledgments
The results introduced in this paper are a part of research project supported by Czech
Science Foundation via grant No. GAP403/11/0849 with the title US GAAP and IAS/IFRS
convergence with respect to the adjustment of methodical procedures of the financial
analysis.
References
Boček, M. (2012). The impact of the potential convergence of US GAAP and IAS/IFRS on
revenue recognition. (Dopady   potenciální   konvergence   systémů   US   GAAP   a IAS/IFRS
v oblasti  vykazování  výnosů). Diploma thesis. Mendel University, Brno.
Bohušová, H. (2009). Development of the common standard for revenue recording as
a part  of  the  US  GAAP  and  IAS/IFRS  systems  convergence.  (Návrh  společného  standardu  
pro  zachycování  výnosů  jako  součást  konvergence  systémů  US  GAAP  a IAS/IFRS). Acta
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Recognition (Topic 605): Revenue from Contracts with customers. Available on:
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<http://www.fasb.org/jsp/FASB/CommentLetter_C/Comment LetterPage%26cid=121822
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40
Performance of Hungarian Mutual Funds and the Financial Crisis
Gábor  Bóta,  Mihály  Ormos
Budapest University of Technology and Economics
Department of Finance
Magyar  tudósok  krt.  2, 1117 Budapest, Hungary
E-mail: bota@finance.bme.hu
ormos@finance.bme.hu
Abstract: We investigate the performance and time varying risk behavior of Hungarian
equity mutual funds by applying the modified version of the four-factor model using the
Central Eastern European index as a market proxy. We classify the funds according to
their target markets (Hungary, Central and Eastern Europe, developed markets) and
separate bullish and bearish periods. We find no significant excess returns for any
circumstances, however market betas can be significantly different for bullish and bearish
periods as well as the explanatory power of book-to-market ratio and market
capitalization. After taking into account the daily percentage changes in the number of
shares outstanding we find  investors’  relation  to  risk  to  be  different  in  bearish  and  bullish  
periods.
Keywords: mutual funds, asset pricing, financial crisis, time varying beta
JEL codes: G01, G12
1 Introduction
We investigate the performance and time varying risk behavior of Hungarian equity
mutual fund returns for the period of January 2001 and February 2013. Based on the
global market returns we separate bullish and bearish periods, and examine the modified
version of Carhart (1997) equilibrium model whether the estimated risk parameters are
constant or the return generating process is different in periods of crisis. We also
investigate performance and the risk in a regional manner as the 30 mutual funds in our
analysis invest in equities of Hungarian, or Central and Eastern European or developed
capital markets. Furthermore we extend our model with an addition variable of the
percentage change of the number of shares outstanding to capture the reactions of
market investors to increasing or decreasing market changes.
The performance of mutual funds are examined for the decades in the financial literature
as a test for market efficiency and the general conclusion is that funds can not
outperform the market on average (Jensen 1968, Malkiel 1995, Gruber 1996). Grinblatt
et al. (1995) find that funds investing in past winner stocks providing higher returns than
others. Gruber (1996) shows that the return of new investors to be better than average
return for all investors and cash in and outflows can be predicted. Kosowski (2011)
argues that average underperformance of mutual funds is appropriate only for expansion
periods but not during recessions. Another avenue of research is related to the
performance of investors with local knowledge and the results show that market analysts
(Bae et al. 2008), hedge funds in Asia Teo (2009) or mutual funds in Europe (Banegas et
al. 2013) with local presence outperform others.
2 Data and methodology
To examine the time varying performance and risk of Hungarian publicly offered openended
mutual funds investing in equities we collect 30 different mutual fund share prices.
These funds invest in three different regions: Hungary (6), Central and Eastern Europe
(11), and developed markets (13). The source of the data is the Association of Hungarian
Investment Fund and Asset Management Companies. Daily returns of funds and the local
and regional indices are calculated in USD terms using WM/Reuters closing spot rates.
The source of the factors for the Fama-French (1992, 1993, 1996) model and momentum
factor  is  the  Center  for  Research  in  Securities  Prices  from  Kenneth  French’s  website.
41
We apply different equilibrium models to capture the difference in excess returns and in
risk before and after the financial crises. To do so we collect returns from the bullish
(increasing market prices) and bearish (decreasing market prices) periods from January
2001 to February 2013. We run ordinary least squares regressions with different set of
explanatory variables. Table 1 shows the coefficients of determination (R2
) for different
models. The average results are presented for our full sample (30 funds) and for the
different regions these funds are investing in (Hungary, Central and Eastern Europe,
developed markets). According to our estimates a one-factor model using the CETOP20
regional index has better explanatory power than a model using the CRSP market proxy
(Mkt-Rf) not only for the Hungarian and CEE funds but for the funds investing in
developed markets as well, which seems to be surprising in a globalized capital market;
however verify the hypothesis of Errunza and Losq (1985).
Table 1 Explanatory power of models with different set of parameters
Mkt C20 SMB HML MOM
Full
sample
HUN
CEE
region
Developed
markets
CAPM x x 0.132 0.101 0.116 0.161
CAPM - CEE x x 0.484 0.525 0.651 0.323
FF 3-factor x x x x 0.143 0.110 0.130 0.170
Carhart 4-factor x x x x x 0.145 0.112 0.131 0.173
FF for CEE x x x x 0.490 0.532 0.654 0.331
Carhart for CEE x x x x x 0.494 0.534 0.656 0.338
FF + C20 x x x x x 0.516 0.534 0.656 0.389
Carhart + CEE x x x x x x 0.518 0.536 0.657 0.391
Notes: This table shows the variables of the applied regression models and the average R2
values
of the estimations for the whole sample and the regional breakdown of the funds.
From the models presented we have chosen the modified version of the Carhart (1997)
four-factor model, using CETOP20 index as a market proxy instead of Mkt-Rf.
3 Performance analyses
If we look at the explaining power of the chosen model for the three set of funds
separated geographically and for the whole examined period and then separated bullish
and bearish periods as well the first result worth to mention is that the model has higher
coefficient of determination for both the bullish (0.556) and bearish periods (0.554) than
for the whole period (0.494). We have obtained opposing results for the funds investing
in Hungary and in the CEE region as in the former case the model has a better explaining
power in the bullish period (0.707 vs. 0.558 in the bearish period) while in the latter case
the model fitted better for the bearish period (0.732 vs. 0.655 in the bullish period). For
the funds investing in developed countries there was no such difference between bullish
and bearish periods (0.403 and 0.401 respectively); however the R2
s are still higher than
for the full sample.
For the whole period we have measured significant excess return only at 10% level for
one fund, for the bullish period there are four cases with significant (and positive) Jensen
(1968) alphas (2 only at 10% and 1 only at 5% level) and in the bearish period there are
four significant (negative) alphas (at 10% significance). The statistically non-significant
results  of  excess  returns  are  also  interesting  from  an  economist’s  point  of  view.  We  find  
25 funds in the bullish period with positive excess returns and 24 funds in the bearish
period with negative excess return. This result shows that the portfolio managers
exaggerate and somehow amplify the market circumstances.
CETOP20 index is significant as a market proxy for all the funds and periods, however
there are substantial differences in the betas in different periods which sign depends on
the geographic focus of the funds. For all the funds investing in the CEE region and for
most (8 of the 13) funds investing in developed markets the betas in the bearish period
were significantly higher. 4 out of the 6 the funds investing in Hungary (and tracking the
Hungarian market index) show higher beta in the bullish period. This opposing result for
42
the Hungarian market suggests that when the market is falling fund managers reduce
the exposure of the funds to equities and invest more in bonds, which is confirmed by
the high R2
results for the regressions containing the CMAX index as a variable.
Table 2 Carhart four-factor model with C20 market proxy for the 30 funds
Fund const C20 SMB HML MOM R2
CE1 0.0002 0.7840***
0.0003 0.0015***
0.0002 0.77
CE2 0.0000 0.8030***
0.0005**
0.0013***
0.0003**
0.83
CE3 0.0001 0.9389***
0.0007**
0.0006*
-0.0002 0.92
CE4 0.0000 0.6965***
-0.0002 0.0010***
0.0006***
0.69
CE5 0.0000 0.7229***
0.0007***
0.0010***
0.0006***
0.80
CE6 0.0001 0.4527***
0.0023***
0.0016***
0.0012***
0.36
CE7 0.0002 0.3141***
0.0013***
0.0012***
0.0005**
0.21
CE8 0.0001 0.7890***
0.0000 -0.0002 -0.0001 0.89
CE9 0.0000 0.8349***
0.0007***
0.0018***
0.0002 0.80
CE10 0.0000 0.6960***
0.0007**
0.0017***
0.0007***
0.67
CE11 0.0001 0.3745***
0.0013***
0.0009**
0.0006***
0.27
Dev1 -0.0002 0.4594***
0.0008**
0.0008***
-0.0024***
0.47
Dev2 -0.0001 0.5502***
0.0006*
0.0021***
-0.0014***
0.57
Dev3 0.0000 0.3621***
0.0020***
0.0032***
-0.0031***
0.35
Dev4 -0.0002 0.4745***
0.0007**
0.0001 -0.0006***
0.51
Dev5 -0.0001 0.3946***
0.0004 0.0003 0.0007***
0.23
Dev6 -0.0003 0.3222***
0.0028***
0.0018***
0.0008***
0.15
Dev7 -0.0001 0.3702***
0.0008*
0.0016***
0.0009***
0.19
Dev8 -0.0002 0.2873***
0.0025***
0.0009*
0.0005**
0.12
Dev9 0.0001 0.6025***
0.0000 -0.0011 0.0004 0.65
Dev10 -0.0001 0.4147***
0.0018***
0.0014***
0.0003 0.32
Dev11 0.0000 0.4390***
-0.0003 0.0019***
-0.0004*
0.31
Dev12 -0.0001 0.3765***
-0.0006 0.0013***
-0.0008***
0.25
Dev13 -0.0001 0.3987***
0.0005 0.0000 0.0009***
0.26
HU1 -0.0002 0.9871***
0.0016***
0.0017***
0.0002 0.77
HU2 0.0003*
0.6715***
0.0006**
0.0015***
0.0000 0.71
HU3 0.0001 0.7053***
0.0034***
0.0016***
0.0017***
0.63
HU4 0.0000 0.4332***
0.0011 -0.0016**
-0.0003 0.19
HU5 0.0001 0.4959***
0.0034***
0.0021***
0.0014***
0.26
HU6 0.0000 0.7237***
0.0015***
0.0011***
0.0007***
0.66
Average R2
for the full sample 0.49
Average R2
for funds investing in Hungary 0.53
Average R2
for funds investing in CEE region 0.66
Average R2
for funds investing in developed markets 0.34
Notes: This table shows the results of the modified Carhart (1997) four-factor model, using
CETOP20 as a market proxy for the full sample period of January 2001 and February 2013. *, **,
*** denote significance levels of 10%, 5% and 1% respectively.
The results for the funds investing in Hungarian shares are surprising (see Table 2)
especially given the fact that these are index-linked funds. The C20 betas are far from
being equal to one, their average value for the full investigated period is only 0.67.
find 20% difference in absolute terms and find that altogether the market betas are
decreasing with 7% in average. This result suggests that when the market is falling these
funds invest in other, lower risk assets as well. To verify this hypothesis we run a simple
regression for the fund returns explaining with C20 and CMAX (Hungarian government
bond benchmark) index. In this setting we get an even higher explanatory (0.596 for the
full period) than for the Carhart (1997) four-factor CEE model.
The HML factor is significant for 25 funds for the whole period, 25 for the bullish period
and only 7 for the bearish period (at 10% significance, at 1% the results are 21, 21 and
2 respectively). So it seems that during a recession the book-to-market equity ratio
behind the HML factor has no explaining power.
Table 3 Carhart four-factor model with C20 market proxy for the 30 funds for bullish and bearish periods
Bull Bear
const C20 SMB HML MOM R2
const C20 SMB HML MOM R2
ΔC20
CE1 0.0003*
0.7217 ***
0.0008 ***
0.0021***
0.0000 0.69 0.0003 0.8911 ***
0.0006 0.0000 -0.0001 0.91 -23%
CE2 0.0000 0.7681 ***
0.0005 *
0.0017***
0.0001 0.77 0.0001 0.8702 ***
0.0014***
0.0007 0.0004 *
0.94 -13%
CE3 0.0001 0.9353 ***
0.0002 0.0007 -0.0002 0.91 0.0001 0.9513 ***
0.0014***
0.0006 -0.0001 0.94 -2%
CE4 0.0000 0.6430 ***
0.0005 0.0019***
0.0002 0.63 -0.0003 0.7810 ***
-0.0004 -0.0008 0.0006 0.82 -21%
CE5 0.0000 0.7075 ***
0.0005 **
0.0014***
0.0004 **
0.75 -0.0001 0.7564 ***
0.0017***
0.0005 0.0008 ***
0.91 -7%
CE6 0.0003 0.5226 ***
0.0006 0.0020***
0.0006 **
0.41 -0.0011 0.3505 ***
0.0048***
0.0025 **
0.0025 ***
0.29 33%
CE7 0.0005**
0.3198 ***
0.0001 0.0015***
-0.0001 0.21 -0.0014 *
0.3280 ***
0.0045***
0.0030 **
0.0023 ***
0.24 -3%
CE8 0.0002 0.7518 ***
-0.0001 0.0002 -0.0001 0.86 -0.0001 0.8407 ***
0.0009**
-0.0004 0.0000 0.94 -12%
CE9 0.0001 0.7889 ***
0.0008 ***
0.0022***
0.0000 0.73 0.0000 0.9210 ***
0.0017***
0.0008 0.0000 0.93 -17%
CE10 0.0002 0.6819 ***
0.0006 0.0022***
0.0004 *
0.62 -0.0007 0.7226 ***
0.0014**
0.0014 *
0.0010 **
0.77 -6%
CE11 0.0004*
0.3622 ***
-0.0001 0.0011***
0.0002 0.24 -0.0011 0.4314 ***
0.0056***
0.0022 *
0.0021 ***
0.36 -19%
Dev1 -0.0001 0.4241 ***
0.0013 ***
0.0006*
-0.0024 ***
0.42 -0.0003 0.5168 ***
0.0003 0.0016 -0.0020 ***
0.57 -22%
Dev2 0.0000 0.5098 ***
0.0013 ***
0.0024***
-0.0014 ***
0.53 -0.0001 0.6098 ***
-0.0004 0.0012 -0.0018 ***
0.65 -20%
Dev3 0.0000 0.3262 ***
0.0036 ***
0.0035***
-0.0026 ***
0.33 -0.0002 0.3831 ***
-0.0010 0.0013 -0.0044 ***
0.40 -17%
Dev4 -0.0003 0.4438 ***
0.0012 ***
0.0007**
-0.0009 ***
0.45 0.0000 0.5202 ***
0.0002 -0.0010 -0.0005 0.66 -17%
Dev5 0.0000 0.3787 ***
-0.0003 0.0007 0.0001 0.20 -0.0009 0.4388 ***
0.0026**
0.0016 0.0022 ***
0.32 -16%
Dev6 -0.0002 0.3457 ***
0.0027 ***
0.0035***
0.0002 0.21 -0.0013 0.2617 ***
0.0019 -0.0021 0.0000 0.08 24%
Dev7 0.0000 0.4091 ***
0.0010 **
0.0025***
0.0002 0.25 -0.0014 0.2788 ***
-0.0009 0.0012 0.0017 *
0.11 32%
Dev8 0.0000 0.3238 ***
0.0005 0.0021***
-0.0002 0.16 -0.0017 *
0.2547 ***
0.0070***
-0.0005 0.0012 0.10 21%
Dev9 0.0000 0.6074 ***
0.0002 -0.0011 0.0003 0.65 0.0003 0.5703 ***
-0.0034*
0.0011 0.0037 **
0.68 6%
Dev10 0.0000 0.4276 ***
0.0005 0.0015***
-0.0004 0.33 -0.0008 0.4245 ***
0.0054***
0.0047 ***
0.0031 ***
0.33 1%
Dev11 0.0001 0.3751 ***
0.0000 0.0025***
-0.0007 ***
0.23 -0.0002 0.5543 ***
0.0006 0.0010 -0.0002 0.52 -48%
Dev12 0.0000 0.3051 ***
0.0002 0.0017***
-0.0008 ***
0.17 -0.0001 0.4938 ***
-0.0010 -0.0004 -0.0013 *
0.43 -62%
Dev13 0.0000 0.3663 ***
0.0001 0.0007 0.0005 *
0.22 -0.0006 0.4668 ***
0.0023**
-0.0009 0.0013 *
0.37 -27%
HU1 -0.0002 1.0168 ***
0.0007 0.0018***
0.0007 **
0.77 -0.0005 0.9550 ***
0.0023**
0.0009 -0.0007 0.77 6%
HU2 0.0004***
0.6147 ***
0.0009 ***
0.0019***
0.0001 0.65 -0.0001 0.7700 ***
0.0012*
0.0001 -0.0005 0.80 -25%
HU3 -0.0001 0.8656 ***
0.0008 0.0014*
0.0010 **
0.68 -0.0002 0.5229 ***
0.0031***
0.0018 **
0.0021 ***
0.63 40%
HU4 0.0006 0.4240 ***
-0.0007 -0.0021**
-0.0014 ***
0.17 -0.0020 *
0.4894 ***
0.0051***
0.0015 0.0023 **
0.24 -15%
HU5 0.0004 0.5769 ***
0.0011 **
0.0023***
0.0008 **
0.30 -0.0018 *
0.3882 ***
0.0075***
0.0045 ***
0.0034 ***
0.20 33%
HU6 0.0000 0.7458 ***
0.0008 **
0.0014***
0.0007 ***
0.64 -0.0005 0.6946 ***
0.0029***
0.0003 0.0005 0.70 7%
Average R2
for the full sample 0.56 0.55
Average R2
for funds investing in Hungary 0.71 0.56
Average R2
for funds investing in CEE region 0.65 0.73
Average R2
for funds investing in developed markets 0.40 0.40
Notes: This table shows the results of the modified Carhart (1997) four-factor model, using CETOP20 as a market proxy for bullish and bearish periods.
*, **, *** denote significance levels of 10%, 5% and 1% respectively.
44
Based on our results SMB factor has more explaining power in recession than expansion
as the SMB factor is significant at 10% in 21, 13 and 20 cases for the whole, bullish and
bearish periods respectively (at 1% significance the results are 13, 8 and 13).
For the momentum factor we have measured no substantial difference for bullish and
bearish periods, at 10% level it was significant for 15 funds in the former and 18 funds in
the latter case (at 1% 8 and 11 funds). The significant momentum factor underlines the
fact the persistence in of mutual fund performance is a relevant risk factor as Bollen and
Busse (2005) show for the US and Filip (2011) argues for the Hungarian mutual funds.
4 Fund flows and returns
We have incorporated an additional variable into our model the percentage change of the
number of shares outstanding in order to separate the changes in the net asset value of
the funds caused by the change of the price and by the purchase of new shares or
redeem existing ones by the investors.
Table 4 Carhart four-factor model with C20 market proxy extended with the trade
volume for the 30 funds
Full period Bullish Bearish
const C20 SMB HML MOM Vol % R2
Vol % Vol %
CE1 0.0002 0.7840***
0.0003 0.0015***
0.0002 0.0019 0.77 0.0021 -0.0038
CE2 0.0000 0.8031***
0.0005**
0.0013***
0.0003**
-0.0101 0.83 -0.0115 -0.0099
CE3 0.0001 0.9384***
0.0007**
0.0006*
-0.0002 -0.0104**
0.92 0.0032 -0.0122**
CE4 0.0000 0.6965***
-0.0002 0.0010***
0.0006***
0.0026 0.69 0.0056 -0.0185
CE5 0.0000 0.7231***
0.0007***
0.0010***
0.0006***
0.0254 0.80 0.0317 -0.0087
CE6 0.0001 0.4522***
0.0023***
0.0016***
0.0012***
-0.0263 0.36 -0.0200 -0.0496
CE7 0.0002 0.3141***
0.0013***
0.0012***
0.0005**
-0.0035 0.21 -0.0035 -0.1096
CE8 0.0001 0.7890***
0.0000 -0.0002 -0.0001 0.0011 0.89 -0.0056 0.0136
CE9 0.0000 0.8349***
0.0007***
0.0018***
0.0002 0.0010 0.80 0.0012 -0.0064
CE10 0.0000 0.6952***
0.0007**
0.0018***
0.0007***
0.0569***
0.67 0.0191 0.2049***
CE11 0.0001 0.3745***
0.0013***
0.0009**
0.0006***
0.0225 0.27 0.0205 0.0522
Dev1 -0.0002 0.4594***
0.0008**
0.0008**
-0.0024***
0.0011 0.47 0.0047 -0.2003
Dev2 -0.0001 0.5501***
0.0006*
0.0022***
-0.0014***
-0.0075 0.57 0.0012 -0.0556**
Dev3 0.0000 0.3622***
0.0020***
0.0032***
-0.0031***
-0.0009 0.35 0.0043 -0.0210
Dev4 -0.0002 0.4729***
0.0008***
0.0000 -0.0006***
0.0791***
0.52 0.0768
***
0.1078**
Dev5 -0.0001 0.3948***
0.0004 0.0003 0.0007***
0.0096 0.23 0.0113 -0.0224
Dev6 -0.0003 0.3219***
0.0028***
0.0018***
0.0008***
0.0131 0.15 0.0097 0.0308
Dev7 -0.0001 0.3703***
0.0008*
0.0016***
0.0009***
-0.0056 0.19 -0.0197 0.0628
Dev8 -0.0002 0.2873***
0.0025***
0.0009*
0.0005**
-0.0007 0.12 -0.0010 -0.1635
Dev9 0.0002 0.6023***
0.0000 -0.0011 0.0004 0.8683 0.65 0.6697 1.4486
Dev10 -0.0001 0.4147***
0.0018***
0.0014***
0.0003 0.0001 0.32 -0.0005 0.0284
Dev11 0.0000 0.4395***
-0.0003 0.0019***
-0.0004*
0.0147**
0.32 0.0016 0.1299***
Dev12 -0.0001 0.3766***
-0.0006 0.0013***
-0.0008***
0.0091 0.25 0.0080 -0.0073
Dev13 -0.0001 0.3985***
0.0005 0.0000 0.0009***
0.0059 0.26 0.0061 -0.0132
HU1 -0.0002 0.9878***
0.0016***
0.0018***
0.0002 0.0125 0.77 0.0111 0.0388
HU2 0.0003*
0.6715***
0.0006**
0.0015***
0.0000 -0.0012 0.71 -0.0010 -0.1161**
HU3 0.0001 0.7051***
0.0034***
0.0016***
0.0017***
0.0093 0.63 0.0002 0.0544**
HU4 0.0000 0.4332***
0.0011 -0.0016**
-0.0003 -0.0100 0.19 -0.0139 -0.0098
HU5 0.0001 0.4959***
0.0034***
0.0021***
0.0014***
0.0021 0.26 -0.0119 0.0940
HU6 0.0000 0.7239***
0.0015***
0.0011***
0.0007***
-0.0268 0.66 -0.0230 -0.1759
Average R2 0.49
Notes: This table shows the results of the modified Carhart (1997) four-factor model, using
CETOP20 as a market proxy extended with daily percentage changes in the number of shares
outstanding for the full sample period of January 2001 and February 2013.
*, **, *** denote significance levels of 10%, 5% and 1% respectively.
For the whole investigated period the trade volume variable is significant for 4 funds for
the whole investigated period, for one fund in the bullish and for 7 funds in the bearish
period (at 5% level, while at 1% level these numbers are 2, 1 and 2 respectively). This
result suggests that when prices are falling on the given trading day the reactions of the
45
investors are more intense, however the signs are positive in 4, and negative in 3 cases,
so investors react by purchasing new shares in the former and redeeming existing shares
in the latter case. (See Table 4)
In the next version of our model we have used the percentage change in the number of
shares outstanding on the day following the day of which return we wanted to explain. At
first glance this may seem a bit odd as we want to explain the return with a next day
data; these regressions can not be used to forecast returns, we just aim to measure the
strength of the relationship between the variables (and not the causality between them).
So the problem we address is whether daily returns of the funds influence investors’  
decisions about purchasing new shares or redeeming existing ones, or to put it another
words after a significant positive or negative return they change their investment in the
fund or not.
The percentage change in the number of shares outstanding in the following day shows
significant relationship with the actual day return in case of 10 funds out of the 30 for the
whole period, 10 funds for the bullish and 6 funds for the bearish period (at 10% level).
So investors change their position in the fund after a day resulting in a positive return
than after a day when prices are falling, however the direction of these changes in their
positions are mixed in both periods: in exactly half of the cases they increase and in half
of the cases they decrease their position during both periods. After a substantial price
change investors are more likely to change their position if this change was positive,
while after a day resulting in a substantial loss they are less likely to change their
investment. So their relation to risk seems to be different in bearish and bullish periods
confirming the Kahneman-Tversky (1979) prospect theory.
5 Conclusions
By applying the modified version of Carhart (1997) model using the Central Eastern
European index as a market proxy we find that mutual fund managers neither can beat
the market for the full period nor in bullish or bearish market circumstances. We find
significant difference in market beta for the bullish and bearish markets, with lower betas
for the bearish periods. Although book-to-market ratio proved to be significant
explanatory power for the whole investigated period and for bullish market; however, in
bearish market circumstances the estimated coefficients are not significant. Conversely
the market cap as an explanatory variable has higher power in recession than in
expansion. For persistence parameter we detect no difference for distinct market
conditions. Concerning the actual trading day fund flows we find that when prices are
falling the reactions of the investors are more intense. Furthermore after a substantial
price change investors are more likely to change their position if this change was
positive, while after a day resulting in a substantial loss they are less likely to change
their investment. So their relation to risk seems to be different in bearish and bullish
periods confirming the Kahneman-Tversky (1979) prospect theory.
Acknowledgments
We  ackonowledge  the  research  assistance  of  Gábor  Hajós.
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47
Insurance of Risks of Small and Medium-sized Enterprises
Viktória  Čejková1
,  Svatopluk  Nečas2
1
School of Economics and Management in Public Administration in Bratislava
Department of Economics and Finance
Furdekova 16, 851 04 Bratislava, Slovak Republic
E-mail: cejkova@vsemvs.sk
2
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  602  00  Brno, Czech Republic
E-mail: necas@econ.muni.cz
Abstract: Insurance of risks of enterprises belongs to the most important parts of every
insurance market with developed market economy. It helps to stabilize the operation of
the business entity and its economy as well. The damages suffered by the company are
covered by insurance benefit and the company can use its available funds for
development activities, instead of having to spend them on renewal of its assets or
processes. Currently, on the insurance market there is a large number of products that
cover the vast majority of insurable risks.
Keywords: insurance, business risks insurance, small enterprises, medium-sized
enterprises, risks of entrepreneurship
JEL codes: G22
1 Introduction
Business of small and medium enterprises (SMEs) plays an important and irreplaceable
role in all countries with a market economy. We can argue that SMEs are the backbone
of the European economy and in the long run also a major employer. The great
advantage of SMEs is their flexibility, lower capital intensity, upgrade and covering more
and more differentiated market segments. They are considered the most effective, most
progressive and therefore the most important part of the economy.
The aim of the article is to characterize the risks of small and medium-sized enterprises,
their insurance options and to present insurance market development of business risks
insurance in the Czech Republic. In order to achieve the aim, the methods
of analysis and comparison will be used. The statistics presented by Czech Insurance
Association will be used as a data source.
2 Risks of Small and Medium-sized Enterprises and Possibilities of its
Insurance
Economic activity carried out by small and medium-sized enterprises has its negative
symptoms. Among the problem areas including, in particular complex legislation, poor
law enforcement, high contribution burden, as well as the administrative burden
of business. On the other hand, enterprise are threaten by risks arising from business
activities (such as manufacturing, technical and technological, market, social, financial
risks etc.). In recent decades, the range of risks faced by business entities extended.
Risks associated with various natural disasters, vandalism and deception were added.
Losses caused by those risks can be solved by insurance.
First of all we should define a small and a medium-sized  enterprise.  “For micro, small and
medium-sized enterprises (SMEs) is considered an entity if:
a) it employs less than 250 employees, and
b) its assets/property do not exceed CZK equivalent amount of 43 million EUR or
the turnover/income do not exceed CZK equivalent of 50 million EUR.
48
A small enterprise is considered an entity if:
a) it employs less than 50 employees, and
b) its assets/property or turnover/income do not exceed CZK equivalent of 10 million
EUR.”  (Czech  Trade,  2009).
If a company is not, in accordance with the above mentioned parameters, among micro
or small or medium-sized enterprises, it belongs among the big companies. “The  
importance of SMEs to the national economy is irreplaceable. For example, in Slovakia,
SMEs constitute 99 % of all enterprises, they are involved in employment by 66 %, their
share on value added is of about 60 % and their share on gross domestic product is
about  40  %."  (Strážovská,  Strážovská,  2010)  They  are  the  biggest  employer  in  Slovakia  
and the main creator of new jobs. They are the driving force of the market economy and
contribute substantially to economic growth. The situation in the Czech Republic is very
similar. Between 2003 and 2010 SMEs constituted more than 99 % of all enterprises,
they were involved in employment in business segment by more than 70 % (in the whole
economy by more than 50 %), their share on book value added was about 1/3 (about
57 % if goes about book value added of business sector) and their share on gross
domestic  product  was  about  1/3  as  well.  (Dubská,  2013)  "SMEs  are  also  able  to  adapt  
quickly and flexibly to market requirements and demand, they are innovative, often
come up with new products and original ideas to satisfy even the most demanding
customer requirements, employ people from the region and provide numerous
opportunities  for  employment."  (Strážovská,  Strážovská,  2010)
2.1 Risks of Enterprises
In order to do business seamlessly, each company must have an overview of the risks
that could threaten its trouble-free operation in the future.
We can note that there are many different risks that can be sorted according to various
aspects - pure and speculative risk, risk caused by natural factors and the risk caused by
human factor, internal and external risks, influenceable and not influenceable risks etc.
As mentioned above, the risks of business entities can also be seen from a point of view,
whether factors operate inside or outside the entity. We talk about internal and external
risks. The internal risks include failure to comply with such safety regulations, which may
damage the health of employees. The external risks include, for example flood that may
cause damage to property and the health and lives of people. Risks occurring mainly as a
result of sudden changes in natural conditions are called natural hazards.
Many risks can be eliminated. They can be influenced and subsequently revised. Such
risks are called influenceable risks. Among these risks include, for example, damaged or
stolen property by theft. A business entity may use various means to reduce the risk of
theft of property such as setting up of guard service, security system, concierge and
more. Some risks are difficult to influence or predict or even absolutely not influenceable.
An example of such a risk is a volcanic eruption or flood.
When insuring risks of SMEs, it is all about insurable risks. For each risk the insurer is
considering whether it is possible to accurately identify the risk, whether a loss may be
quantified after the risk realization and whether the risk is economically viable and
therefore acceptable for the insurer. If the risk is random, identifiable, quantifiable and
cost-effective, so it is possible to insure it. We say that the risk is insurable. Each
insurance company discloses risks that can be insured.
Negative event can cause to people and businesses direct and indirect losses. Direct
losses they are quantifiable - for example, damage or destruction of property. Indirect
losses arise from interruption of the activities of the entrepreneur (such as loss of market
position) or damage to his reputation. Such losses are hardly quantifiable and therefore
hardly insurable.
In practice, various risk factors and risk level are important in determining the amount of
the premium. When calculating the premium, the insurance contracts are classified
49
according to various factors and risk levels for tariff groups. Tariff group is a group of
insurance contracts with approximately the same risk. For example, for motor hull
insurance, there may be these risk factors - age of driver, vehicle type and region.
On  that  basis,  different  levels  can  be  observed.  For  example  factor  “age  of  driver”  can be
divided into three levels - age 18-25 years, 26-58 years, 59 years and more. Number of
tariff groups is then equal to the product of the number of factors and levels. Each group
has a different tariff premium. In this way, the calculation is simplified in practice.
People or business entities may not insure any risks that endanger their life or business.
Some risks can be eliminated by other means. It is important to know the instruments
of coping with risks implement a process called risk management.
Risk management is a process of examining the risk, its influence, prevention and harm
reduction and exploring possibilities for their financial coverage. During the process of
risk management, first of all, it is important to know the risks that threaten the entity.
This includes the identification, determination of severity and probability of their
occurrence. Further this includes assessment of measures which could reduce risks.
These include, for example, various preventive measures, such as various training
courses, using a variety of security features such as fire alarms, usage of security
services etc. An important part of risk management is a risk distribution and financial
coverage, particularly insurance. Before risk identification the owner should make an
“inventory”   of   all   the   values   in the company (physical, financial, intangible assets,
workforce), which could be a subject to loss or destruction. It must be done to determine
risk factors as a potential threat to a company. During the identification process there
can be detected and classified these and other risks (mentioned as a model in Table 1).
Table 1 Types of detected external and internal risks
External Risks
 Natural Hazards
 Theft and Vandalism Risks
 Credit Risk
 Risks of non-payment of Debts
 Payment Risks
 Transport Risks – Risks of National and International
Carriage
Internal Risks
 Risk of occupational accident
 Risk of losing skilled workers
Source:  Čejková  (2012)
Businesses have insurance that they are obliged to have by law, i.e. compulsory
contractual insurance, and beyond it also voluntary contractual insurance contract which
the company decided itself for. The compulsory contractual insurance comprehends e.g.
motor third party liability insurance, environmental damage insurance and other
insurance products. Voluntary insurance comprehends e.g. motor hull insurance with the
coverage of these risks: accident, theft, vandalism, unauthorized use of a vehicle or
natural hazards. Among these risks belong: storms, hail, floods, fires, landslides,
earthquakes etc.
During the process of risk identification number of risks is usually identified, of which
only a tiny part tends to have any insurance coverage. Risks representing catastrophic
impact on the enterprise as natural hazards and risks of theft are sometimes not covered
at all. In turbulent economic times insurance can be the difference between the company
staying afloat or going out of business.
2.2 Insurance Products for Enterprises
In accordance with the Czech Insurance Association we can mention a wide classification
of business risks insurance:
 Property Insurance (theft, vandalism, natural hazards)
 Motor Insurance (Hull insurance, Motor Third Party Liability - MTPL)
50
 River/Sea/Aircraft/Railway Vehicle Hull Insurance
 Technical Risks Insurance/Machinery and Electronics Insurance (Technical Risks
Insurance - insurance of machinery and equipment, insurance of electrical
equipment; construction and installation insurance – this insurance is sometimes
offered separately and does not correspond to machinery and electronics insurance)
 Business Interruption Insurance
 Agricultural Insurance
 Cargo Insurance
 Legal Expenses Insurance
 Credit Insurance (general insolvency, export credit, mortgage credit, business
credit, agricultural credit)
 Financial Risks Insurance (e.g. covering losses incurred to banks and other financial
institutions caused by malversation/embezzlement, fraud or forgery)
 Insurance of Leasing Payments
 Financial Default Insurance of Travel Agency
 Liability Insurance (Management Liability Insurance; Carrier Liability Insurance;
Environmental Damage Insurance; Liability Insurance for Cities and Municipalities;
Farm Entities Operation Insurance/Farm Business Insurance; Selected Professions
Compulsory Liability - for doctors, experts, assessors, interpreters, translators,
accountants, engineers etc.; Product Liability Insurance; Freight Forwarder Liability
Insurance;;   Intermediaries’   Liability Insurance; Employer’s   Statutory   Liability  
Insurance)
 Life and Accident Insurance
 Other risks (Czech Insurance Association, 2007, 2010a)
In the following text, the selected insurance products for SMEs will be discussed.
Property Insurance
Property Insurance includes insurance coverage for a case of natural hazards and theft.
It is important for enterprises to have insurance for a case of realization of these and
also other risks. The subjects of this insurance are various office buildings, repair
workrooms and other service buildings. The basic part of property insurance is the
natural hazards insurance. Property (individual things or their set) is in base insured for
the event of damage or destruction by fire, explosion, lightning strike and collision or
aircraft crash. This basic insurance can be extended to protection of damages caused by
flooding, flash flooding, storms, hale, landslide, falling rocks or earth, avalanches, trees,
earthquake, weight of snow and frost, water leaking from water supply equipment, fluid
escaping from the sprinkler etc. If the building is insured, the insurance can cover also
the water pipe, drainpipe, heating and solar systems. The second part is the theft
insurance that provides protection in a case of theft or robbery, if the offender overcame
barriers protecting the insured object or used violence. Insured matters must be
protected against theft manner consistently with their values (security locks and film,
safes etc.).
Business Interruption Insurance
“Property Insurance may be extended by Business Interruption cover that provides
protection  against  financial  consequences  (lost  profit,  extra  expense)  of  a  material  loss.”  
(AIG, 2013) It replaces business income lost as a result of an event that interrupts the
operations of the business, such as fire or a natural disaster. Business interruption
insurance is not sold as a separate insurance, but is either added to the Property
Insurance. This   insurance   covers   “loss of profit from manufacturing or commercial
activities which the insured would otherwise earn for the period of business interruption,
but not longer than for the insured period, and fixed costs specified in the insurance
contract which the insured must unconditionally carry even in case of business
interruption, for the period no longer than the insured period. Variable costs which do not
happen during business interruption are not covered, for example, costs of materials,
raw material, technological energy supply and delivered goods, if the costs in question
are not maintenance costs or operational costs etc.“  (MAXIMA,  2013b)
51
Cargo Insurance
Cargo Insurance provides coverage against physical damage or loss of goods during
shipping, whether by land, sea or air. Because of the many dangers inherent in shipping,
most individuals and businesses choose to insure their goods while they are in transit.
The entrepreneur can choose an all-risk insurance. All-risk offers broad coverage against
loss  or  damage  from  external  causes.  Although  it  is  called  “all-risk," most policies exclude
many types of damage from coverage. These may include damages from improper
packing, infestation, rejection by customs, abandonment of cargo and employee
dishonesty, among others. It is very important with all-risk coverage to examine the
policy and conditions carefully to determine what types of loss are not covered. The
advantage of all-risk   coverage   is   that   it   is   not   necessary   to   first   prove   the   carrier’s  
liability to claim damages. The insurance will pay out regardless of who is at fault.
(Demand Media, 1993-2013)
Credit Insurance
Credit Insurance is insurance for the risk of non-payment of short-term receivables from
buyers as a result of their insolvency or protracted default (commercial risks), or as a
result of territorial (political) risks on the part of a foreign buyer. The goal of credit
insurance is to secure the non-payment risk (business risk) in relations between business
entities. The price of the insurance is a fraction of a percent of the invoiced amount,
whereas the possible indemnity may be as high as the invoiced amount (minus the
deductible). (KUPEG, 2011) The second form of this insurance is payment protection
insurance (Creditor Protection Insurance), purchased by consumers to insure payment of
credit extended to the consumer. This insurance is offered also by banks when providing
a credit and is available also for entrepreneurs (natural persons). The covered risks are:
death, full disability, temporary inability to work and job loss.
Legal Expenses Insurance
“Legal   Expenses   Insurance is designed to provide cover against the potential costs of
legal action against an institution or individual. The insurance covers the legal costs
involved in pursuing or defending a claim. Legal costs include the appointment of
solicitors, barristers   and   expert   witnesses.”   (Telegraph   Media   Group,   2010)   In   other  
words,   “it   provides   affordable   coverage   for   the   legal   fees   and   expenses   incurred   by   a  
lawyer on specified matters. These matters are defined as fortuitous events, or issues
that are not anticipated. These could include employment disputes, litigation, disciplinary
actions,  Human  Rights  complaints  and  criminal  charges.” (LegalExpenseInsurance, 2011)
General Business Liability Insurance
General Business Liability Insurance covers the entrepreneur's expenses for claims of
third parties which he carries legal responsibility for. In case of a small business, the loss
in question does not necessarily have to happen due to negligence or due to violation of
legal obligations of an entrepreneur. In many cases an entrepreneur is responsible for a
loss regardless of whose fault it is (i.e., objective fault). Losses can incur due to damage
to health or life, material loss and consequent financial loss. (MAXIMA, 2013a)
Business Liability Insurance usually covers losses incurred to the insured person in the
result of operational activities, exercising of the right of ownership, maintenance and
administration of the immovable property and by faulty product (see below).
This liability insurance can also be extended to include insurance for recourse of health
insurance companies in the event of an occupational accident, recourse of sickness
benefits paid out and insurance for damage to items assumed and used. Business liability
insurance can have many forms and the risks mentioned above can be divided in a
different way.
Product Liability Insurance
“Companies  that  manufacture,  wholesale,  distribute,  and  retail  a  product  may  be  liable  
for its safety. Product liability insurance protects against financial loss as a result of a
52
defect product that causes injury or bodily harm. The amount of insurance depends on
the   products   that   the   entrepreneur   sells   or   manufactures.”   (US   Small   Business  
Administration, 2013) “Product   liability   and   the   related   damages   are   governed   by the
provisions  of  act  number  59/1998  Coll.”  (IMG,  2011)  The  (defecte)  product  “can  cease  to  
perform its functions due to faulty construction or material fault and as a result may
cause losses (e.g., faulty refrigerating equipment may cause damage to stored food in a
shop). The subject of the insurance is not the faulty product itself (a mender), but a loss
which   it   incurred.”   (MAXIMA,   2013a)   “The   Act   No.   59/1998   Coll.   stipulates   the  
responsibility of the manufacturer in a case where a product defect causes damage to
health or death or damage to property. The injured party has a choice whether to apply
for damages  under  this  Act  or  under  the  general  rules  on  liability  for  damages.”  (Czech  
Trade Inspection Authority, 2012)
Table 2 Development of selected indicators of business insurance coverage
2012 2011 2010 2009 2008 2007
Non-life Insurance
Premium Written
(thousands of CZK)
67 460 347 71 447 131 72 740 658 73 857 822 74 050 076 70 556 338
Number of policies
(non-life insurance)
19 245 994 19 197 418 18 739 476 18 588 193 17 839 389 16 923 224
Total Business Insurance Coverage
Premium written
(without mandatory
insurance; thousands
of CZK)
18 151 474 17 811 634 17 205 668 17 208 236 16 669 497 15 647 603
Insurance Benefit
(thousands of CZK)
N.A. 9 046 154 9 324 787 8 125 852 7 648 754 6 837 811
Loss Ratio (%) N.A. 50,8 54,2 47,2 45,9 43,7
Business Insurance
Coverage Premium/
Non-life Insurance
Premium (%)
26,9 24,9 23,7 23,3 22,5 22,1
Number of policies
(business insurance
coverage)
807 924 810 911 799 536 818 413 799 108 780 865
Number of policies in
business insurance
coverage / number of
policies in non-life
insurance (%)
4,2 4,2 4,3 4,4 4,5 4,6
Property Insurance in Business Insurance Coverage
Premium Written
(thousands of CZK)
N.A. 12 344 256 12 132 345 12 113 437 11 643 323 11 183 738
Insurance Benefit
(thousands of CZK)
N.A. 7 107 108 7 500 515 6 521 121 5 848 414 5 333 326
Loss Ratio (%) N.A. 57,6 61,8 53,8 50,2 47,7
Business Liability Insurance
Premium Written
(thousands of CZK)
N.A. 4 459 168 4 193 104 4 132 085 4 516 074 3 791 336
Insurance Benefit
(thousands of CZK)
N.A. 1 751 548 1 676 228 1 466 022 1 566 361 1 196 401
Loss Ratio (%) N.A. 39,3 40,0 35,5 34,7 31,6
Source: Czech Insurance Association (2010b, 2011, 2012, 2013)
3 Business Risks Insurance as a Part of Insurance Market
As mentioned above, Business Risks Insurance (Business Insurance Coverage) has a
great importance in every market economy. The importance and level can be shown by
several indicators. In developed economies, there are used (for instance) these
indicators: premium written, insurance benefit or loss ratio.   (Čejková,   Martinovičová,  
Nečas,   2011).   It   can   also   be   very   interesting   to   show   the   share   of   business   risks  
insurance on the whole non-life insurance market. In recent years, there was an increase
53
in the field of business risks insurance in the Czech Republic (see data in Table 2 for
years 2007 – 2012). (Czech Insurance Association, 2013)
It is apparent an increasing trend of companies to protect against consequences of their
risks and to use insurance instead of relying on the use on their own resources. But
unfortunately it is still true that in case of economic problems of a particular business
entity, the insurance is on   the   first   place   when   the   cost   reduction   is   needed.   (Šídlo,  
2011) In the Czech Republic, this insurance has a long tradition and comprises about
¼  of  non-life insurance market according to premium written and about 4 % according to
number of insurance policies (see data in Table 2). Although this data does not seem to
too high, we have to mention that some business risks are covered and thus reported in
other insurance products e.g. motor hull insurance or MTPL. Of course, there is still a
huge space pro insuring the risks of other entities. If goes about financial stability of
business risks insurances, the situation is very good. Loss ratio is only about 50 %
(in property insurance about 60 %, in liability insurance about 40 %) and such results
are satisfactory.
4 Conclusions
The business activities of small and medium-sized entrepreneurs also have its negative
manifestations. These companies threaten various business risks - internal and external
ones. Insurance is an important form of financial coverage of risks. Insurance is aimed to
cover risks whose realization would cause to entrepreneur considerable damages and
could lead even to the collapse of the company. Therefore, nowadays a role of business
risks insurance of small and medium-sized entrepreneurs increasingly accrues.
In this paper we described and presented the typology of risks of enterprises,
a segmentation of possible insurance products for these enterprises and also details of
several selected and very important products. In the final part of the paper, there was a
brief analysis of insurance market of business risks insurance. SMEs conclude various
insurance products for protection from the consequences of their risks. In the first place,
these are mainly insurances, which they are obliged to conclude by legislation norms, as
we stated in our article. Beside compulsory insurances they conclude also voluntary
insurance products, but markedly less than they could and should do.
References
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55
Country Risk as a Phenomenon of the Current European Debt Crisis
Božena  Chovancová, Peter  Árendáš
University of Economics in Bratislava
Faculty of National Economy, Department of Banking and International Finance
Dolnozemská  cesta  1,  852  35  Bratislava, Slovak Republic
E-mail: p.arendas@centrum.sk
bozena.chovancova@euba.sk
Abstract: Current European debt crisis leads to new roles for economic theory that
should try to find solutions of actual problems. There are a couple of new problems in the
financial markets as well. The risk attitude of investors has changed significantly,
especially the perception of sovereign risks. Issues of securities and trading with bonds
and shares are among the most affected. From the investor point of view it is important
to quantify the level of country risk that reflects not only the danger of default but also a
risk premium based on development of various macroeconomic fundamentals. The aim of
this  paper  is  to  analyze  economic  category  “country  risk”  and  methods  of  quantification  
of country risk premiums with focus on current situation in Europe and the change of
country risk perception in the case of developed (Germany, France, Italy, Spain) and
emerging (Slovakia, Czech Republic, Poland, Hungary) European countries, compared to
the pre-crisis era. The country risk premium calculations are based on credit ratings and
credit default swaps that have huge impact on risk perception of the particular country.
As   calculations   show,   the   real   market   situation   doesn´t   match   theoretical   calculations,  
which is caused by various political and psychological factors.
Keywords: country risk, risk margins, risk rating, financial markets, debt crisis
JEL codes: G11, G15, E52
1 Introduction
Risk   on   financial   markets  is  in  the   centre   of   attention   of   today´s   economic   theory   and  
praxis. It represents a wide range of problems which creates space for various spheres of
research, from country risk on the macroeconomic level to sector risks and company
risks on microeconomic level. There are also different types of risks for different
segments of financial markets. Especially impacts of particular risks on stock and bond
markets are being analyzed intensively.
The key problem in analyzing risks on financial markets is the quantifying of country risk
and setting the risk free interest rate correctly. These parameters are highly important
factors for investors to evaluate the effectiveness of investment projects as well as the
incremental value of securities.
There are a lot of opinions on the problematic of country risk in the economic literature
nowadays. The authors tend to use various attempts and methodologies. The attention
should be paid to authors such as Booth, Laurence (1999), P. Santa-Clara and S. Yan
(2006).
A world renowned specialist in this area is A. Damodaran who studies the problematic of
country risks mostly from the point of view of company valuations. His publication
“Security  Analysis  for  Investment  and  Corporate  Finance”  (2006)  is  frequently  cited  and  
well renowned by theorists and investment professionals as well.
Other important authors are A. Goyal and I. Welch (2007), J.Y. Campbell and S.B.
Thompson (2008), I. Alexopoulou, I. Bunda and A. Ferrando (2009), J.H. van
Binsbergen, M.W. Brandt and R.S.J. Koijen (2011). Some of the authors, for example S.
Lyócsa,   E.   Baumohl   and   T.   Výrost   (2011),   focus primarily on emerging markets of
Eastern Europe.
56
This paper is focused on problematic of defining risk free interest rates and quantifying
country risk margins for developed European countries and emerging markets of
Vysegrad Group (V4) countries.
2 Economic  Category  “Risk  Free  Rate”
Financial theory says that risk is the deviation of the actual return from the expected
return. When the actual return equals the expected return, the risk equals zero. Thence
resulting that the actual return of a risk free investment equals the expected return. The
risk free investment must fulfill three conditions:
 There is no default risk
 There is no reinvestment risk
 Time horizon of investment
On  financial  markets  the  role  of  “risk  free”  investments  play  government  securities. They
are supposed to be the safest investment vehicles. The best suitable for this task are TBills
and partially also longer term government bonds. But it is important to note that
there is a threat of the reinvestment risk when investing in 5- or 10-year bonds. The
reason is that the change of interest rates can cause a change in realized yields and
thence there is the threat of deviation of actual yield from expected yield. T-Bills are
short term discounted securities therefore the reinvestment risk is diminished.
The reinvestment risk of longer term bonds can be resolved by issuing of zero bonds. The
actual yield of zero bonds equals the expected return when holding it to maturity. The
issuance of zero bonds is more popular under current conditions in Europe.
But   it   is   important   to   note   that   government   bonds   can´t   be   supposed   to   be   risk   free  
anymore. A lot of European countries face the risk of default. This risk is reflected in
higher risk margins. The simplest way to determine the country default risk is the
country rating.
Table   1   shows   particular   levels   of   country   rating   (Moody´s)   and   related   basis   points.  
Ratings express the probability of default of the rated country. Basis points express the
default risk margins related to particular rating levels.
Table 1 Country Ratings and Default Spreads in Basis Points (January 2013)
Investment range
Rating Aaa Aa1 Aa2 Aa3 A1 A2 A3 Baa1 Baa2 Baa3
Points 0 25 50 70 85 100 115 150 175 200
Speculative range
Rating Ba1 Ba2 Ba3 B1 B2 B3 Caa1 Caa2 Caa3 D
Points 240 275 325 400 500 600 700 850 1000
Source: own processing, using data of www.damodaran.com
According to table 1, default risk equal to zero can be attributed only to countries with
Aaa rating. The differences between rating levels in the investment range are quite small.
The difference between Aaa and Baa3 level are only 200 basis points. On the other hand
we can see significantly growing differences in the speculative range. For example the B1
level brings 400 basis points of default risk spread and B2 level brings 500 points of
default risk spread. The difference between Ba1 and Caa3 is notable 760 basis points. As
the data show, it is in the best interest of every country to have as good credit rating as
possible. High credit rating means lower risk spreads and lower risk spreads result in
lower costs of debt service.
This paper analyses some of the biggest Eurozone economies as well as Vysegrad Group
countries (Slovakia, Czech Republic, Poland, Hungary). The decrease of credit ratings has
impacted the level of default risk spreads of big countries such as France, Italy or Spain.
Only the German economy holds its Aaa rating which means no risk of default. On the
57
other hand Spain has Baa3 rating which means significantly increased default risk (200
basis points compared to Germany).
The best rating out of the Vysegrad group countries has Czech Republic and the worst
rating has Hungary that has slipped to the speculation range. According to table 1, the
risk spread between Czech Republic (A1) and Hungary (Ba1) is 155 basis points.
The key factor for quantifying of country risk is the aforementioned risk free rate. In
Europe we can use Germany as a risk free country because of its Aaa rating and strong
economic fundamentals. Thence the risk free rate is determined by the parameters of
German bond and stock markets.
3 Country Risk Premium
Based on the risk free rate we can quantify risk premium of a selected country. There is
a couple of methods. The simplest one is based on calculating of risk spread as a
difference between yields of short term bonds (T-Bills) of analyzed country and yields of
T-Bills of a risk free country. There are also some more complex mathematical-statistic
methods that calculate risk premiums using various parameters of bond and stock
markets. These methods are often used together in order to get the final result that
involves wider spectrum of various factors.
The aforementioned simple method quantifies country risk spread based on comparison
of short term government bonds (T-Bills) (1). According to this method, the default risk
spread of a country equals the difference between yield of its T-Bills and yield of T-Bills of
a country with triple-A rating. T-Bills of the triple-A rated country represent so called risk
free asset and its yields represent risk free yield.
yFreeCountrRiskBillsTryXBillsCountTcountryX YieldYieldSpreadRiskDefault   (1)
Figure 1 10-year Government Bond Yields (2003 – 2012)
Source: Own processing, using data of Eurostat
0,00%
1,00%
2,00%
3,00%
4,00%
5,00%
6,00%
7,00%
8,00%
9,00%
10,00%
11,00%
12,00%
Germany France Italy Spain
Slovakia Czech Republic Poland Hungary
58
Long term government bonds bring higher level of risk compared to T-Bills. Spread
changes are related not only to ratings but also to market risks of interest rate changes
and currency risk. Most of analyses tend to use 10-year government bonds that bring
reinvestment risk because this time horizon often brings significant changes of capital
markets environment.
As figure 1 shows, yields of 10-year government bonds have changed dramatically during
last decade. Only Germany, France and Czech Republic have managed to maintain their
long term trend of slightly decreasing government bond yields. Slovakia and Poland show
signs of stagnation from 2006 to 2013. Despite high level of volatility, Hungarian
government bond yields have oscilated around the 7-8% range most of the time.
Negative impact of the European debt crisis can be seen especially on Spanish and Italian
bond yields that have risen significantly during 2009-2012.
We can also see a notable increase in risk spreads. France, Spain, Italy and Czech
Republic used to have almost non-existent risk spread compared to German bonds before
2009. But this risk spread has widened significantly during the last years. Specifically
Spain and Italy are eye-catching examples because their risk spreads have risen from
around 0,5% in 2009 to 3-3,5% in 2013, with peak values in 2012 in the range between
5-6%.
A method of quantification of risk premium of stock markets is based on standard
deviations. Standard deviation quantifies the volatility of the stock market. It shows
absolute value of average deviation of measured values from their average value. The
higher is the standard deviation, the higher is the volatility and the higher is the riskiness
of the asset. A. Damodaran (2006) states following relations:
CountryFreeetOfRiskEquityMark
yXetOfCountrEquityMark
Xcountry
DeviationdardS
DeviationdardS
DeviationdardSlative
tan
tan
tanRe  (3)
CountryXuntryRiskFreeCo DeviationdardSlativexpremiumRiskpremiumriskEquity tanRe
(4)
BondMarket
etEquityMark
xspreaddefaultCountrypremiumriskCountry



(5)
Equity risk premium equals risk premium of a risk free country times relative standard
deviation of analyzed country (4). Relative standard deviation of analyzed country equals
standard deviation of its equity market divided by standard deviation of equity market of
a risk free country.
Risk premium of a particular country is calculated as a default spread of the country
times the standard deviation of its equity market divided by standard deviation of bond
market of the country.
For calculation of standard deviation of stock markets and standard deviation of bond
markets historical data are used. The calculations are based on a benchmark stock
market index and a 10-year government bond.
Country default spreads can be determined by two methods. The first one bases default
spreads on country credit ratings. The second one determines it by CDS (Credit Default
Swap) spreads.
As table 1 shows, Germany has credit rating based default risk spread 0% while Spain
has default risk spread 2%. On the other hand equity markets bring significantly higher
risk premiums, from 5,8% to 8,8% compared to bond markets. The overall sovereign
rating based risk premium of Germany is 0%, followed by France (0,38%). Country risk
premiums of Spain and Italy are 2,63% and 3% respectively.
59
Table 1 Country risk based on rating and CDS principles
Country
Sovereign Rating based Equity
Risk Premiums
Country
Risk
Premium
Sovereign CDS based
Equity Risk
Premiums Country
Risk
Premium
Local
Currency
Rating
Rating-
based
Default
Spread
Equity
Risk
Premium
CDS
Default
Spread
Equity
Risk
Premium
Germany Aaa 0,00% 5,80% 0,00% 0,82% 6,03% 0,23%
France Aa1 0,25% 6,18% 0,38% 1,44% 6,96% 1,16%
Italy Baa2 1,75% 8,43% 2,63% 3,03% 9,34% 3,54%
Spain Baa3 2,0% 8,8% 3,00% 3,14% 9,51% 3,71%
Source: own processing, using data of www.damodaran.com
CDS spreads signalize higher default risk compared to credit ratings thence country risk
premiums calculated using CDS spreads are higher. German country risk premium
according to this method is 0,23% which is slightly higher compared to rating based
method. As we can see the difference of results is higher in the case of France, Italy and
Spain where it ranges from 0,71%-0,91%. The quantification using CDS spreads shows
that also a country with risk free rate brings a risk premium.
Table 2 Country risk based on rating and CDS principles (V4 countries)
Country
Sovereign Rating based Equity
Risk Premiums
Country
Risk
Premium
Sovereign CDS based
Equity Risk
Country
Risk
PremiumLocal
Currency
Rating
Rating-
based
Default
Spread
Equity
Risk
Premium
CDS
Default
Spread
Equity
Risk
Premium
Slovakia A2 1,00% 7,30% 1,50% 1,42% 6,93% 1,13%
Czech
Republic
A1 0,85% 7,08% 1,28% 0,89% 6,13% 0,33%
Poland A2 1,00% 7,30% 1,50% 1,30% 6,75% 0,95%
Hungary Ba1 2,40% 9,40% 3,60% 3,16% 9,54% 3,74%
Source: own processing, using data of www.damodaran.com
Table 2 shows country risk premiums of V4 countries. As we can see Slovak, Czech and
Polish country risk premiums are comparable to the German and French ones. Hungarian
country risk premium is more similar to Italian and Spanish ones. As we can see it is in a
contradiction to the real market situation (Figure 1) because yield spreads between
Slovak or Polish government bonds and German government bonds are similar as
spreads between German and Italian or Spanish bonds. Only Czech Republic and France
are in line with calculated results.
4 Conclusions
The  aim  of  this  paper  was  an  analysis  of  economic  category  “country  risk”  and  methods  
of quantification of country risk premiums with focus on current situation in Europe. The
base for quantification of the country risk is credit rating that shows probability of default
of particular country. An alternative method is based on CDS spreads. The country risk is
also related to the stock market that brings even higher level of risk compared to bond
market. As we can see, country risk affects also countries with risk free rating although
their country risk is much smaller compared to other countries.
It  is  also  important  to  note  that  in  reality  the  country  risk  premiums  doesn´t  reflect  the  
theoretical quantifications well. Real risk premiums of most of the analyzed countries are
higher compared to risk premiums calculated using aforementioned methods.
60
Acknowledgments
This   contribution   is   the   result   of   the   project   implementation   “Creating   excellence   in  
economic research department for addressing the challenges of civilization in the 21th
century”   (ITMS   26240120032)   supported   by   the   Research   &   Development Operational
Program   funded   by   the   ERDF   and   VEGA   (1/0329/11)   ” Capital market and its role in
supporting  the  real  economy  of  eurozone”.    
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61
Rate of Investments as a Parameter of the VIM Model
for Appraising Trademarks of Unlisted Companies
Romana  Čižinská, Tomáš  Krabec
ŠKODA  AUTO  University
Department of Financial Management
Tř.  V.  Klementa  869,  293  60 Mladá  Boleslav. Czech Republic
E-mail: cizinska@is.savs.cz
krabec@is.savs.cz
Abstract: The value of a trademark is based on its economic benefit to its owner or the
economic entity owning/using the trademark. All methods of appraising the fair value of
a brand are based on three approaches - comparability, cost and income approach. In
appraising intangible assets of unlisted (private) companies operating in developing
markets, appraisers have to face the problem of the lack of empirical data, or their low
quality. This makes the application of standard appraising methodology disputable.
Therefore we suggest the VIM (Verifiable Interdependent Model) model, which has been
designed specifically for the conditions of use in this environment. VIM model is a
combination of methodological approaches, however results in the income based
valuation approach. We found the income based value of the brand to be the most
sensitive to the rate of the brand relevance, to the amount of reinvestment of earnings
from the brand after taxes in ensuring and maintaining the brand value creation and to
the assumed rate of growth (g) during the second stage of the valuation process. The
goal of the paper is to discuss the costs of branding and to suggest appropriate level of
earnings reinvestments to the brand necessary to preserve and increase the brand value
under different conditions.
Keywords: brand value, intangible assets, costs of branding, investments
JEL codes: M20, M30, G32
1 Introduction
According to International Valuation Standards, (IVS, 2007, GN 4), trademarks of
products and services belong among intangible marketing assets, which are used in
advertising and marketing. The same category also includes geographical designations
and appellations of origin, trade name, industrial designs, corporate wear, Internet
domains, and non-competition contracts.
A trademark which is regulated in the Czech legal order by Trademarks Act no. 441/2003
Sb., can be, under the conditions specified by that Act, any designation that can be
graphically represented, especially words, including personal names, colours, drawings,
numerals, the shape of a product, or its packaging, provided that this designation can
differentiate products and services of one person from products and services of another
person. Trademarks are protected designations which are either entered in a relevant
register (e.g. a register maintained by the Office for Harmonization in the Internal
Market)  or  are  “generally  known”.  
Instead   of   the   “trademark”   term,   marketing   literature talks about a brand in general,
which  is  defined  by  Hanzelková  et  al.  (2009)  as  a  name,  designation,  symbol  and  set  of  
unique associations connected with the name, designation, or symbol, which differentiate
from rival brands and strongly influence the buyer decision process (similarly also with
the American Marketing Association).
Apparently, there is (or can be) a difference between the brand value perceived by
customers and the financial value and/or the value for an enterprise (see also Pelsmacker
et al., 2003). According to Keller (2007), the brand value perceived by the customer is
created  if  the  consumer’s  response  to  marketing  activities  differs  depending  on  whether  
the customer knows the brand or not.
62
According to Aaker (2003), the brand value   is   formed   by   five   categories   of   “assets”:  
awareness of the brand name, brand loyalty, perceived quality, associations connected
with the brand, and other ownership assets (e.g. patents), which increase or decrease
the value brought by a product or service to the enterprise and/or customer in various
ways. The assets:
- increase the value for the customer, if they increase/improve the interpretation and
processing of information, confidence in buyer decisions and user satisfaction;
- improve the value for the company, if they increase/improve the efficiency and
effectiveness of marketing programmes, brand loyalty, price/margin, brand
extension, the leverage effect in business activities, and competitive advantage.
The brand value for a company is therefore created if the risk-free benefits of the brand
(higher income or lower costs) exceed the costs (investment) required for creating and
maintaining brand value.
The goal of this paper is to investigate more the microeconomic factors influencing the
rate of investments in the VIM model for appraising trademarks of unlisted companies.
Therefor we approach the task from top down by designing the model in the first place
and going to the elaboration of the parameters having an impact on the model results
next.
2 Methodology of Appraising Trademarks of Unlisted Companies
According to Keller (2007), a strong brand can bring many benefits regarding higher
revenues  and  lower  costs.  According  to  Hanzelková  et  al.  (2009),  a  quality  brand  is  able  
to strengthen the loyalty of customers, contribute to maintaining stable relationships
between the customer and the producer, improve distribution channels, and reduce
threats to the company from customers. A strong brand reduces marketing costs and can
bring more stable sales and profits. For more details about the issue, see also Aaker
(2003, p. 9).
The financial value and appraisal of a trademark is based on its economic benefit to its
owner or the business that owns the trademark. Products and services produced by a
business with a successful trademark are sold at premium prices. The business owning
the trademark receives this bonus because it can sell at higher prices than its rivals who
are either without a trademark or with a weaker trademark, or because it sells and
produces more, and thus it has saved more fixed costs per production unit.
For the purposes of this paper, we will only deal with the category of the value of
intangible assets, i.e. the fair value. The interpretation and content of this term can be
found in the International Valuation Standards (IVS, 2011) and, in a slightly different
version, within the IFRS 13 standard (for context, see Krabec, 2009).
As   Krabec   and   Čižinská   (2013b)   state,   all   methods   of   appraising   the   fair   value   of   a  
trademark are based on three approaches (see IFRS 13, IVS, 2007, 2011, Krabec, 2009,
Svačina,  2010):
• Comparability approach – this approach is based on the balance principle =
competitive intangible asset markets are able to create balanced prices of intangible
assets  with  a  comparable  utility.  This  approach  serves  as  the  basis  for  the  “market  
multiples  method”.
• Cost approach – based on the principle of economic substitution = a prospect is not
willing to pay more for the relevant assets than he would spend on their creation. In
this case, we consider two types of costs: reproduction costs and substitution costs.
• Income approach – based on the expectation principle = a prospect is not willing to
pay more for the relevant assets than the present amount of expected income from
the use of the assets.
In general, it can be concluded that the utility, and also therefore an economic benefit
from the ownership of a trademark, must be analysed in relation to market chances of
the products and services designated by the trademark on one side, and any savings in
63
the costs of creating the differentiated brand perceived by target groups of customers on
the other (especially in the cases where the enterprise shows a technologically high rate
of fixed costs, and the use of a licensed trademark leads to a realisation of the effects of
the operating leverage thanks to the accelerating dynamics of earnings. These economic
benefits can be examined empirically (the comparability approach and its methods)
within the relevant segment. Because the empirical data is often significantly variable,
the methods deriving the value on the basis of comparison will generally offer a less
accurate estimation. This has many reasons, starting from the lack of data, through a
different structure of realised transactions, to a different value generator, which the
royalties can follow from (gross or net earnings, i.e. earnings less discounts, rebates, and
returns). The income-based valuation is another method, which enables taking more
account of the specifics of a particular enterprise and/or transaction.
A different perspective on the classification of approaches to valuation is offered by
Zimmermann (2002), who, in addition to the standard quantity (monetary) approach,
distinguishes   models   based   partly   or   wholly   on   quality   aspects.   Zimmermann’s  
categorisation extends the aforesaid classification by another three groups of methods,
which, within the determination of the brand value, take more account of the quality
factors. In addition to the commercial-financial methods (see e.g. Fischer, Hermann &
Huber, 2001, Zimmermann, 2003), there are psychographical-behavioural methods (see
e.g. Aaker, 1991, Andersen, 1991, Kapferer, 1992, Keller, 1993, Riesenbeck, 2000),
combined financial-behavioural (see e.g. Schulz & Brandmeyer, 1989, Maretzki &
Wildner,1994, Bekmeier & Feuerhahn, 1998, Sattler, 1999) and input/output models,
and/or   portfolio   models   (see   e.g.   Preißner,   1990). The particular methods within
individual  approaches  are  summed  up,  for  example,  by  Krabec  and  Čižinská  (2013b).
3 VIM Model – Combination of Methodological Approaches
In applying the aforesaid basic or somehow modified valuation methods, appraisers
usually obtain results which differ significantly. This is usually caused by the subjectivity
of parameterisation of relevant valuation models, and mainly by the lack and/or high
scatter of market data. Therefore, we suggest the following combined application of
competitive and income based methods so that the results obtained from independent,
mutually confirming calculations can be verified.
We have suggested a phase VIM (Verifiable Interdependent Model) model (see Krabec a
Čižinská,  2013a,  2013b),  where  the  calculation  procedure  includes  steps  in  the  following  
order:
1) Calculation of the enterprise value of the whole company
It is appropriate to use one of the income based methods for the calculation, preferably
the economic value added method.
Interim step: Partial analysis of brand strength and its evaluation: Subsequently a
corporate discount rate is to be transformed into a discount rate corresponding to the
brand-specific risk on the basis of the evaluation of brand strength.
2) Application of the Benchmarking Valuation Method
Application of multipliers resulting from the share of a price of comparable intangible
assets on the selected economic characteristics related to those assets – the values of
various multipliers can be found in many empirical surveys, e.g. the long-term quantity
research   of   the   Corporate   Branding   Index®   by   the   CoreBrand   company,   which   works  
with the contribution of the brand value to the market capitalisation of the business).
3) Application of Income Based Valuation Methods
We set up brand relevance parameter so that we are able to identify the portion of
economic profit EVA attributable to the brand. The cash flow attributable to the brand is
discounted at a discount rate adjusted by the brand strength – see interim step 1. The
calculated income based value of the brand is an important information input for the
64
calculation of an implicit royalty rate. The procedure is similar to the valuation of a
business by the method of discounted cash flow, the operating profit being replaced with
the  total  income  and  implicit  royalty  rate.  Such  “profit”  is  subject  to  an  effective  income  
tax rate. Then, investment that is necessary in order to ensure and maintain the brand
value driving, is deducted. This determines free cash flow on the FCFF level. The real
nature of the calculated implicit royalty rate must be evaluated in the context of available
information about market transactions.
4) Calibration and Reconciliation of Results
The last step involves a detailed study of differences in results, if any, detailed
economically and methodologically justified calibration of model parameters of income
based valuation and further specification of empirical methods according to step 2. If we
deem these parameters of income based value on the level of the enterprise value of the
whole company to be indisputable, the comparability of results within the determination
of the income based value of the brand is most sensitive to values of the following
information inputs:
- Brand relevance in the form of a share of cash flow of the brand in the economic
profit of the business as a whole (method of cash flow attributable to the brand)
- Amount of reinvestment of earnings from the brand after taxes (earnings * implicit
royalty rate) in ensuring and maintaining the brand value creation
- The assumed rate of growth of the operating profit and/or free cash flow during the
second stage of the valuation process (if going concern assumption is justifiable and
if the two-stage method has been applied).
4 Investments in Ensuring and Maintaining the Brand Value
As stated above, Aaker (2003) perceives the brand value as a set of assets. Therefore,
brand value management also involves investments required for creating and
strengthening these assets. As for investments in the brand, Keller (2007, p. 70) talks
about the rapid growth of the costs involved in launching new products, or of promoting
existing products, which results from the considerable growth of competition in markets
in recent years. The database of trends of test and repeated purchases of average
consumer products (A.C.Nielsen and NPD) indicates that the rate of testing a product
(when a household buys a particular consumer product at least once during an
introduction year) decreased from 15% in the second half of the 1970s to below 10% at
the beginning of the 1990s.
We found out that the rate of investment (and/or reinvestment of income from the brand
after taxes) in ensuring and maintaining brand value is one of the key parameters which
may influence the resulting income-based value of the brand significantly. The purpose of
this chapter is to specify the factors, on the basis of which this parameter should be
quantified, in detail.
4.1 Impact of the Investment Rate on the Calculation of the IncomeBased
Brand Value in the VIM Model
This chapter specifies in detail how the quantified investment rate (and/or the absolute
volume of investments) enters the calculation of the income-based value of the brand by
means of the VIM model. In this model, the brand value is calculated in two ways so that
the income-based valuation parameters (see step 4 of the VIM model) can be checked
and calibrated by comparing the results. Both methods work with the investment rate in
a slightly different way – of course, they differ only in the calculation technique.
Brand Value Based on Flows on the EVA Entity Level
On the basis of the EVA calculation for an enterprise as a whole, cash flow of the brand in
individual years of the first phase is calculated, taking brand relevance into consideration.
FCFFZt = (NOPATt – NOAt-1 * WACC) * RZ (1)
65
where NOPATtis the net operating profit after tax in year t
NOAt-1arethe net operating assets at the beginning of year t (and/or year t-1)
RZ is brand relevance in buyer decisions
The duration of the first phase of the valuation (T) corresponds to the number of years
for which a detailed plan of individual valuation parameters can be drawn up. For the
calculation of the current value of cash flow, a discount rate on the level of a corporate
rate of WACC, adjusted for the brand strength calculated in the first interim step (see
Chapter 3), is used. The corporate WACC rate is divided into a risk-free rate and risk
premium. According to the brand strength, the resulting adjusted discount rate (WACCadj)
can range between <rf; WACC>, or can be higher than WACC.
Within the calculation of the income-based value of the brand in the second phase of the
valuation process, for which we assume the stability of key parameters (rate of growth,
investment   rate,   return   on   investment),   Gordon’s   formula   is   applied   (see   e.g.   Mařík   et  
al., 2012) as follows:
PHZ = {[NOPATT * (1+g) – NOAT * WACC] * RZ}/(WACCadj-g) (2)
where g is the rate of growth in the second phase of the valuation process
Within this approach, the rate of investment in the brand can be quantified as the
product of year-on-year growth of NOA for an enterprise as a whole, and the brand
relevance coefficient. The discount rate for the calculation of the current value of cash
flow is adjusted as against WACC, which also means a change in the requirement for the
return on investment in the brand, in comparison with the return on investment of the
enterprise as a whole.
Brand Value Based on the Rate of Royalties
On the basis of the resulting brand value verified by a valuation using the method of
discounted cash flow of the brand and method of market multipliers, an implicit royalty
rate or internal rate of royalties is calculated retrospectively (iteratively) by the
determined portion of total revenues. However, the calculation technique actually
corresponds to the DCF entity valuation method, using free cash flow, which is assignable
to the brand in individual years, while:
FCFFZt = Tt * LSi * (1-d) – It (3)
It = mIZT * [Tt * LSi * (1-d)] (4)
Where Ttis income (revenues) of an enterprise in year t
LSiis an implicit royalty
dis an effective income tax rate
Itare investments in year t
mIZtis a rate of investment in brand management from brand revenues after taxes
in year t
The duration of the first phase of valuation (T) should, as in the previous case,
correspond to the number of years for which a detailed plan of individual valuation
parameters could be drawn up. For calculation of the current value of cash flow, a
discount rate on the level of corporate WACC is used. Within the calculation of the
income-based value of the second phase of the valuation process, for which we assume
the stability of key parameters (rate of growth, investment rate, return on investment),
Gordon’s  formula  is  applied  (see  e.g.  Mařík  et  al.,  2012)  as  follows:
PHZ = FCFFZT+1/(WACC – g) (5)
FCFFZT+1 = [Tt * LSi * (1-d)] * (1 + g) * (1 – mIZ2) (6)
where WACC (weighted average cost of capital) is a corporate discount rate
gis a rate of growth in the second phase of the valuation process
mIZ2is a rate of investment in brand management from brand revenues after taxes
in the 2nd
phase
66
4.2 Determinants of the Rate of Investment in the Brand Management
Svačina  (2010)  summarises  cost  items  related  to  the  creation  of  intangible  assets  in  the  
form of a trademark as follows:
- Logo creation: one of the basic cost items, which includes the graphic design of a
logo, final form (specification), and licence to use it;
- Industrial-legal protection: the category containing the costs of protection as such
(registration fee, territorial distribution, re-registration) and costs of services of a
patent agent in registering trademarks;
- Monitoring: the costs typical for more mature stages of the existence of designation,
which are based on the assumption that more significant designations are imitated or
used in a way that violates trademark law. Monitoring therefore consists in keeping
track of counterfeits and registered trademarks;
- Advertising costs: the costs connected with the launch of a new designation, and, in
the second stage, the costs of advertising an established designation;
- Opportunity costs: opportunity costs of creating intangible assets, because the costs
incurred could have been invested alternatively;
- Amortisation after the benefit from tax deductibility of the asset has been taken into
account: however, designation should be subject to amortisation, because its
significance grows over time.
According to Svačina   (ibid.),   the   amount   and   significance   of   individual   types   of   costs  
differ in various phases of the brand lifecycle (see e.g. Vysekalová  et  al.,  2011). In this
connection,   Hanzelková   et   al.   (2009)   state   that   each   phase   of   the   brand’s   lifecycle   is  
dominated  by  a  different  part  of  the  brand’s  value.  However,  brand  value  is  determined  
by individual assets which form brand value (see Aaker, 2003) in various ways. For
example, brand loyalty leads to a decrease in marketing costs; perceived quality can be a
source of price premiums, etc.
It can be assumed that there will be problems within the quantification and prediction of
individual costs, especially at the level of advertising costs, or, generally speaking, at the
level of costs of a marketing programme. According to Pickton and Broderick (2005, In.
Hanzelková   et   al.,   2009),  marketing   budgets  range   from   1%   to   25%   of   the   volume  of  
annual sales. However, a particular amount depends on many field factors that influence
the enterprise.
The planning and implementation of marketing programmes and activities are based on
the identification and determination of brand positioning and values, which consists of
making decisions about planned offer and image of a company aimed at placing the
brand   into   consumers’   minds so that any potential advantage of the company is
maximised. According to Keller (2007), it means to persuade customers about the
advantages of the brand compared to competing brands, and also to reduce their interest
in potential disadvantages. However, the marketing costs (or rather marketing
investments) are especially related to:
- Product research, development, and design;
- Trade or support of middlemen (support of distribution channels);
- Differentiation from competitors by extra features offered by the brand (e.g. specific
guarantees, security guarantees, or reliability guarantees);
- Marketing communications (e.g. advertising, promotion, sponsorship, direct and
interactive marketing, face-to-face selling, publicity, and public relations).
For a detailed analysis of the issue of marketing investments (and/or investments in
marketing programmes), we will use the analysis of the value chain of the brand, which,
according to Keller (ibid.) refers to a structured approach to the appraisal of sources and
benefits of the brand, and how marketing activities create brand value. This approach
assumes that brand value is definitely based on customers – the process of creating
brand value therefore begins with corporate investment in a marketing programme,
which focuses on existing or prospective customers. The transfer between a marketing
67
programme and subsequent stages of creating value is carried out by three sets of
multipliers:
1) Programme multiplier: The ability of a marketing programme to influence the way
consumers think, which is determined by quality (not just quantity) parameters of
financial investments in the marketing programme (it especially involves the clarity,
relevance, difference from competitors, consistency).
2) Customer multiplier: The extent, to   which   the   value   created   in   customers’   minds  
influences performance in the market. The extent is high if the brand attracts a
sufficient number of profitable customers, if members of distribution channels and
other mediators provide the brand with strong support, and primarily if the quality
and quantity of marketing investments of rival brands is not effective enough.
3) Market multiplier: The extent to which the market performance of the brand is
reflected in the value of an enterprise for its owner. Market performance is connected
with  customers’  opinion  on  the  brand,  as  well  as  their  responses.  For  example,  if  a  
market share is high, customers are willing to pay a price premium for the brand,
they respond non-flexibly to increases in prices, and flexibly to their decreases; there
is a positive impact on the flow of profits over time. The profitability of the brand is
then supported if these aspects are combined with the ability of the enterprise to
improve its source of profits (expansion of the brand, product range, or launching
new products) and with savings resulting from the reduction of costs of a marketing
programme, thanks to the prevailing opinion on the brand. The market multiplier is
then determined by parameters of the financial market (capital costs, approaches to
risk), growth potential of the brand and industry, risk profile of the brand, and its
significance in the general corporate brands portfolio.
The analysis described above indicates that an effectively designed, financed, and
implemented marketing programme is an inevitable, but not sufficient, precondition for
creating brand value, because some factors of the value chain can be out of reach of
marketers. Moreover, the effects of some marketing investments can only be seen after
a long period of time. On the other hand, the image of the brand is often connected with
the image of a guarantor or bearer of the brand –as a result, any additional marketing
costs of the brand are reduced.
5 Conclusions
Brand value can be perceived from the point of view of a customer or from the point of
view  of  an  enterprise.  From  the  customer’s  point  of  view,  the  value  is  determined  by  the  
level  of  the  effect  of  the  awareness  of  the  brand  on  the  consumer’s  response  and  on  the  
marketing of the brand. The financial value of the brand is then realised on the basis of
the ability to use the effects of the brand potential by the enterprise itself – by its
management or owner. However, we can talk about creating a positive income-based
value of the brand in the case that the risk-free benefits of the brand (higher income or
lower costs) exceed the costs (and/or expenses or investments) required for creating and
maintaining brand value.
The goal of this paper was to specify determinants of the rate of investment in brand
management in detail. Chapter 4.1 indicates that the rate of investment significantly
influences the resulting brand value calculated by the VIM model (i.e. in multiple ways)
for the first and second phase of the valuation process. Chapter 4.2 specifies in detail the
individual types of investments required for creating and maintaining brand value.
Acknowledgments
The   paper   was   produced   within   the   “Characteristics   of   Financial   Systems   of   Central  
European Economies and Impacts on Valuation of Business Enterprises”   project,  
conducted  by  IGA  ŠKODA  AUTO  a.s.  Vysoká  škola.
68
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70
Co-movements  of  bank  stocks’  prices  
as signs of interdependence and contagion
Oleg Deev
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  60200 Brno, Czech Republic
E-mail: oleg@mail.muni.cz
Abstract: This paper examines the interrelations between six bank stock indices,
illustrating world's biggest banking systems, to test the directions of market integration
and possible contagion. Employing cointegration analysis methodology, we find evidence
of cross-border banking sector interdependence, apparently formed separately by
investment and commercial banks, and contagion, which in our case follow the timing
convention in business activities of the chosen countries.
Keywords: bank stocks. cointegration analysis, interdependence, contagion
JEL codes: G15, G21
1 Introduction
The study of the co-movements between asset markets is a central issue in finance as it
has many crucial practical implications in asset allocation and risk management.
Moreover, the integration of financial markets, especially between those of developed
countries, is nowadays considered an established fact. However, the majority of studies
is focused on the integration of the stock markets overall or major stocks only, while the
interdependence on the lower level (between stocks representing one economic sector of
different countries) is not yet considerably studied. We believe that investigation of such
processes could give us better understanding of the drivers of the global financial
integration. For this reason, and considering the importance of banking system for the
stability of financial and economic development and dominance of banking system's
contagion channels in the propagation of crises, the co-movements of bank stocks' prices
are of main interest for the deeper understanding of financial integration.
It is extremely hard to distinguish the difference between interdependence (integration)
and contagion. Interdependence and contagion in times of extreme shocks between
banks or banking systems arises on the larger scale from common asset exposures. Bank
portfolio diversification is privately beneficial but increases the likelihood of systemic risk,
as portfolios become more similar (for the discussion see Ibragimov et al., 2011 and
Allen et al., 2010). The situation is worsened when biggest banks are exposed to
sovereign debt securities. Therefore, it might be equally critical to assess indirect
financial linkages, which may arise from exposure to common risk factors such as similar
business models, common accounting practices, etc. One method to extract this
information   consists   of   tracking   the   market’s   perception,   usually   reflected   in   securities  
prices. Not knowing to what type of securities certain banks are generally exposed to and
taking the assumption, that, if investors can identify the condition of banking accurately,
bank’s   stock   prices   provide   useful   information   on   their   economic   condition   (Flannery,
2010), we suggest detecting possible financial contagion through the co-movement of
banks’   stock   prices.   In   terms   of   cointegration   analysis,   possible   contagion   is   seen   as  
short-term linkages between markets to transmit common shocks established in the long
run.
Studies investigating relationships among world stock markets find evidence of
comovement among them. Most of the studies focus on the equity markets of the United
States, South-East Asia and Western Europe using pair-wise and group analysis. It
should be stated that direct comparison of such research is not completely appropriate,
since different studies employ different differencing intervals (daily, weekly, monthly),
different time periods and sub-periods, and different market indices as representatives of
71
the studied markets. Hence, these studies draw different conclusions about the
interdependent relations in the selected markets.
2 Data and methodology
To depict the structure of the global banking market, six national bank systems with
highest assets and capital were chosen, namely US, UK, Germany, France, Japan and
China. For our empirical investigation we use FTSE banking sector indices collected from
Datastream, which captures more than 75% of the total market. FTSE Banks indices are
capitalization-weighted indices of publicly traded domestic banks' stocks, which are
designed to measure the performance of the banking sector of the chosen countries.
Index for each market is evaluated in US dollars, logs are subsequently calculated. The
sample data consists of weekly closing index prices from March 2nd, 2009 till August
13th, 2012. Our investigation is conducted on the most recent market situation, since
several studies have shown that financial integration increases after the major financial
turmoils.
Figure 1 describes data in levels. Causal observation of levels implies that each stock
price series appears to be non-stationary and that these six stock price indices tend to
move more or less together over time without a trend. The differences of chosen
variables seem to vary over a constant level of zero, although there are few large
outliers, which should be accounted for in the model. From the inspection of properties of
data seen in first differences (not reported here due to space constraints, but available
on request), it is indicated that the assumption of multivariate normality might be
accepted with minor changes of the model in order to obtain better specification and
robustness of further analysis.
Figure 1 Co-movements of bank indices' prices (in logs, scaled)
Source: Bloomberg
The study employs cointegration analysis and follows theoretical formations described in
Johansen (1996) and Juselius (2006). Stepwise cointegration analysis allows an
identification of the structure of interdependence/integration (long-run structure of the
cointegrated system), common trends driving the integrated system as well as contagion
channels (short-run dynamic adjustment structure).
The baseline VAR model in its vector equilibrium correction form is specified by:
72
∆𝑥 = 𝛼 𝛽 , 𝜌
𝑥
𝐷 + ∑ 𝛤 𝑥 + ∑ 𝜃 𝐷 + 𝛷𝐷 + 𝜀 (1)
where 𝑥 = [log 𝐺𝐸𝑅 ;   log 𝐽𝐴𝑃 ;   log 𝑈𝐾 ;  log 𝐹𝑅 ;  log 𝑈𝑆 ;  log 𝐶𝐻 ] is a data vector, 𝛤
is the short-term matrix, measuring how an increase or decrease (in the form of a
positive  or  negative  difference  respectively)  in  last  period’s  log  price  affects  this  period’s  
log price, 𝛼 is the long-run adjustment matrix, measuring how a shock to the
cointegration  relations  affects  this  period’s  log  price  (the  ”weights” of the cointegrating
variables), and 𝛽 defines the stationary linear combinations (cointegration relations)
between variables. Clear linear trends are not observed in the data and were rejected by
testing as well. Inclusion of constant term in the model is not seemed to be reasonable,
since it will indicate that some of the stock portfolios comprising the chosen indices are
more profitable than the others. Otherwise, we would expect returns to have a zero
mean.
We include a shift dummy 𝐷 to illustrate market awareness of the European sovereign
debt crisis (several ECB measures were taken during that week) and its effect on the
integration process or, in terms of our model, the cointegration relations between
markets. Two more dummies 𝐷 , and 𝐷 , are included in days March 30th, 2009 and
May   11th,   2009   to   account   for   the   “blips”   in   the   variables'   differences   and   they  
correspond to the announcements of banks' write-downs to reach market-to-market
valuations of structured credit assets.
The residual analysis is conducted to determine whether chosen model specification is
statistically well-specified, or in other words, to check the assumption of the error terms
being independently normally distributed (results are summarized in Table 1). It is worth
mentioning that valid statistical inference is sensitive to violation of certain assumptions,
such as autocorrelated or skewed residuals and parameter inconstancy, and quite robust
to violation of others, such as residual heteroskedasticity or excess kurtosis.
We cannot reject the null hypothesis of no first or second order autocorrelation. Model
has no severe skewness effects, however, there is still an evidence of kurtosis (which
does not interfere with the asymptotic results of the model). The multivariate test of
normality is rejected, while univariate tests indicate normal distribution for all variables,
except for Chinese index. Model exhibits moderate ARCH effects, which will not influence
the test of cointegration rank. Moreover, we believe that non-normality in the error terms
of the Chinese banking index and presence of ARCH-effects in the error terms of the
Chinese and German banking indices would not effect the explanatory power of following
analysis, since the situation of no autocorrelation is achieved.
Table 1 Misspecification tests (p-values in brackets)
Multivariate tests
Residual autocorrelation LM(1) 𝜒 (36) =38.594 [0.353]
Residual autocorrelation LM(2) 𝜒 (36) =42.288 [0.218]
Test for normality 𝜒 (12) =53.325 [0.000]
Test for ARCH LM(1) 𝜒 (441) =575.126 [0.000]
Univariate tests
log 𝐺𝐸𝑅   log 𝐽𝐴𝑃   log 𝑈𝐾   log 𝐹𝑅   log 𝑈𝑆   log 𝐶𝐻
Skewness -0.080 0.094 -0.110 -0.226 -0.143 -0.217
Kurtosis 3.082 3.430 2.680 2.951 3.471 3.471
ARCH
7.563
[0.023]
0.872
[0.646]
5.053
[0.080]
5.199
[0.074]
2.606
[0.272]
7.431
[0.024]
Normality
0.571
[0.751]
2.659
[0.265]
0.866
[0.649]
1.626
[0.443]
3.093
[0.213]
10.561
[0.005]
For the determination of the lag length we estimated several unrestricted VAR(k) models
with different lag length k=1,2,3....,kmax and than compared these models using general
to specific testing. The same effective sample is used to ensure models to be nested. The
73
p-values of the LR-tests are reported in Table 3, where model in the column is tested
against the model in the row. Results indicate the choice of lag k=2.
Table 2 Lag length determination
VAR(7) VAR(6) VAR(5) VAR(4) VAR(3) VAR(2)
VAR(6) 0.0131
VAR(5) 0.0097 0.1372
VAR(4) 0.0071 0.0800 0.1663
VAR(3) 0.0020 0.0217 0.0391 0.5500
VAR(2) 0.0002 0.0023 0.0035 0.0038 0.1222
VAR(1) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
The investigation of co-movements between banks' stock prices allow us to test several
hypotheses:
1. Global banking system, depicted by the indices of world's biggest national banking
systems, is highly integrated.
2. Sovereign debt crisis in Europe had a significant impact on the interdependence
and contagion in the global banking system.
3. Chinese banking system, mostly consisting of state banks, is independent (to
some degree) from the developments in other markets.
In order to confirm Hypothesis 1, any number of cointegration relations between all
chosen variables should be found. The significance of European debt crisis would be
established, if we discover the presence of a break in the equilibrium. The variable for
Chinese banks' stock prices should be considered weakly exogenous to verify selfsufficiency
of the Chinese banking system.
Except for the above-mentioned hypotheses, we also assume that shocks to the system
might be determined geographically as American, European and Asian shocks (3 or 4
common stochastic trends are expected) and not coming from each country individually,
and hence indicating regional integration. We consider the structure of short run linkages
between bank markets to specify the distribution channels of contagion.
3 Tests of model specific data properties
Estimation of the baseline VAR model without the rank imposed (not reported here but
available on request) allows us to conduct several tests to specify model properties with
economic meaning.
Table 3 Lag length determination
p-r r Eig.Value Trace Frac95 p-value
For the model without deterministic components
6 0 0.337 144.610 139.890 0.024
5 1 0.178 72.860 104.599 0.898
4 2 0.104 38.657 74.387 0.992
3 3 0.069 20.814 49.300 0.988
2 4 0.054 9.197 29.187 0.957
1 5 0.000 0.076 13.882 0.998
For the full model with the level shift (after simulation)
6 0 0.337 144.610 95.063 0.000
5 1 0.178 72.860 71.297 0.035
4 2 0.104 38.657 49.188 0.313
3 3 0.069 20.814 31.576 0.460
2 4 0.054 9.197 17.186 0.489
1 5 0.000 0.076 7.744 0.983
The trace test is performed in order to determine cointegration rank of the model (upper
part of Table 3). Since model contains several dummies, asymptotic tables of the trace
test should be corrected with 𝜒 (1) for each dummy (the results of the trace test based
on the simulated asymptotic properties are given in the lower part of Table 3). According
to the test, the rank of r=2 should be chosen.
74
We run several test to ensure more parsimonious specification of the model, namely the
possibility of long-run variable exclusion, individual stationarity, weak exogeneity of the
variables (corresponds to a zero row in 𝛼), and their exclusive adapting (corresponds to
a unit vector in 𝛼).
Test of exclusion (Table 4) suggest the exclusion of the variable for German market,
however, test based on the model with rank r=1 gives lower (5,6%) confidence of
exclusion, also indicating that German market participates at least in one cointegration
relation. The shift dummy is not long run excludable either.
Table 4 Test of long-run variable exclusion
r log 𝐺𝐸𝑅   log 𝐽𝐴𝑃   log 𝑈𝐾   log 𝐹𝑅   log 𝑈𝑆   log 𝐶𝐻
1
3.640 10.236 5.402 0.626 0.784 24.706
[0.056] [0.001] [0.020] [0.429] [0.376] [0.000]
2
4.020 10.425 19.653 10.979 7.392 24.707
[0.134] [0.005] [0.000] [0.004] [0.025] [0.000]
3
4.142 17.200 21.618 11.003 7.853 29.522
[0.247] [0.001] [0.000] [0.012] [0.049] [0.000]
4
6.959 19.230 22.006 13.824 7.972 32.018
[0.138] [0.001] [0.000] [0.008] [0.093] [0.000]
*LR-test, Chi-Square(r), P-values in brackets
The hypothesis of long-run weak exogeneity, or no levels feedback, of a variable 𝑥 for
the long-run parameters 𝛽 means that the variable 𝑥 has influenced the long-run
stochastic path of the other variables in the system but has itself not been influenced by
them. In our study, stock prices of Chinese banks are found to be weakly exogenous
(Table 5). These findings prove our hypothesis of Chinese banking system self-
sufficiency.
Table 5 Test of weak exogeneity
r log 𝐺𝐸𝑅   log 𝐽𝐴𝑃   log 𝑈𝐾   log 𝐹𝑅   log 𝑈𝑆   log 𝐶𝐻
1
22.250 9.630 32.603 14.273 37.511 2.224
[0.000] [0.002] [0.000] [0.000] [0.000] [0.136]
2
30.544 12.791 39.430 27.074 48.147 2.442
[0.000] [0.002] [0.000] [0.000] [0.000] [0.295]
3
34.999 18.475 42.811 33.904 48.700 8.530
[0.000] [0.000] [0.000] [0.000] [0.000] [0.036]
4
37.077 20.729 44.971 36.653 48.700 9.075
[0.000] [0.000] [0.000] [0.000] [0.000] [0.059]
*LR-test, Chi-Square(r), P-values in brackets
We cannot reject the null hypothesis of a unit vector in alpha (Table 6) for the variable
characterizing French banking market. Therefore, stock prices of French banks are only
adjusting to the long-run equilibrium, while shocks in the French market simply have
transitory effects.
Table 6 Test of unit vector in 𝛼
r log 𝐺𝐸𝑅   log 𝐽𝐴𝑃   log 𝑈𝐾   log 𝐹𝑅   log 𝑈𝑆   log 𝐶𝐻
1
55.938 60.982 52.020 42.411 51.340 60.818
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
2
20.285 22.552 20.019 6.220 24.022 28.901
[0.000] [0.000] [0.000] [0.183] [0.000] [0.000]
3
6.683 7.874 8.120 6.200 9.743 13.707
[0.083] [0.049] [0.044] [0.102] [0.021] [0.003]
4
0.618 6.578 1.253 1.469 3.013 10.224
[0.734] [0.037] [0.535] [0.480] [0.222] [0.006]
*LR-test, Chi-Square(6-r), P-values in brackets
75
4 Identification of the long-run structure (interdependence)
We impose a restriction on the model that the rank of Π  matrix is r=2, meaning that
between the six variables in the model (banking systems) we have r=2 cointegration
relations and p-r=4 common stochastic trends. In order to give economically meaningful
explanation to the estimations, we should test restrictions on 𝛽 and identify the set of
cointegration relations. Since we previously have not assumed any directions of
integration  relations,  we  allow  the  data  “to  speak  freely”.  Hence,  the  best  identification  of  
the long-run structure should have the highest statistical explanatory power (highest pvalue
for the test of the restricted model). 𝛽 coefficients for three model specifications
with highest p-values are reported in Table 7.
Table 7 Chosen specifications of the long-run structure
𝛽′ log 𝐺𝐸𝑅   log 𝐽𝐴𝑃   log 𝑈𝐾   log 𝐹𝑅   log 𝑈𝑆   log 𝐶𝐻 Ds(2011:08:08)
Model 1: p-value 0.8809
𝛽′(1)
0.000
[NA]
0.000
[NA]
1.000
[NA]
−0.606
[-9.805]
-0.437
[-7.376]
0.000
[NA]
-0.219
[-6.186]
𝛽′(2)
−0.532
[−2.327]
0.611
[4.000]
0.000
[NA]
−0.470
[−2.424]
−0.830
[−5.534]
1.000
[NA]
−0.486
[−6.456]
Model 2: p-value 0.6094
𝛽′(1)
0.000
[NA]
0.029
[3.909]
1.000
[NA]
-0.614
[-9.909]
-0.482
[-7.495]
0.000
[NA]
-0.224
[-6.307]
𝛽′(2)
0.024
[2.212]
0.000
[NA]
1.000
[NA]
-0.593
[-9.923]
-0.444
[-7.194]
-0.045
[-6.781]
-0.202
[-5.948]
Model 3: p-value 0.4148
𝛽′(1)
0.000
[NA]
-0.247
[-3.941]
1.000
[NA]
-0.218
[-7.320]
0.000
[NA]
-0.514
[-15.885]
0.000
[NA]
𝛽′(2)
-0.187
[-1.943]
0.000
[NA]
1.000
[NA]
-0.408
[-4.703]
-0.330
[-4.274]
-0.131
[-2.198]
-0.186
[-6.325]
Test of the restricted model with better economically meaningful explanation and
statistical properties gives Chi-squared statistics of 0.254 with the p-value of 0.881. The
estimations of the final model specifications are stated in Table 8.
Table 8 The estimations of the final model specification
𝛽′ log 𝐺𝐸𝑅   log 𝐽𝐴𝑃   log 𝑈𝐾   log 𝐹𝑅   log 𝑈𝑆   log 𝐶𝐻 Ds(2011:08:08)
𝛽′(1) 0.445 −0.518 1.000 −0.200 0.236 −0.853 0.196
𝛽′(2) −0.104 0.051 1.000 −0.551 −0.467 −0.002 −0.232
𝛼 𝛼(1) 𝛼(2)
Δ  log 𝐺𝐸𝑅
−0.233
[−6.219]  
0.231
[2.737]
Δ log 𝐽𝐴𝑃
−0.070
[−3.601]  
0.090
[2.045]
Δ log 𝑈𝐾
−0.193
[−7.262]  
0.084
[1.397]
Δ log 𝐹𝑅
−0.230
[−5.355]  
0.431
[4.433]
Δ log 𝑈𝑆
−0.232
[−8.012]  
0.042
[0.643]
Δ log 𝐶𝐻
−0.039
[−1.588]
0.030
[0.547]
Π log 𝐺𝐸𝑅   log 𝐽𝐴𝑃   log 𝑈𝐾   log 𝐹𝑅   log 𝑈𝑆   log 𝐶𝐻 Ds(2011:08:08)
Δ  log 𝐺𝐸𝑅
−0.110
[−6.455]
0.127
[6.455]
−0.029
[−0.322]
−0.080
[−1.554]
−0.158
[−3.740]
0.207
[6.455]
−0.094
[−4.529]
Δ log 𝐽𝐴𝑃
−0.034 0.039 0.019 −0.042 −0.062 0.063 −0.035
[−3.823] [3.823] [0.419] [−1.562] [−2.823] [3.823] [−3.253]
Δ log 𝑈𝐾
−0.089 0.103 −0.111 −0.011 −0.087 0.168 −0.057
[−7.375] [7.375] [−1.757] [−0.315] [−2.887] [7.375] [−3.874]
Δ log 𝐹𝑅
−0.114 0.131 0.173 −0.205 −0.259 0.214 −0.142
[−5.806] [5.806] [1.684] [−3.474] [−5.333] [5.806] [−5.921]
Δ log 𝑈𝑆
−0.106 0.122 −0.193 0.023 −0.074 0.199 −0.055
[−8.029] [8.029] [−2.798] [0.588] [−2.260] [8.029] [−3.389]
Δ log 𝐶𝐻
−0.018 0.021 −0.017 −0.006 −0.020 0.034 −0.013
[−1.628] [1.628] [−0.292] [−0.170] [−0.732] [1.628] [−0.945]
76
As we have previously established that the stocks of French banks are exclusively
adjusting to the equilibrium, the pulling forces of the banking market should be in the
best way described by two relations: between the US and UK banks and between US,
European and Asian banks. In our opinion, these relations depict the difference between
the investment and commercial banks (the majority of the investment banks are
originated from the US and UK) as also seen and important to the investors.
5 Common driving trends
The restricted model in the simplified MA-form (without short-run dynamics and
deterministic components) is given by:
𝑥 = 𝛽 𝛼′ ∑ 𝜀 + 𝐶∗(𝐿)𝜀 + 𝐴 (1)
where 𝛽 = 𝛽 (𝛼′ 𝛽 ) , 𝛽 and 𝛼 are respective orthogonal complements to 𝛽 and 𝛼,
𝐶∗(𝐿) is a lag polynomial, an A depends on initial values. The term 𝛼′ ∑ 𝜀 defines
the common stochastic trends driving the system and 𝛽 defines their loadings,
describing how the common trends are transmitted to the system variables.
With a cointegration rank of r=2 we have p-r=4 common stochastic trends. Taking into
consideration previously revealed weak exogeneity, the common stochastic trends, which
drive bank stocks' prices, are comprised by the shocks to:
 UK and US banks;
 Japanese banks (with small insignificant effects from UK);
 Chinese banks;
 European (German, French and UK) banks.
Table 9 The MA-representation of the restricted model with the imposed weak exogeneity
𝛼′ log 𝐺𝐸𝑅   log 𝐽𝐴𝑃   log 𝑈𝐾   log 𝐹𝑅   log 𝑈𝑆   log 𝐶𝐻
CT(1) 0.000 0.000 -1.483 0.186 1.000 0.000
CT(2) 0.000 1.000 -0.191 −0.198 0.000 0.000
CT(3) 0.000 0.000 0.000 0.000 0.000 1.000
CT(4) 1.000 0.000 −0.826 −0.349 0.000 0.000
𝛽 CT(1) CT(2) CT(3) CT(4)
  log 𝐺𝐸𝑅
−0.487
[−3.257]
0.179
[1.687
0.762
[6.110
0.910
[6.173
log 𝐽𝐴𝑃
−0.114
[−1.074]
1.006
[13.31
0.284
[3.194
−0.081
[−0.769
log 𝑈𝐾
−0.081
[−0.769
0.255
[3.397]
0.694
[7.869]
−0.126
[−1.206]
log 𝐹𝑅
−0.770
[−5.089]
0.161
[1.499]
0.584
[4.623]
0.100
[0.670]
log 𝑈𝑆
0.681
[5.099]
0.332
[3.510]
0.720
[6.468]
−0.392
[−2.982]
log 𝐶𝐻
0.017
[0.166]
−0.071
[-0.666]
1.083
[8.656]
0.217
[1.465]
C log 𝐺𝐸𝑅   log 𝐽𝐴𝑃   log 𝑈𝐾   log 𝐹𝑅   log 𝑈𝑆   log 𝐶𝐻
Δ  log 𝐺𝐸𝑅
0.910
[6.173]
0.179
[1.687]
−0.063
[−0.157]
−0.444
[−1.829]
-0.487
[−3.257]
0.762
[6.110]
Δ log 𝐽𝐴𝑃
−0.081 1.006 0.044 −0.193 −0.114 0.284
[−0.769] [13.310] [0.154] [−1.113] [−1.074] [3.194]
Δ log 𝑈𝐾
−0.126 0.255 0.266 −0.033 −0.142 0.694
[−1.206] [3.397] [0.937] [−0.193] [−1.346] [7.869]
Δ log 𝐹𝑅
0.100 0.161 1.030 −0.210 −0.770 0.584
[0.670] [1.499] [2.531] [−0.856] [−5.089] [4.623]
Δ log 𝑈𝑆
−0.392 0.332 −0.749 0.198 0.681 0.720
[−2.982] [3.510] [−2.088] [0.913] [5.099] [6.468]
Δ log 𝐶𝐻
0.217 -0.071 −0.191 −0.058 0.017 1.083
[1465] [-0.666] [−0.474] [−0.240] [0.116] [8.656]
77
In terms of achieved financial integration, it is obvious that higher degree of integration
exists between US and UK banking systems and in Europe. We have not tried to find the
structural MA representation of the system, since it directly contradicts the aim and main
hypothesis of complete integration between biggest banking systems, which was
confirmed earlier.
6 Identification of a short-run dynamic adjustment structure (contagion)
The short-run dynamics between the bank markets help us identify contagion in the
banking system, which will reflect in asset prices and may occur only in the situation of
extreme shocks. It might be also argued that weekly data of financial markets do not
correspond to the short-run dynamics, but it should be equally seen as a sample of daily
data reduced to every fifth (last working day of the week) observation.
Table 10 Short-run linkages
Π Δlog 𝐺𝐸𝑅   Δlog 𝐽𝐴𝑃   Δlog 𝑈𝐾   Δlog 𝐹𝑅   Δlog 𝑈𝑆   Δlog 𝐶𝐻
Δ  log 𝐺𝐸𝑅 − − − − − −
Δ log 𝐽𝐴𝑃
−0.1973
[0.025]
−0.1687
[0.010]
− −
−0.2672
[0.003]
−
Δ log 𝑈𝐾 − − − − −
0.3722
[0.000]
Δ log 𝐹𝑅 − − − − − −
Δ log 𝑈𝑆 −
0.1720
[0.000]
− −
0.1944
[0.000]
−0.3418
[0.000]
Δ log 𝐶𝐻 − − − −
−0.1773
[0.004]
−
𝐶𝐼𝑎 − −
−0.1110
[0.003]
0.1810
[0.000
−0.1736
[0.002]
−
𝐶𝐼𝑏
0.2152
[0.000]
0.0652
[0.000]
0.1772
[0.000]
0.2263
[0.000]
0.1969
[0.000]
0.0382
[0.042]
Dp(2009:03:30) −
−0.0648
[0.045]
−0.1471
[0.000]
−0.1349
[0.000]
−0.2388
[0.000]
−
Dp(2009:05:11) −
0.1524
[0.000]
0.0797
[0.001]
− − −
Ds(2011:08:08)
−0.2016
[0.002]
−0.1007
[0.002]
−0.1726
[0.000]
−0.2038
[0.005]
−0.2773
[0.000]
−0.1261
[0.002]
To identify the short-run causality we impose restrictions on the variables with the lowest
t-values. According to the LR-test of over-identifying restrictions (𝜒 (37)=29.975), the
final structure is accepted with a p-value of 0.7880 and all remained coefficients are
significant. The dummy corresponding to European debt crisis is found significant in all
studied markets. Taking into consideration the previous findings (the weak exogeneity of
Chinese banks' stock prices and pure adjustment of French banks' stock prices), the
identification of a short run dynamic adjustment structure easily draws the following
causality chain:
UK → US → Japan → Germany
In the times of extreme shocks it should be considered a main distribution channel of
contagion, which to higher extend corresponds to the timing convention in business
activities of the chosen countries.
Other techniques of short-run structure identification, such as identification of simulation
effects, give similar results, however with a lesser degree of statistical significance. At
the same time, the usage of same-time period variables is somewhat inconclusive, if we
take into consideration the time difference of trading activities and index calculation in
the each market.
7 Conclusions
Using cointegration analysis we have documented the evidence of interdependence
between globally biggest banking markets. Through the examination of banks' stock
prices co-movements we were able to confirm initially formulated hypotheses. The
78
presence of cointegration relations among bank stock indices is an evidence of
integration and interdependence of world's biggest banking systems. In times of extreme
market shocks these relations change; as an example we found a significant impact of
the European sovereign debt crisis both in long and short run. At the same time, Chinese
banking system, mostly consisting of state banks, is found to be partially independent
from the developments in other markets.
The structure of short run linkages between bank markets as an indicator of contagion is
reasonably not the best option to depict market contagion. However, it displays the
distribution channels of contagion, which rather coincide with the timing convention in
business activities of the chosen countries.
Probably the main implication of our study for the practical usage is that diversification in
bank stocks is hard to achieve with the only exception of Chinese banks. On the other
hand, the existence of cointegration relations between bank stocks suggests the
possibility to predict its prices.
Acknowledgments
This research is supported by the Masaryk University Student Project Grant MUNI/A/
0743/2012   “Research of integration and optimization in banking and insurance
(Integrační  a  optimalizační  postupy  v  bankovnictví  a  pojišťovnictví)”.
References
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Stock Prices: Evidence From a Cointegration Study, International Review of Financial
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Flannery, M. J. (2010). Market discipline in bank supervision. In: Oxford Handbook of
Banking, Oxford University Press.
Gropp, R., Moerman, G. (2003). Measurement of Contagion in Banks' Equity Prices, ECB
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Ibragimov, R., Jaffee D., Walden, J. (2011), Diversification Disasters, Journal of Financial
Economics, 99-2, pp. 333-348.
Johansen, S. (1996). Likelihood-based Inference in Cointegrated Vector Auto-Regressive
Models, Oxford University Press.
Jokipii, T., Lucey, B. (2007). CEE Banking Sector Co-Movement: Contagion or
Interdependence?, Economic Systems, 31, pp. 71-96.
Juselius, K. (2006), The Cointegrated VAR Model: Methodology and Applications, Oxford
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Voronkova, S. (2004), Equity Market Integration in Central European Emerging Markets:
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79
Resolving the consequences of work-related injuries
in the conditions of the Czech Republic
Eva  Ducháčková
University of Economics
Department of Banking and Insurance
Nám.  W.  Churchilla  4,  Prague  3,  130  67,  Czech  Republic
E-mail: duchack@vse.cz
Abstract: Work-related injuries can have serious consequences for employees, often
long-term. Currently, in the Czech Republic is the current issue of the future shape of
financial instrument to deal with the consequences of work-related injuries. Insurance
required by law, which is at present in connection with claims of work-related injuries is
not relevant, from an economic perspective and from the perspective of prevention of
work-related injuries. Therefore, it is considered for solving two basic solutions: inclusion
in social insurance or use of commercial principles (compulsory contractual insurance).
The aim of this paper is to analyze the various possible solutions.
Keywords: work-related injuries, commercial insurance, social insurance, Insurance
required by law, compulsory contractual insurance
JEL codes: G22, G28, G29, G38
1 Introduction
The risks of work-related injuries and occupational illnesses are considered to be
significant from the point of view of their impact on employees. From the legal point of
view, it is the employer who is liable for these events. Financial compensation for the
consequences of work-related injuries is provided for in different countries by various
forms of security. In the conditions of the Czech Republic the consequences of workrelated
injuries are currently resolved through a system of Insurance required by law,
which, as an operation commissioned by the state, is carried out by two commercial
insurance companies. This approach is in conflict both with insurance theory and the
general principles of the functioning of commercial insurance. Entrusting the task in
question to selected insurance companies means giving them a privileged position. The
aim of this contribution is to analyze the situation regarding the consequences of workrelated
injuries in the conditions of the Czech Republic, and to analyze and evaluate the
various approaches to the financial resolution of the consequences of such injuries.
When processing is based on an analysis and an analysis of the possible approaches to
solutions based on theoretical models and findings from other countries . Furthermore,
analysis of the impacts of the current approach to the Czech insurance market . Here are
mainly used data published by the Czech Insurance Association.
2 Possible approaches to the resolution of the consequences of workrelated
injuries
Security in cases of work-related injuries was considered, in the past, to be a social
priority, which led to priority being given to work-related injuries – insurance for workrelated
  injuries   was   the   first   branch   of   workers’   social   insurance   to   develop   (Vostatek,  
2000). Various approaches to the security of compensation for work-related injuries have
been developed over the years.
There are, in theory, two basic approaches to the problem of work-related injuries and
occupational illnesses (Vostatek, 2000).:
 social insurance – This is financed by contributions paid by both employees and
employers, and may be supported by state subsidies (when work-related injuries are
resolved with the emphasis on finance from the side of the employer). So-called
continuous financing is used. The claim to payments from social insurance by the
80
insured parties is based on the payment of insurance premiums or on the length of
time of employment; a solution within the framework of social insurance means that
compensation for work-related injuries and occupational illnesses is administered by
the state or a public institution, and the insured party is the employee, as in other
areas of social insurance, and employers pay social premiums and do not pay
compensation to their employees. The insured parties therefore have the right to
claim social benefits provided by the relevant state institution. Payments outside the
framework of accident insurance would have to be covered by voluntary additional
insurance, as is the case in other areas.
 company provisions, which fall into the following two types, according to the form
of security a company has for the eventuality of work-related injuries:
a) Company provisions without the use of insurance. This means that the company
provides its employees with social security payments from its own resources (from
its current income or from appropriate reserves created to cover the payment of
benefits) according to law or on the basis of an agreement with its employees;
b) Company  provisions  with  the  use  of  insurance  (insurance  to  cover  the  employer’s  
liability for damage arising from work-related injuries). Nowadays, this type
involves the mandatory contractual insurance of the employer, which functions on
a commercial basis, which means that the employer is obliged to take out
insurance from one of the commercial insurance companies which offer third party
insurance. Third party insurance is offered by commercial insurance companies in
accordance with the technical principles of the insurance business.
In relation to social security benefits, compensation for work-related injuries and
occupational illnesses can take two different forms:
 A completely independent system of benefits, which provides full security in the case
of an injury.
 A system which supplements pension, sickness and other benefits paid irrespective of
the cause of inability to work.
In Europe, there are two systems of insurance for cases of work-related injuries and
occupational illnesses:
- A system in which the main responsibility is borne by state institutions. Such a
system   operates,   for   example,   in   Germany,   Great   Britain   and   France   (Mesršmíd,  
2012). This model focusses mainly on work-related injuries (as well as occupational
illnesses).
- A system based on the activity of private subjects (commercial insurance companies)
– Such a system operates, for example, in Portugal, Belgium, Finland and Denmark
(Mesršmíd,   2012).   The   commercial model deals with all injuries within a single
system and does not make great distinctions between them. The insurance company
reacts in the same way whether an employee has broken his arm working in a factory
or while doing the cleaning at home. This model spread rapidly through Western
Europe mainly because, in the course of the last few decades, the proportion of
different types of injuries has been reversed – while, in the past, accidents at work
predominated, nowadays injuries incurred outside the workplace are more common.
This model is transparent and has balanced tariffs.
Both systems have their pros and cons. The advantage of the public systems is, above
all, that they maintain continuity with a minimal risk of collapse. Their disadvantage is
that they are generally slower in settling insurance claims. The advantage of the private
systems  lies  in  the  insurance  companies’  experience  with  settling  claims  and  their  ability  
to adapt quickly to changes in the work environment. Their disadvantage is that they
offer no guarantee of continuity.
In the Czech Republic there is currently a system of Insurance required by law, for the
liability of the employer for losses arising from work-related injuries and occupational
illnesses. The establishment of Insurance required by law was justified by the necessity
81
to cover losses arising from work-related injuries or occupational illnesses while, at the
same   time,   respecting   the   Czech   Republic’s   international   legal   obligations   arising   from  
the conventions of the International Labour Organisation, which impose the obligation to
ensure compensation for work-related injuries and occupational illnesses even in cases
where the employer has ceased to exist and has no legal successor, or when the
employer is insolvent.
3 The current situation in the conditions of the Czech Republic
The   current   system   of   insurance   was   established   at   the   beginning   of   the   1990’s   as   a  
temporary solution at a time when thousands of new companies were appearing on the
scene and it was necessary to provide cover for their employees in cases of work-related
injuries or occupational illnesses. This temporary system was meant to be transformed
several times. Indeed, there was already a separate functioning insurance company with
salaried employees which was meant to administer a transformed system of accident
insurance instead of the two commercial insurance companies.
The   employer’s   liability   for   a   work-related injury or occupational illness is an objective
liability. This means that there is no requirement  to  prove  the   employee’s  fault  or  any  
illegal act, merely the existence of an objective fact. The employer is liable for losses
arising from a work-related injury, even if he has broken no legal obligation or directive.
It is enough that one of his employees has suffered an injury while carrying out a task at
work or in direct connection with it. This is so-called objective liability, according to which
the employer is liable for the incident and its consequences. The generally binding legal
document which  governs  statutory  insurance  for  the  employer’s  liability  for  work-related
injuries and occupational illnesses is the currently valid version of the Labour Code,
which sets the conditions for the establishment and operation of statutory insurance for
the   employer’s   liability   for   losses   arising   from   work-related injuries and occupational
illnesses. The conditions for Insurance required by law are governed by the
accompanying decree of the Ministry of Finance (Decree of the Ministry of Finance no.
125/1993 Sb.).
Insurance   for   the   employer’s   liability   for   losses   arising   from   work-related injuries or
occupational illnesses takes the form of statutory insurance, which gives rise to the way
in which it is financed.
Figure 1 Proportion to the volume of received premiums in % - Česká  pojišťovna  and  
Kooperativa
Source: Annual report of the Czech Insurance Association, information on the insurance market,
available at www.cap.cz
A consequence of the conception of Insurance required by law and its structure is the
fact that, strictly speaking, it is not really insurance, but, rather, from the point of view of
the operating insurance companies, an operation commissioned by the state. The
0
20
40
60
80
100
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Česká  pojišťovna Kooperativa
82
employer is obliged to take out an insurance policy for a specified type of insurance, as
well as to pay premiums to the appropriate insurance company at a specified tariff and
according to a specified timetable. The nature of the Insurance required by law means
that its operation was entrusted to specifically identified insurance companies. Those
companies  are  Česká  pojišťovna  (which  at  present  has  an  approximately  20%  share  of  
the volume of received premiums, see Figure 1.), and Kooperativa (80% of the volume of
received premiums, see Figure 1). At the same time, because of the statutory nature of
the insurance, these two insurance companies cannot compete with each other, because
they have precisely defined classes of employers to whom they can provide insurance,
and   strict   rules  for   operating.  Kooperativa’s   rising   share   stems   from the change in the
structure   of   the   market   (Česká   pojišťovna   administers   insurance   for   companies   which  
had already taken out insurance for liability for work-related injuries before 1993).
The insurance is not operated using an insurance method, but rather a kind of
combination of an insurance method and a budget method. In practice, it is financed
continuously on the principle of intergenerational solidarity. The operation of statutory
insurance   of   the   employer’s   liability   for   losses   arising   from   work-related injuries or
occupational illnesses is regulated as a non-profit making business (commercially based
insurance with a state guarantee). In reality this means that any losses incurred by the
insurance companies during the operation of this type of insurance are covered by the
state budget, while, on the other hand, any surplus they may have from the operation of
insurance, is transferred by the companies to the state budget. The administrative costs
incurred by the insurance companies are fixed (see Table 1). At the same time, the
premium tariffs are regulated (Decree of the Ministry of Finance no. 125/1993 Sb.,). The
level  of  the  premium  is  set  as  a  percentage  of  the  tax  assessment  base  (the  employees’  
gross earnings), and is differentiated according to the main economic activity of the
employer (it ranges from 2.8 per mille of the tax base to 50.4 per mille of the tax base,
according to the risk level of the economic branch in question).
Table 1 The estimated sizing of the administrative costs
Period
Percentage of the size of the administrative
costs of premium income
1993-1994 29,5 %
1995-2001 25,0 %
2002- 2011 13,5 %
2012 - 9,0 %
Source: Decree of the Ministry of Finance No. 125/1993 Sb., which sets out the conditions and
rates of employer liability insurance for damage accident at work or occupational disease, as
amended, section 16, Labour Code as in effect from 1..1. 2013, section 365, paragraph 2.
One significant deficiency of Insurance required by law is its economic basis. The
mechanism functions on the principle of continuous financing: the received premiums are
used up during the appropriate year to pay compensation and to cover administrative
costs (the difference between the received premiums and costs is made up by the state
budget). The system does not, therefore, allow the entrusted insurance companies to
create the technical reserves necessary for the payment of long term compensation
payments (annuities) in the future. The system does not, therefore, anticipate the basic
technical insurance principles and the basic theoretical principles for the functioning of
insurance  as  a  category  of  activity  (Ducháčková,  Daňhel,  2012).  In  the  light  of  the  fact  
that compensation is predominantly in the form of annuities (payments of compensation
often  spread  over  a  long  period  of  time),  this  “non-insurance”  approach  generates  future  
commitments, for which adequate technical reserves have not been created, which
means future commitments for the state. From the point of view of the functioning of the
insurance business as an economic category, the approach to the setting of premiums
does not correspond to the basis of the business: fixed premium tariffs (arising from the
statutory nature of the insurance) do not allow the insurance companies to vary
insurance premiums according to the risk level of individual employers, that is, to base
83
the premiums on the size of the risk, which would be in line with insurance principles. It
also makes it impossible to influence the size of the risk through the individualisation of
premiums on the basis of the level of risk of an employer and his insurance history.
The level of compensation provided in cases of a work-related injury or occupational
illness does not sufficiently motivate the injured party to return to work, and, likewise,
does not motivate the employer to take effective measures to prevent work-related
injuries. The original motivating element, that is the participation of the employer, was
abolished   by   an   amendment   of   the   law   in   1995.   Today’s   system   of compensation for
work-related injuries is defective because it is practically impossible to ascertain whether
all those who should pay premiums do so, whether they pay the amount stipulated by
law, and whether the compensation paid out is at all justified. The current version of
statutory insurance does not, therefore, contain certain important elements which are
found in similar laws in other countries, for example incentives for employers to take
preventative measures which lead to a reduction in the number of work-related injuries
and occupational illnesses. At the same time, the law makes it impossible for the
insurance companies to engage in the prevention of work-related injuries and
occupational illnesses.
Table no.2 shows the development of the amount of premiums received and the costs,
both of compensation payments and administration. The amount of premiums is rising
(in 2002 the more marked growth was the result of a change in the insurance tariff),
while on the other hand there has been a long-term decline in the number of insurance
events (see graph no.2), although the volume of compensation paid out is rising (as a
result of an increase in compensation tariffs in 2002, when various changes took place to
the rules governing the size of compensation).
The current set-up in the Czech system of Insurance required by law also does not fulfill
the requirements of the European Union to protect economic competition. During the
negotiations on the accession of the Czech Republic to the European Union and the
harmonisation of Czech law with the laws of the EU member states it was noted several
times that the legal system of the Czech Republic, in unprecedented fashion, gives
precedence to two commercial insurance companies by allowing them to operate
statutory insurance. The EU demanded a change in the law to allow all insurance
companies to operate that form of insurance, or, alternatively, to exclude the insurance
companies and transfer statutory insurance to the state.
4 New approaches to a solution
In theory, there are several approaches to resolving the situation relating to work-related
injury compensation which can be considered, based, above all, on theoretical models for
resolving the consequences of work-related injuries.
From a practical point of view there are two possible options:
- a commercially based solution, that is, the operation of insurance on commercial
principles involving the opening-up of insurance to other companies,
- the allocation of cover for work-related injuries to social insurance.
It  is  the  second  option  that  was  selected  within  the  framework  of  the  Law  on  Employees’  
Accident  Insurance  (Act  no.    266/2006  Sb.,  on  employees‘  accident  insurance. This law
was passed, but did not take effect, It was postponed first until 2010, then 2012 and
then until 1. 1. 2015), where, on the basis of this amendment, compensation for workrelated
injuries is regarded as an element of social insurance. This approach to a solution
means that people injured at work (or suffering from an occupational illness) receive
social benefits irrespective of their individual needs.
The  Act  on  Employees’  Accident  Insurance  changes  the  existing  concept  of  the  provision  
and   legal   regulation   of   statutory   insurance   of   the   employer’s   liability   for   losses   arising  
from work-related injuries and occupation illness in line with the position adopted by the
Czech   Republic   during   the   negotiations   on   the   conditions   of   the   Czech   Republic’s  
84
accession to the European Union, leading to a shift to insurance within the framework of
the social system, which is formulated as insurance complimentary to the statutory
system of social security. Accident insurance should complement health insurance,
sickness benefit and social security.
Table 2 The indicators of employer liability insurance for work injuries and occupational
diseases
Year
Premium income in CZK
million
Insurance benefits in
CZK million
Administrative expenses
in CZK million
1995 1 656,5 588,3 432,7
1996 1 922,2 883,8 480,6
1997 2 151,3 1 180,0 537,8
1998 2 181,9 1 460,3 570,5
1999 2 356,0 1 650,8 589,0
2000 2 402,2 1 639,8 600,5
2001 2 618,7 1 849,1 654,7
2002 4 067,3 2 079,2 549,1
2003 4 297,0 2 416,0 580,1
2004 4 551,0 2 595,6 614,4
2005 4 859,3 2 713,9 656,0
2006 5 199,6 2 973,3 701,9
2007 5 675,9 3 098,8 766,3
2008 6 222,4 3 269,1 840,0
2009 6 139,9 3 397,8 828,9
2010 6 094,1 3 317,7 822,7
2011 6 285,6 3 502,7 848,6
2012 6 408,1
Source: Annual report of the Czech Insurance Association, information on the insurance market,
available at www.cap.cz
In cases of work-related injuries and occupational illness, the Czech Administration of
Social   Security   (ČSSZ)   would   make   regular   payments   of   compensation   directly   to   the  
injured employees. This compensation should therefore be a form of social accident
insurance with its own system for providing payments, in which insurance events and
compensation for losses are replaced by a social event and social payments, which may
be smaller in value than in the current system. The basic feature of the new system of
insurance is that it introduces a system of payments similar to that used in social
security, which, from the point of view of the employee, maintains the current set-up, in
which the employee does not have to demonstrate that his work-related injury or
occupational illness was the fault of the employer.
This approach means the introduction of the principle of general solidarity. When the
level of compensation is set, no regard is given to the loss of income incurred, and the
type of work carried out by the injured party is immaterial. An employee who suffers
35% damage to his health, without regard to the resulting disability, receives only a oneoff
payment, not an annuity, while, at the same time, a ceiling is set for compensation
payments.
The Act on Employees’  Accident  Insurance  means  a  fundamental  systemic  change  which  
transfers responsibility for operating accident insurance to the state. It is a complete
transformation of the nature of compensation.
5 The compulsory contractual form of insurance of liability for losses
arising from work-related injuries
The second option is to solve the problem using commercial principles – that is, to
implement compulsory contractual insurance. This approach enables the implementation
of  the  employer’s  liability  for  losses  arising from work-related injuries in full. At the same
85
time, this system enables the use of the classic concept of the insurance product with all
its characteristic features. The insurance of liability for losses makes it possible, at the
same time, to look to an amendment in the Civil Law Code which is based on the
principle of compensation for losses incurred to property and other losses (a worsened
position in society, pain, mental suffering). A system of commercial insurance means
individual assessment of the risks of each employer by the insurance company (with
preferential treatment for employers who actively engage in the prevention of workrelated
injuries and occupational illnesses), the use of incentives, such as participation,
or  a  “bonus  malus”  system (no claims bonus). At the same time, the implementation of
classic insurance principles enables active risk management.
Another important fact for the implementation of this type of solution is that the
commercial insurance market is one of the most regulated branches of the economy. A
similar change was implemented from the year 2000 onward within mandatory insurance
for  liability  for  losses  incurred  through  the  use  of  vehicles  (Ducháčková,  2011);;  that  is,  a  
move from statutory insurance to compulsory, contractual insurance. That means:
- the  employer’s  obligation  to  take  out  third  party  insurance,
- compensation according to insurance for liability for losses, in its current form
(compensation for lost income, for losses to property, for physical pain suffered, for
damage to social position etc),
- compensation paid directly to employees who have suffered a loss,
- the   introduction   of   a   limit   to   compensation,   the   employer’s   participation   in  
compensation, and the implementation of the bonus-malus system (no claims bonus)
- monitoring of the ability to fulfil commitments, within the framework of regulation of
the insurance business and with regard to fixed amounts of compensation and the
creation of reserves for future payments of compensation,
- the creation of a guarantee fund to cover events which have no connection with
existing insurance contracts (failure to fulfil the obligation to take out insurance etc.),
and the setting-up  of  the  institution  of  an  “Office”,  which,  as  well  as  administering  the  
guarantee fund, would deal with the prevention of work-related injuries and monitor
issues connected with accidents at work. It would have a similar role to that fulfilled
by  the  Czech   Insurers’   Office  (Česká   kancelář   pojistitelů)  with  regard  to  mandatory  
liability insurance for losses incurred through the use of vehicles.
One problematic issue connected with the shift to a commercial approach to the
resolution of work-related injuries and occupational illnesses is how to move to that
system. Because the two insurance companies which currently carry out that task, in
effect on behalf of the state, do not apply the technical principles of the insurance
business, a financial deficit has arisen during the period that the current system has been
in operation. This deficit consists of financial liabilities arising from the claims of injured
parties. It stems from the failure to create technical reserves to cover future
compensation for events occurring during the period that the system has been in use
(This deficit ranges at present between 40 and 50 billion CZK. In the coming years of the
application of statutory insurance in the monitored area, this deficit will grow). The
question of which source to use to finance annuities arising from claims connected with
the statutory nature of the insurance system remains unanswered. Because the
operation is a state commission (the commercial insurance companies currently carry out
a commission for the state, and that commission is strictly specified), the appropriate
source of finance appears to be the state. There are several different possible solutions to
be considered, each, in a different way, proposing financing from the state budget. The
most realistic options appear to be one that proposes a one-off transfer of an appropriate
amount  of  finance  to  the  “Office”,  and  another  option  which  proposes  a  gradual,  annual  
release of resources from the state budget (Analysis, 2012).
6 Conclusions
There has been a long-term attempt to resolve the consequences of work-related
injuries, because the current solution based on Insurance required by law operated by
86
two specified commercial insurance companies is in conflict with the principles of
commercial insurance, and due to its character, it is not an instrument of social insurance
either. It is a non-standard approach to the problem. On the other hand, the character
and structure of compensation payments within this system do comply with the rules for
compensation  for  losses  arising  from  liability.  A  solution  based  on  the  Act  on  Employees’  
Accident Insurance would mean a shift to compensation for work-related injuries based
on the solidarity principle, using social security payments, meaning that the existing
principles for compensation would be suppressed. With regard to the development and
habits of the field of compensation for work-related injuries, the commercial approach
seems more appropriate, that is, the introduction of a form of compulsory contractual
insurance and the application of insurance principles in full. This approach is used to
cover the consequences of work-related injuries in a number of countries. At the same
time, this approach is attractive because it offers the possibility of incentives for
employers, the prevention of work-related injuries and individual risk assessment, which
mean a fairer solution. The main problem is the move to this system, which could be the
basic issue affecting its implementation. It also is necessary to resolve the financing of
claims for compensation (annuities) in the future.
Acknowledgments
This article was written as a contribution to the research project of the Faculty of Finance
and Accounting at the University of Economics, Prague, Czech Republic, which was
carried out within the framework of the institutional support of the University (IP
100040).
References
Analysis of the problem of work-related injuries and occupational illnesses, together with
a proposed practical solution (2012). Available at www.asocr.cz
Czech Insurance Association. Annual Report. Available at www.cap.cz
Decree of the Ministry of Finance no. 125/1993 Sb. Available at www.atre.cz/zakony
Ducháčková,  E.  (2011)  Current  Issues of Automobile Insurance. European Financial and
Accounting Journal. 6(3), pp. 111-122.
Ducháčková,  E.  and  Daňhel,  J.  (2012).  Insurance markets: changes in insurance status
in the global era. Professional Publishing, Prague, pp. 252.
Act no. 266/2006 Sb., on employees‘   accident   insurance.   Available at
www.atre.cz/zakony
Mesršmíd,   J.   Foreign Models insurance against work-related injuries and occupational
diseases, Pojistné  rozpravy, 28, pp. 84–93.
Vostatek, J. (2000) Social and private Insurance, Codex, Prague, pp. 602.
87
Trends in Integration of the Electronic Banking
Jiří  Dvořák1
,  Martina  Janková2
1
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  507/41a,  602  00  Brno, Czech Republic
E-mail: dvorakj@muni.cz
2
Brno University of Technology
Faculty of Business and Management, Information Systems Division
Kolejní  2906/4,  612 00 Brno, Czech Republic
E-mail: jankova@fbm.vutbr.cz
Abstract: A summarization of the current state of electronic banking (e-banking) from
the perspective of the general e-business in the new economy is presented. It is an
expression of system integration in the technical and economic models. It is a description
of the dynamics of the structure and behaviour of systems in the integration process of
the system concept of contemporary reality for the new economy. Trends of a dynamic
integration process in real systems are presented. There is a brief description of the
models of electronic banking and their likely trends in the upcoming system integration of
electronic banking. Expected trends in selected areas of electronic banking system from
the perspective of technical solutions, design and user experience are shown.
Keywords: e-banking, e-business, new economy, system integration, integration process,
system, model
JEL codes: 032
1 Introduction
The current state of electronic banking is seen in the information resources of the world
as a trend of the new economy and upcoming knowledge society.
To the creation of this article contributed a perception of processes as system solutions of
economic, legal, technical and technological processes in the world on the basis of new
theoretical disciplines supported by philosophical and physical knowledge of principles
and laws. And with a precise use of artificial languages as a means of communication
with the scientific proving of new effects and expected benefits of the model of Applied
Informatics with regard to the creative use of mathematical language and other
languages arising from basic research. First of all it is the perfect application of modern
Systems Theory, the Theory of Modelling and Simulation and completely new
specialization   of   Applied   Cybernetics   (highly   developed   in   the   world   of   Medicine’s  
Cybernetics, now very progressive in the world's superpower of the Military Cybernetics,
and upcoming Economic Cybernetics and others) from the position of a scientific
knowledge of Theoretical Cybernetics principles.
The Electronic Banking is thus part of the rapidly developing Information and
Communication Technologies (ICT), a base of developing Economic Cybernetics and from
the user point of view dynamically developing Financial markets and their banking.
The current state of Informatics in this user environment is understood as:
 very fast developing area of processing and transfer of information (ICT) on the
basis of new technical means and appropriate technology (electronics yet,
gradually optoelectronics, nanotechnology, and then later use promising bionic
devices and techniques),
 highly dynamic process using high-quality data (state space of spread
hierarchical cybernetic systems), information and gradually integrated
information transformed into knowledge spaces and gradually integrating skills in
new spaces of the knowledge in developing intelligent cybernetic and robotic
systems in the near future,
88
 highly rapidly growing needs of the emotional intelligence growth of social
systems as a environment of:
o projection (using the Theory of Artificial Intelligence to a new "smart"
application environment of information society and in the future
knowledge society),
o structural (using new resources of automated design of intelligent
information and robotic systems based on a perfect application of
methods and resources of Technical Cybernetics),
o user (using effective methods of learning to a sophisticated using of a
system of effective E-learning in expanding education systems of the
Virtual Schools Environment and in the future World Universities).
The development of the modern concept of electronic banking (e-banking) can be
understood only as systemically integrated environment of these areas and also from the
point of view of the general nature of very sophisticated electronic business (business),
electronic commerce (e-commerce), the electronic environment of the state and public
administration (e-government) and others in the new world economy based on
applications of electronic communication and transformation in the cyber environment so
far. This environment would solve demanding needs:
 transmission speed and processing all kinds of information, accuracy and
reliability of information for the management and cyber security of the modern
economy environment,
 sensitive to the environment for attacks on cyber security and understanding of
new and highly dangerous manifestation of a new cyber war in the world.
2 Methodology and Data
Information sources used for the conducted research activity in the field of e-commerce
and the modern teaching of EBAN (electronic banking) in the Department of Finance of
Masaryk University in Brno, mainly describes the systematically defined environment
suitable for modelling, simulation and optimization of the structure and behaviour of
these systems (such as electronic banking).
In general terms, in this research, teaching, bachelor and master thesis of students, it is
a very interesting problem of the relationship of two environment which are simplified
shown on (Figure 1 System Integration of modelled e-banking), where it is illustrated:
A. Relationship of technical and social environment (relationship "human-machine")
 technical  environment  defined  by  the  current  means  which  allow  clients’  contact  
(social environment) implemented by:
o credit cards (cash machines of various kinds),
o internet banking (different PCs and the Internet),
o television banking (various means of TV service) etc.
 social environment defined by a literacy of clients is given by:
o possibilities of understanding to this new environment and capabilities
based primarily on information literacy of clients,
o availability of technical and service resources of technical security of
electronic banking.
 interface defined by the technical and social environment which is constantly
changing and there is the general effort to minimize this environment as much as
possible.
B. Relationship of models for all three environments - the technical environment, the
interface between these two environment and the social environment - are given
by:
 modern means for developing a transformation of system to models and
modelling in the appropriate environment in order:
o to create an optimal model (of technical environment with regard to the
transmission characteristics of that environment, security and
89
confidentiality of performed banking transactions by these means,
reliability, diagnostics and others),
o to create a suitable model for the effective operation of this environment
with option of economic adaptability to other means of electronic banking,
o the future integration of the current environment of electronic banking in
the process of introducing new technical, organizational, client and
operational capabilities of electronic banking,
o the new banking resources (such as digital money), etc.
 modern trends in the integration of all three quoted environments in order
(modelling and optimization of electronic banking):
o to create a suitable environment for a communication in the field of
integrated activities of electronic banking that is undertaken with trends in
e-commerce, economic cybernetics and other promising areas,
o to create conditions for intelligent environment of robotic systems in
cybernetic concept of the new banking world,
o by automating the entire environment using intelligent learning
environments of technical and social facilities in order to minimize the
quoted interface and replace it by a training environment with adaptive
learning in environment of multi-criteria optimization.
Figure 1 System Integration of modelled e-banking
The research can show an approximate data so far, which clearly highlights the quality of
the technical means of electronic banking and the ease of communication with higher
security transactions (Figure 2).
Technical
environment
e-banking
Social environment
e-banking
Interface of technical
environment and social
environment
e-banking
Model technical
resources
suitable for current and
prospective components of
electronic banking
Model of the social
environment
suitable for current and prospective
components of electronic banking
Modelling and Simulation
electronic banking environment
System integration of electronic banking environment
90
Figure 2 Current evaluated possibilities of e-environment for business and banking
3 Conclusions
Trends in integration of electronic banking must be an essential part of models for a
modern e-business and an application of modelling process for a possible methodology
for designing intelligent e-business in environment of technical, economic and social of
new economy.
This paper is the result of progressively acquired information and knowledge in field of
developing modern electronic environment and trends in the world of electronic
economy.
Based primarily on innovated subject EBAN (e-banking) taught at the Department of
Finance FES Masaryk University in Brno and surveys conducted by students in
assignments for this subject.
Acknowledgments
This paper is a partial outcome generally formulated specific research project "ICT and
mathematical methods in business management" in a particular module 3: "System
integrated environment for the design of intelligent models, modelling and simulation of
modern cyberspace of enterprise." The project is under the Internal Grant Agency of the
Technical University in Brno.
References
JANKOVÁ,   M.;;   DVOŘÁK,   J.   (2012).   Zaměření elektronického   podnikání.   In   Informatika
XXV/2012. Brno:  Mendelova  univerzita  v  Brně,  pp.  25-27.
SUCHÁNEK,   P.,   DVOŘÁK,   J.   (2012).   Impact   of   the   Economic   Crisis   on   E-business Negative
or Positive?. In In 7th International Symposium on Business Administration.
Çanakale, Onsekiz Mart University.   Canakkale:   Çanakale   Onsekiz   Mart   University,   pp.  
120-128.
91
Implied forward rates as a source of market expectations –
implications for monetary policy in Poland
Ewa Dziwok
University of Economics in Katowice
Faculty of Finance and Insurance, Department of Applied Mathematics
40-276 Katowice, ul Pulaskiego 25, Poland
E-mail: ewa.dziwok@ue.katowice.pl
Abstract: A yield curve which could be understood as a plot of zero-coupon rates for
different terms plays an important role in monetary policy. With the assumption of
expectation hypothesis (with risk premium different from zero), the implied forward rates
are particularly interesting for central banks to extract market expectations from yield
curves. The aim of the paper is twofold: to derive the implied forward rates from the
assets’  prices  with  two  parametric  models  and  three  different  goal  functions.  The  second  
is to compare risk premium depending on source of data and estimation method to find
the most transparent asset and the most useful method of estimation. This paper applies
data for several segments of Polish market: inter-bank deposits, forward rate
agreements (FRA) and interest rate swaps. During the analyzed period we found that
there is a period of time when a higher positive premium in the form of calculated
differences was noticed. The result could be used as a leading indicator of market
disturbances – especially for the central bank which wants to assure markets that it
controls the situation and does not let investors lose the mutual confidence.
Keywords: yield curve estimation, parsimonious models, market expectations
JEL codes: C53, C92, E43, E58
1 Introduction
Last disturbances on financial markets show how important is a good understanding of
market behaviors. Extracting market expectations from asset prices and their further
analysis plays a crucial role in modern monetary policy management (Bernanke et all
1999), (Mishkin 2007). The idea assumes that monetary policy should be focused on
transparency because it let minimize shocks caused by unexpected interest rate
movements. Following expectation hypotheses central bank managers can control how
market participants predict future move of the central bank and may check if the
prediction is correct.
Generally central bank extracts the expectations from the instantaneous term structure
which is understood as the relationship between the yield of the investment with the
same credit quality but different term to maturity (Nawalkha, Soto, Beliaeva 2004).
Because financial markets offer only discrete data, one of the most important problems
to be solved is a model selection for fitting data. There are several methods which let
create a yield curves (James, Weber 2000) but in countries with well developed debt
markets central banks use both parsimonious models coming (Nelson, Siegel 1987)
(Svensson 1994) and a method based on cubic splines (Fisher, Nychka, Zervos 1995).
The main aim of the article is an analysis of the difference between implied forward rate
and a reference rate understood as a risk premium (ex post analysis) to show that it
could be utilize as an indicator of market disturbances. Then to compare calculated risk
premiums depending on source of data and estimation method to find the most
transparent asset (the best source of expectations) and the most useful method of
estimation. To find the sensitivity of expectations to market disturbances the construction
of six term structures for every of four types of data was taken into account.
The paper is structured as follows: Section 2 provides a general overview of the term
structure modeling, the evolution of methods and shows their applications into monetary
policy, Section 3 shows the methodology of term structure modeling based on
92
parsimonious models, Section 4 is an empirical one – it involves risk premium analysis
which illustrates an influence of market expectations (fears and believes) on its value.
The final part covers concluding remarks.
2 Expectations and monetary policy
A forward rate ),( tsf extracted at time  is an agreement made to lend money at some
future date s and t (Tuckman 2002), but sometimes analysts differentiate forward rates
taken directly from the market from these implied forward rates which are derived from
the spot interest rates (theoretical ones) (Choudhry 2004). Predictive power of implied
forward rates (as an estimation of the future spot rates) depends on the efficiency of the
market and various hypotheses try to explain both the relation itself and several biases
which may be included in forward rates. According to one of hypothesis, the preferred
habitat theory (Modigliani, Sutch 1966), the term structure reflects directly the market
expectations of future rates with a risk premium.





),(
),()],([),(
ts
tstsiEtsf


(1)
where: ),( tsi - the reference rate of the central bank
)],([ tsiE - expected at moment  the level of the reference rate of the central bank
),( ts – a risk premium
With the assumption of the predictability of monetary policy understood in its narrower
definition as the ability of financial markets to correctly anticipate the next monetary
policy decisions of a central bank (ECB 2006) we can observe:
),()],([ tsitsiE  (2)
Then the equation (1):





),(
),(),(),(
ts
tstsitsf


(3)
And the risk premium could be explain as:
),(),(),( tsitsfts   (4)
The shorter is the period s between term of establishing of the implied forward rate
and the beginning of the spot contract, the lower should be the risk premium (market
participants receive more information about future moves of the reference rate and are
able to predict them properly).
The implied forward rates are particularly interesting for central banks, especially if the
time period of calculated implied forward is equal to the duration of the central  bank’s  
key  open  market  operation.  To  evaluate  the  central  bank’s  predictability  we can analyse
the difference between a reference rate and an implied forward rate (ex post analysis of
risk premium). The lower the difference is the clearer and more transparent monetary
policy has been before a decision-making meeting. If the level of the risk premium is
high, the question arises what are the reasons for – sometimes it is an inefficiency of the
market (lack of liquidity), sometimes biases mentioned earlier: either lack of trust to
current monetary policy or the incomprehension of the central bank purposes.
It is also possible that market participants permanently overestimate the scale of central
bank decisions as an effect of market behavior. Being familiar with determinants that
shape the term structure, the central bank is able to improve its transparency through
official and unofficial messages sent to the market to keep the inter-bank rates as stable
as possible (Choudhry 2002).
93
3 A yield curve construction
To derive the implied forward rates from asset prices two parametric models were taken
into account. At the first step one should explain the process of zero-coupon yield
construction with the assumption of the continuously compound interest.
The parametric models, proposed here, the Nelson-Siegel model with four parameters
and the Svensson model with six parameters respectively are described in the forms:
1
1
210
)()( v
s
e
v
s
sf

  (5)
2
2
3
1
1
210
)()( v
s
v
s
e
v
s
e
v
s
sf

  (6)
where: )(sf - instantaneous forward rate
],,,[ 1210 v , ],,,,,[ 213210 vv – vectors of parameters describing the curve:
0 - parameter which shows a limit of )(sf as s approaches infinity, 00 
1 - parameter which shows a limit of )(sf as s approaches zero, 010  
2 - parameter which shows a strength of first curvature
2 - parameter which shows a strength of second curvature
1v - parameter which shows a place of first curvature, 01 v
2v - parameter which shows a place of second curvature, 12 vv 
The forms of parametric models allow to receive implied forward rates directly. It
guarantees different shapes of theoretical term structures. Having both theoretical and
market vector of prices we try to find such value of parameters to achieve as good
approximation of theoretical data to market ones as possible. The sets of parameters:
],,,[ 1210 v in case of the Nelson-Siegel model and ],,,,,[ 213210 vv in case of the
Svensson are to be estimated by three different goal functions which let minimize mean
square errors between:
- market prices and theoretical ones:
k
PP
k
l ll 
1
2
*
)(
min

(7a)
- market prices and theoretical ones divided by duration of the asset:
k
D
PPk
l
l
ll
 

1
2
*
)(
min

(7b)
- market yields and theoretical ones:
k
ii
k
l ll 
1
2
*
)(
min

(7c)
where: ll PP  – a price error of l -th asset
ll ii  – a yield error of l -th asset
lD - duration of the l -instrument
k – number of bonds




modelSvenssonthefor],,,,,[
modelSiegel-Nelsonthefor],,,[
213210
210*
vv
v


β
94
When a vector of parameters is known, it is easy to construct a set of zero-coupon rates
and then an implied forward term structure which let analyze a current situation in the
market.
l
l
l
t
t
ti
)(ln
);(

  for every cash flow times lt l=1,2...m (8)
According to the rules laid down by the National Bank of Poland this analysis uses a both
parametric models. To calculate the implied forward rate the estimation of four
],,,[ 1210 v and six parameters ],,,,,[ 213210 vv was performed in three different
ways – by minimizing the difference between theoretical and market prices, prices
divided by duration, and yields of given assets (formula 7a, 7b, 7c). This is why for each
set of assets we receive six different term structures for every research day.
Generally a term structure is typically built with a set of liquid and common assets; the
problem arises in a case of not so liquid market (as in Poland) with a small number of
data. One solution, proposed here, is to analyze several types of models and then choose
this one which let achieve the best approximation.
4 Data and results
The term structure building begins by gathering the sample of the instrument to be used.
In Polish money market, which is analyzed here, there is lack of short term data (apart
from money market fixing quotations), that is why all available quotations were taken
into account with no quality check.
For several years Polish money market has become one of the most important places of
trade in Eastern Europe attracting both speculators and long term investors. Following
the openness and liberalization, Polish market has started to be more liquid and can be
used as a source of market participants behavior. Three types of instruments were taken
into a forthcoming research. They vary in terms of their liquidity, maturity and default
risk.
 Interbank lending rates are represented by WIBOR (Warsaw InterBank Offer Rate)
seven in total: 1-week, 2-weeks, 1-month, 3-, 6-, 9-months, one year,
 FRA rates: first set involved thirteen 3-months FRA rates (1x4, 2x5, 3x6, 4x7, 5x8,
6x9, 7x10, 8x11, 9x12, 12x15, 15x18, 18x21, 21x24) and second with eight 6-month
FRA rates (1x7, 2x8, 3x9, 4x10, 5x11, 6x12, 12x18, 18x24),
 Swap rates, ranged from one to ten years plus POLONIA index.
Since 1999 due to the implementation of the direct inflation targeting strategy (IT
strategy) into Polish monetary policy, the National Bank of Poland defines the inflation
target and then adjusts basic interest rates in order to maximize the probability of
achieving the target. The NBP maintains interest rates at a level consistent with the
adopted inflation target by influencing the level of nominal short-term interest rates in
the money market. The minimum yield of the 7-days open market operations is one of
the instruments used by the NBP to determine interest rates in the market (the reference
rate adopted by the Monetary Policy Council).
The research takes into account the period between 2005 and 2009, when the National
Bank of Poland (NBP) firstly decreased its main reference rate from 6,5% to 4% (2005-
2006). From the beginning of 2007 it started to increase the reference rate to the level of
6,25% in 2008. The disturbances in money market, lack of trust among participants and
high volatility caused by Lehman Brothers bankruptcy made the NBP change the direction
of Polish monetary policy – from that time the process of decreasing interest rate was
begun and lasted till the beginning of 2011. We focus on dates of Monetary Policy Council
decision-making meetings that took place between January 2005 and June 2009 and the
dates 14 days before the meetings. For the first period (2005-2007) two instruments
were taken (inter-bank deposit rates, swap rates) and for 2007-2009 period four: interbank
deposit rates, 3-months FRA, 6 months FRA and swap rates.
95
For each of these particular days, and for each instrument, six implied forward term
structures were constructed using different models and estimation processes (through
three estimation functions for each of two parametric models). Having six estimated
vectors of parameters for each dates and asset, a set of 7-days implied forward rates
was calculated. Additionally, the difference between an implied 7-days forward rate
(calculated 14 days before the decision of the Monetary Policy Council - MPC) and the
reference NBP rate settled on that day was taken into account. We wanted to examine
the level of the risk premium and check if the disturbances in financial markets have had
influence markets expectations. The research then is based on the equation:
NBPi
s
s
ss 


)(
)(
ln
7
365
)
365
7
,(
365
7


 (9)
where: )
365
7
,(  ss – risk premium.
Because for each date and asset we receive six different levels of risk premium we
assumed following notations:
NS_P – the level of risk premium where the implied forward rate was calculated from the
Nelson-Siegel model with the fitting criteria based on prices;
NS_P/D - the level of risk premium where the implied forward rate was calculated from
the Nelson-Siegel model with the fitting criteria based on prices divided by the
duration;
NS_Y - the level of risk premium where the implied forward rate was calculated from the
Nelson-Siegel model with the fitting criteria based on yields;
Sv_P - the level of risk premium where the implied forward rate was calculated from the
Svensson model with the fitting criteria based on prices;
Sv_P/D - the level of risk premium where the implied forward rate was calculated from
the Svensson model with the fitting criteria based on prices divided by the
duration;
Sv_Y - the level of risk premium where the implied forward rate was calculated from the
Svensson model with the fitting criteria based on yields.
Figure 1 Risk premium for implied forward rate taken from WIBOR and SWAP quotations
14 days before the MPC meeting (2005-2007)
2005-02-12
2005-03-17
2005-04-14
2005-05-12
2005-06-16
2005-07-14
2005-08-18
2005-09-14
2005-10-12
2005-11-16
2005-12-07
2006-01-18
2006-02-15
2006-03-15
2006-04-12
2006-05-17
2006-06-14
2006-07-12
2006-08-16
2006-09-13
2006-10-11
2006-11-15
2006-12-06
2007-01-17
2007-02-14
2007-03-14
2007-04-11
2007-05-16
2007-06-13
SWAP NS_P
SWAP NS_Y
SWAP NS_P/D
SWAP Sv_P
SWAP Sv_Y
SWAP Sv_P/D
WIBOR NS_P
WIBOR NS_Y
WIBOR NS_P/D
WIBOR Sv_P
WIBOR Sv_Y
WIBOR Sv_P/D
0 -25 25 -50 50 -75 75 -100
Source: own computations based on WIBOR and swap data
96
2005-2007 period
Looking at Figure 1 we can observe how market participants foresaw 7-days reference
rate 14 days before and how their expectations had influenced different data (two types
of assets) and estimation method (three types shown in 7a-7c). The differences between
an implied 7-days forward rate and the reference NBP rate settled on that days show that
WIBOR rates almost every time were a good predictor of Polish monetary policy, and that
the monetary policy was transparent through that kind of asset.
2007-2009 period
The results shown in Figure 2 could be used as a leading indicator of market disturbances
– since October 2008 the predicting power of all data (especially WIBOR) has become
lower. The higher positive premium (understood in a form of calculated differences)
reflects a fall in banks' mutual confidence. This situation affects asset prices and their
strong changes were observed, so they did not truly reflect the expectations of future
interest rates.
Figure 2 Risk premium for implied forward rate taken from WIBOR and SWAP quotations
14 days before the MPC meeting (2007-2009)
Source: own computations based on WIBOR and swap data
In January 2009 the reversal behavior have been noticed – in spite of the financial crisis
the asset prices showed the tendency to return back to its common value. It was
probably  the  effect  of  messages  sent  by  central  bank’s  officials  who  wanted  (like  other  
central bankers around the world) to assure markets that they control the situation and
do not let investors lose the mutual confidence.
The similar results can be observed in Figure 3 when FRA instruments were taken into
account. They also could be used as a leading indicator of market disturbances. The
higher is the positive premium the lower is the confidence between the investors and
traders whose reaction (to new information, here about the financial crisis,) is
exaggerated.
In a whole analyzed period the implied forward rates overestimated future short rates.
Such surprisingly results (regardless of data were used to create the implied forward)
give market participants a chance to make excess profits. For the central bank it is
additional information which should be taken into account during an analysis of a
monetary transmission mechanism.
2007-09-12
2007-10-17
2007-11-14
2007-12-05
2008-01-17
2008-02-14
2008-03-13
2008-04-16
2008-05-14
2008-06-12
2008-07-16
2008-08-13
2008-09-10
2008-10-16
2008-11-13
2008-12-12
2009-01-14
2009-02-12
2009-03-11
2009-04-15
2009-05-13
2009-06-10
SWAP NS_P
SWAP NS_Y
SWAP NS_P/D
SWAP Sv_P
SWAP Sv_Y
SWAP Sv_P/D
WIBOR NS_P
WIBOR NS_Y
WIBOR NS_P/D
WIBOR Sv_P
WIBOR Sv_Y
WIBOR Sv_P/D
0 -25 25 -50 50 -75 75 -100 100 -125
97
Figure 3 Risk premium for implied forward rate taken from FRA 3M and FRA 6M
quotations 14 days before the MPC meeting (2007-2009)
Source: own computations based on FRA data
5 Conclusions
For several years central banks have focused on the techniques to extract market
expectations from financial instruments to judge their influence on real economy and
analyze theirs transparency (ECB 2006). In countries with well developed debt market
there are parsimonious models that play an important role in term structure building
process. For research purposes, the most useful source of rates were WIBOR and FRA
rates which becomes an important segment of financial markets. This paper took data for
several segments of Polish market: interbank deposits, forward rate agreements (FRA)
and interest rate swaps taken from 2005-2009 period. Parameters were compared every
14-days before a central bank decision-making meeting to examine if the market
participants expected movements correctly.
During the period 2005-2009 we found that there is a period of time when a higher
positive risk premium was noticed. The result could be used as a leading indicator of
market disturbances – especially for the central bank who wants (like other institutions
around the world) to assure markets that it controls the situation and does not let
investors lose the mutual confidence. These results should be interpreted with caution,
because a small open market, sensitive to external shocks and speculative attacks (like
Polish one) is too changeable to recognize results as typical. This is why this research did
not relay on one source of data but examined several types of assets to get a wider
spectrum of market situations. It is also too early to indicate with no doubts the best
fitting method. The result we received show how wide is spectrum of implied forward rate
for the same date. We should be very careful about formulating the conclusions.
The useful supplementary information is that market participants overestimate
(overreaction in a behavioral sense) future movements of the Polish central bank as an
effect of bank transparency. It is an additional argument for the central bank authorities
to inform (officially and unofficially) about future movements of the key interest rates to
keep the interbank rates as stable as possible.
2007-09-12
2007-10-17
2007-11-14
2007-12-05
2008-01-17
2008-02-14
2008-03-13
2008-04-16
2008-05-14
2008-06-12
2008-07-16
2008-08-13
2008-09-10
2008-10-16
2008-11-13
2008-12-12
2009-01-14
2009-02-12
2009-03-11
2009-04-15
2009-05-13
2009-06-10
FRA 3M NS_P
FRA 3M NS_Y
FRA 3M NS_P/D
FRA 3M Sv_P
FRA 3M Sv_Y
FRA 3M Sv_P/D
FRA 6M NS_P
FRA 6M NS_Y
FRA 6M NS_P/D
FRA 6M Sv_P
FRA 6M Sv_Y
FRA 6M Sv_P/D
0 -25 25 -50 50 -75 75 -100 100 -125
98
References
Bernanke B. S., Laubach T., Mishkin F. S., Posen A. S. (1999), Inflation Targeting:
Lessons from the International Experience. Princeton University Press, Princeton.
Choudhry M. (2002), An introductory guide to analyzing and interpreting the yield curve.
in F.J. Fabozzi (ed): Interest rate, term structure and valuation modelling. John Wiley &
Sons, Hoboken, pp. 73-92.
Choudhry M. (2004), Analysing and Interpreting the Yield Curve, John Wiley & Sons, Inc
Singapore.
ECB (2006), Monthly Bulletin, January.
Fisher M., Nychka D., Zervos D. (1995), Fitting the term structure of interest rates with
smoothing splines. Federal Reserve Board Finance and Economics Discussion Series,
Working Paper, nr 95-1.
James J., Webber N. (2000), Interest Rate Modelling. John Wiley & Sons, Inc. Chichester.
Mishkin F. S. (2007), Monetary Policy Strategy, MIT Press Books.
Modigliani F., Sutch R. (1966), Innovation in interest rate policy. American Economic
Review, Vol. 56, pp. 178-197.
Nawalkha S.K., Soto G.M., Beliaeva N.A. (2005), Interest Rate Risk Modeling, John Wiley
& Sons, Inc. Hoboken.
Nelson C. R., Siegel A. F. (1987) Parsimonious modelling of yield curves. Journal of
Business, 60 no.4: pp.473-489.
Svensson L.E.O. (1994), Estimating and interpreting forward interest rates: Sweden
1992-1994. NBER Working Paper Series, No. 4871.
Tuckman B. (2002), Fixed Income Securities, 2 ed., John Wiley & Sons, Inc. Hoboken.
99
Factors influencing the microeconomic and fiscal effects
of the reduced VAT on books
Martin Geško
University of Economics in Bratislava
Faculty of National Economy, Department of Finance
Dolnozemska cesta 1, 811 05 Bratislava, Slovak Republic
E-mail: martin.gesko@euba.sk
Abstract: Value added tax as the general tax on consumption has a significant influence
on the consumer behaviour and is a significant source of public revenues. Many tax
policies in response to the current financial and public finance crisis employ the increase
of the VAT rate on previously exempted or tax-favoured goods. On the other hand, in
previous years some countries adapted decreased VAT rates on specific goods, e.g.
books among others, in order to achieve certain non-economic goals. The effectivity of
such actions depends strongly on the nature of the demand for the specific good, i.e.
how will consumers react in situations of increased or decreased tax rate. The goal of this
paper is, on the example of the changes of the VAT rate on books, to identify crucial
factors determining the nature of the demand for books and to develop a basic logical
framework for further analysis.
Keywords: indirect tax, VAT, microeconomic effects, demand elasticity
JEL codes: D11, H22, H31
1 Introduction
The actual role of taxation is not only fiscal, but due to their influence on the taxpayer's
behaviour social as well. Taxes through the income and substitution effect have the
opportunity to significantly influence the economic decisions of taxpayers. General
consumption tax plays an important role in the most of the current modern economies.
For instance in Slovakia the income from VAT constitutes some 50 % of the total state
budget tax revenues. Therefore it would be recommendable to use the indirect tax policy
measures for non-fiscal purposes exclusively in cases when there is a strong probability
that the effects of the measure will be desirable. Otherwise we risk the loss of the tax
revenues without any relevant response in social behaviour.
By the so-called tax reform in 2003 in Slovakia all the goods and services were included
under the standard VAT rate of 19%. In case of the books it probably caused an increase
in the price level. In 2007 the Slovak government adopted amendment to the Value
Added Tax Act which, with effect from 1.1.2008, has assigned books to a group of
products with a reduced VAT rate (from 19% to 10%, i.e. decrease of 9 percentage
points). The explanatory memorandum to the Act No. 222/2004 Coll. on Value Added Tax
to the point 53-60 (Annex No. 7) of the amendment states:
Slovak Republic has the smallest book market among EU Member States, therefore in
Slovakia, in comparison with other Member States, fewer books are being issued. After
transferring books to the basic tax rate of 19% the literature became beyond the reach
for certain groups for financial reasons. Decline in supply of books for public and school
libraries remains as well as the decline in the development of the state language, which
affects the overall educational level of the Slovak population in European and global
context. In order to improve the availability of literature we suggest shifting books from
the basic tax rate of 19% to reduced tax rate of 10%.
The  financial  impact  …  constitutes  accrual  decrease  of  state  budget  revenues  in  2008  of  
SKK   320   million   (circa   €10,6   millions),   in   2009   amounted   to   SKK   338   million   (€11,2  
millions),  and  in  2010  of  SKK  357  million  (€11,85  millions).  
With this measure Slovak Government decided to shift the books back to the reduced
VAT rate group in order to make the books more affordable to the common consumers.
100
However, doing so in time of the financial and economic crisis shall raise some questions
regarding the timing and substance of such measure. The justification for this political
action could be based on the recommendations from the European Commission or the
experience from other countries. For example, analysis of the Copenhagen Economics
(2007) focused on the effects of reduced VAT rates gives the example of Sweden, which
in 2002 reduced rate of VAT on books from the standard 25% to 6% (i.e. a reduction of
19 percentage points). Then we analyse further developments on the book market after
the adoption of this measure, and according to their findings 19% decrease in the rate of
VAT at 12 months led to a 16% decline in the book prices. These data were confirmed by
the information from the The  Swedish  Publishers’  Association  (2003).
But the question is whether it is possible to consider such a measure in the Slovak
Republic as justified from the economic point of view? Are the assumptions on which the
measure is based, correct? What factors determine the way taxpayers will react to this
action? It is the goal of this paper to address these questions in our paper.
1.1 Logical assumptions of the tax policy measure
In order to assess the economic rationality of the measure we shall first look at the
substantial structure of it. The logical structure of the proposed measure could be put as
follows: Reduction of VAT on books would have the effect of reducing the price of books
to final consumers (1), which would then increase the availability of literature and lead to
increased demand and consumption of books (2). Increasing consumption of books
should be reflected in the better development of the state language and increase the
overall "educational level of the Slovak population in the European and global context"
(3).
The main goal of this fiscal policy measure is apparently of non-economic nature, and is
based on three logical assumptions. In this paper we will not deal with the analysis of the
third assumption, according to which an increase in "consumption" of books shall be
reflected in the better development of the state language and the overall educational
level of the Slovak population in the European and global context. Assumptions 1 and 2
are interrelated and represent the economic substance of the measure. Basically, the tax
rate reduction should lead to lower prices (assumption 1), which should in turn lead to an
increase in demand (assumption 2). We will focus on the rationality of these two
assumptions.
1.2 Book-price level development – 2002 to 2012 reality
According to the Statistical Office of the Slovak Republic the development of the price of
item 09.5.1.1 Books was as follows (aggregated data for better comparability):
Figure 1 Average prices of books in 2002 – 2012 : aggregated data
Source: Statistical Office of SR 2013
We have studied the intensity of the price increase as well since if the overall inflation
would be higher than the book-price increase in the observed period then the VAT rate
decrease would only diminish the increase of the price.
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
0,00  €
2,00  €
4,00  €
6,00  €
8,00  €
10,00  €
101
Figure 2 Inter-year change in the book-price growth (intensity)
Source: Statistical Office of SR 2013
This was clearly not the case since the overall inflation (increase in consumer prices)
however did not show any differences compared to the book-price development. In case
of the book prices the price increase was consistent with the previous year and the
overall inflation as well. In following years 2009 and 2010 the book-prices rose even
more intensively than the inflation level.
Figure 3 Inflation measured by CPI (national methodology) - total, core, net
(2002 - 2012)
Source: Statistical Office of SR 2013
The development of the VAT rate in observed period was as follows:
Figure 4 VAT rate on books in period 2002 - 2012
Source: The Tax Directorate of the Slovak Republic 2013
Along with the statistic data also non-professional  observations  (Záborský  2008)  of  price  
changes at bookstores after the introduction of lower VAT rate suggests that prices did
not change after the rate decrease. As could be seen from the figures the book prices did
not react to the VAT rate decrease in 2008. Observed slowdown in book prices increase
started already in 2005 therefore it could not be directly attributable to the VAT rate
decrease.
2 Analysis
As was shown in the previous overview we could argue that the tax measure in form of
decrease of VAT rate on books was not successful since the decreased VAT rate on books
did not result in decreased book prices. Further we will try to find the reasons why.
2.1 Theoretical background
Income effect is the first and necessary consequence of imposing or increasing any tax.
As result the taxpayer loses part of his disposable resources without receiving adequate
remuneration in the form of goods and services. Impact on behaviour is caused by the
tendency of taxpayers to keep (or increase) the level of benefits deriving from
consumption over time. Another important effect of taxation, a direct consequence of the
income effect, is the substitution effect: an imposition of the commodity tax reduces
available resources of the taxpayer through the change the relative prices of goods and
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
0,00%
5,00%
10,00%
15,00%
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
0%
10%
20%
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
0,00%
5,00%
10,00%
102
services   and   thus   "motivates"   a   taxpayer   to   change   economic   behaviour   (Zubaľová  
2012). The elementary theory counts with the shift of the tax into consumer through the
prices. In reality, however, it is not always the case. The way how consumers react to
the change in the tax could be measured by the price elasticity of the demand (PED).
And this elasticity is the most important factor influencing the ratio of the shifting the tax
onto consumers. The PED determine how will the changed tax burden disperse among
the suppliers and consumers.
The PED is determined by several factors, from our point of view we consider as the most
important the following:
1) The existence of substitutes – relatively cheaper substitutes allow the consumer to
keep his level of the utility he previously received from the consumption of the nowtaxed
and more expensive good. If the consumer could substitute the supplier would be
aware of increasing the price. Therefore we could say that the higher availability of the
homogenous substitutes results in higher PED.
2) The level of the disposable income or the gross household expenditure
respectively – this is probably the most important factor at all. The disposable resources
which could be used to satisfy the needs form the final limits for the consumption. We
will describe in more details in Ad 5) how people satisfy their needs, however, we could
say that with the growing disposable income we could afford more goods which satisfy
the less urgent needs and thus we are becoming more sensitive to the price change =
the PED is growing.
3) The share of expenditure on a good in total consumption expenditure – in
general we could say that the lower is the share of the certain expenditure on the total
expenditures the lower is the PED. If the expenditure on certain good constitutes just a
small percentage of our resources we are not so aware about the price change. The
reason could be that the increase as well as decrease of the price will change our current
level of utility minimally.
4) The size of the tax change – this is closely related to the previous factor. In case of
a high sensitive demand even the small change in the price could create a significant
response of the demand. However, with the lower PED, the price change shall be greater
in order to create a relevant demand response.
5) The nature of the need to be satisfied by the relevant good - the basic
motivation of human activity is to satisfy the needs. The need is felt as a lack of
something, a bad feeling. Satisfying the need makes us feel relief, a good feeling. We
prefer to feel good before bad. Needs can be virtually endless, while the means or
resources to satisfy them are limited. Men satisfy needs gradually until exhausts its
resources. People satisfy their needs in some order. We could draw the relation between
the needs, PED and income as follows:
1. Basic physiological and living needs – air, food, basic clothes, living space and other
basic living conditions. We are forced to satisfy the basic physiological and living
needs first. In accordance to the Engel's law the share of the necessity goods
expenses decreases with the increase of income.
2. Imposed and acquired needs – needs externally imposed to us by the law or other
social rule, or acquired as a custom or addiction. Their share on the total expenditure
is given by the external factors. We try to minimise them as much as possible since
we do not valuate them subjectively.
3. Social and cultural needs – higher needs, social status, self-realisation, etc. Its
intensity could vary a lot and is very subjective. However, we tend to treat them as
more price sensitive in general. Their share depends solely on the disposable income
and satisfaction of the more intense needs of the lower grades. In general we could
say the higher is the intensity of the need the lower would be the price elasticity of
the demand.
103
Normally we satisfy the needs in order bottom-up with growing disposable income. The
intensity of the needs tends to fall moving up with the growing income. As we see from
the diagram the elasticity tends to rise along with rising disposable income and level of
needs. The effects of these main factors form the final behavioral response of the
consumers and suppliers to the tax rate change.
All the demand for goods is determined by the nature of the need which it is supposed to
satisfy. What needs are books are supposed to satisfy? Various needs could by satisfied
by the books. Reading custom is a form of the cultural heritage of the individual obtained
in the learning process. This custom is being satisfied through the use of libraries or
purchase of books. It could be said that first basic layer of the book demand is formed by
the relatively less elastic demand of people not willing to stop purchasing books
regardless of cost. For these people is to read book one of the more intense needs as
part of their intellectual life. Another part of the overall demand for books would be
constituted by the imposed demand of students, parents, researchers. These  are  “forced”  
to purchase books by the external factors and this kind of the demand is less price elastic
as well. If these two types of demand prevail, the commodity tax increase would be
probably, to the great extent, shifted forward onto consumers. On the other hand, tax
rate   decrease   would   be   probably   “consumed”   by   the   sellers   and   thus   not   reflected   in  
price.
Another type of book demand is the common demand for books as type of the leisure or
cultural activity. With higher income and other more urgent need satisfied become
reading a form of relaxation and cultural life of a person. This need would be higher
within the hierarchy and therefore it would be more price sensitive. In our opinion this
demand for books is an expression of "higher" need for cultural life. If we accept the idea
that the higher within the hierarchy of the needs the specific need is, the lower is the
intensity of motivation for its fulfillment (compared to the basic needs). To meet this
cultural need requires a relatively high level of satisfaction of lower needs.
Demand for books is obviously not homogenous. Each of the mentioned demand-types
plays a role in its composition. It could be said that to a certain level of income
(purchasing power) the less price elastic demand prevails. The higher the disposable
income of households in the economy, the higher the price elasticity of demand for
additional (marginal) books.
After the brief overview of our premises we shall take a look at the real case.
2.2 Real case analysis
What do we know as certain is that the VAT rate decrease was not reflected in book-price
by the sellers. This could be because the sellers choose not to decrease the price since
they were not expecting significant rise in sales if they do. Therefore we could conclude
that their experience with the Slovak consumers led them to belief that the demand is
relatively less price elastic. This belief is quite surprising since the experience from
Sweden showed that the demand for books in Sweden is price elastic. Why is the
situation so obviously different in Slovakia?
If we look at the situation from the bottom, we shall first analyse the main factors
forming the final response of the consumers to the price change (i.e. the PED). Then we
can formulate some premises which could explain the behavioral response of the
consumers on the book market.
In the previous part we have identified five main factors forming the final response of the
customers and suppliers to the VAT rate change. Further we look briefly on statistical
data characterising the real status of the Slovak consumers and thus determining their
consumption behaviour.
1) The existence of tax-free substitutes. Books and the reading as the very specific
“higher”  cultural  activity  have  no  real  and  homogenous  substitute. Of course, there is a
modern phenomenon of e-books which could change our perspective on the issue.
104
However,  it  is  not  widespread  enough  yet  and  even  its  homogeneity  with  the  “real”  books  
is questionable, therefore we will not include this into our considerations. Influence of the
e-books and related tax consequences shall be analysed by the further research.
2) The level of the disposable income (or the gross household expenditure
respectively) which could be used for consumption expenditure limits the degree to
which we satisfy our needs. As we have mentioned in previous part our needs are being
satisfied in some order with the physiological needs on the first place and the cultural
followingly. Therefore, if we consider a particular situation, we must examine the income
levels and overall purchasing power in the Slovak Republic. Tables and graph below
document the average level of disposable income of households and its usage
(expenditures type) as well as the estimate of purchasing power in the Slovak Republic.
Figure 5 Gross money incomes of private household (EUR/month/person) in period 2004
– 2012 in Slovakia
Source: Statistical Office of SR 2013
According to the Statistical Office of the Slovak Republic the development of disposable
income of households was in years 2004-2012 as showed in Graph 5. Slight increase in
2004 to 2008 period has been replaced by the decrease in 2008 to 2010 period, followed
by the recovery period until 2012. The overall income level remains still at the low level
compared to the economically more developed European countries. For example,
according to the Austrian Statistical Office, the gross household income of the median
household in Austria for 2011 is EUR 21 319 per year. In 2008 an estimated purchasing
power of the Slovak population reached the 48.8% of the European average, and only
40.7% of the EU 27. For comparison, the purchasing power of the population in Slovakia
in 2008 with the amount of EUR 6 102 reached 34.8% of the purchasing power of the
Swedish population with the estimated amount of EUR 17 552 (GfK 2009). According to
the EUROSTAT figures the median household income in 2011 was: Slovakia EUR 6 303,
Sweden EUR 22 498, Finland EUR 21 826, Austria EUR 21 319, Czech Republic EUR 7
451.
3) The share of expenditure on specific good in total consumption expenditure.
Statistical item Recreation and culture includes expenditures on books as well. However,
expenditure on books constitutes only the minor part of the item since it includes the
holidays, theatre and cinema visits as well.
Table 1 Gross money expenditures of households on Recreation and Culture
(EUR/month/person) in period 2004 – 2012
Expendeture 2004 2005 2006 2007 2008 2009 2010 2011 2012
Recreation and
culture
5,49% 5.21% 5.50% 5.79% 6.11% 5.81% 6.31% 6.10% 5.99%
Source: Statistical Office of SR 2013
For the year 2012 item represents the average amount of EUR 272 per year (including
holidays, but also books), representing 5,99% of the total gross household expenditures
per year. For comparison, according to the Statistical Office of Germany the average
amount of the total household expenditure in Germany is EUR 2 252 per month, i.e. EUR
27 024 per year. Item Recreation and culture constitutes 10.8% of it, representing an
average annual expenditure of EUR 2 919. In Sweden the average expenditures on
2004 2005 2006 2007 2008 2009 2010 2011 2012
0
100
200
300
400
500
105
Leisure (Recreation) and culture counts for 18% (out of the total household disposable
income in amount of EUR 22 498).
4) The size of the tax change. The VAT rate decreased from 19% to 10%, i.e. 9%
percentual points. Rough calculation: if the average consumer price for the regular (not
textbook or for children) book in the Czech Republic for 2010 was CZK 208,90, i.e. EUR
8,34, the VAT decrease, if fully reflected in price, would cause the decrease in price to
EUR 7,59, i.e. EUR 0,75. For comparison, the average book price for the hardcover in
Sweden as of 2009 was app. EUR 25.
5) The nature of the need to be satisfied by the relevant good. As we have already
written in the theoretical background the demand for books has some specific features.
We could conclude that on the lower levels of the income the greater part of the overall
demand for books would consists mainly of the imposed (students, researchers, parents,
libraries, etc) demand, as well as with the demand of the people with stronger reading
habit who are satisfying their basic needs for reading (reading as the intellectual
activity). With the increasing income the proportion of the imposed demand would
probably remain stable. The demand of the people with reading habit would probably rise
more than proportionally since the readers with the stronger habits would be satisfying
their marginal needs along with the new consumers – common readers with the common
reading habit. This latter demand is probably more price elastic.
2.3 Summary
To sum up the situation in Slovakia we could conclude that:
1. There were not relevant substitutes for books at the time.
2. The overall level of household income and expenditures is still relatively low
compared to other European countries.   The   share   of   the   more   “urgent”   needs   is  
significant  and  the  space  for  “higher”  needs  remain  low.
3. The share of the expenditures on books compared to the overall expenditure level is
low.
4. The VAT rate decrease from 19% to 10% is relatively low with the possible price
decrease app. EUR 0,75 which probably could not provoke any change in consumer
behaviour. This is more obvious if we take in to account previously mentioned
income/expenditure level and the share on overall expenditures factors.
5. Demand for books is obviously not homogenous. Each of the previously mentioned
demand-types plays a role in its composition. It could be said that to a certain level
of income (purchasing power) the less price elastic demand prevails. Important is the
intensity and the degree of fulfillment of other needs. As we have seen from the
statistic data the overall income level in Slovakia is relatively low, therefore the less
price elastic demand would probably create the more sizeable share on the overall
demand.
3 Conclusions
Our theoretical insight as well as the empirical surveys shows that the decrease of the
VAT rate on books in Slovakia did not bring expected results. We believe that the main
reason for this failure of the tax policy measure lies in insufficient understanding of the
tax principles and microeconomics of taxation.
The Copenhagen Economics on the example of Sweden shows the relatively high price
elasticity of demand for books. As we see, what works in Sweden does not have to work
elsewhere. The economy is created by people with their customs, needs and history
operating under different circumstances and conditions. These differences could create a
difference in the economic activities, therefore the good economic policy shall reflect
these differences and special circumstances.
All the factors we analysed indicate the low price elasticity of the overall demand for
books at the time in Slovakia. Therefore, taking into account the mechanism how the
indirect tax reacts in case of the low price elasticity, we would not recommend
106
undertaking the VAT rate decrease. Undertaking such measure in order to lower the book
price would be ineffective and would transfer the money resources from the state budget
to the book supplying chain (app. EUR 32 mil. from 2008 to 2010). According to our
opinion, if the subsidy to book supplying chain would be the purpose of the measure, we
would recommend to rather undertake the direct means instead. Doing so through the
indirect tax measure could not be seen as targeted and effective, causing the distortion
of the tax system neutrality. The question which inevitably pops up by analysing the
issue  is:  Cui  bono?  As  Randall  Holcombe  (2002)  warned  in  his  paper:  “The information
necessary to set taxes in conformance with the Ramsey rule is not directly observable,
which will lead to rent-seeking activities as interest groups try to influence the political
determination  of  tax  rates  they  face.”
We hope that the future tax policy measures will be more sophisticated and will bring
better results.
Acknowledgments
Supported by VEGA project No. 302: The effectiveness of the tax revenue and public
expenditure use in relation to long-term sustainability of public finances, and new nontraditional
options of budget revenues.
References
Copenhagen Economics (2007). Study on reduced VAT applied to goods and services in
the member states of the European Union, Final report, 6503 DG TAXUD, Denmark
Explanatory memorandum to the Act No. 222/2004 Coll. on Value Added Tax in wording
as of January 1st, 2008.
GfK Aktiengesellschaft (2009). A survey of the purchasing power in Slovakia and 40
other European countries, Germany, Available at: www.gfk.com
Holcombe R.G. (2002). The Ramsey Rule Reconsidered, Public Finance Review November
2002, vol. 30, no. 6, pp. 562-578
Maslow A. H. (1943), A Theory of Human Motivation, Psychological Review, 50.
The  Swedish  Publishers’  Association  (2003).  Reduced VAT on books (and magazines) in
Sweden, Available at: http://www.forlagskansli.se
Záborský   J.   (2008). Harry Potter zlacnel. Knihy nie, Journal Trend, Available at:
http://ekonomika.etrend.sk/ekonomika-slovensko/harry-potter-zlacnel-knihy-ni.e.html
Zubalova A. and coll. (2012), Tax theories in practice – Tax Theory and Policy II, IURA
Edition, Bratislava.
107
Value at Risk estimation using a Monte Carlo simulation
Ján  Gogola
University of Pardubice
Faculty of Economics and Administration
Institute of Mathematics and Quantitative Methods
Studentská  84,  532  10  Pardubice, Czech Republic
E-mail: jan.gogola@upce.cz
Abstract: In this paper we deal with a value at risk (VaR) based on simulation approach,
known as Monte Carlo method and its predictive performance is evaluated with respect to
a given portfolio of 4 equities. The aim of this paper is to apply Monte Carlo simulation
for value at risk (VaR) estimation and other goal is to check our results with empirical
obtained values. There is paying attention to the importance of back testing. When
testing the model results are compared to actual future results. During testing, therefore,
expected losses of the portfolio compared with actual losses of the portfolio in order to
know the accuracy of the model.
Keywords: VaR, Monte Carlo Simulation, Back testing.
JEL codes: C16, G22, G32
1 Introduction
VaR is nowadays frequently used model for risk analyses mostly in the banking and
insurance companies. Following the characteristics of the model principle, we focus to the
Monte Carlo method. Monte Carlo method is used to simulate a variety of scenarios for
the portfolio value on the target date.
By the method VaR (Value at Risk) is estimated worst loss that can occur with a specified
probability of a specified future period. The result of this method could be, for example
daily VaR 10,000 €  with  95%  confidence, there is 5% probability that the potential daily
loss of more than  10,000  €.
In banks, it is one of the most used approaches in the so-called internal models to
calculate capital requirements for market risk. The results of this method are used:
- In determining the capital requirement
- The allocation of investment funds
- The evaluation of individual traders (risk of their investment activities)
- Financial risk management
- The integration of various types of risk into a single value
The VaR method is characterized by two factors that must be determined in advance,
holding period and confidence level. Holding period – characterizes, during which period
the possible loss considering. The choice of holding period depends on:
- the market liquidity,
- no changes in portfolio (constancy portfolio),
- the verifiability of results.
The choice of both factors is indeed subjective, but in some systems is fixed, for
instance: RiskMetrics (American bank J.P. Morgan) uses a one-day holding period and a
95% confidence level, while the recommendations of the Basel Committee's 10-day VaR
with a 99% confidence level. (In practice, however there are other values such as
Citibank uses a 94.5% confidence level). In calculating capital requirements, the value of
VaR sometimes is multiplied by a "safety factor" whose value is predetermined.
Denote the profit or loss of the investment portfolio as a random variable X. If we
consider the daily holding period, then X is the daily profit or loss. If we denote the
108
required confidence level as c (eg for system RiskMetrics c = 0.95) then the respective
value VaRabs
is defined as
cVaRXP abs
 )( , (1.1)
i.e. –VaRabs
is  a  100·(1–c) percent quantile cq 1 of a random variable X, and from theory
of probability we know: cVaRXP abs
 1)( .
As follows defined value is called the absolute value at risk VaRabs
(or absolute VaR).
It is used the so-called relative value at risk VaRrel
(relative VaR) which refers to the
mean (expected) value of E(X) of random variables X and is defined as
)(XEVaRVaR absrel
 . (1.2)
Figure 1 VaR as 5 percent quantile
In practice, in the investment context, it is preferred to work with profit rates.   Let’s  
consider the investment portfolio with the price P and the rate of profit during the
relevant time horizon as a random variable r with mean .
If the 100·(1-c) percent quantile cq 1 of a random variable r we denote r*
, then absolute
value at risk for the considered holding period and confidence level c is

 rPVaRabs
, (1.3)
and the corresponding relative value at risk is
)()(   
rPPrPXEVaRVaR absrel
. (1.4)
Remark 1.1 The price changes can be defined in different ways. We will consider the
logarithmic rate of profit rt (it is also known as a profit rate of continuous interest) which
is defined 








1
ln)1ln(
t
t
tt
P
P
Rr , (1.5)
where Pt is the price of asset at time t, Pt-1 is the price of asset at time t-1 and Rt is the
discrete rate of profit at time t.
The logarithmic rate of profit seems to be more complicated than discrete. However, it
has some advantages, especially here, that it can be modeled by a normal distribution,
which implies a log-normal distribution of the ratio
1t
t
P
P
.
2 Methodology and Data
Parametric VaR estimation can take place if we know the distribution of the rate of profit
in the period described by a distribution of the estimated parameters. For this purpose a
normal distribution is most commonly used r  );( 2
N . For   the   100·(1–c) percent
quantile r*
of random varible r it holds
109
cqr 

 1 , (2.1)
where cq 1 is (1-c) percent quantile of N(0;1).
For relative value at risk we receive
cc
rel
qPqPrPVaR 

 11 )()(  (2.2)
Remark 2.1 For simplicity, we will further denote relative value at risk without
superscript refers to as VaR.
Approximation by the normal distribution may not always be satisfactory. In terms of
calculating the value-at-risk bothers to approximate by normal distribution the "heavy
tails" distribution rate of profit (and it actually just heavy negative tail). Improper use of
the normal distribution approximation value comes in lower risk than should properly be,
which is in practice very dangerous. For the calculation of VaR is therefore also used
other distributions namely heavy-tailed distribution as the gamma distribution, Pareto
distribution etc.
Remark 2.2 One of the major criticisms of the wide use of the normal distribution to
estimate risks is American scientist (Lebanese origin) Nassim Nicholas Taleb, author of
the  book  “The Black Swan”.
The calculation of VaR by Monte Carlo uses large number of simulations on the price of
the portfolio to calculate VaR. On the basis of mathematical and statistical models we
simulate random processes governing the price development of tools creating portfolio.
Figure 2 Market price of Google
Source: Yahoo Finance
We notice (Figure 2) that market price is much more random. From the course of price is
visible the randomness, which means that prices cannot be modelled by deterministic
functions. Therefore, we use stochastic processes.
Wiener process is the basic process from which are derived many others. Wiener process
)(xWt is stochastic process having the following properties:
i. W0 = 0,
ii. The function t → Wt is with probability one everywhere continuous
iii. Wt has independent increments with Wt−Ws ~ N(0, t−s)  (for  0  ≤  s < t),
Monte Carlo method proceeds as follows:
– We determine the descriptive statistics of each instrument in the portfolio,
– In modelling the evolution of the share price Pt we use Wiener process
110
tttt zPtPP ddd   , (2.3)
where  is a rate of profit of share,  is volatility of share price a dzt is random variable
with normal distribution N(0;dt). For practical purposes (for a better understanding) the
model is used in the differential form
tPtPPP tttttt   , (2.3)
where t  N(0;1), whereas it holds
tPPPE tttt  )( , tPPP tttt   )( . (2.4)
If we generate t independently for each time interval, then we can write
tt
t ePP 
 
0 , (2.5)
where   N(0;1).
In the case of more instruments in the portfolio, it is necessary to determine their
correlation matrix R. Simulation of random vectors T
(T
is a row vector) with a
multidimensional normal distribution N(0; R) is possible to get by a simple simulation of
random vectors T
with distribution N(0; I ), where I is an identity matrix, i.e.
components of the vector T
are mutually independent random variables with distribution
N(0;1).
When
T
LLR  , with lower triangular matrix L, then it is called the Cholesky
decomposition of matrix R (such a decomposition exists for every positive definite
matrix), and then we can write
TTT
Lηε  . (2.6)
By repeating and mutually independent use of a suitable random number generator we
simulate the probability distribution of prices of the portfolio and by the histogram we will
find the value at risk VaR.
3 Results
We will use Monte Carlo simulation for Value at Risk to evaluate the risk of a portfolio of
four financial instruments. We use one-day holding period and the confidence level 95%.
From the historical prices (source: finance.yahoo.com), we calculate the mean, standard
deviation and other statistical characteristics of our assets (Table 1.). We chose
ConocoPhillips, Dow Chemicals, Gannett and Masco which were a part of the Pension
fund  “Progress”  of  Allianz  d.s.s.  Historical  prices  are  from  4.1.  2009  to  9.11.  2011,  that  
we draw from finance.yahoo.com
Table 1 Descriptive statistics of assets
Conoco DowChem Gannett Masco
Mean 0,071% 0,003% -0,083% - 0,028%
St. dev.  1,657% 1,333% 1,141% 1,629%
Skewness - 0,244386 - 0,788095 - 0,036463 0,2309727
Kurtosis 0,2279892 8,1025948 0,732302 3,4038782
Correlations are calculated from series of historical prices, too (Table 2).
Table 2 Correlation matrix
ConocoPhillips 1 0,250802384 0,196232538 0,243851077
Dow Chemicals 0,250802384 1 0,292515638 0,344501424
Gannett 0,196232538 0,292515638 1 0,315929936
Masco 0,243851077 0,344501424 0,315929936 1
111
Now we decompose the correlation matrix by the Cholesky decomposition (Table 3).
Table 3 Cholesky decomposition
1 0 0 0
0,250802384 0,968038307 0 0
0,196232538 0,25133308 0,947799807 0
0,243851077 0,292698119 0,205226567 0,901524553
We work with the assumption we have invariable (fictive) portfolio (Table 4).
Table 4 Portfolio
Value of each
asset in the
portfolio ($)
Value of
assets on
9.11.2011
Number of
shares
ConocoPhillips 600 000 82,56 7267
Dow Chemicals 750 000 41,81 17938
Gannett 250 000 39,84 6275
Masco 400 000 23,17 17264
Sum 2 000 000
Then we simulate returns of each asset by multiplying the decomposed matrix by a
random vector (Table 5) of independent standard normal random variables.
Table 5 Random vectors
N(0;1) N(0;1) N(0;1) N(0;1) e1 e2 e3 e4
1. 0,52458 -1,71488 -0,20495 1,05307 0,52458 -1,52850 -0,52231 0,53329
2. 0,09227 -1,16861 0,42689 -1,04683 0,09227 -1,10811 0,12901 -1,17568
3. -1,22788 0,22522 -0,96634 -0,98966 -1,22788 -0,08992 -1,10024 -1,32402
4. 1,39434 -1,57640 0,07024 0,87932 1,39434 -1,17631 -0,05600 0,68575
5. -1,41195 0,26052 0,19808 -0,95602 -1,41195 -0,10192 -0,02385 -1,08927
…
…
…
…
…
…
…
…
…
We ran 500 hypothetical trials of daily returns for each asset in our portfolio.
Table 6 Generated prices of each asset
Conoco DowChe
m
Gannet
t
Masco Value of the
portfolio
Profit/Loss
1. 83,34 40,97 39,57 23,37 1 992 250,55 -7 749,45
2. 82,74 41,20 39,87 22,72 1 982 829,70 -17 170,30
3. 80,95 41,76 39,31 22,67 1 975 483,31 -24 516,69
4. 84,55 41,16 39,78 23,42 2 006 827,38 6 827,38
5. 80,71 41,75 39,80 22,76 1 978 119,21 -21 880,79
…
…
…
…
…
…
…
Prices (Table 6) are generated by the formula tt
t ePP 
 
0 , where t =1, since there
are one-day values.
We sort the 500 prices of our portfolio from the lowest to the highest and read the price
which corresponds to the 5% quantile, since we want to get the VaR at 95% confidence
level. We will get -33 558,28.
Our result therefore leads to the following conclusion: with 95% confidence, we do not
expect to lose more than 33 558,28 $. If we ran it again, we would get a different result,
although the differences would be narrow.
112
A part of each methodology should be a verification of accuracy of used procedures.
Therefore, in the framework of Value-at-Risk are used different methods to resolution of
the relevant discrepancies. In practice the most commonly we use back testing. The most
common test of a VaR model is to count the number of VaR exceptions, i.e. days (or
holding periods of other length) when portfolio losses exceed VaR estimates. If the
number of exceptions is less than the selected confidence level would indicate, the
system overestimates risk. On the other hand, too many exceptions give a signal of
underestimation of risk.
Denoting the number of exceptions as k and the total number of observations as T, we
may define the failure rate as
T
k
. In an ideal situation, this rate would reflect theselected
confidence level. For instance, if a confidence level of 95 % is used, we have a null
hypothesis that the frequency of exceptions is equal to 5%. This type of statistical test
was proposed by Kupiec. According to Kupiec, the POF-test (proportion of failure) is best
conducted as a likelihood-ratio (LR) test. The test statistic takes the form
𝑙 = 2 ln 1 − − 2 ln((1 − 𝑝) 𝑝 ), (3.1)
where
𝑇 – number of observed historical values
𝑘 - number of exceedances of the VaR during the observed time-horizon
p – confidence level
The idea is to find out whether the observed failure rate pˆ is significantly different from
p, the failure rate suggested by the confidence level. Under the null hypothesis that the
model is correct, LRPOF is asymptotically χ    (chisquared) distributed with one degree of
freedom.
Figure 3 Backtesting results
The following table shows the number of exceedances to the Value-at-Risk during the
monitored time horizons T for the specific probability.
Table 7 k - number of exceedances of Value-at-Risk
𝒑 𝑻 = 𝟐𝟓𝟓 𝑻 = 𝟓𝟏𝟎 𝑻 = 𝟏𝟎𝟎𝟎
0,01 𝑘 < 7 1 < 𝑘 < 11 4 < 𝑘 < 17
0,05 6 < 𝑘 < 21 16 < 𝑘 < 36 37 < 𝑘 < 65
0,10 16 < 𝑘 < 36 38 < 𝑘 < 65 81 < 𝑘 < 120
Source: Cipra (2002)
-40 000,00
-30 000,00
-20 000,00
-10 000,00
0,00
10 000,00
20 000,00
30 000,00
40 000,00
50 000,00
60 000,00
0 50 100 150 200 250
VaR
Loss/Profit
113
We tested the value of the portfolio VaR calculated by Monte Carlo methods. On the back
testing we chose 255 observed time horizons (from 12.11. 2011 to 15.11. 2012) and
Kupiec test results showed us the following chart. (Where loss is positive and profit is
negative.)
4 Conclusions
Based on Kupiec test, we found that the number of exceedances of VaR over 255
observed time horizons is 18. Thus, during the 255 observed time horizons number
exceeded the Value-at-Risk is in the interval (6, 21) at the significance level of 5% we
accept the hypothesis that the probability of 0.05 is correct. We came to the conclusion
that the Monte Carlo method is suitable for the calculation of Value-at-Risk of our
portfolio with the chosen and confidence level 5% and the holding period one day.
References
Ambrož,  L.  (2011). Měření  rizika  ve  financích, Praha: Ekopress, 2011
Cipra, T. (2002). Kapitálová  přiměřenost  ve  financích  a solventnost v poišťovnictví, Praha:
Ekopress.
114
Nowcasting business cycles using transportation index
Andrius Guzavicius, Vytas Barkauskas, Vidas Tamulis
Kaunas University of Technology
Faculty of Economics and Management,
Department of Business Economics
Kestucio 8-203, Kaunas, Lithuania
E-mails: andrius.guzavicius@ktu.lt
vytas.barkauskas@ktu.lt
vidas.tamulis@gmail.com
Abstract: Today's relationship between financial market and real economy is one of the
main topic in scientific debate in the field of many different approaches. It is practically
impossible separate economics science from others disciplines, like psychology, human
behavior, philosophy, ethic and etc. Financial markets after losing the Gold standard in
1970 exploited to enormous amount, very active monetary policy in USA, EU and Japan
last few years made the situation even more complicated. Some economist see current
situation in financial markets as normal, their argument is that expansive monetary
policy increasing liquidity in financial market and provide efficient recourse allocation in
real economy. On the other hand, such intensive policy could lead to misallocation of
recourses, high cyclical fluctuations and even possibility for hyperinflation. For the real
business society it is very important to have a reliable forecasting tools to identify
current phase of a business cycle. Because of the complexity financial instruments and
difficult economical situation today instead of forecasting it is useful to have real-time
and reliable data, that might identify current stage of a business cycle. There are
different scientific opinions, that some financial indicators have power to predict market
action, one of them is transportation sector. The research object of this article is
possibility of identification a business cycle phase using transportation index providing
readers with understanding of nowcasting approach regarding regional heterogeneity
using transportation index.
Keywords: market driven economy, business cycle, nowcasting, transportation sector
JEL Classification: G01, G15, G17
1 Introduction
According to traditional economics theory, financial markets acting depending on
situation in the real economy. The positive or negative expectations in stock market
reacting to change in main economic indicator as GDP, unemployment level, inflation and
ect. On the other hand, there are different opinions about correlation between financial
markets and real economy: financial markets are primary factors to change expectations
and sentiments, and they impact directly to real economic indicators. But for the real
business society it is very important to have a reliable forecasting tools to identify
current phase of a business cycle. Because of the complexity financial instruments and
difficult economical situation today instead of forecasting it is useful to have real-time
and reliable data, that might identify current stage of a business cycle (Klus, LandonWhite,
2005; Schumacher, Breitung, 2008; Giannone, Reichlin, Small, 2008; Kuzin,
Marcellino, Schumacher, 2012). There are different scientific opinions, that some
financial indicators have power to predict market action, one of them is transportation
sector (Gordon, 1992; Han, Fang, 2000; Bosworth, 2001; Lahiri, Young, 2004;, Lahiri,
Yao, 2006; Fenz, Schneider, 2009; Askitas, Zimmermann, 2012)
The research aim of this article to describe and evaluate different approaches of
nowcasting business cycle regarding selected levels of regional heterogeneity using
transportation indexes. The research object is identification a business cycle phase using
transportation index and ability to forecast market action in particular regions. The
results of research is to provide readers with understanding of nowcasting approach
regarding regional heterogeneity using transportation index.
115
2 Nowcasting in market driven economy
Using different econometrical tools for nowcasting it is crucial to have reliable and
precise data sets. This problem of reliability is even more actual for different economic
research in small economy country like Lithuania. A good example of this might be
famous expression of former prime minister of Lithuania G. Kirkilas at the beginning of
year 2008: "there will be no crisis in Lithuania, this is happening somewhere else, it is
not affecting our economy... ", but second year decreasing of Lithuania GDP was 16
percent.
Many economists trying to find "golden key" indicator, that would forecast real situation
in economy. There are many theories (beginning whit Dow Jones theory regarding
industrial and transportation sector's interaction in 19 century in USA) that one of the top
indicator might be transportation sector. To substantiate or negate this theory, it is
necessary to evaluate predictive power of transportation index.
Figure 1 Heterogeneity of levels of liquidity in financial markets and availability of data
Looking at the world economy living is small economy country gives an advantages to
find best solution for problem of poor data set availability. Lithuania became
independent just 20 years ago, there has been different economic systems from planning
economy, transition economy to market economy. It is complicated to compare long
term data, it is necessary look wider in to the economies with long term data set
available. In market driven economy the financial sector is leading parameter and it
might be helpful looking for contemporary data. Because of high liquidity in financial
markets, fluctuations of business cycle became global instead of local, however, there is
a risk of relying on liquidities reliability because of complicated action of trading
computer's algorithms, so called HFTP (high frequency trading platforms).
Identifying high liquidity and data availability as main factors for successful
econometrical nowcasting process, we are suggesting separate levels for different
countries approaches (see figure 1). The first level including countries (USA, Europe) whit
very high liquidity in financial markets and excellent availability of long term data, those
countries dominating in the world economy. The second level including countries,
participating in the world economy very actively, but having middle liquidity and data
availability. The most difficult and interesting level are countries whit very small
High liquidity
Middle liquidity
Low liquidity
Poor
<20 years
Liquidity
Availability
of data
Middle
±50  years
High
>50 years
USA,
GDP
15584
Bln.
$US
Europe,
GDP
16584
Bln.
$US
China, GDP
8227 Bln. $US
Mexico, GDP
1200 Bln. $US
Lithuania, GDP
42 Bln. $US
116
economies, having almost no impact on global financial system, with very poor data
availability and low liquidity in financial markets. Verification of transportation index as
nowcasting tool for economy in such environment might be interesting and serve as
substantiation of reliability.
Starting whit the USA, the bankruptcy of Lehman Brothers in 2008 precipitated what is
likely to be judged to be the most global recession ever. The causes of the current
recession are being debated, but it is widely believed that the housing downturn, which
started in 2006, is a primary cause of the broader economic failure. The fall of housing
prices from its peak levels affected very negatively on home building and home
purchases. This also caused a sharp rise in mortgage foreclosures for many institutions
that had exposure to mortgage securities (see figure 2). There has been many crises in
the last decade, but the last financial crisis was unprecedentedly global and effected all
countries in the World.
Figure 2 Recent market crises and Dow Jones transportation Average index (DJ-20)
Looking at the strongest economy in World, there are different scientific approach
regarding possibility to forecast future action. In the 19th century in USA was created so
called Dow Jones theory, and one o the main postulates was that stock market averages
(indexes) must confirm each other (Dixon, 1924; Rhea, 1932; Cowles, 1934;
Henricksson, Robert, 1981). In Dow's time, the USA was a growing industrial power. The
US had population centres but factories were scattered throughout the country. Factories
transport their goods to market, usually by rail. Dow's first stock averages were an index
of industrial companies (Dow Jones Industrials, DJ-30) and rail companies (Dow Jones
Transport Averages, DJ-20). According to this logic, if manufacturers' profits are rising, it
follows that they are producing more. If they produce more, then they have to transport
more goods to consumers. If an investor is looking for signs of health in manufacturers,
they have look at the performance of the companies that transports production to
market. The two averages should be moving in the same direction. When the
S&P 500
FTSE 100
HSI
MXF
DJT
1
1
2
4
8
16
32
64
128
256
512
1024
2048
4096
8192
16384
32768
1985.01.03
1986.01.03
1987.01.03
1988.01.03
1989.01.03
1990.01.03
1991.01.03
1992.01.03
1993.01.03
1994.01.03
1995.01.03
1996.01.03
1997.01.03
1998.01.03
1999.01.03
2000.01.03
2001.01.03
2002.01.03
2003.01.03
2004.01.03
2005.01.03
2006.01.03
2007.01.03
2008.01.03
2009.01.03
2010.01.03
2011.01.03
2012.01.03
Value
Indexes
1987 stock
marketcrash
Asian financial
crisis 1997
technology
bubble 2000 Financial crisis
2008
Mexican crisis
1994
Financial crisis
2008
117
performance of the averages diverge, it is a warning signal for investors. Transportation
index (e.g. DJ-20) usually acting first, probably because to follow transportation data and
to have an interpretation is more possible then evaluate the real value of an industry
company regarding fundamental analysis of financial data. However, despite of some
higher probability of forecasting accuracy in short run period, long run forecasting using
transportation data is more complicated. Predicting crises there is a hope, that the crisis
will  not  be  worse  or  will  somehow  be  “controlled”  by  economic  policy  but,  we  see  more  
and more developments that look unsustainable from the macroeconomic perspective. An
example might be European sovereign debt crisis. Domestic demand continues shrinking
in large parts of the euro area countries, and uncertainty remains high, effectiveness of
monetary policy stimulus remains limited.
Even more difficult situation dealing with forecasting main macroeconomics indicators at
the regional level using different indicators (see figure 3). Using national forecasts for
business purpose can lead to mis-estimation because of a high degree of regional
heterogeneity (e.g., different economic structures).
Figure 3 Transportation sector and gross national product (GDP) in Lithuania
Source: Eurostat, Statistic Lithuania
Overcoming  the  problem  of  a  ’data-poor  environment’  at  the  sub-national level there has
to be included not just regional, but also national and international indicators. An
example might be situation in Lithuania. There is very clear correlation of transportation
sector and gross national product (GDP) in Lithuania. Despite short economic history
(Lithuania became independent in 1991) and limited availability of data set, we see, that
last two crises affected our market to (crisis 1999 and 2007).
Different nowcasting models using heterogeneous set of predictors, including diferent
statistics, as also surveys. The data are monitored by market participants to identify the
state of the economy. This methodology might be designed to include essential
characteristics of the economic problem in real time. Because of shortage of available
data about transportation sector, the biggest Employers organization in country
Lithuanian Confederation of Industrialists is providing survey of highest level Lithuanian
company's management opinions and prognosis analysis. It is called Industry
Expectations Index (IEI). The goal of IEI – provision of constant and easily interpreted
indicators of Lithuanian manufacturing industry company expectations. Survey result answers
received from the CEOs of 130 largest Lithuanian manufacturing industry
143769
117113 115135
101325
109076
115127 116563
132203 128989
136902 133516 138177 134773
105845
115192
121297 120688
5,1
8,5
7,5
-1,5
4,1
6,6 6,9
10,3
7,3 7,6 7,8
9,8
2,9
-14,8
1,5
5,9
3,7
0
20000
40000
60000
80000
100000
120000
140000
160000
-20,0
-15,0
-10,0
-5,0
0,0
5,0
10,0
15,0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Goods carried by all modes of transport (tonnes) Gross domestic product (GDP, %)
118
companies on the current status and future perspectives for the upcoming quarter. By
the statistical method and calculations, answers to the questions presented are converted
into an interactive numerical expression.
Business entities always depend on accurate information, including reliable short-term
analysis of economic activity. Forecasting and time lags in the publication of national
accounts data imply that main economic indicators must be given on the basis of higher
frequency data from indicators which are available earlier than the national accounts
estimates. Nowcasting of economy provide important inputs into macroeconomic
projections, as they help to improve the understanding of economic developments in the
current time period.
3 Conclusions
The definition nowcasting has recently become popular in economics sciences.
Forecasting used to identify the state of an economy, for example, gross domestic
product, inflation, unemployment are only determined after a long delay, nowcasting only
have to identify present and even the recent past.
The transportation indexes representing sector, that plays a main role in facilitating
economic activities between sectors and across regions, and can be useful in monitoring
and identifying cyclical turning points the current state of the economy, however,
regional heterogeneity and different approaches regarding liquidity in financial markets
and data availability are necessary.
Techniques of nowcasting have been based on simplified heuristic approaches and can be
formalized in a different statistical models. Models collecting information from a large
quantity of data series at different frequencies and with different time lags. Signals about
the direction of change in main economic indicators can be extracted from heterogeneous
set of information sources, data are used to compute the real time flow of data.
Among the coincident indicators that the institutions in Europe uses to date business
cycle turning points, none specifically represents the transportation sector. It is
important to provide the usefulness of the transportation sector indexes as coincident
indicators in determining the peaks and bottoms of economy.
References
Klus, A., Landon-Lane, J.S., and White, E.N. (2005), How Could Everyone Have Been so
Wrong? Forecasting the Great Depression with the Railroads, Explorations in Economic
History, 42, 1), 27-55.
Schumacher, C. & Breitung, J. (2008). Real-time forecasting of German GDP based on a
large factor model with monthly and quarterly data. International Journal of Forecasting,
24 (3), 386–398.
Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: The real-time informational
content of macroeconomic data. Journal of Monetary Economics, 55 (4), 665–676.
Kuzin, V., Marcellino, M. and Schumacher, C. (2012). Pooling versus Model Selection for
Nowcasting GDP with Many Predictors: Empirical Evidence for Six Industrialized
Countries. Journal of Applied Econometrics.
Dixon, F.H. (1924). Transportation and Business Cycle. In Edie, L.D. (ed.), The
Stabilization of Business, New York: The Macmillan Company, 113-163.
Askitas, N. and K. F. Zimmermann (2012), Nowcasting Business Cycles Using Toll Data.
Journal of Forecasting. DOI:10.1002/for.1262.
Fenz, G. and M. Schneider (2009), A Leading Indicator of Austrian Exports Based on
Truck Mileage. Monetary Policy & Economy Q1/09: 44-52.
Lahiri, K., W. Yao and P. Young (2004), Cycles in the Transportation Sector and the
Aggregate Economy. University of Albany Discussion Papers 03-14.
119
Bosworth, B. (2001), Output and Productivity in the Transportation Sector: An Overview,
The Brookings Institution.
Gordon, R.J. (1992), Productivity in the Transportation Sector. In Griliches, Z. (Ed.),
Output Measurement in the Service Sectors, 371-428.
Han, X. and Fang, B. (2000), Four  Measures   of  Transportation’s   Economic  Importance,  
Journal of Transportation and Statistics, 3 (1), 15–30.
Lahiri, K., and Yao, W. (2006), Economic Indicators for the U.S. Transportation Sector,
Transportation Research – A, 40, 872-887.
120
Financial and operational performance of hospitals
in the Czech Republic
Tana Hajdikova1
, Lenka Komarkova1
, Petr Pirozek2
1
University of Economics, Prague
Faculty of Management
Jarosovska 1117/II, Jindrichuv Hradec, Czech Republic
E-mails: hajdikova@fm.vse.cz, komarkol@fm.vse.cz
2
Masaryk University
Faculty of Economics and Administration
Lipova 41a, 602 00 Brno, Czech Republic
E-mail: pirozek@econ.muni.cz
Abstract: Article aims to describe and analyze the performance of hospitals in times of
economic instability. It describes the important dimensions of financial and operational
performance in both profit and non-profit hospitals in the Czech Republic during selected
period. Years 2008 and 2011 were chosen quite intentionally, because of the differences
in findings between the time of the very beginning of the global economic crisis that
started in 2008 and last possible researchable year. The year 2011 is so far the latest
year for which hospitals submitted their annual reports. Empirical research has been
conducted by evaluating secondary data, which were selected from reporting and internal
materials of hospitals in years 2008 and 2011. The authors identify potential factors geographical
location and operating subsidies, and by using statistical methods they
explore the relationship between these factors and identify differences for individual
years. The research results demonstrate differences in selected areas in health care
sector.
Keywords: hospitals, non-profit hospitals, net income, economics, performance, subsidies
JEL codes: H2
1 Introduction
Performance of organizations is at present closely observed discipline monitoring and
evaluating success of an organization. Performance of organizations is primarily the
domain of business organizations and is associated with a variety of approaches to its
determination  (Šiška and  Kozáková  2012),  (Částek  and  Pokorná  2013).  Considering the
importance of responsible management of public resources it is a subject of interest in
the area of public services or in the administration of public and government (Krajicek
and Valouch, 2011). Besides, the concept of performance is also found in other human
activities.
The performance of the organization is essentially the same as with any living organism
whose behavior is consciously focused on achieving a certain level of performance and in
conjunction with the fulfillment of organizational goals. The starting point of the
performance viewpoint lies in the approaches used in the field of performance
management. Basis can be described as a trust in the stability of the basic parameters of
the business environment, business performance ranging from the operational to the
tactical horizon. Business owners are usually directly involved in the management of the
company in senior management positions and are users of performance information (Král  
et al, 2012). As the basic performance measures are considered profit or loss if its
recognized concept of surveys is based on the so called accrual principle. The broader
concept of business performance includes in its measurement the impact of current
actions on the future development of business performance. In the area of performance
benchmarks for external users there is the effort to harmonize both the content and the
methods through the development of International accounting standards (IAS) and their
successors International Financial Reporting Standards (IFRS), US Generally Accepted
121
Accounting Principles (US GAAP) and the Court of the European Communities (Wagner
2009).
Business performance is measured by many different methods, whether simple or
complex, both conceptually and mathematically. The crisis in 2008 and 2009 means
undeniable change in perspective. During this period, many companies dealt with their
existential issues and with maintaining or increasing the market share in terms of
position against the competition (Kislinger, 2009).
It is necessary to remember that hospitals are more complex and considerably sparser
than most other types of organizations. We can mark the so called four hospital worlds the
world of therapy, care, administrative hierarchy and the world administrators. These
worlds correspond to four sets of activities, four different ways of organizing and thinking
(Glouberman and Mintzberg, 2001).
The topic in many countries of Central and Eastern Europe is the transformation of legal
form of hospitals (Albreht, T., Klazinga, N. 2009), (Mishtal, J. 2010). In the Czech
Republic it came to a transformation of the legal form of the companies after the year
2000. The original district hospital, maintained by the state, passed to the newly
established regions. Changing the form from contributory organization to commercial
company was accompanied by the idea that the council of the region or city will,
according to valid regulations, have the maximum decision-making powers of inspection.
With the transition there was a promotion of an entrepreneurial approach to some health
care services. The expectation was to optimize the decision-making and stabilizing the
company's  economy  (Střítecký,  R.,  Pirožek,  P.  2002),  (Pirožek,  P.  Střítecký,  R.  2003).
Opponents of this transformational change argued that health services have the
character of mainly public service and their providing system must come from the public
sector  system  (Holčík,  J.  2004).
The aim of this paper is to describe and analyze the performance of hospitals in times of
economic instability, which dates back to 2008, compared to the last possible
researchable year - the year 2011. In this period the effects of hospital management
have been examined, i.e. whether the management of hospitals and hospital facilities
has been influenced.
2 Methodology and Data
To  describe  and  analyze  the  performance  of  hospitals  we  used  hospitals’  annual  reports  
for 2008 and 2011 with regard to the evaluation of the effect of the transformation of
their legal form. A total number of 112 for 2008 and 134 for the year 2011 have been
gathered. Due to the need of determining comparable data, the annual reports of
hospitals, which were not represented in both years, were excluded from the sample. The
annual reports of hospitals from both years, where it was not possible to determine the
required data were also excluded. The final number of annual reports for each year was
fixed at number 64 out of 188 annual reports, which can be obtained for the period 2008
and 2011.
For each hospital the following information was logged: year of publication of the annual
report, hospital name, legal status and geographical location (region). The economic
parameters logged were net income and an operating subsidy received.
2.1 Geographical location of hospitals
Structure of the sample of hospitals by geographical location was as follows. Information
was obtained from 64 health facilities in 2008 and 2011. The dislocation of hospitals
according to the survey data is as follows - in South Bohemian region there are 7
hospitals, in the South Moravia region 8 hospitals, in the Karlovy Vary region 1 hospital,
in Hradec Kralove region 1 hospital, in Liberec region 0 hospitals, in the MoravianSilesian
region 14 hospitals, in Olomouc region 2 hospitals, in Pardubice region 5
hospitals, in Pilsen region 5 hospitals, in Prague 9 hospitals, in the Central Bohemian
122
region   2   hospital,   in   Usti   Region   3   hospital,   in   Vysočina   region   3   hospitals   and   in   Zlín  
region 4 hospitals.
Figure 1 Dislocation of health facilities by region in 2008 and 2011
2.2 Legal status of hospitals
Structure of the sample of hospitals by legal status was as follows. Information was
obtained from 64 health facilities in 2008 and 2011. Numbers of hospitals based on the
survey data are as follows - 29 joint-stock companies, 27 contributory organizations, 7
companies with limited liability and there was also 1 non-profit organization.
Figure 2 Legal status of hospitals in 2008 and 2011
2.3 Net income
Economic parameters of managing the hospitals' economy were identified through
indicators of net income. Determining of the net income was based on public data
statements that hospitals process in accordance with Act No. 563/1991 Coll., on
Accounting and decrees implementing this law. Hospitals, whose annual reports do not
contain these data and their statements could not be found on the web www.justice.cz
11%
13%
2% 2%
0%
22%
3%
8% 8%
14%
3%
5% 5%
6%
0%
5%
10%
15%
20%
25%
45%
11%
0%
2%
0%
42%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Joint-stock company Limited liability
company
Non-governmental
non-profit health care
organization
Public benefit
organization
Civic association Contributory
organisation
123
were excluded from the sample. For this reason the number of surveyed hospitals
reduced significantly because it was necessary to unify net income for the years 2008
and 2011. Net income is a key measure of performance and was selected due to the
harmonization of its conception in the Czech Republic in relation to abiding the laws and
regulations.
Since net income did not grossly match a normal distribution, the nonparametric
statistical methods were used for analysis. All presented analyzes were performed in the
statistical software R, version 2.15.0 (R Development Core Team 2012).
Table 1 Net income in thousands CZK vs. legal form – median (lower, upper quartile)
year joint-stock limited liability Others
2008 70 (-8254, 1995) 1538 (-2885, 6734) 1988 (-62, 23486)
2011 506 (-3367, 1776) 1866 (-9234, 11830) 1676 (66, 5529)
Note: others = 27 contributory organizations and one public benefit organization
The median shows that the joint-stock company stands, in terms of the net income, for
the worst. Nevertheless, at the 5% significance level it cannot be proved using the
Kruskal-Wallis test that the net income would significantly differ between the companies
of a different legal form, both in 2008 (p = 12.4%) and 2011 (p = 26.7% ). For the year
2008, where the p value is lower, we additionally conducted multiple comparisons, where
the p values of two-sample Wilcoxon tests for each pair were adjusted for multiple
comparison using the Holm's correction (Holm, S. 1979). The post-hoc analysis shows
that the closest to the statistical significant difference in net income is between the
limited liability companies and contributory (or public benefit) organizations (p = 13.5%).
2.4 Operating subsidies
Another parameter examined in the hospitals' economy was operating subsidies received.
Determination of operating subsidies is quite problematic. Operating subsidies are not
fully specified in the annual reports by many processors. The reports prepared in
accordance with applicable regulations (the preparation of financial statements) do not
show a clear separation of subsidy data from other grants, gifts or other types of income.
The actual amount was mostly determined from the auditor's report, if this report has
been included in the financial statements. Most hospitals, however, provide data outside
reports and draw operating subsidies from the owner or founder.
Even operating subsidies received did not match a normal distribution, thus the
nonparametric statistical methods were used for analysis. All presented analyzes were
performed in the statistical software R, version 2.15.0 (R Development Core Team 2012).
Table 2 Subsidies in thousands CZK vs. legal form – median (lower, upper quartile)
year joint-stock limited liability others
2008 5703 (2739, 11623) 923 (259, 2745) 17345 (3618, 50219)
2011 5989 (2890, 11345) 1523 (1176, 4924) 11246 (2400, 19749)
Regarding subsidies, there can be statistically significant differences demonstrated
between hospitals with different legal form in 2008 (p = 2.6%), but cannot be
demonstrated in 2011 (p = 18.6%). Detailed post-hoc analysis for 2008 identifies the
biggest difference in subsidies between limited liability companies and contributory (or
public benefit) organizations, where contributory (or public benefit) organizations are
subsidized by most according to the median.
124
2.5 Net income vs. operating subsidies
The authors measured the dependence of the net income on operating subsidy in the
examined period and within each region. This dependence is demonstrated using the
Spearman correlation coefficient (p-values).
Table 3 Net income vs. subsidy – Spearman correlation coefficient (p-value)
2008 0.144 (p=35.2%)
2011 0.397 (p=1.3%)
Statistically significant relation between net income and the amount of subsidies can be
proved only in 2011. Analysis of the regions has not been made because the type of legal
form, in some cases, is closely linked   with   the   region   that   administers   the   hospital’s  
management. (e.g. in the South Bohemian region there are only joint stock companies,
while in Vysocina region there are only contributory organizations), moreover some
regions are relatively underrepresented in the dataset.
3 Conclusions
Paper evaluated the performance of hospitals in a period of economic instability, which
dates back to the year 2008, compared to the last possible researchable year - the year
2011. Given the sources used it was quite difficult to interpret the set of economic
parameters determined through the hospitals' annual reports. Many results reported
were either incomplete or did not match the amounts reported in the financial
statements. This was mainly the issue of the amount of the operating subsidy, which has
very significant impact on the total amount of the net income. However, it is possible to
draw conclusions from the interactions between selected parameters and years.
From the statistical processing, a relation between the legal form and the net income was
not proved. Nevertheless, joint-stock companies seem to stand, in terms of the net
income, for the worst. On the other hand, relationship of the legal form to subsidies was
statistically significant in the year 2008. The biggest difference was identified in subsidies
between limited liability companies and contributory (or public benefit) organizations,
which are subsidized by most. Relation between net income and the amount of subsidies
can be proved only in 2011.
Acknowledgments
The work of the first author was funded from the resources of the project No. 17/2013, a
negotiated contract between the researchers and IGA (Internal Grant Agency), University
of Economics in Prague. The second and the third author were supported by the Czech
Science Foundation grant GAČR  P403/12/1557.
References
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1992–2008, Health Policy 90, pp. 262–269.
Glouberman, S., Mintzberg, H.M. (2001). The Care of Health and the Cure of Disease.
Part I: Differentiation & Part II: Integration. Health Care Management Review, 26(1),
56–84.
Holčík,  J.  (2004).  Zdraví  a  peníze.  ČAS  Lék.čes. 143, 1. pp.4-8.
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian
Journal of Statistics, 6, 65–70.
Kislingerová,  E.  (2009). Jak  měřit  výkonnost  podniku  v  časech krize, Časopis  ÚSPĚCH PRODUKTIVITA
A INOVACE V SOUVISLOSTECH 4/2009:  Měření  a  vyhodnocování  procesů
Krajíček,   J.,   Valouch,   P.   (2011). FINANČNÍ   SEKTOR   A   JEHO   VLIV   NA   REGIONÁLNÍ  
ROZVOJ   ČESKÉ   REPUBLIKY.   In   MVK   Hradecké   ekonomické   dny   2011. 1. vyd. Hradec
Králové:  Gaudeamus,  Univerzita  Hradec  Králové,  pp.  149-152.
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Kral, P., Tripes, S., Pirozek, P., & Pudil, P. (2012). Corporate Governance Against
Recommendations: The Cases of the Strong Executive and the Strong Ownership. Journal
of Competitiveness, 4(3).
Mishtal, J. (2010). Neoliberal reforms and privatisation of reproductive health services in
post-socialist Poland, Reproductive Health Matters 18, pp. 56–66.
Pirožek,   P.,   Střítecký,   R.   (2003).   Iluze   a   fakta   – právní   postavení   nemocnic   v   krajích  
vyžaduje  novelizaci, Euro 42, pp. 52-53.
Pokorná,  J.,  Částek,  O.  (2013). How to measure organizational performance in search for
factors of competitiveness. Acta universitatis agriculturae et silviculturae Mendelianae
Brunensis, Brno: Mendelova univerzita  v  Brně,  LXI, 2, pp. 451-461.
R Development Core Team (2012). R: A language and environment for statistical
computing. R Foundation for Statistical Computing: Vienna.
Stritecky, R., Pirozek, P. (2002). Review of Possible Approaches to the Health Market,
Proceedings of the 18 International Case Mix Conference PCS/E, pp. 250-263, Innsbruck.
Šiška,  L.,  Kozáková,  P.  Strategic  Positioning  and  Company's  Long-term Performance. In
Advances in Finance&Accounting. Proceedings of the 1st WSEAS International Conference
on Finance, Accounting and Auditing (FAA  ´12).  Zlín:  Tomas  Bata  University, 2012. pp.
338-342.
Wagner, J. (2009). Měření  výkonnosti, Grada.
126
Analysis of the problem of pro-cyclicality in the Eurozone
and pro-cyclicality solutions in Basel III
Eva  Horvátová
University of Economics in Bratislava
Department of Banking and International Finance
Dolnozemská  1,  85235 Bratislava, Slovakia
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  60200,  Brno,  Czech  Republic
E-mail: eva.horvatova@econ.muni.cz
Abstract: The main causes of changes in banking regulation were in negative impacts of
the financial crisis, which has shown the weaknesses of Basel II. Basel Committee on
Banking Supervision adopted in September 2009 the basic principles of international
regulatory framework for banks under Basel III designation. In December 2009 has been
published consultative documents Basel III. Concrete actions and implementation were
adopted in September 2010 and finally published on 16 December 2010. The main
objectives of the new regulation were the ability of the banking sector to absorb shocks
caused by economic crises, improvement of the banks management system and
increasing of the banking business transparency. The way for analysis of the problem of
pro-cyclicality in the Eurozone and solutions pro-cyclicality in Basel III document leads
through analysing and increasing of quality and quantity of capital, liquidity and procyclicality
solution, as main tasks of Basle III.
Key words: Basle III, pro-cyclicality of the regulation, quality of capital
JELClassification: E58, G28, G21
1 Objectives, instruments and key priorities of Basel III
Mr Nout Wellink, Chairman of the Basel Committee on Banking Supervision and President
of the Netherlands Bank, described the Basel III Framework as "a landmark achievement
that will help protect financial stability and promote sustainable economic growth. The
higher levels of capital, combined with a global liquidity framework, will significantly
reduce the probability and severity of banking crises in the future." He added that "with
these reforms, the Basel Committee has delivered on the banking reform agenda for
internationally active banks set out by the G20 Leaders at their Pittsburgh summit in
September 2009".1
The purpose of capital requirements of credit institutions and investment firms in
documents Basle III is to overcome difficult times. Basle III introduces two levels of risk
control:
1. Micro-prudential regulation of the commercial banks;
2. Macro-prudential regulations of systemic risks in the banking sector.
Objectives and instruments of micro-and macro-prudential regulation are illustrated in
the Table 1.
Table 1 Objectives and instruments of micro- and macro-prudential regulation
Objectives Indicators
Macro-prudential
regulation
Financial system stability Dynamics of the GDP
Micro-prudential
regulation
Financial (credit) institution
stability
Protection of the consumer on
financial market
1
Basel III: A global regulatory framework for more resilient banks and banking systems
127
Among main solutions for reducing of the pro-cyclical effects of financial regulation are
included:
• monitoring of systemic risk,
• creation of countercyclical instruments in the capital and reserves,
• improving of accounting rules,
• creation a framework for better systems of employee remuneration.
1.1 Key priorities of Basel III
Key priorities of Basel III can be summarized as follows:
• Support of banking institutions at the macro level (macro-prudential regulation).
• Removal of pro-cyclicality of credit institutions regulations.
• Solution of problems resulting from negative externalities and from action of
systemically important financial institutions.
• Identification and risks mitigation of OTC derivatives.
• Support of the credit institutions liquidity and limiting of leverage ratio.
To achieve these objectives of Basel III is important:
• Increasing of the regulatory and economic capital quality.
• Actions to eliminate cyclicality.
• Liquidity risk management.
• Reducing the leverage ratio.
• Return character of capital from government bailouts to banks.
The financial crisis has shown that a risk in the banking sector is too large regarding to
the capital to risks covering. Objective of Basel III in improving the quality and quantity
of capital will be implemented on the basis of concrete steps. In the quantitative criteria
notably the gradual implementation of the indicators in a time sequence expressed in the
following figure.
2 Pro-cyclicality in the Eurozone
Pro-cyclicality represents the largest problem in existing approaches to capital adequacy
regulation.
• Pro-cyclicality is manifested by regulation as an enhancing of the actual tendency
of the economic cycle. In times of crisis, makes deepening of the crisis and during
boom supports overheating economy.
Figure 1 Development of credits and capital (left) and capital and reserves and GDP
(right)
Source: European Central Bank
• Pro-cyclicality is supported by the possibility of saving capital through the using of
advanced methods of capital calculating introduced under Basel II. The following
graph shows that the volume of credit (CRED) and capital including reserves
8e+006
1e+007
1,2e+007
1,4e+007
1,6e+007
1,8e+007
2e+007
1998 2000 2002 2004 2006 2008 2010 2012
600000
800000
1e+006
1,2e+006
1,4e+006
1,6e+006
1,8e+006
2e+006
2,2e+006
2,4e+006
CAPREZ (right)
CRED (left)
85
90
95
100
105
110
1998 2000 2002 2004 2006 2008 2010 2012
600000
800000
1e+006
1,2e+006
1,4e+006
1,6e+006
1,8e+006
2e+006
2,2e+006
2,4e+006
CAPREZ (right)
GDPIND (left)
128
(CAPREZ) did not develop evenly, capital lagged behind credits at the time of
incoming crisis.
The situation changed in 2011 in according due Basle III. Addition of capital there were
also pro-cyclical, because it takes place in a time of growing financial crisis (see left part
of Figure 1).
Capital and reserves lagged behind the growth of GDP. After 2010, the situation has
changed due to the tightening of regulation as a result of the financial crisis. Procyclicality
is reflected in the fact that capital growth was slower in the pre-crisis period,
and vice versa capital was growing faster than the volume of loans after crisis period.
The right part of Figure 1 shows the evolution of capital and reserves on one side and
GDP growth on the other.
Pro-cyclicality is also affected by the 2009 financial crisis. It is also seen on the
development of indicators in the euro area (Figure 2). Another example of pro-cyclicality
is leverage ratio. Leverage (Leverage ratio) represents the requirement that the ratio
between capital and assets (unweighted risk) will be at least 3%. Indicator of financial
leverage will become part of Pillar 1 since 2018.
Figure 2 Euro area economic indicators
Note: CAPREZ = capital and reserves; DEPOS = deposits; CRED = credits; DCRED = changing of
credits; DLOMB = lombard credits.
Source: European Central Bank
3 Steps to eliminate pro-cyclicality and the market reaction
In order to reduce of pro-cyclicality are intended the Capital conservation buffer and the
countercyclical buffer. Capital conservation buffer shall be 2.5% above the level of core
capital of Tier 1. In order to fulfil this task will be involved restrictions according to profit,
especially to payment of dividends to shareholders.
Counter cyclical buffer will be up to 2.5% of RWA (Risk Weighted Assets). Signal to the
formation of countercyclical buffer will give the national regulator in accordance to the
evolution of the business cycle. The national regulator will be responsible for ensuring
that excessive credit growth does not lead to systemic risk.
600000
800000
1e+006
1,2e+006
1,4e+006
1,6e+006
1,8e+006
2e+006
2,2e+006
2,4e+006
1998 2001 2004 2007 2010 2013
CAPREZ
8e+006
1e+007
1,2e+007
1,4e+007
1,6e+007
1,8e+007
2e+007
1998 2001 2004 2007 2010 2013
CRED
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
1998 2001 2004 2007 2010 2013
DCRED
85
90
95
100
105
110
1998 2001 2004 2007 2010 2013
GDPIND
129
Table 2 The sequence of steps in raising capital requirements in Basel III
2011 2012 2013 2014 2015 2016 2017 2018 2019
Minimum CET1
ratio 3,5 % 4,0 % 4,5 % 4,5 % 4,5 % 4,5 % 4,5 %
Capital
conservation
buffer
0,625% 1,25% 1,875% 2,5 %
Minimum Tier 1
capital 4,5 % 5,5 % 6,0 % 6,0 % 6,0 % 6,0 % 6,0 %
Minimum
regulations
capital
8,0 % 8,0 % 8,0 % 8,0 % 8,0 % 8,0 % 8,0 %
Minimum
regulations
capital + Capital
conservation
buffer
8,0 % 8,0 % 8,0 % 8,625% 9,25% 9,87 % 10,5 %
The phasing out
of less quality
capital -
supplementary
Tier 1 and Tier 2
The phasing out of less quality capital
CCB - Counter
cyclical buffer
Start of
monitoring
Minimum
standard
validation
LCR – Liquidity
coverage ratio
Start of
monitoring
Minimum
standard
validation
NSFR – Net
stable funding
ratio
Start of
monitoring
Minimum
standard
validation
In addition, Basel III requires more capital. In the current is also changed capital
structure in order to improve its quality and stability. Global banks operate with Tier 1
part  capital  including  hybrid  “innovative”  instruments  with  debt-like character. We have
had Tier 1 capital, innovative Tier 1, upper and lower Tier 2, Tier 3 capital, with own
limits and with functions of other capital elements.
The complexity in the definition of capital made it difficult to determine what capital
would be available to cover which of the losses. This combination of weaknesses
permitted tangible common equity capital, the best form of capital, to be as 1% of riskweighted
assets.“1
Repayment of government capital assistance provided to commercial banks
• Many banks have had government support during the financial crisis.
• The support has the form of joint ownership or the form of loans.
Government assistance is repayable and must be efficiently utilized. Much capital
assistance had not absorbed losses as was expected.
• Therefore are important principles of management remuneration.
• Today is a very timely idea that management has to be paid not by profit, but
by increasing value of managed property.
3.1 Analysis of pro-cyclicality in the practice of Eurozone
Using regression analysis, we examine the relationship between dependent variable and
explanatory variables (for example between GDP growth and credit growth). Regression
analysis examines the functional relationship between changes in dependent variable Y
and changes in the independent variables x1, x2, xn. Regression model has the form:
1
Hannoun, H.: The Basel III Capital Framework: a decisive breakthrough. BoJ-BIS High Level Seminar on
Financial Regulatory Reform: Implications for Asia and the Pacific Hong Kong SAR, 22 November 2010.
Dostupné  na  internete:  http://www.bis.org/speeches/sp101125a.pdf .
130
Yi =  α  +  βxi +  εi
where: i  =  1,  2,  …,  n
Estimate of the regression function Y is: ŷ  =  a  +  bx
Parameters   α,   β   of   the   regression   function   a,   b   are   calculated   by the method of least
squares. We have also focused on the comparison of longer term and shorter term,
reflecting the impact of the crisis.
Figure 3 Connection between credit increasing and GDP increasing 1997 - 2012
Source: European Central Bank
Model 1 OLS, using observations 1997:3-2012:3 (T = 61)
Dependent variable: CRED
Coefficient Std. Error t-ratio p-value
GDPIND 139465 3391,94 41,1167 <0,00001 ***
Mean dependent var 13603506 S.D. dependent var 3453237
Sum squared resid 4,11e+14 S.E. of regression 2618602
R-squared 0,965726 Adjusted R-squared 0,965726
F(1, 60) 1690,581 P-value(F) 1,17e-45
Log-likelihood -987,5183 Akaike criterion 1977,037
Schwarz criterion 1979,148 Hannan-Quinn 1977,864
Rho 0,998660 Durbin-Watson 0,005078
According to the results of the regression analysis showed a positive correlation between
loans and GDP, which indicates strongly pro-cyclical developments. For the period
1Q2008 to 3Q2012 came following results:
Model 2 OLS, using observations 2008:1-2012:3 (T = 19)
Dependent variable: CRED
Coefficient Std. Error t-ratio p-value
GDPIND 170696 1032,23 165,3659 <0,00001 ***
Mean dependent var 17879842 S.D. dependent var 358601,9
Sum squared resid 4,00e+12 S.E. of regression 471231,4
R-squared 0,999342 Adjusted R-squared 0,999342
F(1, 18) 27345,89 P-value(F) 4,28e-30
Log-likelihood -274,6452 Akaike criterion 551,2904
Schwarz criterion 552,2348 Hannan-Quinn 551,4502
Rho 0,721667 Durbin-Watson 0,204102
85
90
95
100
105
110
8e+006 1e+007 1,2e+007 1,4e+007 1,6e+007 1,8e+007
GDPIND
CRED
GDPIND versus CRED (with least squares fit)
Y = 74,4 + 1,78e-006X
101
102
103
104
105
106
107
108
0 0,5 1 1,5 2
GDPIND
DCRED
GDPIND versus DCRED (with least squares fit)
Y = 104, + 1,62X
131
Table 3 Correlation coefficients, using the observations 2008:1 - 2012:3
5% critical value (two-tailed) = 0,4555 for n = 19
CAPREZ DEPOS CRED DCRED DLOMB
1,0000 0,8154 0,9597 -0,0436 -0,9928 CAPREZ
1,0000 0,8885 -0,2853 -0,7791 DEPOS
1,0000 -0,2543 -0,9262 CRED
1,0000 0,0125 DCRED
1,0000 DLOMB
The relationship between credit growth and GDP growth for the period 1Q2008 to
3Q2012 is shown in the right part of Figure 3. Slope of the line is less steep. It shows
that the growth rate of GDP is less than the growth rate of credits. It notes the pro-
cyclicality.
Pro-cyclicality is also reflected by the fact that during the crisis to reduce the amount of
loans faster than the reduction in GDP growth (Figure 4 left). Relationship between loans
and capital (Figure 4 right) shows that during the crisis has increased the slope line,
thus credits grew faster than capital, which also indicates the pro-cyclicality. Olson model
points to a negative correlation between capital and GDP growth, hence the lack of
capital formation in boom times.
Figure 4 Connection between GDP growth and equity (reserves) growth (left) and
between the ratio of credits and capital & reserves (right)
Source: European Central Bank
The next section explores the relationship between capital as the dependent variable and
GDP, loans and deposits. It turns out, that GDP and loans had the highest impact on the
creation of reserves in 2008 - 2012. In the period 2008-20012 their capital formation
slowed.
Model 3 OLS, using observations 1997:3-2012:3 (T = 61)
Dependent variable: CAPREZ
Coefficient Std. Error t-ratio p-value
CRED 0,152932 0,0045458 33,6425 <0,00001 ***
GDPIND -7185,99 645,134 -11,1388 <0,00001 ***
Mean dependent var 1372453 S.D. dependent var 490434,1
Sum squared resid 5,02e+11 S.E. of regression 92205,12
R-squared 0,996122 Adjusted R-squared 0,996056
F(2, 59) 7576,712 P-value(F) 7,33e-72
Log-likelihood -782,8765 Akaike criterion 1569,753
Schwarz criterion 1573,975 Hannan-Quinn 1571,408
Rho 0,966446 Durbin-Watson 0,067455
85
90
95
100
105
110
115
800000 1e+006 1,2e+006 1,4e+006 1,6e+006 1,8e+006 2e+006 2,2e+006
GDPIND
CAPREZ
GDPIND versus CAPREZ (with least squares fit)
Y = 82,3 + 1,19e-005X
8e+006
1e+007
1,2e+007
1,4e+007
1,6e+007
1,8e+007
2e+007
2,2e+007
800000 1e+006 1,2e+006 1,4e+006 1,6e+006 1,8e+006 2e+006 2,2e+006
CRED
CAPREZ
CRED versus CAPREZ (with least squares fit)
Y = 4,13e+006 + 6,90X
132
Model 4 OLS, using observations 2008:1-2012:3 (T = 19)
Dependent variable: CAPREZ
Coefficient Std. Error t-ratio p-value
CRED 0,341423 0,0650902 5,2454 0,00007 ***
GDPIND -39220,2 11114,3 -3,5288 0,00258 ***
Mean dependent var 1996139 S.D. dependent var 204529,4
Sum squared resid 2,88e+11 S.E. of regression 130132,7
R-squared 0,996235 Adjusted R-squared 0,996013
F(2, 17) 2249,015 P-value(F) 2,48e-21
Log-likelihood -249,6531 Akaike criterion 503,3061
Schwarz criterion 505,1950 Hannan-Quinn 503,6258
Rho 0,880999 Durbin-Watson 0,240387
Recently also increased the impact of LTRO on credits (Figure 5). This dependence may
indicate inflation based on credit growth, which clearly growing.
Figure 5 Connection between long-term refinancing operations and loans
Source: European Central Bank
4 Conclusions
Analysing the degree of pro-cyclicality in terms of the euro area we can make conclusion
that actions under Basel III are correct. Risks in terms of the euro area can be divided
into two groups. On the one side, there are risks, which are reflected lack of credit and
capital. On the other side, there are risks associated with macroeconomic developments,
especially with inflation due to rising consumer loans. Limiting the scope of assets under
leverage can significantly contribute to the elimination of risks, which could put pressure
on public resources. Problem is in that - the solution of pro-cyclicality comes at a time of
crisis and therefore currently Basle III operates pro-cyclical. Basel III is thus solution to
the problem in the future.
References
Basel III: A global regulatory framework for more resilient banks and banking systems Original
version available at: http://www.bis.org/publ/bcbs189_dec2010.htm. Revised
version available at: http://www.bis.org/publ/bcbs189.pdf.
Gleeson, S. (2010). International Regulation of Banking. Oxford: Oxford University Press.
-2
0
2
4
6
8
10
12
14
2000 2002 2004 2006 2008 2010 2012
9e+006
1e+007
1,1e+007
1,2e+007
1,3e+007
1,4e+007
1,5e+007
1,6e+007
1,7e+007
1,8e+007
1,9e+007
uvery (right)
INFLA (left)
DMTRI (left)
133
Hannoun, H. (2010). The Basel III Capital Framework: a decisive breakthrough. In BoJBIS
High Level Seminar on Financial Regulatory Reform: Implications for Asia and the
Pacific. Hong Kong SAR, 22 November 2010. Available at: http://www.bis.org/
speeches/sp101125a.pdf
Hatrák,  M. (2007). Ekonometria. Bratislava: IURA Edition.
Lapetit, L. (2008). Bank income structure and risk: An empirical analysis of European
Banks. Journal of Banking and Finance, vol. 32.
134
The yield curve as a predictor of economic activity –
the case of the Visegrad group
Jana Hvozdenska, Veronika Kajurova
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipova 41a, Brno 602 00
E-mail: 174974@mail.muni.cz
vkajurova@mail.muni.cz
Abstract: In this paper we study the ability of the yield curve to predict GDP activity in
four countries of the Visegrad group – Czech Republic, Slovakia, Poland and Hungary.
The dataset contains the spread between 10-year and 3-month sovereign bonds and real
GDP of the countries mentioned above between the years 2002 and 2012. The results
showed that the prediction of the GDP growth or decrease was proven after year 2008
(the financial crisis) in the Czech Republic, Slovakia and Hungary, the predictive power of
the yield curve was lowered in those countries before the year 2008. Certainly the simple
yield curve growth forecast should not serve as a replacement for the complex predictive
models, it does, however, provide enough information to serve as a useful check on the
more sophisticated forecasts. These findings provide further evidence of the potential
usefulness of the yield curve spreads as indicators of the future economic activity
Keywords: yield curve, slope, Visegrad, GDP, spread
JEL codes: E43, E44, E47, G01
1 Introduction
Predicting of the future has always fascinated people, moreover economic forecasting
doubles the interest by adding the chance of profit.
The yield curve – specifically the spread between long term and short term interest rates
is a valuable forecasting tool. It is simple to use and significantly outperforms other
financial and macroeconomic indicators in predicting recessions two to six quarters
ahead.
The yield curve simply plots the yield of the bond against its time to maturity. Many
market  observes  carefully  track  the  yield  curve’s  shape,  which  is  typically  upward  sloping  
and convex. However when the yield curve becomes flat or slopes downward (the spread
between 10-year and 3-month bond is negative) it may signal GDP decrease (recession).
This paper builds on a wide range of previous researches, but differs in some ways.
Bernard and Gerlach (1998) in their paper showed empirically on eight countries that the
slope of the yield curve is a good predictor of the real economic activity. Berk and van
Bergeijk (2001) examined 12 euro-area countries over the period of 1970-1998 and
found that the term spread contains only limited information about future output growth.
Their work is based on the previous theoretical researches of Estrella and Hardouvelis
(1991), Estrella and Mishkin (1996). There was proven the evidence that the slope of the
yield curve and the future GDP activity are related together. However it is necessary to
say that this rule was true until the end of 20th
century and it mostly disappeared at the
beginning of 21st
century and appeared again during the financial crisis (from 2008) and
later on (De Pace, 2011; Giacomini and Rossi, 2005; Chinn and Kucko, 2010). Most of
the studies are focused on the relationship of the yield curve and GDP activity of United
States of America.
The aim of this paper is to show if the yield spread possesses the predictive power of
future economic activity in the countries of Visegrad group and to examine if this rule
was weaken at the beginning of 21st
century and appeared again during the financial
crisis.
135
Despite various researches, there is not any comprehensive theory that would prove the
correlation between the yield spread and economic development of the country yet.
Often we come across the statements that have only theoretical basis without generally
valid empirical evidence. Economic models are largely based on the argument that the
yield curve tends to be flatter in the situation of the tight monetary policy and the
economic slowdown typically occurs with a slight time lag.
Almost perfect tool containing the relevant future data provides the yield spread of
government bonds. The simplest interpretation of the yield spread is through monetary
policy of the country. Based on this criterion - relatively low spread reflects the restrictive
and tight monetary policy and vice versa - high spread reflects loose monetary policy. We
can find the theoretical justification for using of the spread in expectations hypothesis. It
assumes that long term rate of return is the average of the current and expected future
short   term   yields.   The   investor’s  decision   to invest in short term or long term asset is
completely irrelevant (Mishkin, 1990).
Dependence of the yield spread and GDP can be derived from their connection to the
monetary policy of the state. As bond yields react to monetary policy as well as monetary
policy is able to respond to the output of the economy, the yield curve assumes
overlapping of policy measures and responses. The yield curve had the ability to reflect
future production either directly or indirectly. Indirectly it comes to predicting of the
future interest rate and the future monetary policy. It may also reflect the future
production directly because of the 10-year yields may depend on estimates of the output
of the economy in 10-years.
A question arises – how many months, quarters, years of future economic activity can be
predicted by the yield spread? Based on the study of Bonser-Neal and Morley (1997)
spread has the greatest ability in predicting one-year horizon (four quarters ahead).
2 Methodology and Data
There are many ways of using the yield curve to predict the future real activity. One
common method uses inversions (when short term rates are higher than long term rates)
as recession indicators. Obtaining predictions from the yield curve requires much
preliminary work. There is the principle which needs to be hold: keep the process as
simple as possible. Thus we avoided both complicated nonlinear specifications and a
detailed search for the best predictor.
A yield curve may be flat, up-sloping, down-sloping or humped. The standard solution
uses a spread (difference between two rates). The problem is to choose the spread
between the right terms. The most used spread is between 10-year and 3-month bonds.
The problem is that there are rarely bonds which mature exactly in 10 years (or 3
months). In that case the best solution is to use the yield curve, which shows us the yield
of each maturity. Creating and calculating of the yield curve is a rather difficult task
because there are many ways how you can do it. There are many models which try to
model the best fitted yield curve for each country.
The yield curves are constructed by Bloomberg, therefore we used the data for spreads
from Bloomberg. For the spread we chose 10-year bond rate minus 3-month bond rate
(Estrella and Hardouvelis, 1991; Estrella and Mishkin, 1996). We use quarterly data for
the spreads because the data for the economic activity are taken on quarterly basis as
well. The data for real GDP can be used from Eurostat or Bloomberg as well. The data of
real GDP obtained and used in this paper are from Eurostat.
The selected countries are Czech Republic, Slovakia, Poland and Hungary. There is no
previous research which would prove or reject the hypothesis of real GDP and bond
spread dependence. This hypothesis was proven first for United States of America and
later on for other countries in North America, Europe (western countries) and Asia
(Japan).
136
As our measure of real growth we use four-quarter percent change in real GDP (thus we
calculate the percent change of the quarter against  the  last  year’s  same  quarter,  e.g.  we  
calculate the change from 1Q2004 and 1Q2003 real GDP). GDP is standard measure of
aggregate economic activity and the four-quarter horizon answers the frequently asked
question – what happens the next year?
Our sample period starts from 3Q2002 and ends on 4Q2012. This mainly covers the
period before financial crisis, period of financial crisis and period after financial crisis. Our
basic model is designed to predict real GDP growth/decrease four quarters into the future
based on the current yield spread (Bonser-Neal and Morley, 1997).
We accomplish this by running of a series of regressions using real GDP activity and the
spread between 10-year and 3-month bond yields lagged four quarters (e.g. the interest
rate spread used for 3Q2004 is actually from 3Q2003).
The last step is to find out if there is the change of behaviour of the spreads and GDP
activity at the beginning of 21st
century and after the year 2008 (De Pace, 2011).
To generate the GDP predictions we run the regression using the whole sample, and later
two divided samples of real GDP and spreads of each selected country (the sample is
divided in 4Q2007/1Q2008, because this year was the previous year of financial crisis
and should show some changes in prediction of the yield curve spread).
The following equation is estimated for each country:
𝑅𝑒𝑎𝑙  𝐺𝐷𝑃 =∝ +𝛽 ∗ 𝑠𝑝𝑟𝑒𝑎𝑑 (1)
The program software used for regression (ordinary least squares) is Gretl.
3 Results
Does the yield curve accurately predict the future GDP? First we can look at the data.
Figure 1 shows the growth of real GDP and the lagged spread (4 quarters) between 10year
and 3-month bond yields in the Czech Republic (the similar figures can be
constructed for the rest of the countries, this one is for example).
Figure 1 Real GDP and spread in the Czech Republic (spread lagged four quarters)
Source: Bloomberg, Eurostat
A decline in the growth or real GDP is usually preceded by a decrease in the yield spread
and narrowing yield spread often signals a decrease in real GDP growth. A negative
spread usually precedes recessions, but not always. You can see at the first sight that the
dependency between real GDP and spread is more visible form the year 2008 than
before. The recession that began in 2009 was preceded by many quarters of decreasing
-0,06
-0,04
-0,02
0
0,02
0,04
0,06
0,08
0,1
0,12
2002Q3
2003Q1
2003Q3
2004Q1
2004Q3
2005Q1
2005Q3
2006Q1
2006Q3
2007Q1
2007Q3
2008Q1
2008Q3
2009Q1
2009Q3
2010Q1
2010Q3
2011Q1
2011Q3
2012Q1
2012Q3
2013Q1
GDPchange,spread
Years
Czech Republic spread lagged 4 quarters
CZ spread
CZ GDP
137
spread and at the end the spread was very close to zero. The same situation repeated in
2011 and 2012. The prediction of the future GDP for 2013 is also quite clear – Czech
Republic should remain in recession due to down-sloping spread going very close to zero.
When we constructed a scatterplot with each point representing a particular combination
of real GDP growth and the lagged yield spread of Czech Republic, it showed that the
relationship between the two variables is mostly positive. It means that positive real GDP
growth is associated with a positive lagged yield spread and vice versa. Plotting the data
gives a strong impression that the yield spread predicts future real activity.
To generate the GDP predictions we run a regression using the whole sample to generate
each predicted data point.
Results of regression for Czech Republic, Slovakia, Poland, Hungary – whole
sample
The whole sample of dataset contains the real GDP from 3Q2002 to 4Q2012. We ran a
regression of the whole sample and got the results as seen in Table 1.
Table 1 The results of all countries and whole sample from OLS regression
Whole sample Constant Spread
P - value
(F - test)
R2
Czech Republic 0,042029 -0,02796 0,9744 0,000029
Slovakia 0,081257 -1,37952 0,0229 0,13558
Poland 0,069407 0,422761 0,2384 0,038395
Hungary 0,049057 -0,66949 0,076 0,084874
For example for the Czech Republic it means:
Real GDPCzech Republic = 0,042029 – 0,02796 * spreadCzech Republic
However it is necessary to say that we cannot contribute this model as statistically
significant because of very poor R2
and very high p-value. Thus this model cannot be
used as predictive model. It might be because of the different behaviour of the spread
and GDP before and after the year 2008. This hypothesis will be tested later on.
The p-value seems to be good (at low level under 10%) for dataset of Slovakia and
Hungary, however the R2
coefficients, which show us how many percentage of the
sample can be explained by these models, are very poor for all countries.
We have to say that there is not any proven dependency between the spread and real
GDP.
Table 2 The results of all countries and sample of period 3Q2002 – 4Q2007 from OLS
regression
3Q02 – 4Q07 Constant Spread
P - value
(F - test)
R2
Czech Republic 0,0436233 1,53286 0,062477 0,162926
Slovakia 0,104807 -0,249324 0,638152 0,011274
Poland 0,0773092 1,86793 0,000045 0,573331
Hungary 0,0771587 0,138518 0,758746 0,004824
138
Results of regression for Czech Republic, Slovakia, Poland, Hungary – divided
samples
We continued with the research as follows – we divided the sample into two samples. The
first one is from 3Q2002 to 4Q2007, the second one is from 1Q2008 to 4Q2012 in order
to show if there is any dependency between the variables before or after the financial
crisis. We ran a regression of the first sample and second sample. The results for the
time span of 3Q2002 – 4Q2007 (first sample) are possible to see in Table 2, the results
for the period of 1Q2008 – 4Q2012 (second sample) are in Table 3.
Table 3 The results of all countries and sample of period 1Q2008 – 4Q2012 from OLS
regression
1Q08 – 4Q12 Constant Spread
P - value
(F - test)
R2
Czech Republic -0,031118 1,31273 0,001533 0,523567
Slovakia -0,059265 3,48847 0,000062 0,693569
Poland 0,0515843 0,508590 0,180447 0,124307
Hungary -0,006203 1,91095 0,003610 0,465161
It is clearly visible, that the dividing of sample made a great difference in results. In the
first period (2003 – 2007) only model for Poland was statistically significant and could be
used for GDP future prediction. It is because its p-value was very low and R2
showed us
that this model explains 57,33% of the sample, which is good. All the other models could
not be used because of their statistical insignificance (high p-values and low R2
).
The second period (2008 – 2012) showed big differences. Models for the Czech Republic,
Slovakia and Hungary can be used for future real GDP prediction, however the model for
Poland cannot be used due to its statistical insignificance.
The models for the Czech Republic, Slovakia and Hungary have very low p-values (under
1%) and high R2
. The models are therefore usable for future prediction of GDP.
We can say that:
Real GDPCzech Republic = - 0,03118 + 1,31273 * spreadCzech Republic
Real GDPSlovakia = - 0,059265 + 3,48847 * spreadSlovakia
Real GDPHungary = - 0,06203 + 1,91095 * spreadHungary
For example if there would be a change of 1% up in the spread of the Czech Republic
then the GDP would increase about 1,28% (-0,03118+1,31273*1%).
The findings of De Pace (2011) were confirmed in the Czech Republic, Slovakia and
Hungary. The models should predict the future GDP well after 2008, however the model
in Poland worked well before the financial crisis but not anymore.
Table 4 The prediction of real GDP in the Czech Republic, Slovakia and Hungary
Prediction
of GDP
Czech Republic Slovakia Hungary
spread GDP spread GDP spread GDP
1Q2013 0,0249 0,00151 0,028088 0,03872 0,0166 -0,0303
2Q2013 0,02079 -0,0039 0,029829 0,04479 0,0079 -0,0469
3Q2013 0,01637 -0,0097 0,025603 0,03005 0,01153 -0,04
4Q2013 0,01439 -0,0123 0,023523 0,02279 0,00916 -0,0445
139
Prediction of real GDP in 2013 – Czech Republic, Slovakia, Hungary
At the end we can compute the future real GDP for the Czech Republic, Slovakia and
Hungary. The spreads are known from the year 2012. The results are in the Table 4.
The GDP of the Czech Republic should decrease from 0,39% in 2Q2013 to 1,23% in
4Q2013. However the GDP of Slovakia should increase approximately about 2,5% in
each quarter. The future economic activity of Hungary does not look good, the model
predicts the decrease of GDP around 3 – 4% (depending on the quarter).
4 Conclusions
Does the yield curve accurately predict the real economic growth? Answering this
seemingly simple question requires a surprising amount of preliminary work. The 10year,
3-month spread has substantial predictive power and should provide good forecast
of real growth four quarters into the future. Nevertheless from 2002 to 2008 the
predictive power of the yield curve was lowered in the Czech Republic, Slovakia and
Hungary, however in Poland the model had a good predictive power. Our results
presented above confirm that 10-year, 3-month yield spread has significant predictive
power for real GDP growth after the year 2008 in the Czech Republic, Slovakia and
Hungary. This paper confirms the previous work of De Pace, that there was a break in
the time of financial crisis and the hypothesis that future growth of GDP can be explained
by spread of bonds did not work properly at the beginning of 21st
century, however it
started to work after 2008. It also proves that this spread model works even in the
countries of Eastern Europe, as the previous researches were done only on countries of
North America, Western Europe and Asia.
The simple yield curve growth forecast should not serve as a replacement for the
predictions of companies, who deal with predicting of many economic indicators, it
however does provide enough information to serve as a useful check on the more
sophisticated forecasts.
Acknowledgments
Support of Masaryk  University  within  the  project  MUNI/A/0753/2012  “European  financial  
systems  2013”  (Student  Project  Grant  at  MU,  Faculty  of  Economics  and  Administration,
Department of Finance) is gratefully acknowledged.
References
Berk J. and P. van Bergeijik (2001). On the Information Content of the Yield Curve:
Lessons for the Eurosystem? Kredit und Kapital. pp. 28 – 47.
Bernard, H. J. and S. Gerlach (1998). Does the Term Structure Predict Recessions? The
International Evidence, CEPR Discussion Papers 1892.
Bonser-Neal, C. a T. R. Morley (1997). Does the Yield Spread Predict Real Economic
Activity? A Multicountry Analysis, in Economic Review: Federal Reserve Bank of Kansas
City. No. 3.
Chinn, M. and K. Kucko (2010). The predictive power of the yield curve across countries
and time. NBER Working Paper Series, 16398.
De Pace, P (2011). GDP Growth Predictions through the Yield Spread: Time – Variation
and structural Breaks, in Euro Area Business Cycle Network.
Estrella, A. and G. A. Hardouvelis (1991). The Term Structure as a Predictor of Real
Economic Activity, Journal of Finance, Vol. 46, No. 2, pp. 555-576.
Estrella, A. and F. S. Mishkin (1996). The Yield Curve as a Predictor of U.S. Recessions,
in Current Issues in Economics and Finance, Federal Reserve Bank of New York, Vol. 2,
No. 7.
Giacomini, R. and B. Rossi. (2005). How stable is the Forecasting Performance of the
Yield Curve for Output Growth. Duke University and University of California, Los Angeles.
Mishkin, F. (1990). Yield curve, NBER Working Papers Series, No. 3550.
140
Mergers in Czech Agricultural Companies
Eva  Hýblová,  Zuzana  Křížová,  Jaroslav  Sedláček
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová,  41a, 602 00 Brno, Czech Republic
E-mail: hyblova@econ.muni.cz
krizovaz@econ.muni.cz
sedl@econ.muni.cz
Abstract: Mergers of trading companies are significant trading transactions that
represent a combination of companies from an economic as well as legal perspective. The
aim of mergers is usually a further development of a company. One of the factors that
influence the number of mergers and their results is the field of business of relevant
companies. Agricultural production is a field which takes one of the last places as
concerns the number of implemented mergers. Agricultural companies are specific due to
their subject of business and the number of factors that influence them. That is why their
analysis is highly interesting. The aim of the paper is to analyze and evaluate mergers in
Czech agricultural companies.
Keywords: mergers, financial position, performance, agriculture, economic development
of a company
JEL codes: M 41, Q19
1 Introduction
Mergers of trading companies are significant trading transactions that represent a
combination of companies from an economic as well as legal perspective.
Reasons for mergers can be related to business or speculative. Business reasons include
situations when the company aims at a further development so it needs to grow, change
its structure, expand its share in the market or find a new market. Speculative reasons
include an effort for a maximum appreciation of capital in a short time, when the gained
companies can be gradually sold out. Another significant   effect   is   tax   savings.   (Mařík,  
1995)
Statistical data concerning mergers and acquisitions in the Czech market are hard to
obtain. The trade register, in which the movement is captured, does not provide these
data in a temporal series. Mediators, who participate in these transactions by counselling,
do not provide these data either as mainly in large counselling agencies the data overlap
and duplicity occurs (in some transactions these agencies are listed as counsellors for
purchase, in others for sale or other types of counselling services related to mergers and
acquisitions). Therefore, the data provided by individual counselling agencies differ and
cannot  be  used  for  obtaining  the  real  state  of  mergers.  (Hýblová  et  al.,  2012)
As regards classification of mergers based on fields, the first place in the number of
implemented mergers is taken by fields of production and services. Agricultural
production appears in one of the last places. Although the number of mergers within this
field is small, the analysis of their results is important.
2 Characteristics of Agricultural Companies
Agriculture represents a significant part of national economy. It secures production of
food and other products and influences the related processing industries. Agricultural
production is a specific field. Its main features are:
 biological character of production,
 smaller human control over the production process,
 long-term production cycle,
 seasonal character of production,
 dependence on natural and climatic conditions. (Dvořáková,  2012)
141
Agricultural production has a biological character; natural biological processes are the
value-forming essence of agricultural production. This leads to a relatively smaller control
of the production process by human beings as it cannot be always directed. The
production cycle is long, neither a use of modern technologies can bring any essential
changes in this aspect. The development and intensification of production may get into
conflict with the environment protection. The production is seasonal and thus a
fluctuation of the need of workforce occurs. Agriculture is dependent on natural
conditions; there are risks related to adverse weather conditions and natural disasters.
Although agriculture is a significant field, the proportion of agricultural production in the
gross domestic product is decreasing in developed economies. The Czech Republic has a
considerably lower proportion of agriculture in GDP than EU-15 countries. The Czech
proportion is 0.5%, while the EU-15 average is 1.2%. In the past 14 years, the
proportion of agriculture in GDP in the Czech Republic decreased by 83%, while in EU-15
countries  it  only  decreased  by  29%.  (Smítal,  2012)  
The drawbacks appearing from the comparison of the Czech agriculture with EU-15
average (due to the high demands of EU legislation) are the high indebtedness, longterm
persistence of a lower sale of agricultural production and lower European or national
subsidies. Social disparities between agriculture workers and other workers in national
economy are increasing; the attractiveness of agriculture for young and qualified people
is decreasing mainly due to the salary disparities.
The problems arise also in financial reporting and evaluate the performance of agriculture
enterprises. The specifics of agricultural production and biological transformation
connected with that type of production are not taken into account by most of the existing
accounting systems. (Bohušová,   et   al.   2012)   According   to   Sedláček   (2010),   in  
comparison with other economic branches, the agricultural activity is characterized by
specific activities that require the appropriate accounting treatments.
3 Mergers in Agriculture
The aim of the paper is to analyze and evaluate mergers in Czech agricultural companies.
Results of mergers were analysed using companies that merged in the period of 2001–
2010. The data were acquired from financial statements of the companies published in
the Trade Register of the Czech Republic. Out of the total number of 2676 mergers, 95
were mergers of agricultural companies, which is 3.55%. The result of the research is
only a comparison of absolute values, using modern statistical methods is not possible,
the number of rated firms is too small.
The overview of mergers in individual years is presented in Table 1.
Table 1 Development of the number of mergers in 2001–2010
Year
Total
number of
mergers
Number of
mergers of
agric. comp.
Proportion of agric.
comp. mergers in the
total number (%)
2001 69 5 7.24
2002 142 5 3.52
2003 249 7 2.81
2004 185 4 2.16
2005 135 3 2.22
2006 300 12 4
2007 320 9 2.81
2008 429 15 3.49
2009 415 15 3.61
2010 432 20 4.63
Total 2676 95 3.55
Source: Authors based on data in Trade Register of Czech Republic
142
A comparison of mergers implemented in all fields with mergers in agricultural companies
is shown in Figure 1.
Figure 1 Comparison of the number of agricultural mergers with the total number of
mergers in 2001–2010
Source: Authors based on data in Trade Register of Czech Republic
Table 1 and Figure 1 show that agricultural companies merged to a minimum extent in
2001–2005 and there was a slight increase after 2006. This trend also occurs in the total
number of mergers.
The consequences of mergers from the perspective of financial position and company
performance can only be evaluated for 12 out of the 95 companies; the others have not
published complete data in the Trade Register.
The following items were analysed in the companies where the data was available for the
year of merger and three years after merger.
- total assets;
- equity;
- earnings after taxes.
Out of the total number of 12 companies that published their data, 4 companies
implemented a merger in 2001, 4 companies in 2002, 1 company in 2004, 3 companies
in 2007. The data of 2003, 2005, 2006, 2008, 2009, 2010 are not available, see Table 2.
Table 2 Number of companies that published their financial statements
Year of
merger
Number of
companies
2001 4
2002 4
2003 X
2004 1
2005 X
2006 X
2007 3
2008 X
2009 X
2010 X
Total 12
Source: Authors based on data in Trade Register of Czech Republic
The basic aim of business of each company generally is to increase its market value in a
long-term  horizon.  The  energy  which  allows  business  to  be  done  (company’s  activities)  is  
143
capital. From a purely financial point of view, a company can be seen as a money
machine where the invested capital is gradually transformed during the production
process into various assets whose final demanded form is money (new capital). Financial
statements provide initial economic data to evaluate  a  company,  (Sedláček,  2001).
Total  assets  express  the  size  of  the  company’s  possessions  which  serve  for  the  increase  
in the future economic profit of the company. They represent a sum of long-term assets
and current assets, which are used to ensure the  company’s  activities.  The  values  of  total  
assets are presented in Table 3. The found data show that the value of assets decreased
in the years after the mergers.
Table 3 Value of total assets in thousands CZK
Year of
merger
As of the
merger date
1st
year after
merger
2nd
year after
merger
3rd
year after
merger
2001 1191378 1059283 994695 991297
2002 3127152 670353 628345 587303
2004 260302 226067 218238 227296
2007 1616660 1626253 1609122 483833
Total 6195492 3581956 3450400 2289729
Source: Authors based on data in Trade Register of Czech Republic
Equity is the share of company assets remaining after debts are deducted. It expresses
the  value  of  net  trade  property,  i.e.  the  assets  that  are  financed  from  the  company’s  own  
sources. The values of equity are presented in Table 4. It shows that this value decreases
in the years after mergers.
Table 4 Value of equity in thousands CZK
Year of
merger
As of the
merger date
1st
year after
merger
2nd
year after
merger
3rd
year after
merger
2001 434320 406665 338677 337729
2002 2787192 435357 403937 411038
2004 186407 139125 151981 171786
2007 1480571 1526246 1522587 383450
Total 4888490 2507393 2417182 1304003
Source: Authors based on data in Trade Register of Czech Republic
Profit or loss is a measure of the company performance. It expresses the relationship
between revenues and expenditures during the accounting period. Earnings after taxes
are a basis for the creation of funds and distribution among owners. They are also a basis
for the calculation of other indicators. Its values are presented in Table 5. In total, the
value fluctuated.
Table 5 Value of earning after taxes in thousands CZK
Year of
merger
As of the
merger date
1st
year after
merger
2nd
year after
merger
3rd
year after
merger
2001 43771 -13854 18141 23322
2002 -12109 5276 -2483 9009
2004 4704 3414 14268 20707
2007 1913 44351 -3216 -7567
Total 38279 39187 26710 45471
Source: Authors based on data in Trade Register of Czech Republic
The relationship between total assets and equity is presented in Figure 2.
144
Figure 2 Development of total assets and the equity
Note: Legend for x axis: 1- Data for the date of merger; 2- Data for the first year after merger; 3Data
for the second year after merger; 4- Data for the third year after merger
Source: Authors based on data in Trade Register of Czech Republic
Total assets and equity in the years after the merger decreased when compared to the
day of merger. The decrease could be caused either by reclassification of assets and sale
of redundant properties, or due to the shrinking of the companies that were not able to
meet the demanded merger aims. The change in the value of assets has to be considered
in correspondence with the other indicator - earnings after taxes - shown in Figure 3.
Figure 3 Development of earnings after taxes
Note: Legend for x axis: 1- Data for the date of merger; 2- Data for the first year after merger; 3Data
for the second year after merger; 4- Data for the third year after merger
Source: Authors based on data in Trade Register of Czech Republic
Earnings after taxes fluctuate: after a slight increase in the first year after the merger,
there is a sharp drop in the second year followed by a rise in the third year. To clarify the
reasons for earnings fluctuation, we conducted the analysis of the development of added
value, see Table 6. The results show that the development of earnings after taxes was
considerably affected by the development of revenues and expenses in the field of
product sale and service provision.
145
Table 6 Development of added value in thousands CZK
As of the
merger date
1st
year after
merger
2nd
year after
merger
3rd
year after
merger
Added value
in total
418180 482176 270086 406207
Source: Authors based on data in Trade Register of Czech Republic
When compared with Figure 3 and Table 5, we can see that the development of added
value follows the development of earnings after taxes.
4 Conclusions
Agriculture is a specific field and the number of mergers implemented in this field is very
small. In relation to the total number of mergers (mergers of agricultural companies
forming only 3.55% of this), the data are not really comparable. Another significant
impediment to the evaluation is the fact that out of the total number of 95 agricultural
mergers, only 12 companies, i.e. 12.6%, published information from their financial
statements. In spite of the small number of companies and lack of data, the evaluation is
highly important, although in this case, you can not use modern statistical methods.
Agriculture represents a very important field, which ensures production of food and
creates inputs for related processing industries.
The analysis confirmed a decreasing value of assets and equity in the years after the
mergers. Earnings after taxes fluctuated and depended on the development of added
value.
The analysed agricultural companies were from the category of small and medium-sized
companies. Assets of small and medium-sized companies within all fields of economy
increased (Valouch, et al. 2012), while those of agricultural companies decreased.
This result can have various reasons. It can be affected by the small number of samples
– 12 companies cannot provide relevant information. The result can also be distorted by
measuring of assets in agricultural companies whose specific nature can affect the
pricing. Act on accounting or the related regulations do not deal with measuring of
biological assets in detail. For example, measuring of lands or permanents stands for sale
depends on the market value established by an expert. Also valuation of animals and
plants  gained  by  the  companies’  own  production  can  be  inaccurate.  The  last  reason  can  
be the fact that agricultural companies are so special as regards their position and
subject of business that they are incomparable with other fields.
Acknowledgments
The paper was prepared with the support from Grant Agency of the Czech Republic
project No. P403/11/0447 The Analysis of Taxation and Accounting Practices during
Mergers.
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University, pp. 226–231.
147
The calculation of the output gap in the machinery sector
of the Slovak Republic
Eva Kafková,  Igor  Hudák
University of Economics in Bratislava
Faculty  of  Business  Economy  with  seat  in  Košice
Tajovského  13,  Košice,  Slovak Republic
E-mail: eva.kafkova@euke.sk
igor.hudak@yahoo.com
Abstract: The aim of this paper is the calculation and evaluation of the output gap by
Cobb-Douglas function in the terms of the Slovak economy with focus on the mechanical
sector as an important part of the national economy during the period 2000 – 2012. The
advantage of the chosen calculation of the output gap is the possibility to use it for the
part of economy. The output gap is an important economic indicator determining the
difference between actual and potential output of the economy, respectively of its part.
In the first part of the paper we present the methodology and data used for current
calculation. Consequently, we evaluate the growth of machinery sector by achieved
results. Important information for the formulating strategic objectives in the machinery
sector is to determine its potential growth, which means whether the industry is above or
below its value. In this paper, we investigate the value of output gap, the source of
economic growth in the machinery sector and their contribution on the performance of
the Slovak economy in the period 2000 – 2012.
Keywords: output gap, Cobb-Douglas function, sectorial growth, economy performance.
JEL codes: E23, E24
1 Introduction
Successful decisions of a country's economy and its policy are related not only with the
knowledge of the current status of the economy, but also with knowledge of the
circumstances that led to it. The countries in order to ensure a desirable status of their
economy proceed with regard to the sustainability of economic activity, its
appropriateness and balance. Negative deviations from this level represent a burden to
the economy and create economic imbalances in the country. The output gap represents
the difference between actual and potential output of the economy and plays an
important role as economic indicator. Whether the economy is above or below its
potential growth, is very important information in relation to the formulation of monetary
policy of the country, as well as other economic policies. The aim of this paper is to
evaluate the output gap using the Cobb-Douglas function in conditions of the Slovak
economy, in particular in machinery industry, the economic growth in the sector and its
contribution to the performance of the Slovak economy.
The difference between real and potential product reflects the output gap. Since it is not
commonly measurable, in practice the calculation means rather its estimation. Among
the simplest statistical methods for estimating the output gap are included calculations
using the deterministic linear and quadratic trend, which decompose output into two
elements, trend element and cyclical element (output gap). The calculation of the
potential product by using linear trend brings constant potential growth and does not
allow changes of individual growth factors.
Structural methods extend the capabilities of variables examination in calculation of the
output gap, most frequently of the labor productivity and capital. The output gap is
calculated using the production function, in our case the Cobb-Douglas function. This
method uses the OECD (Orlandi, 2012) and also International Monetary Fund.
148
2 Methodology and Data
Coubb-Douglass function determines the value of GDP, with an average utilization of
production factors. Establishment of Cobb-Douglas function is attributed to the American
economist Paul Douglas, who in the twenties of the last century noticed that the share of
labor and capital on U.S. GDP is stable in the long term view. Mathematician Charles
Cobb subsequently derived production function, which satisfies the requirements of
stable shares. In the production function is the value of potential product dependent on
primary sources of production - labor, capital and effective use of resources aggregate
factor productivity. Two-factor Cobb-Douglas function adjusted for some part of the
economy, for the particular industry, has the form:
𝑌 = 𝐴𝐹𝑃 ∗ 𝐶 ∗ 𝐿 , (1)
where Y is product, L is labor, C is capital, AFP is aggregated factor productivity and α is
share of labor on the product.
The share of labor α was determined as an average share of employees compensation on
GDP at current prices in the monitored period. If we know the number of employees in
the sector (L) and the capital stock at constant prices (K) we obtain AFP from equation
(1) as a residue. Potential GDP is calculated by substituting the aggregate factor
productivity, capital stock and potential employment to the production equation. The
data necessary for the calculation were obtained from the Statistical Office and from the
SR Ministry of Economy materials which deal with the development characteristics of the
machinery industry in the observed period. The labor was calculated as an average
number of workers in machinery industry. The capital is expressed in constant prices and
includes the gross stock of machinery and equipment, buildings and constructions. The
share of labor α on GDP in the observed period was 41%, therefore the share of capital
was 59 %. The value of a share depends on the maturity of that country and in
developed countries it is higher. Labor in SR is in comparison with developed countries
worse evaluated (Konuki, 2008).
ln  𝑌 = 𝑂𝐹𝑃 + α ∗ ln  𝐿 + (1 − α) ∗ ln  K, (2)
The potential employment is defined as the highest value of employment without
inflationary pressures. The calculation uses the indicator NAWRU (Non-Accelerating Wage
Rate of Unemployment). It expresses the unemployment rate at which is growth of
wages is stable. The bottom line is that wages are rising when unemployment is below
the equilibrium value.
𝐿 = 𝐼𝑃𝑃 ∗ 𝑃𝑃 ∗ (1 − 𝑁𝐴𝑊𝑅𝑈), (3)
where Lpot
is potential employment, IPPpot
represents the participation rate of employed
population in active age to working process, PP is population in working age and NAWRU.
Theoretical definition of NAWRU can be found in works of Elmeskov (1993), Holden and
Nymoen (2002), Hofer (2010). Orlandi (2012) defines NAWRU as the following equation:
Δ𝑤𝑔 = −𝑐 ∗ (𝑈 − 𝑈 ), 𝑐 > 0, (4)
where  Δwgt is wages growth in time t, Ut represents unemployment growth in time t, Ut
NAWRU is NAWRU rate in time t. If the value NAWRU is not changing between two
following observations, the relation (4) will be used for expression of ct parameter for
each observation separately.
𝑐 = Δ 𝑤𝑔 /ΔU . (5)
The NAWRU value will be calculated by substituting ct from (5) to (4).
𝑈 = 𝑈 − (ΔU /Δ 𝑤𝑔 )Δwg . (6)
Within the evolution of the Slovak machinery industry growth we paid the attention to
the sectional development of AFP and to selected quantitative performance indicators in
machinery industry. Quantitative indicators of business performance in machinery sector
are processed on the base of the statistical classification of economic activities (NACE
149
Rev. 2), which is used and effective since 2008. Statistical classification of economic
activities SK NACE Rev. 2 contained in the Decree no. 306/2007 of the Slovak Republic
Statistical Office from the June 18th
, 2007 follows-up a European Parliament regulation
and Council (ES) regulation no. 1893/2006, classifies industrial production in the
structure of economic activities in section "C". The entire section has divisions 10 to 33.
We divided the reviewed period into three time intervals in which we expressed an
individual values as averages of the periods (2000-2004, 2005-2008, 2009-2012).
Industrial production is the most significant contribution to the economic growth of
countries and also plays an important role in the development of developed countries.
The main part of the industrial production is machinery industry. The categories CH, CK,
CL of classification NACE Rev. 2, which defines machinery industry as the sum of selected
divisions, classes and groups of economic activities, it is a division 25 - Manufacture of
fabricated metal constructions except machinery and equipment, division 28 Manufacture
of machinery and equipment, division 29 - Manufacture of motor vehicles,
trailers and semi-trailers and division 30 - Manufacture of other transport equipment.
3 Results
For adapting an indicator NAWRU in SR conditions, we used data about real wages trends
and about the level of unemployment. Development of unemployment and NAWRU
indicator is shown on Figure 1. According to the NAWRU theory, the growth of
unemployment and wage growth are inversely proportional (Orlandi, 2012). However, if
unemployment raises and at the same time the wages are growing, we can expect
fluctuations in the NAWRU indicator. In Figure 2 we depict Cobb-Douglas function applied
to the conditions of Slovak machinery industry, with an average utilization of production
factors. The level of potential product is dependent on the basic production sources labor,
capital and AFP.
Figure 1 Development of unemployment and NAWRU indicator in SR
Source: Statistical Office of the SR (2013)
Calculated values of NAWRU, except years 2003 and 2008, copy the actual
unemployment throughout the period under review. The higher is the unemployment rate
in the country, the greater is the structural unemployment.
In 2008, Slovakia reached the lowest unemployment rate since the establishment of
independence. Its value according to the Statistical Office of the SR was 9.57%, which is
in absolute terms 257 500 unemployed persons. The impact of the economic crisis was
reflected in the second half of 2008. The crisis began to manifest itself by reducing
employees in machinery industry (especially in automotive industry) and in chemical
150
industry. Rapid rise in unemployment has stabilized in 2011, but now there has been
reported increase in unemployment again.
Production function takes into account the structural changes in the economy of the SR
which were made at the beginning of the period under review. By 2005, the growth in
the machinery sector was below its potential capabilities, as well as the whole economy
of the SR. The industry came to its production possibility limits one more time, in 2009.
Currently, the economy of the SR together with the machinery industry is above the
potential product growth level (Figure 2). Potential output growth since 2004 has been
supported by increased capital accumulation in machinery enterprises. In 2009, however,
there are significant economic problems in areas with traditionally high employment, not
excluding the area of machinery industry. Gradually, the share of labor costs on potential
output growth (a gradual increase in employment in machinery enterprises), which
shows an increasing trend until the end of the period.
Figure 2 Output gap calculation using Cobb-Douglas function in the machinery sector
Table 1 Basic data about SR machinery sector in period 2000 – 2012
2000 - 2004 2005 - 2008 2009 - 2012
Enterprises* 489 712 605
Revenues (mil. €) 9 875 17 610 18 025
Added value (mil. €) 1 020 2 701 2 986
Export (mil. €) x 16 854 17 243
Import  (mil.  €) x 13 598 13 128
Number of employees 78 000 120 218 110 445
Consumption of materials,
energy and other non-stocking
supplies  (mil.  €)
x 14 520 12 753
Average  monthly  salary  (in  €) x 785 858
Returns  (mil.  €) 11 485 21 451 19 338
Costs  (mil.  €) 10 988 20 756 18 566
Asset  (mil.  €) 4 596 8 062 9 984
Revenues/Employees  (€) 126 600 146 483 163 203
Added value/Employees 13 076 22 467 27 036
Added value/Revenue 0,103 0,153 0,166
*number of enterprises with 20 and more employees
Source: Statistical Office of the SR (2013)
During the years 2008 - 2012 is development of Slovak manufacturing production
characterized by the drop in sales, revenues, employment and worsening of performance
outputs. Recession, the decline in consumption and the increase in debt have a direct
151
impact on the enterprises functioning as the essential elements of global economy
(Pukala, 2010). On the other side, the positive of development is growth of the
manufacturing productivity indicators measured by the productivity parameters and
value  added  rate  (Kafková  and  Hudák,  2012).
Slovak Republic is among the countries of the European Union, where the values of basic
indicators in machinery sector compared with industrial production are well above the
average of EU-27 (Table 1). Machinery is the largest manufacturing industry sector in
Slovakia. In 2012, revenues from machinery sector reached 36 % of industrial production
and employment reached 32%. (SR Ministry of Economy, 2013). Machinery industry has
a significant share on production and on employment across industries in SR. As a
consequence of the economic crisis, machinery production in SR experienced decline in
revenues and employment.
Predominance of production in the section of motor vehicles has manifested mainly at the
beginning of the second half of the period under review, when along with the production
of parts for motor vehicles has created 60 % of all revenues. Despite of the decline in
automotive industry revenues in 2005 we can, based on the averages of individual time
intervals, to evaluate the period 2005 to 2008 as a period of growth in the machinery
sector (Table 1). The increase of the goods production in the industry and in other
sectors helped to this growth, which partially eliminated the drop in revenues of the own
machinery performances and goods.
4 Conclusions
The output gap plays an important role not only in analyzing the economic development
of the country but also in the implementation of decisions relating to its future
development. The calculation of potential product was influenced by the character of the
time series in SR and its structural changes at the beginning of the period under review.
This was also reflected on the shape of output gap curve in SR machinery industry, which
resembling a sinusoid, passing from the position below the production possibilities of the
industry (-2,5 % in the year 2003) to the changes over the production possibilities
frontier, which is associated with the economic crisis (0,4 % in the year 2009 until 0,55
% in the year 2012) . It proves how sensitively responded Slovak industry, specifically
machinery industry, on the sharp drop in demand and on a significant reduction of
products exported abroad. Currently, the Slovak machinery industry is above potential
output growth. This fact has its exogenous but also endogenous reasons. The exogenous
reasons include the influence of economic policy of the SR government, which seeks to
provide a positive economic growth by investment incentives for foreign investors. To the
endogenous reason belongs stable investment expectations related to the participation of
Slovakia in the EU and in the Eurozone. Positive expectations of investors are conditioned
also by better economic policy of SR in comparison with other EU countries.
Acknowledgments
This paper was elaborated within the project VEGA no. 1/0519/12 "Business insurance as
an essential part of strategic management in the period of debt crisis"
References
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Hofer, H. et al. (2010). Practice and Prospects of Medium-Term Economic Forecasting.
Ruhr Economic Paper No. 177, pp. 1-23.
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Kafková,  E.,  Hudák,  I.  (2012). Determination of the general value of selected engineering
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Konuki, T. (2010). Estimating Potential Output and the Output Gap in Slovakia. Eastern
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Ministry of Economy of the Slovak republic. (2013). Characteristics of the development of
the machinery industry. [online]
Orlandi, F. (2012). Structural unemployment and its determinants in the EU countries.
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Pukała,   R.   (2010).   Zarządzanie   ryzykiem   przedsiębiorstwa   w   warunkach   recesji.  
Podkarpackie   przedsiębiorstwa   po   przystąpieniu   Polski   do   Unii   Europejskiej, K. Kaszuba
(red.),  MIG,  Rzeszów,  pp.  233-247.
Regulation (EC) No. 1893/2006 of the European Parliament and of the Council of 20.
December 2006 establishing the statistical classification of economic activities NACE
Revision 2 and amending Council Regulation (EEC) No. 3037/90 as well as certain EC
Regulations on specific statistical domains.
Regulation (EC) No. 177/2008 of the European Parliament and of the Council of 20.
February 2008 establishing a common framework for business registers for statistical
purposes and repealing Council Regulation (EEC) No. 2186/93.
Statistical Office of the SR. (2013). Business Statistics and Indicators of Economic
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Decree of the Statistical Office of the Slovak Republic from 18. June 2007 issuing the
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153
Credibility Theory and Its Using In Insurance Practice
Silvie  Kafková
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41,  602 00 Brno, Czech Republic
E-mail: 175424@mail.muni.cz
Abstract: The subject of this paper is to introduce the theory of credibility. Credibility
theory is a set of quantitative tools that allows an insurer to estimate future premiums
based on past experience of policyholder. First, risk factor for policyholders is defined.
Then sorts of premium are described and the credibility premium is introduced as a linear
function of the past data.  The  simplest  credibility  model,  the  Bühlmann  model,  is  shown.  
It is tryed to use for the determination of the amount of premium. The model example is
solved with the use of mathematical and statistical methods.
Keywords:  Bühlmann  model,  premium,  theory  of  credibility
JEL codes: C11, C63
1 Introduction
In insurance practice it often occurs that one must set a premium for a group of
insurance contracts. The insurer has information about past claim experience regarding
the group itself or a large group of contracts that are more or less related. The problem
is  then  to  set  up  an  experience  rating  system  to  determine  next  year’s  premium.    The  
premium should be set not only on the basis of individual experience but also on the
basis of collective experience (Kaas et al., 2008).
This problem can be solved by using the credibility theory. Credibility theory:
 is the mathematical tool to describe heterogeneous collectives;
 answer the question of how one should combine individual and collective claims
experience;
 belongs mathematically to the area of Bayesian statistics;
 is motivated by questions arising in insurance practice (Bühlmann  et al., 2005).
We try to define credibility premium and then we introduce the simplest credibility
model,   the   Bühmann   model.   This   model   is   used   in   computation   of   premium   in   simple  
example.
2 Methodology
We assume that we have observed n exposure units of past claims X =(𝑋 , … , 𝑋 ) for a
particular policyholder. We have a manual rate 𝜇 applicable to this policyholder, but the
past experience indicates that it may not be appropriate, e.g. 𝑋 =   𝑛 (𝑋 + ⋯ + 𝑋 ), as
well as 𝐸(𝑋), could be quite different from 𝜇. Should premiums for the next year be based
on 𝜇, on 𝑋 or on combination of the two?
The insurer must consider whether the policyholder is really different from what has been
assumed in the calculation of 𝜇, or has it just been random chance that has caused the
differences between 𝜇 and 𝑋 (Klugman et al., 2008).
We assume each policyholder could be characterized by a risk parameter 𝜃. The risk
parameter is not known and it is unobservable. 𝜃  could be viewed as representative of
residual factors that affect the risk level. 𝜃 is changeable and varies according to the
policyholder. We assume the probability distribution with probability function 𝜋(𝜃)  is
known. 𝜃 is a scalar parameter with distribution function Π(𝜃). It can be interpreted as a
probability the proportion of policyholders in the rating class with risk parameter Θ less
than or equal to 𝜃 (Θ is a random variable).
154
We  would  like  to  determine  next  year’s    premium.    If  the  portfolio  is  homogeneous  (all
policyholders have identical means of claim) we can use pure premium (collective
premium) . Pure premium is suitable when we know nothing about the individuals. Then
𝜇 = E(𝑋 ) = E[𝐸(𝑋 |Θ)] = E[𝜇 (Θ)].
It is not depended on a risk parameter 𝜃 and does not use observed experience 𝒙.
On the other side is the individual premium (hypothetical mean) 𝜇 (𝜃). Individual
premium is justified if the portfolio is heterogeneous ( policyholders have different
means of claims). This premium is a function of the individual risk parameter 𝜃. But it is
impossible to determine individual premium because 𝜃  is unknown. Therefore it has been
replaced by Bayesian premium. It uses the past data. Then it is possible to determine
the Bayesian premium as
E(𝑋 |𝑿 = 𝒙) = ∫ 𝜇 (θ)𝜋 |𝑿(𝜃|𝒙)d𝜃,
where 𝜋 |𝑿(𝜃|𝒙) is the posterior distribution.
As a compromise between these options we may ask credibility premium
𝑃 = 𝑍𝑋 + (1 − 𝑍)𝜇.
This premium is weighted average of past experience of policyholders 𝑋  and pure
premium 𝜇. 𝑍 is called credibility factor, 𝑍 ∈ [0,1]. Charging this premium is justified
because the portfolio is general neither completely homogeneous, nor completely
heterogeneous.
3 The credibility premium
Bühlmann   suggested   an   alternative   premium   in   1967   (Bühlmann,   1967).   We   can  
estimate a premium as a linear function of the past data. Consider premiums in the
following form
𝑍𝑋 + (1 − 𝑍)𝜇 = 𝛼 + 𝛼 𝑋 ,
where 𝛼 , 𝛼 ,, … , 𝛼 we must choose. We minimize the square error loss to do so. That is
𝑄 = 𝐸 𝜇 (Θ) − 𝛼 + 𝛼 𝑋 ,
where we use joint distribution of 𝑋 , 𝑋 , … , 𝑋 and Θ. Now we differentiate by 𝛼
𝜕𝑄
𝜕𝛼
= E 2 𝜇 (Θ) − 𝛼 + 𝛼 𝑋 (−1) .
We use the notation 𝛼 ,  𝛼 ,…  𝛼 for minimized values of 𝛼 , 𝛼 ,, … , 𝛼 . We solve
the equation
𝜕𝑄 𝜕𝛼 = 0.⁄
Then we get
E[𝜇 (Θ)] = 𝛼 + 𝛼 E( 𝑋 ).
Since E(𝑋 ) = E[𝐸(𝑋 |Θ)] = E[𝜇 (Θ)], we can write
155
E(𝑋 ) = 𝛼 + 𝛼 E( 𝑋 ).
When we differentiate 𝑄 by 𝛼 , 𝑗 = 1, … , 𝑛, and equate it to 0 to get
E[𝜇 (Θ)𝑋 ] = 𝛼 E(𝑋 ) + 𝛼 E( 𝑋 𝑋 ).
We can rewrite the left-hand side as E[𝜇 (Θ)𝑋 ] =   E(𝑋 𝑋 ) and then we have
E(𝑋 𝑋 ) = 𝛼 E(𝑋 ) + 𝛼 E( 𝑋 𝑋 ).  
  The system of these equations together with the equation for E(𝑋 )  are called normal
equations. The solution of these equations 𝛼 ,  𝛼 ,…  𝛼 can be used to determine the
credibility premium
𝛼 + 𝛼 𝑋 .
3.1  The  Bühlmann  model
The   Bühlmann’s  model   is  the   simplest   credibility   model.   This   model   considers for each
policyholder that pass losses 𝑋 , 𝑋 , … , 𝑋 have the same mean and variance and that data
are independent identically distributed conditional Θ (Klugman et al., 2008).
Thus, we can define
𝜇(𝜃) = E 𝑋 Θ = θ   
and
𝑣(𝜃) = Var 𝑋 Θ = θ ,
where 𝜇(𝜃) is the hypothetical mean and 𝑣(𝜃) is the process variance. Further we define
𝜇 = E[𝜇(Θ)],
𝑣 =   E[𝑣(Θ)],
and
𝑎 = Var[𝜇(Θ)].
We use 𝜇 when   we   don’t   have   information   about   policyholders   (about   𝜃). It can be
proved
E 𝑋 = 𝜇,
Var 𝑋 = 𝑣 + 𝑎  .
and
Cov 𝑋 𝑋 = 𝑎,
(Klugman et al., 2008). Thus the credibility premium is
𝛼 + 𝑋 = 𝑍𝑋 + (1 − 𝑍)𝜇,
where
𝑍 =
𝑛
𝑛 + 𝑘
and
156
𝑘 =
𝑣
𝑎
=
E Var 𝑋 Θ
Var E 𝑋 Θ
.
Thus determined factor 𝑍 we call the Bühlmann  credibility  factor.
Premium obtained in this way is a weighted average of the sample mean 𝑋 and the
manual rate 𝜇. When 𝑛  increases the formula gives more importance to 𝑋 rather than 𝜇.
This corresponds to the intuitive assumption that importance of 𝑋 increases when we
have more past data. When we have homogeneous population with respect to the risk
parameter Θ, the variance of hypothetical mean is small. Thus 𝑎 is small relative to 𝑣,
then 𝑘 is larger and 𝑍 is closer to 0. It makes sense, because when we have homogenous
population importance of 𝜇 (manual rate) increases. In the other side, when we have a
heterogeneous population, the hypothetical means are more variable and 𝑍  is closer to 1.
This means that when we have heterogeneous population we give more importance to
the past experience of each policyholder.
4 Use  of  the  Bühlmann  model
Consider an insurance company which have two types of driver. Good drivers represent
80% of the population and bad drivers represent other 20% of the population. Good
drivers have zero claims with probability 0.75, one claim with probability 0.2 and two
claims with probability 0.05 in one year. Bad drivers have zero, one or two claims with
probability 0.5, 0.35 and 0.15, respectively, in one year.
We can describe this process with the use the risk parameter 𝜃.
Table 1 Probabilities for insurance company
𝐱 𝑷(𝐗 = 𝐱|ϴ=G) 𝑷(𝐗 = 𝐱|ϴ=B) 𝛉 𝑷(ϴ=θ)
0 0.75 0.50 G 0.8
1 0.20 0.35 B 0.2
2 0.05 0.15
Suppose the insurance company has this observation for a particular policyholder: 𝑥 = 1
and 𝑥 = 0.   The   insurance   company   wants   to   determine   the   Bühlmann   estimate   of  
Bayesian premium.
First we must compute the hypothetical means. Thus
𝜇(𝐺) = E 𝑋 |𝐺 = (0)(0.75) + (1)(0.20) + (2)(0.05) = 0.3
𝜇(𝐵) = E 𝑋 |𝐵 = (0)(0.50) + (1)(0.35) + (2)(0.15) = 0.65.
We know 𝜋(𝐺) = 0.8 and 𝜋(𝐵) = 0.2. Now we can determine pure premium 𝜇 and variance
of the hypothetical means 𝑎
𝜇 =   E[𝜇(Θ)] = 𝜇(𝜃)𝜋(𝜃) = 0.3(0.8) + 0.65(0.2) = 0.37
and
𝑎 =   Var[𝜇(Θ)] = 𝜇(𝜃) 𝜋(𝜃) − 𝜇 = 0.09(0.8) + 0.4225(0.2) − 0.37 = 0.0196.
The process variance is computed as
𝑣(𝐺) = Var 𝑋 𝐺 = 0 (0.75) + 1 (0.2) + 2 (0.05) − 0.3 = 0.31
𝑣(𝐵) = Var 𝑋 𝐵 = 0 (0.5) + 1 (0.35) + 2 (0.15) − 0.65 = 0.5275.
For the process variance the expected value is determined as
157
𝑣 =   E[𝑣(Θ)] = 𝑣 (𝜃)𝜋(𝜃) = 0.31(0.8) + 0.5275(0.2) = 0.3535.
Then
𝑘 =
𝑣
𝑎
=
0.3535
0.0196
= 18.0357
and it gives
𝑍 =
𝑛
𝑛 + 𝑘
=
2
2 + 18.0357
= 0.09982.
Now we can determine the Bayesian premium. Thus
𝑍𝑋 + (1 − 𝑍)𝜇 = 0.09982
1 + 0
2
+ 0.90018(0.37) = 0.38298.
5 Conclusions
We met with the theory of credibility and we showed that it is very useful when we have
a heterogeneous portfolio in the insurance company. We showed that we can determine
premium as a linear  combination  of  the  manual  rate  and  past  data.  The  Bühlmann  model  
was presented. We also showed how we can use it for determine Bayesian premium for
vehicle insurance. There exist other models witch used Bayesian methods – for example
Bühlmann-Straub model.
References
Bühlmann,   H.   (1967).   Experience   rating   and   Credibility.   ASTIN Bulletin, vol.4, issue 3,
pp. 199-207
Bühlmann,   H.,   Giser,   A.   (2005).   A course in credibility theory and its applications.
New York: Springer.
Klugman, S. A., Panjer, H. H., Willmot, G. E. (2012). Loss models: from data to decisions.
4th ed. Hoboken, N.J.: Wiley.
Kaas, R., Panjer, H. H., Willmot, G. E. (2009). Modern actuarial risk theory: using R.
2nd ed. New York: Springer.
158
Linkages between bonds and credit default swaps
of the European financial institutions
Veronika  Kajurová,  Jana  Hvozdenská
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  602  00  Brno,  Czech  Republic
E-mail: vkajurova@mail.muni.cz
E-mail: 174974@mail.muni.cz
Abstract: Credit default swap markets have been considered as a leading indicator
of the future development of creditworthiness, which can point out the potential situation
in economy. The aim of the study is to find out whether the role of credit default swap
markets and bond markets has been changed by the financial crisis and the debt crisis.
The attention is paid to the credit default swaps and bonds of 22 financial institutions,
which are included in Markit iTraxx Europe Senior Financial index. Granger causality tests
are employed in order to discover short-run causality. Findings can be favourable for all
participants in the financial markets, especially for investors and regulators as a possible
indicator of credit risk.
Keywords: credit default swap market, bond market, causality
JEL codes: G20
1 Introduction
Increased attention to the credit derivatives started to be devoted after the outbreak of
the financial crisis. Because of the limited availability of data for credit derivatives, most
published empirical studies focus on credit default swaps primarily. Authors obtained
data concerning credit default swaps directly from bank or other institutions. Since the
credit default swap indices have started to emerge, the credit default swap market can
be investigated by means of time series.
First empirical studies paid attention mainly to the relationship between investment
grade bonds and CDS. Blanco, Brennan, Marsh (2003) published first study which
investigates credit default swap prices with time series application. They found out that
spreads of credit default swaps are close to bond spreads. Their next finding was that the
credit  default  swap  markets  “overtake”  the  bond  markets.  In  our  study,  we  try  to  explore  
interaction between credit default swap markets and bond markets by investigation of
prices and we try to confirm the theoretical statement that the credit default swap
markets  “overtake”  the  bond  markets.
There are two main reasons why credit default swap markets are expected to lead the
bond markets. 1) The information is reflected earlier in the credit default swap market
than in the bond market. 2) Institutional features of the credit default swap markets
facilitate a continuous flow of transactions, because short positions in the bond markets
are more difficult to establish (Norden and Weber, 2009, p. 532).
The aim of the study is to find out whether the role of credit default swap markets has
been changed by financial crisis. Theoretically, credit default swap markets are
considered as a leading indicator of the future development of creditworthiness, which
can point out the potential situation in economy.
2 Data and methodology
In our research, we pay attention to 22 financial institutions which are included in the
Markit iTraxx Europe Senior Financial index. This index comprises 25 equally weighted
credit default swaps on investment grade European entities. It covers spectrum of
financial institutions e. g. banks, insurance companies, asset management companies
etc. All data were obtained from Bloomberg on daily basis. We omitted financial
159
institutions which did not issue bonds during observed period from our research
(Hannover Rueckversicherung SE, Swiss Reinsurance Co Ltd and Credit Suisse Group
AG). All observed financial institutions are listed in Table 1 together with country of origin
and industry.
Figure 1 Development of Markit iTraxx Europe Senior Financial index (last price, EUR)
Source: Bloomberg
Table 1 Observed financial institutions
Company Abbr. Country Industry
Aegon NV AEGON Netherlands Insurance
Allianz SE ALVGR Germany Insurance
Assicurazioni Generali SpA ASSGEN Italy Insurance
Aviva PLC AVLN United Kingdom Insurance
AXA SA AXASA France Insurance
Banco Santander SA SANTAN Spain Banking
Barclays Bank PLC BACR United Kingdom Banking
BNP Paribas SA BNP France Banking
Commerzbank AG CMZB Germany Banking
Credit Agricole SA ACAFP France Banking
Credit Suisse Group AG CSG Switzerland Financial Services
Deutsche Bank AG DB Germany Financial Services
HSBC Bank PLC HSBC United Kingdom Banking
ING Bank NV INTNED Netherlands Banking
Intesa Sanpaolo SpA ISPIM Italy Banking
Lloyds TSB Bank PLC LLOYDS United Kingdom Banking
Muenchener Rueckversicherungs AG MUNRE Germany Insurance
Royal Bank of Scotland PLC RBS United Kingdom Banking
Societe Generale SA SOCGEN France Banking
Standard Chartered Bank STANLN United Kingdom Banking
UBS AG UBS Germany Financial Services
UniCredit SpA UCGIM Italy Banking
Zurich Insurance Co Ltd ZURNVX Switzerland Insurance
Source: Bloomberg
0
50
100
150
200
250
300
350
400
160
The total sample period (January 2005 – February 2013) is divided into three sub-periods
according to trends in development of the Markit iTraxx Europe Senior Financial index,
see Figure 1:
a) pre-crisis period (01/01/2005 – 05/31/2007),
b) financial crisis period (06/01/2007 – 10/31/2009),
c) debt crisis period (11/01/2009 – 02/28/2013).
The crisis period is understood as period of the biggest turmoil in the financial markets.
Then the crisis has been transformed into a sovereign debt crisis that has not yet
finished, therefore the period after the financial turmoil is denoted as the debt crisis
period.
Model Specification
We employ Granger causality tests to estimate linkages between prices of bond and
credit default swaps. It is employed in order to discover short-run causality. This
causality test was developed by Granger (1969). General concept of Granger causality is
related to prediction. In case that one time series has an influence on the second time
series, the first one should help to improve predictions of the second one (Cipra, 2008, p.
439). Granger causality means only correlation between present value of one variable
and past values of other variables (Brooks, 2008, p. 298). Traditional Granger causality
test is defined for two time series only; in order to build a network of interactions from a
collection of time series, a simple approach applies it to all possible pairs of signals (Guo
et all, p. 76).
The standard Granger causality model for two variables can be represented as:
𝑌 = ∑ 𝛼𝑦 𝑌 + ∑ 𝛼𝑥 𝑋 + 𝜀 (1)
𝑋 = ∑ 𝛽𝑥 ∆𝑋 + ∑ 𝛽𝑦 𝑌 + 𝜀 (2)
where 𝑌 and 𝑋 are stationary time series, 𝜀 is uncorrelated white noise,  α𝑥 and β𝑥
are coefficients chosen to minimize  σ , ρ is finite and shorter than the given time series
(it can equal infinity but in practice, it is finite due to the length of the available data).
The   null   hypothesis  “X   does  not   Granger   cause   Y”   (formula   1)   or   “Y   does   not   Granger  
cause  X”  (formula  2)  is  rejected  if  the  coefficients    α𝑥 and β𝑦 are jointly significant.
In our research, Granger causality test tries to find if credit default swap prices do
“Granger-cause”   bond   prices   (past   values of credit default swap prices improve the
prediction  of  bond  prices),  and  vice  versa  if  bond  prices  do  “Granger-cause”  credit  default  
swap prices (past values of bond prices improve the prediction of credit default swap
prices). Before application of Granger causality test, the continuously compounded rates
of returns (log returns) are calculated; we denote rate of return of credit default swap as
R_CDS and rate of return of bond as R_BOND. We include five lags (in our case 5 days)
within lag specification of Granger causality test.
3 Results
The Granger causality tests enabled us to examine two null hypotheses:
1)  R_CDS  “does  not  Granger  cause”  R_BOND.
2)  R_BOND  “does  not  Granger  cause”  R_CDS.
3.1 Full sample
Table 2 shows summary results of Granger causality tests in all periods. For full sample
data, results indicate that rates of return of credit default swap of 18 financial institutions
(from  total  22)  “Granger  causes”  rates  of  return  of  bond.  Detailed  results  are  shown  in  
Table 3. Probabilities are lower than 10 % significance level in these cases, therefore the
null  hypothesis  “R_CDS  does  not  Granger  cause  R_BOND”  can  be  rejected.  The  second  
161
null   hypothesis   “R_BOND   does   not   Granger   cause   R_CDS”   can   be   rejected   for   15  
observed financial institutions.
Table 2 Summary of Granger causality test results (at 10% significance level)
Period
No. of
institutions
No. of causal relationships
CDS -> bond Bond -> CDS Mutual
Full sample 22 18 15 13
Pre-crisis period 17 0 2 0
Financial crisis 22 15 5 4
Debt crisis 21 18 11 9
A unidirectional relationship from R_CDS to R_BOND was proved in three cases. It means
R_CDS is strongly exogenous. Both null hypotheses can be rejected in 13 cases
simultaneously; it shows that there is a backward relation (feedback) between variables.
Full sample results do not indicate how the relationships between variables were
changing in time, therefore our attention was paid to the particular periods.
3.2 Pre-crisis period
Almost any Granger causality was not detected in the pre-crisis period. The null
hypothesis  “R_CDS  does  not  Granger  cause  R_BOND”  cannot  be  rejected  in  any  case  and  
the second null hypothesis can be rejected only in two cases. Financial markets were
quite stable during this period without doubts about creditworthiness.
3.3 Financial crisis period
In the financial crisis period, we observed 22 financial institutions. Results of the Granger
causality  test  indicate  that  the  null  hypothesis  “R_CDS  does  not  Granger  cause  R_BOND”  
can be rejected in 15 cases, R_CDS is a strongly exogenous variable in 11 cases.
The  null  “R_BOND  does  not  Granger  cause  R_CDS”  can  be  rejected  in  five  cases;;  only  in  
one case is R_BOND strongly exogenous. Four relationships are mutual. Increased
number of relationships can be seemed as a consequence of turmoil in the financial
markets.
3.4 Debt crisis period
The number of relationships increased compared to the financial crisis period. Nine of
relationships are feedback. In this period, we found 18 causal relations from 21 observed
cases in total for our first null hypothesis. R_CDS is strongly exogenous in nine cases. 11
relationships were found for our second null hypothesis. R_BOND is strongly exogenous
in two cases.
4 Conclusions
The aim of the study was to find out whether the roles of credit default swap markets
and bond markets have been changed by the financial crisis and following debt crisis.
Granger causality tests were employed in order to discover relationships in the particular
periods. Our research showed that the role of both markets has changed. We found out
that the number of causal relations grew during the financial crisis period and has grown
in the debt crisis period. According to obtained results, the credit default swap markets
can be comprehended as a leading indicator in most observed cases and as a potential
indicator of increasing credit risk. It has to be taken into consideration that the results
are valid in this research for financial institutions, which have the most important role in
the credit derivative markets. Findings can be useful for all participants in the financial
markets, policy makers and regulators. Future research of other interactions among
markets is needed as research of determinants of credit default swap prices. Attention
should be paid to long-run causality. The research should be applied on sovereign credit
162
default swaps and bonds, it could have interesting implications for all financial markets
participants.
Table 3 Detailed results of pairwise Granger causality tests
Institution
Null
hypothesis
Start of
the period
Probability
Full
sample
Pre-crisis
Financial
crisis
Debt crisis
AEGON
CDS -> Bond
01/01/2005
6.E-07* 0.9225 0.0049* 5.E-06*
Bond -> CDS 0.0007* 0.2212 0.0287* 4.E-05*
ALVGR
CDS -> Bond
01/01/2005
5.E-13* 0.6964 0.0016* 8.E-15*
Bond -> CDS 0.0354* 0.2148 0.1379 0.1969
ASSGEN
CDS -> Bond
06/27/2006
6.E-10* 0.8858 0.0906#
5.E-11*
Bond -> CDS 0.7544 0.3623 0.8849 0.3021
AVLN
CDS -> Bond
01/01/2005
5.E-15* 0.7232 0.0017* 1.E-10*
Bond -> CDS 0.3964 0.0408* 0.8063 0.0575#
AXASA
CDS -> Bond
01/01/2005
2.E-05* 0.9741 0.0376* 2.E-06*
Bond -> CDS 5.E-06* 0.7769 0.0033* 0.0001*
SANTAN
CDS -> Bond
01/01/2005
9.E-05* 0.5649 0.0724#
4.E-09*
Bond -> CDS 0.0069* 0.7952 0.2692 0.1502
BACR
CDS -> Bond
01/01/2005
4.E-09* 0.2897 0.0517#
1.E-07*
Bond -> CDS 0.0040* 0.0198* 0.2137 0.0548#
BNP
CDS -> Bond
01/01/2005
0.4380 0.6626 0.0517#
0.7365
Bond -> CDS 0.5976 0.3972 0.9389 0.0024*
CMZB
CDS -> Bond
06/15/2008
0.0008* - 0.0787#
0.0005*
Bond -> CDS 0.7384 - 0.6325 0.1019
ACAFP
CDS -> Bond
06/16/2008
0.0062* - 0.0170* 8.E-09*
Bond -> CDS 0.0607#
- 0.4429 0.0008*
DB
CDS -> Bond
01/01/2005
0.0134* 0.4859 0.5949 5.E-08*
Bond -> CDS 0.8018 0.7852 0.9667 0.4116
HSBC
CDS -> Bond
09/14/2005
0.0021* 0.3168 0.2539 0.0001*
Bond -> CDS 0.0176* 0.3662 0.2993 0.3267
INTNED
CDS -> Bond
01/01/2005
5.E-08* 0.2658 0.0065* 0.0003*
Bond -> CDS 0.0163* 0.9877 0.1718 0.0183*
ISPIM
CDS -> Bond
01/01/2005
0.0002* 0.6285 0.2763 2.E-06*
Bond -> CDS 0.0077* 0.8531 0.1192 0.7451
LLOYDS
CDS -> Bond
01/01/2005
0.0013* 0.5449 0.2482 0.0002*
Bond -> CDS 0.0540#
0.6346 0.4337 0.7560
MUNRE
CDS -> Bond
01/01/2005
6.E-13* 0.7112 2.E-05* 9.E-07*
Bond -> CDS 0.2457 0.4420 0.2197 0.0648#
RBS
CDS -> Bond
01/01/2005
0.8166 0.4722 0.5826 0.4581
Bond -> CDS 0.3440 0.1813 0.0012* 0.9142
SOCGEN
CDS -> Bond
01/01/2005
0.1062 0.6883 0.6101 0.0202*
Bond -> CDS 0.0380* 0.4127 0.2260 0.9509
STANLN
CDS -> Bond
06/17/2005
3.E-11* - 0.0460* 3.E-17*
Bond -> CDS 1.E-05* - 0.0010* 0.0050*
UBS
CDS -> Bond
06/28/2007
0.0002* - 0.0140* Bond
-> CDS 0.0820#
- 0.2478 -
UCGIM
CDS -> Bond
01/01/2005
3.E-17* 0.9126 0.0197* 6.E-18*
Bond -> CDS 6.E-05* 0.6407 0.0106* 0.0005*
ZURNVX
CDS -> Bond
08/28/2008
0.8787 - 0.7977 0.4171
Bond -> CDS 0.0368* - 0.1895 0.0631#
* denotes significance at 5% level; # denotes significance at 10% level
163
Acknowledgments
Support  of  Masaryk  University  within  the  project  MUNI/A/0753/2012  “European  financial  
systems  2013”  (Student  Project  Grant  at  MU)  is  gratefully acknowledged.
References
Blanco, R., Brennan, S. and Marsh, I. W. (2003). An Empirical Analysis of the Dynamic
Relationship between Investment Grade Bonds and Credit Default Swaps. Working paper.
Bank of England, 44 p.
Brooks, C. (2008). Introductory Econometrics for Finance. 2nd ed. UK: Cambridge
University Press, 674 p.
Cipra, T. (2008). Finanční  ekonometrie. 1st
ed. Prague: EKOPRESS, 538 p.
Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and
Cross-spectral methods. Econometrica, vol. 37, no. 3, pp. 424-438.
Guo et al. Granger Causality: Theory and Applications (Chapter 5) [online]. [2013-03-
22], pp. 73-98. Available at: <http://ccsb.fudan.edu.cn/eWebEditor/uploadfile/2011
0316110832540.pdf>
Norden, L. and Weber, M. (2009). The Co-movement of Credit Default Swap, Bond and
Stock Markets: an Empirical Analysis. European Financial Management, vol. 15, no. 3,
pp. 529-562.
164
Alternative bankruptcy models – first results
František  Kalouda1
,  Roman  Vaníček2
1
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  602  00  Brno,  Czech  Republic
E-mail: kalouda@econ.muni.cz
2
Ivasoft, ltd.
R&D Department
Kupkova 72, 638 00 Brno, Czech Republic
E-mail: roman.vanicek@ivasoft.cz
Abstract: The article is focused on showing first results of two newly created
(alternative) bankruptcy models. The used database contains available data of Czech
companies (CreditInfo database). Based on result comparison with standardized
bankruptcy models (IN 05, Z-fce) the two new models are equivalent in one case and
significantly better in the second case.
Keywords: discriminant function, alternative bankruptcy models, standard models
JEL codes: G17, G32, G33
1 Introduction
The exclusive position of bankruptcy models in financial analysis methods is out of
question. However their accuracy is regularly being discussed.
The usual praxis of expressing successful predictions in percentages is more and more
often supplemented or substituted with ROC curves (see later).
Two newly created alternative discrimination functions (CZ2 and FK) are presented in this
article. Their accuracies are evaluated using ROC curves. Relative success of these
functions is based on the comparison of their results with standard bankruptcy model
benchmarks – Z function and IN05.
2 Aim and Methodology
The objective of this article is to provide the first objective view of the applicability of the
new created alternative bankruptcy models (CZ2 and FK) in Czech Republic (hereinafter
as CR).
The following shall be used to fullfil the above mentioned aim of this article:
a) proposal of new discriminant function (model) and empirical verification of theirs
predicative ability in a full set of available data on businesses based in the CR
b) comparison of the predictive ability this new discriminant functions (models) with
other bankruptcy models.
The used set of methodical instruments consist mainly of:
- literature review,
- comparison,
- analysis,
- historical analogy and
- synthesis.
The methodology of the construction of the new created models is out of the scope of
this article. It does not allow us to neither present the process of financial ratios selection
nor the process of calculating the weights of the models.
165
3 Data
3.1 Database of Businesses
All   data   used   in   this   work   come   from   the   database   Firemní   monitor   (Creditinfo   Czech  
Republic 2010), formerly known as Albertina. It is a comprehensive database of all
registered firms and organizations in the Czech Republic. It captures the basic data on
more than 2,400,000 business and non-profit economic entities. It has the largest set of
financial statements processed into a structured form. Only financial statements and
industry classification CZ-NACE  (Český  statistický  úřad  2011)  was  used.
The above suggests that as far as the data base, with which the proposed discriminant
function operates, is concerned; it is basically about working with the base selection. All
adjustments to this set described above follow only one viewpoint - to eliminate
irrelevant data.
3.2 Data on Export and Import
Even though the source database contains basic information on more than two million
entities, at least one financial statement is available for only 149,423 entities. On
average, there are three financial statements - not necessarily consecutive - available for
each entity. The total amount of financial statements, which meet the verification
conditions stated below, is 538,162.
During the import of the database, the data redundancy in the financial statement was
used to detect and sometimes also to repair the incorrect values. The set of these
functions were named verification conditions. Part of the verification conditions requires
that the difference between the summands and their declared sum is insignificant. One
condition is also formed by the balance equation. The value of CZK 10,000 was
determined as insignificant. 2.4% of financial statements did not meet the verification
conditions.  This  article  uses  data  from  the  database  Firemní  monitor  as  of  March  2010.  
All monetary values are in thousands of CZK, unless otherwise indicated.
3.3 Selection of the Bankrupt and the Surviving
Every financial statement, which precedes the date of bankruptcy, variably depending on
the selected time horizon - in this paper 720 days (min. 2 years) - was considered a
bankrupt business over time. The issue of insolvency is governed by the Insolvency Act
No. 182/2006 Coll. with effect from 1st January 2008. Before this date, insolvency was
governed by the Act No. 328/1991 Coll., on Bankruptcy and Settlement.
3.4 Selection of Sample and Retained Data
Each financial statement prepared for the period of 12 months is considered an individual
case entering the mathematical model. A randomly selected half of all cases is always
used as the sample used for the calculation of the model. The other half of the retained
(validation) data serves to verify the model.
3.5 Data Profile
In terms of frequency of bankrupt firms over time, the database is of the following
nature. Among all financial statements, there are 1,619 firms two years before
bankruptcy, i.e. 0.3%. If we limit the information ability of the group to 3, there are
1,017 bankrupt firms, i.e. again 0.3%. If we focus on the frequency of bankruptcies
declared in individual years, which is the number of businesses, the database covers
32% of all bankruptcies declared in legal entities in 2009 with a gradual decrease to 12%
in 2005. The database contains a total 1,863 businesses in bankruptcy, for which at least
one financial statement before the date of bankruptcy exists. In addition, the database
contained a sign of bankruptcy in 1,259 businesses without a date of bankruptcy - these
are ceased businesses (or in liquidation for a long time) which cannot be found in the
Commercial Register or ongoing insolvency proceedings. All statements of these
businesses were excluded.
166
An unpleasant aspect of the Czech business environment is occasional huge delays
between a debt that is unpaid for 30 or 90 days and the beginning of the insolvency
proceedings. Delays of four years are not an exception (Klima 2009, p. 2). The result of
this situation is the fact that any bankruptcy model will see the financial data with the
goal to classify it as a healthy business for a time period shorter than the mentioned
delay. If it classifies positively (as bankrupt), it will be penalized in the form of an error
of second kind.
4 Results and Discussion
4.1 Resulting Models CZ2 a FK
The model CZ2 was obtained from the MDA analysis using the Fisher discriminant
includes eight ratio indicators: CapitalReinvested, DaysPayableOutstanding,
DaysSalesOutstanding, InventorySales, CashLiquidity, LiabilitiesHealthPension, ROA,
InterestCoverageRatio.
The model FK was obtained by analysis of causalities between financial ratios. It includes
only three ratios, which are listed in Table 2.
The coefficients of the CZ2 model for the horizon of two years are stated in Table 1. The
coefficients of the FK model for the horizon of two years are stated in Table 2.
The definition of financial coefficients is based on standard approach. Exact definitions
are not the goal of the article. Also beware of the coefficients mentioned in Table 1. They
are to be used solely for economic interpretation not for direct evaluation from financial
ratios.
Table 1 Linear discriminant model CZ2 with failure time horizon two years
Ratio Coefficient
CapitalReinvested 0,416
DaysPayableOutstanding -0,160
DaysSalesOutstanding -0,103
InventorySales -0,047
CashLiquidity 0,321
LiabilitiesHealthPension -0,685
ROA 0,317
InterestCoverageRatio 0,340
Source:  Author’s  construction  based  on  data  base  Firemni  monitor
Table 2 Linear discriminant model FK
Ratio Coefficient
CurrentAssets/ShortLiab 2,0
CurrentLiquidAssets/ShortLiab 1,0
Inventory/ShortLiab -1,0
Source:  Author’s  construction based on data base Firemni monitor
4.2 Models Comparison
The output of some discriminant models such as MDA or logistic regression is x, to which
it applies that the more the value approaches infinity, the higher is the probability that
the correct classification of the given case. In the field of financial risk, this value is called
the score. A user of these models must determine a threshold value of the score,
according to which they will classify businesses as healthy or acutely at risk of
bankruptcy. Each such choice implies the size of the error of the first kind (FP or false
positive) (classification of a healthy business as a business at risk of bankruptcy) and the
error of the second kind (FN or false negative) (classification of a business at risk of
bankruptcy as a healthy business). The function, which puts both these characteristics
indirectly into a relationship, is called ROC curve and is frequently used in the newer
167
studies on bankruptcy models (Altman, Sabato, and Wilson 2010), (Escott, Kocagil,
Rapallo, and Yague 2001) and (Castro 2008).
The relation between specificity and sensitivity follows the definition in equation 1.
The accuracy of the model is defined as the area under the curve (AUC) which is related
to the Gini coefficient G given by the equation 2 where the meaning of the symbols A and
B is shown in Figure 1. Some works (Escott, Kocagil, Rapallo, and Yague 2001), (Castro
2008) use the Gini coefficient instead of the AUC.
The actual accuracy of the model is not directly compared to another model, but
indirectly through so-called benchmark which is one version of the Z-function by Edward
I. Altman.
(1)
(2)
Figure 1 ROC curve
Figure 2 ROC curve model comparison on two year time horizon
Source:  Author’s  construction based on data base Firemni monitor
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
Sensitivity
1 - Specificity
Random
Sample CZ2
Holdout CZ2
FK
Z-fce 1968
IN05
168
4.3 Comparison of the Resulting Models
When calculating the model CZ2, the accuracy of the model on the input sample was Gini
= 0.701 and on the holdout (validation) sample it was Gini = 0.703. The difference is
less than half a percentage point. When calculating the model FK, the accuracy of the
model was Gini = 0.43. There is no need to use a validation sample for model FK as the
weights were not directly based on the input data.
We perform the comparison of the accuracy of the model not only against the benchmark
Z-function models (as mentioned in 4.2) but also against other works (Neumaier and
Neumaier 2005). For the comparison only values calculated on the validation (holdout)
data were used. The comparison is divided according to data source in Figure 2.
Table 3 Model comparison using Gini and AUC values on two year time horizon
Ratio Gini AUC
CZ2 0.703 0.852
IN05 0.500 0.750
Z-fce 1968 0.495 0.748
FK 0.434 0.717
Source:  Author’s  construction  based  on  data  base  Firemni monitor
5 Conclusions
First tests of the newly created bankruptcy models provided encouraging results.
Prediction horizon in this case was chosen to be two years.
The accuracy of all the tested models using the ROC curve with AUC criterion and sorted
descending by accuracy is shown in Table 3. It is clear from the results that the
prediction accuracy of the newly created model is fully comparable with standard models
(model FK), and also clearly better (model CZ2).
The results obtained are considered as a work in progress. The final assessment will be
done after a second round of tests; this time with five year accuracy. It is appropriate to
remark that the information capability of the new models is thanks to the input data base
practically absolute.
References
Act No. 328/1991 Coll., on Bankruptcy and Settlement.
Altman, E. I., G. Sabato, and N. Wilson (2010). The value of non-financial information in
small and medium-sized enterprise risk management, The Journal of credit Risk 6, 1–33.
Castro, C.E. (2008). Estimating a financial distress rating system for Spanish firms with a
simple hazard model.
Czech Statistical Office (2011). Klasifikace   ekonomických   činností   (CZ-NACE)[online]
Available at: http://www.czso.cz/csu/klasifik.nsf/i/klasifikace_ekonomickych_cinnosti_
(cz_
Escott, P., A.E. Kocagil, P. Rapallo, and M. Yague, (2001). Mody’s  RiskCalcTM  For Private
Companies: Spain Rating Methodology, Discussion  paper  Moody’s  Investor  Service.
Insolvency Act No. 182/2006 Coll. (2006). With effect from 1st January 2008.
Klima, J. (2009). Zpráva  o  činnosti  insolvenčního  správce  a  hospodářské  situaci  podniku  
dlužníka, Available at: <https://isir.justice.cz/isir/doc/dokument.PDF?vedlejsi=ne&
rowid=AAAE1GAAYAAL%>
Neumaier, I. and I. Neumaier (2005). Index IN05, In Evropské  finanční  systémy.
169
Crisis Management of Banks in the Context of Financial Crisis
Maria Klimikova, Martin Vovk
University of Economics
Faculty of National Economy
Dolnozemskácesta  1,  852 35, Bratislava, Slovak Republic
E-mail: klimikova@centrum.sk
martin.vovk@euba.sk
Abstract: The aim of the paper is an analysis of impacts of the existing financial crisis on
the management of a commercial bank. The present financial crisis has drawn attention
to the fact that there was not created a system of management of financial institutions
for the crisis period and only few rules and measures which should be applied in the case
of emergency of a bank crisis were made. The absence of effective instruments to
manage institutions in the period of crisis often resulted in the necessity of using public
means to restore confidence in the banking sector to prevent even higher negative
impacts on the real economy. The drawbacks in the sphere of instruments for solving the
crisis situations of financial institutions have shown the need to create a system for an
early and effective intervention in the problematic institution. At the level of regulation
and supervision the measures, which are creating their own systems for solving crisis
situations are simultaneously being elaborated in the management activities of the
banks. To meet this aim, the influence of the financial crisis on the banking sector will be
analyzed, and consequently we will analyze the impact on commercial banks in the
selected parts of the EU legislation in the sphere of banking regulation. The preliminary
results present the activities of commercial banks in case of early signals of crisis.
Keywords: management of commercial bank, crisis management of bank, financial crisis,
EU supervision, measures of the NBS
JEL codes: G18, G21
1 Introduction
At the beginning the European countries were rescuing the financial sector in different
ways. At early stages of the crisis they initially provided financial assistance to the
institutions in the form of monetary assistance and the solutions of averting local crises
by accepting measures of regulation and supervision of financial markets were taken.
The result of the efforts of the central banks of the EU Member States and the ECB to
support the liquidity of the banking sector was a decision of common procedure to reach
the stability of financial systems (27 Member States). The agreement on renewal of the
markets focused on three main areas.
The first area was the government guarantees for interbank loans. The second area was
in the form of recapitalization of financial institutions in various ways, even in the form of
cash injection or possible nationalization of the problematic financial institutions. The
third area concerned the decision on a common deposit insurance up to EUR 100,000.
The adopted agreement is aimed at maintaining a stable financial market in terms of
transparent competition. Aid must be temporary and well-targeted, with no
discrimination to avoid the possible "spillover". Discrimination would include only the
financial support to domestic financial institutions, excluding the foreign ones. Another
common measure was the reduction of key interest rates, in particular to achieve the
required bank capital.
To improve the regulation of financial markets Larossiere Commission suggested focusing
on reorganization of regulation and supervision of the banking system and the problems
of regulation, which the financial crisis had revealed. The aim was both to create an
efficient, integrated and sustainable European system of supervision and regulation, and
170
to improve cooperation between the European supervisory bodies. Currently, the results
of these efforts are to create a single banking supervision in the banking union.
For various commercial banks operating in the European Union, these changes have
brought the changes in terms of banking supervision, as well as of an effective
management of banks concerning the demands of shareholders. This has been reflected
in the changes in approach to the management of commercial banks themselves. Part of
this change is an expansion of the portfolio of indicators that commercial banks are
required to follow in order to be considered safe for their business based on a going-on
concern principle.
To meet this aim, the influence of the financial crisis on the banking sector, and
consequently the impact on commercial banks will be analyzed in the selected parts of
the EU legislation in the sphere of banking regulation.
2 Reasons for changes in the approach to the management of banks
In terms of the European Union at the legislative level, certain steps have been made to
prevent the recurrence of financial crises. They are particularly the legal standards
relating to the prudential treatment of commercial banks creating safe conditions for the
banking sector.
2.1 Directive of the European Parliament and the Council
Its aim is to achieve a timely and early intervention in the declining financial companies,
ensuring continuity of essential functions and minimizing the impact of decline of an
institution on the financial system. In several Member States the supervisory authorities
have powers to manage the bankruptcy of financial institutions. Discrepancies between
the Member States in terms of crisis management raised the need to harmonize the
procedures in dealing with emergencies. In 2010 the Commission adopted a plan for the
crisis management framework in the financial sector, which set the timetable, specific
actions, plans and tools for the comprehensive framework for the EU crisis management.
Subsequently, in 2012 the European Commission adopted the "Proposal for the Directive
establishing a framework for recovery and crisis management of credit and investment
firms. The proposed directive deals with the impacts of the crisis to prevent significant
adverse effects on financial stability, inter alia, to prevent "contagion" and to maintain
market discipline, to protect public resources by minimizing extraordinary reliance of the
public financial support, to prevent unnecessary spoilage and to seek the ways of
minimizing the costs of crisis management, to protect depositors covered by the Directive
94/19/EC, and investors covered by the Directive 97/9/EC, and to protect client funds
and property. "
The Directive enables the financial companies and the authorities responsible for crisis
management to draw up plans to be used in the case of a crisis situation, such as a
recovery plan and a plan for crisis management.
Recovery plan is made by the financial institution. The member States should ensure that
"... the institutions included in the appropriate recovery plans and procedures to ensure
timely implementation of corrective measures, as well as a wide range of recovery
options" [4] and that these institutions have tested their recovery plans using a range of
scenarios of financial distress.
The plan for crisis management is made by the Member States' authorities responsible
for crisis management, and for each institution. The full spectrum of scenarios, including
systemic crisis has to be taken into account.
2.2 Liikanen report
The report of the Expert Group of the European Commission on the structural reform of
banks or also called Liikanen report, published on 2 October 2012, divided the financial
crisis into 5 phases:
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Phase 1 - the mortgage crisis (from 2007 to September 2008): This phase begins with
bursting of the mortgage bubble in the U.S., which also caused the collapse of prices
of mortgage-backed securities. Due to the loss of values of these securities, which
some banks kept in large quantities in their portfolios, the banks had to endure
considerable depreciation resulting in large losses. No one knew how many banks
owned those bad assets, which led to mistrust in banks and greatly hurt the interbank
market.
Phase 2 - systemic crisis (September 2008 - early 2009): At the beginning of this phase
there was a crash of the international investment bank Lehman Brothers, which led
to the mortgage crisis, which spread to the whole system and caused a systemic
crisis. Within a few days there was a great panic and volatility in the global capital
markets, which made the investors realize that even large financial giants may not
always be rescued by the Government.
Phase 3 - economic crisis (2009): The major rescues and rehabilitation of the banking
sector in the U.S. but also in Europe in 2008 resulted in the quiet year 2009. The
prices in the financial markets began to recover and for some banks they showed
gains. Although several countries approved of incentive packages, which were
intended to prevent global depression, there was a significant rise in unemployment,
decline in GDP and contribution of tax revenues to the state budget.
Phase 4 - sovereign phase (2010-2011): At this stage the heart of the global crisis
became Europe, namely when in November 2009, the new Greek Government
revealed a fair amount of debt in the country. Speculations about who might hold
Greek debt started. The investors believed that most of this debt was held by the
European banks, which further reduced the confidence of international investors in
the European banks. The most affected small and medium-sized banks throughout
Europe, especially in the troubled countries, such as Greek banks, lost the ability to
operate in the capital markets. In May 2010 the European Union and International
Monetary Fund decided to help Greece by the rescue package of 110 billion euro.
Since Greece's rating further reduced, and thus the possibility of its return to the
financial markets was unrealistic, the bailout of Greece had to be extended by further
109 billion euro. The requested financial assistance of Portugal and Ireland was made
from a special rescue fund created by the EU countries.
Phase 5 - the crisis of confidence in Europe (at present): The problems in the banking
sector and some EU member countries during the crisis led to the breach of trust in
the European countries and the European Union as a whole. In order to remedy this
trust, in June 2012 the President of the European Council, the European
Commission, the Eurogroup and ECB President issued a joint report, which sets out
four cornerstones of the European Monetary Union, which should be achieved in the
future as "integrated financial framework, integrated budgetary framework,
integrated economic policy framework and enhanced democratic legitimacy and
accountability."
2.3 Vickers report
In parallel with the activities of the Liikanen Commission, the Independent Commission
on Banking (led by J. Vickers) was established in the UK. Its objectives are as follows: to
reduce a systemic risk, to mitigate moral hazard and to reduce the possibility of negative
impact of the bank failure on the entire system. The main recommendation of the group
is to introduce the so-called ring-fence, the main goal of which is separation of
commercial banking from investment banking. Managing, monitoring and control of these
banks would be easier.
Services could be performed only by ring-fenced banks, which would provide mandated
services including the acceptance of deposits and provision of overdraft loans and
payment services to individuals and small businesses. Other services would remain out of
the ring-fence and their implementation should be banned from ring-fenced banks.
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The Group has also proposed the ring-fenced banks to be split off from the investment
banks. Defined ring-fenced part of the bank should have its own board of directors and
must be legally and functionally separated from the parent bank. Capital buffer should be
increased to 20% and include the equity of 10% and another 10% may be other assets,
such as bonds. The largest banks should have at least 17% equity and bonds and other
loss-absorbing instruments up to 3% in the case the bank supervision would be
concerned about their ability to solve the crisis situation with no burden for the
taxpayers. The capital may be moved from a ring-fenced bank to an investment bank, if
the capital adequacy of the split-off bank does not fall below 10%.
Table 1 Comparison measures against crisis situations
Directive of the
European Parliament
and the Council
Liikanen report Vickers report
Adoption
Draft published on 6
June 2012, directive not
yet
Published in October
2012
Published in
September 2011
Territory European Union European Union United Kingdom
Investment and
commercial
banking
It does not impose
allocation of investment
from commercial
banking
Proposes mandatory
unbundling dealing on
its own account and
other risky operations
by the custodian of
banks
Proposes separation
of commercial
banking from
investment banking
by the so-called. ring-
fence
Capital
adequacy
It does not address
solvency, which is
governed by Directive
2006/49/EC on the
capital adequacy
Proposes massive
capital requirements
for unbundled
investment of banks
It does not address
capital adequacy
The main
contribution in
the field of
crisis
management of
banks
Requirement to draw up
a recovery plan living
wills and introduce
possible means to solve
the crisis
Separation of retail
from investment
activities of the bank
activities and
strengthen corporate
governance of banks
Greater protection of
basic banking
activities that are
within the ring-fence
2.4 Risk management
Being clear from the above reports, it is necessary to prepare an effective framework for
the crisis management of banks, which could prevent transfer of infection to other
participants in the financial system. To be able to use this crisis management framework,
the relevant government authorities should possess the instruments to be effective in the
event of an emergency and should ensure financial stability and minimize losses of
taxpayers.
Bank crisis management is a process of preparing for and responding to unforeseen
negative events as a manifestation of a particular risk in such a way as to avoid
deepening the negative impact on the bank. Crisis management includes performing
coordinated measures to control the damage and to preserve or restore public confidence
in efficient management of a wide range of risks in the banking system during the crisis
from a macro and micro point of view.
The  bank’s  primary  risk  management  objective  is  to  achieve  a  position  where  it  will  be  
able to identify all significant risks it faces, to assess their potential impact and have the
relevant policies to manage them effectively.
The most important categories of risk, that the bank faces, include:
• Credit risk is the risk of a loss arising from default by a creditor or counterparty.
173
• Operational risk is the risk of a loss resulting from inadequate or failed internal
processes, people and systems or from external events; it includes legal risk, but
does not include strategic and reputation risks.
• Market risk is the risk of losses in on and off-balance-sheet positions arising from the
movements in market factors, i.e. prices, interest rates, foreign exchange rates,
volatilities, etc.
• Liquidity  risk  is  defined  as  an  inability  to  meet  bank’s  cash  obligations  as  they  come  
due because of an inability to liquidate assets or obtain adequate funding.
• Concentration risk is the risk of losses due to potential adverse consequences which
may arise from concentrations in risk factors or risk types, such as the risk arising
from loans to the same client, to a group of connected clients, to clients from the
same geographic region or industry, etc. Concentration risk may be both intra-risk
and inter-risk, and is not limited to credit risk only.
• Fraud risk is the risk of financial or reputation losses originating from the intent to
defraud the bank or its entities by falsifying information or by misrepresentation by
employees, existing or potential customers, or any third parties.
• Compliance risk is the risk of breaching regulatory rules and related litigation risk
(with regulators or clients), financial risk (fines, compensation of damage),
reputation risk and the risk of breaking corporate culture.
• Reputation   risk   is   the   risk   of   losses   arising   from   failure   to   meet   stakeholders’  
reasonable expectations of  the  bank’s  performance  and  behavior.
• Strategic and business risks are risks to earnings and capital arising from changes in
the business environment and from adverse business decisions, improper
implementation of decisions or lack of responsiveness to changes in the business
environment.
• Macroeconomic risk is the risk of losses due to adverse changes in the overall
macroeconomic environment.
The ultimate risk management body is the Board of Directors. It delegates some of its
authority for particular risk management areas to respective committees (ALCO, ORCO
and CRC). Currently, the Chairman of the Board of Directors and CEO also serve as Chief
Risk Officer (CRO). Asset & Liability Committee (ALCO) has ultimate authority over
market risk of both trading and banking books (including interest rate risk) and over
liquidity risk. As for corporate credit risk, the ultimate decision making body is the Credit
Committee (CRC) which consists of the members of the Board. Operational Risk and
Compliance Committee (ORCO) is the authorized body to make decisions on operational
risk strategies and procedures, it decides on risk appetite and tolerance levels, and
decides on mitigation measures for operational risk, compliance, financial crime, and
anti-money laundering issues. ALCO, ORCO, and CRC are composed of the members of
the board and senior managers. Chief Risk Officer is a member of all three committees.
Operating Liquidity Committee (OLC) governs the execution of liquidity management.
OLC reports directly to ALCO. It analyzes the liquidity situation of the bank on a regular
basis and proposes measures to be taken. OLC consists of members of Treasury, BSM
and Strategic Risk Management units. Risk Appetite and Stress Testing Committee
(RAST) consists of senior managers of BSM, Accounting & Controlling, and SRM. It serves
as an advisory body which defines the overall risk appetite of the bank and handles all
issues related to comprehensive stress testing. Risk Advisory Committee (RAC) is
composed of senior risk managers and specialists from risk management areas. It
analyzes overall credit risk development on a monthly basis and proposes measures and
follow-ups to be taken. Watch List Committee (WLC) analyzes actual and expected credit
risk development of non-retail watch list clients (closely monitored clients are typically
assigned to worse rating grades). It proposes actions to be taken, including decrease of
client’s   exposure,   increase   of   collateral,   rescheduling,   etc.   The   members   of   WLC   are  
senior managers, responsible risk managers from Corporate Credit Risk Management,
Restructuring & Workout and representatives of business lines.
174
The structure of risk management organization consists of five crucial units:
- Strategic Risk Management (SRM) – is responsible for integrated risk management
(ICAAP), operational risk, liquidity risk, market risk (overall as well as particular
trading and banking books), and credit risk control, provisioning, and credit risk
statistical and rating models.
- Corporate Credit Risk Management Division - carries out all activities concerning
operative credit risk of corporate clients.
- Retail Credit Risk Management Division – is responsible for management of credit
risk for retail lending, specifically credit policy, organization of lending process, early
collection process and portfolio management of the retail segment.
- Restructuring & Workout – is responsible for effective debt recovery and write-off
management. It is also responsible for monitoring, restructuring of receivables
overdue, and for specific provisions and collateral management.
- Financial Crime & Compliance – is responsible for compliance risk management (e.g.
code of ethics, full compliance with legal requirements, anti-money laundering
program) and for fraud risk management (prevention, detection, investigation,
deterrence, and recovery of financial fraud).
The risk management function is completely independent from commercial business
lines. Overall risk management has the following roles:
• setting strategies and policies for risk management,
• building a risk-aware culture within the bank,
• designing and reviewing processes of risk management,
• risk identification, calculation, measurement, and control,
• risk reporting,
• setting of risk premium and risk price,
• implementation, calibrating and periodical reviewing of models for risk measurement,
• risk management action, including risk acceptance, elimination, mitigation, limits,
etc.
Crisis prevention in commercial banks has a rich variety of new regulatory actions to
ensure quantitative aspect of "measuring" risk prevention. Those indicators are related to
the currently approved legislative standards in the European Parliament 4/16/2013,
which approved of significant pieces of legislation representing a breakthrough of
steering commercial bank under the name Capital Requirement Directive IV. (CRD IV.)
and Capital Requirement Regulation (CRR).
This is particularly the modification of existing and adoption of new prudential indicators
of commercial banks.
3 Conclusions
The global financial crisis has forced the country to consider the way of preventing crisis
situations and to propose appropriate measures. The main task of the received or
proposed reforms is to strengthen the banking system to overcome another potential
crisis without bailout.
The most significant measure of the European Parliament and the Council is establishing
the forthcoming framework for recovery and crisis management of credit and investment
institutions. Several Member States use bank resolution instruments which are applied to
other entities that have become insolvent. Such an approach to the banking sector is not
correct, since these standard tools do not always ensure sufficiently rapid intervention,
175
continuity of essential functions of banks or do not preserve financial stability. Due to the
deficiencies indicated in the instruments for crisis financial institutions, the proposed
directive is to create a system that will provide the competent authority tools for early
and effective intervention in the credit institutions, in order to ensure the continuity of
essential functions, to minimize the impact of failure on the financial stability and ensure
that losses shall be borne by its shareholders and creditors.
We can conclude that, if this Directive is adopted, the Member States should follow its
provisions. It cannot exclude the emergency of a banking crisis, but it would significantly
help to ensure that in such a situation the participation of taxpayers would be minimal.
On the other hand, the proposed European system of funding mechanisms with the
above ex ante contributions from the financial institutions would also burden taxpayers,
as the banks are likely to move the costs to their clients. The proposed plans should be
prepared for the case of a crisis, so that recovery and crisis management plans can be
effective measures against bankruptcy, but only if they are regularly updated, based on
realistic assumptions, applicable in a range of scenarios and reflect the systemic
importance of an institution or group.
Vickers and Liikanens reports focus on separation of investment banking from
commercial one. Their main objective is to separate the most risky part of banking
business from the one which deals with the insured deposits, i. e. that funds received
from clients in the form of deposits, should not be used for investment in venture funds.
It is evident that similar efforts in the thirties of the last century in the United States
(Glass-Steagal Act) helped to deal with the consequences and repercussions of the
economic crisis.
The main objective of the crisis management is to ensure such a banking sector, which
would be able to finance real economy and carry out its other functions. This goal will
not be achieved without restoring banks' resilience and confidence in the banking sector
as a whole. The banking sector needs to become sustainable and should not rely on any
special support from taxpayers. The measures, that we have analyzed, should contribute
to the mentioned stability of the banking sector and set a clear framework for the
financial sector ensuring all relevant authorities to be well prepared to deal with the
difficulties of banks and properly handle possible bank failures.
References
BIS: Basel III: (2011) A global regulatory framework for more resilient banks and
banking systems, Available at: http://www.bis.org/publ/bcbs189.pdf
BIS: Basel III: (2011) International framework for liquidity risk measurement, standards
and monitoring, Available at: http://www.bis.org/publ/bcbs188.pdf
BIS: Basel III: (2013) The Liquidity Coverage Ratio and liquidity risk monitoring tools,
Available at: http://www.bis.org/publ/bcbs238.pdf
EUROPEAN COMMISSION (2012). Proposal for a Directive of the European Parliament
and of the Council COM(2012) 280, p. 74. Available at: <http://eur-lex.europa.eu/
LexUriServ/LexUriServ.do?uri=COM:2012:0280:FIN:EN:PDF>
EURÓPSKA   RADA   (2012) . Posilnenie   eurozóny:   smerom   k   skutočnej   hospodárskej a
menovej   únii. Brusel:   Európska   rada,   2012. Available at: <http://www.european-
council.europa.eu/home-page/highlights/summit-impact-on-the-eurozone?lang=sk>
Klimiková,  M., Vovk, M. (2012). Vybrané  otázky  regulácie  finančného  trhu  v  EÚ  a USA. In
Financie a riziko, Bratislava:  Vydavateľstvo  EKONÓM.
176
Cash Management in Practice
Jan Krajíček
Masaryk University
Faculty Economics and Administration, Department of Finance
Lipová  41  a,  602  00  Brno,  Czech  Republic
krajicek@econ.muni.cz
Abstract: One of the major components of banking is commercial banking and
innovation of its products. Banks in an effort to retain significant clients create new,
sophisticated banking products that safeguard the bank even at the cost of lower profits,
if these clients remain with her, it will make a profit through the economies of scale and
creating competition to other banks. Cash Management and Cash Pooling are considered
as innovative banking products. These products have been in important position in recent
years. Paper is devoted to Cash Management from the sight of its provision in banking
practice and the motivation of involved subjects in Cash Management in its use.
Keywords: Bank, Client, Cash Management, Cash Pooling, Profit
JEL codes: G21, G39
1 Introduction
Cash Management is one of the determining tasks each entrepreneurial entity has to deal
with to optimize its management. The fundamental types of participants in Cash
Management relations are banks and companies. These are both connected and divided
by their interest in achieving higher profits, which can only be reached at the expense of
another entity, either from a different category or from the same category.
A change in the amount of profit (an increase or a decrease) needs to be evaluated at
the level of individual entities and not at the level of all entities within a category. Nonbanking
entrepreneurial entities achieve a higher profit by cost saving with a better use
of financial flows, which is reflected in a   reduction   of   used   borrowed   (bank’s)   financial  
means, a better use and appreciation of their own financial means.
As regards banks, there is a decrease in incomes within the entire banking sector; but
individual banks increase their profits at the expense of other banks that do not offer
Cash Management. Cash Management can be defined as:
- management of the available amount of money in the right place and at the right time
in the necessary volume;
- cash  management  is  related  to  the  organization’s  liquidity   and includes decisions on
the management of cash flows in the field of its planning and monitoring;
- activities   that   are   directly   or   indirectly   harmonized   with   the   organization’s   financial  
potential;
- a complex of measures and solutions following from a short-term prognosis of surplus
and deficit.
There are two opposing tendencies within the relations of banks to Cash Management.
On  the  one  hand,  banks  are  aware  that  they  need  to  offer  their  top  clients  from  the  “blue  
chips”   category   most   interesting   and   innovated banking products and services. On the
other hand, they are aware that these offers reduce the profit made from these clients.
These are also marketing strategies aimed at gaining and keeping a client – especially a
client who is strategically significant. Banks perceive these costs as costs of keeping
clients in the environment of growing competition and with the purpose of achieving
economies of scale.
In this paper we draw mainly of the following methods: positivist research methodology,
followed by the methodology of pragmatism, and on the methodology of critical
rationalism, followed by economic and statistical methods to evaluate lessons learned.
177
2 Current situation
For an application of Cash Management, it is necessary to know basics of banking and
particular banking products and services which are interconnected within Cash
Management so that they form a complex banking product. This is necessary to
understand the position and function of Cash Management.
The most often stated aim of banking is achievement of profits, which is also a
prerequisite for a stable position of a bank at the market. A bank should not aim at profit
maximization at the moment (in the short term). The amount of the achieved profit
should respect a long-term perspective of the bank (long-term achievement of profit) and
its permanent competitiveness.
Besides the direct achievement of profit, we can define the aims of banking as follows:
- achievement of the maximum market price of the bank or achievement and
maintenance of a significant position at the banking market; these aims are closely
related to the achievement of the maximum profit;
- other  possible  aims  are  banking  stability,  security  for  the  client  and  the  bank’s  owner,  
but also here the original aim of banking – profit achievement – is reflected.
The primary aim (profit achievement) has to be put in harmony with secondary aims
(profitability, bank growth and goodwill). Prerequisites necessary to achieve these aims
are security and liquidity of the bank. The bank has to search for a compromise between
the aim of profit and security (stability).
The formation of the Eurozone enabled organizations to centralize a huge part of their
cash and liquidity, not only within a state, but also on an international level. This allows
for a better control of treasury operations in banks as well as businesses. At the
international level, it is necessary to take into account the different currencies, legal
environments, regulatory frameworks, payment systems and tax regulations in particular
countries. Especially tax regulations are highly autonomous in each member state as the
tax   harmonization   is   not   one   of   the   European   Union’s   priorities   at   the   moment.   This  
means that it is necessary to know all the above mentioned legal conditions and their
application. Companies (also the big international ones) often have no capacity to deal
with them themselves. As a consequence, they need to use sophisticated banking
systems and products.
In spite of all this, the current situation within the field of banking services does not fully
reflect the needs of entrepreneurial non-banking entities for sophisticated banking
services and everything that is related. This is not a problem of the Czech Republic only;
we can see similar tendencies in the surrounding countries. The current Czech literature
deals with Cash Management only to such an extent that it states that such a
phenomenon exists. Mainly the clarification of Cash Pooling and its significance and
impact on economies of banks and organizations is insufficient.
Multinational holdings that operate in several countries cannot coordinate financial flows
in  all  their  subsidiaries  at  the  same  time.  And  these  are  the  entities  that  banks’  offers  of  
Cash Management are designed for as in large volumes one can reach the necessary
savings that follow from its application. Naturally, banks are also interested in non-profit
organizations, organizations of the state administration and mainly self-government for
which they have offers of Cash Management as well. However, this issue does not relate
to multinational organizations only. This also relates to all medium-sized and mainly
large companies that have their accounts in more currencies or are divided into several
independent entities with their own accounts and differing financial consumptions.
The requirement concerning the information systems securing Cash Management are
huge and they follow from the need to process large volumes of financial data. Banks
especially have mechanisms for their monitoring and liquidity management and it is
better to use these within the business sector.
178
The determining factors for further development of banking services, especially Cash
Management, are:
- Development of information technologies. Their development and new opportunities
they have brought in the last 30 years (since the mid-1980s) have enabled banks to
use them fully within their offers to clients. The development of information
technologies enabled the creation and development of sophisticated banking services
as well as the possibility to offer banking services to a higher number of clients,
mainly the development of banking for a wider range of small clients. Retail banking is
directly related to a growing capacity of bank computers that are able to process a
huge amount of data. This is then reflected in an increase in profits of the banking
sector.
- Pressure of large clients and clients who are significant for the banks, and mainly their
consultants, to use and create new and more sophisticated products that bring cost
savings.
- Marketing strategies of banks that focus on gaining the market and needs of large and
multinational clients who are able to bring huge profits even with the low margins, due
to the low costs of services (much fewer bank employees are needed for them than for
a comparable volumes and profits in the case of smaller clients). This indicates the
need to offer these clients services that are attractive for them.
There are two opposing tendencies in the relations of banks to Cash Management. On the
one hand,  banks  are  aware  that  they  need  to  offer  their  top  clients  from  the  “blue  chips”  
category most interesting and innovated banking products and services. On the other
hand, they are aware that these offers reduce the profit made from these clients. These
are also marketing strategies aimed at gaining and keeping a client – especially a client
who is strategically significant. Banks perceive these costs as costs of keeping clients in
the environment of growing competition and with the purpose of achieving economies of
scale.
These strategies are applied to gaining and keeping clients that are significantly
interesting  for  banks.  These  are  the  clients  that  are  related  to  other  bank’s  clients or its
potential clients.This all increases the significance of Cash Management as a highly
innovative product of modern banking in the conditions of growing competition at the
banking market.
Cash Management is in its basis formed by a combination of classical banking products.
An important innovation is its part – Cash Pooling. A significant role is also played by
Shared Services Centres whose use is offered by banks within Cash Management. Thanks
to the interconnection and compilation of classical banking products into the resulting
product offered to clients, Cash Management is considered a sophisticated banking
product.
The centralized management of liquidity performed by banks for their clients (Cash
Management) was created in the 1980s and the 1990s as a result of:
- pressure of top bank clients;
- demand for new, more sophisticated banking services;
- development of information technologies, fast and trouble-free connection that allows
banks to offer new products;
- expansion of the clearing interbank payment system, which is considerably cheaper
and faster than correspondence payments;
- implementation  of  “clearing  centres”  within  bank  holdings;;
- initiatives of the European Commission, which in 2004 cancelled the tax on crossborder
interest payment in most EU member states and thus made the real Cash
Pooling more attractive; in January 2006 charges for cross-border transactions up to
50,000 EUR were reduced as they cannot be higher than charges for inter-state
transactions (in both cases this concerns interbank transactions and is valid for the
states of the Eurozone).
179
These factors made banks able to expand the range of Cash Management products
(especially its determining part – Cash Pooling) for organizations. Cash Management and
Cash  Pooling  are  offered  to  banks’  best  clients,  especially:  
- clients who actively show interest in these products;
- as an offer during acquisitions of the clients banks are interested in;
- to keep significant clients other banks are interested in.
2 Model of Cash Management and Cash Pooling application suitability
Based on the analysis and the conducted survey, discussions with experts in banks,
authors’  own  experience,  and  SWOT  analysis  of  Cash  Management  and  Cash  Pooling,  a  
model was designed that allows for evaluation of Cash Management and Cash Pooling
suitability for a client.
The model is not to   establish   exactly   to   what   extent   of   the   client’s   final   ranking   it   is  
suitable   to   apply   Cash   Management   or   Cash   Pooling,   but   to   demonstrate   a   bank’s  
expected interest in the client. Each bank has its own conditions for acquisition of clients
and this model aims to make their work easier. The model design will include its testing
and establishment of approximate values for the suitability of Cash Management and
Cash Pooling offer and application. The model results can be used as a marketing tool for
acquisition of clients; the bank can demonstrate the suitability of these applications for
its clients using the mathematical model.
Further,  they  can  be  used  by  banks’  clients  as  a  tool  that  declares  their  attractiveness  for  
banks and also from the perspective of  their  clients’  interests.  Due  to  the  parameters  of  
the   correlation   analysis,  where   individual   factors   are   put   in   relation   to   clients’   savings,  
the  model  also  presents  the  client’s  suitability  for  application  of  Cash  Management  and  
Cash Pooling from their own point of view, related to cost savings.
If the conditions for Cash Management to be offered are met, conditions for Cash Pooling
are tested. Some conditions are repeated, they need to be evaluated separately for Cash
Pooling, where the risk of active banking trades is partially included. These can be not
only operating loans and investment credits but also off-balance sheet trades on the
asset side and credit cards. A complex modular method was used for the evaluation
itself. The method allows us to create an evaluation model for Cash Management and
Cash Pooling application suited to the current needs, while keeping the needed input
parameters.
After  we  had  studied  the  options  modular  methods  provide,  we  chose  Mařík’s  “Complex  
Modular  Model”,  which  provides the best conditions thanks to its variability. The model
enables the user to change individual criteria if necessary. The significance weights within
the model were established based on the results of the correlation analysis. The results
of the correlation analysis reached values of correlation coefficients from 0.08975
(negligible correlation) to 0.94654 (very high degree of correlations). Correlation
coefficients were used for the establishment of significance weights of individual criteria
within the  “Complex  Modular  Model”.
For the evaluation of Cash Management and Cash Pooling, the following factors and
criteria enter the model and are specified closer if necessary based on the division
between Cash Management and Cash Pooling:
- Factors – basic factors that are established so that they express the suitability of a
client for the application, e.g. external factors, internal factors, etc.
- Criteria – divide the factors; the criteria that express the necessary extent of the
factor are chosen: e.g. external factors include criteria such as local interest rates,
inflation,  client’s  solvency  and  the  sector  where  the  client  is  active.
- Value of criterion – four values are used.
- n – total number of criteria within the model for Cash Management or Cash Pooling.
- RP – increment per one degree of significance,
180
RP = c * r1, where: (1)
- c = ax
– 1. (2)
- x – degree of criterion significance (value of criterion), this number of degrees
is the same for all factors. The significance of the criteria is established so that the
criterion that is of the highest quality and the most suitable for Cash Management
or Cash Pooling application is marked 1 and the one which is of lowest quality is
marked 4.
- a – coefficient of scoring range (evaluating increment) for one criterion
established as follows:
  𝑎 = =    = 1.82116. (3)
- r – the final ranking of the client within the factors, based on the degree of their
significance.   Their   sum   defines   the   client’s   suitability   for   Cash   Management   and  
Cash Pooling application. The lower the final ranking, the more suitable the client
is   for   their   application.   The   final   value   of   client’s   ranking   for   Cash   Management  
and Cash Pooling is between 10 at minimum and 100 points at maximum (10
points were chosen), where r1 = 1 and r11 = 11, at most a client can reach a
value  of  10  within  the  final  ranking  of  client’s  significance.  
Individual factors, criteria, their significance and weight are designed so that they
express   the   potential   bank’s   interest   in   a   client   in   the   best   possible   way. They were
designed using mainly:
• results of the survey in the banking sector;
• author’s  own  experience;;
• conclusions and recommendations following from discussions with employees of
banks who work in departments of corporate banking.
• Factors establishment was based on:
• results of the survey in banks and the business sector;
• experience from the practice;
• simplicity of their application;
• discussions with employees of banks;
• requirements the model should meet.
Weights of individual factors were assigned based on:
- results of the correlation analysis of the relationship between individual factors and
the savings achieved by application of Cash Pooling, reflected in Cash Management;
- qualified evaluation of the results of the correlation analysis: experience from the
practice, results of the survey in banks and the business sector and verification by
regression analysis.
3 Conclusions
The conducted analyses confirmed that the company size has generally an important
position within the motivation to use Cash Management and Cash Pooling and these
products  being  offered  by  banks.  We  used  Pearson’s  X2
– test to verify the associations
and   Pearson’s   coefficient   to   determine the degree of association. Regression analysis
verified  that  from  the  perspective  of  client’s  financial savings the amount of used loans
and loan costs with Cash Pooling are of the highest significance.
References
Krajíček,  J. (2012). Cash Management and his application in practice, PhD Thesis, Brno:
Masaryk Univerrsity, 161 p.
Mařík   M. (2007). Metody   oceňování   podniku:   proces   ocenění   - základní   metody   a  
postupy, 2nd
ed., Praha: Ekopress, 492 p.
Sodomová  E.  at  el. (2010). Štatistika  pre  bakalárov, Bratislava: Ekonom, 279 p.
Werdenich, M.(2008). Modernes Cash Management, 2nd
ed., München:  Verlag,  279 p.
181
Polluter Pays Principle in the Czech Republic in the Context
of Public Environmental Protection Expenditures
Pavla  Kubová,  Karina  Mužáková
Technical University of Liberec
Faculty of Economics, Department of Insurance Management
Studentská 2, 461 17 Liberec, Czech Republic
E-mail: karina.muzakova@tul.cz
pavla.kubova@tul.cz
Abstract: In light of the significant economic, technical, and technological growths
are increasingly being implemented environmental risks. In practice, there are situations
where the polluter does not have enough funds to repair and implementation of financial
responsibility, which then passes to the state. Remediation of damage implemented
within the environmental risks are not only time consuming but also particularly
expensive. In April 2004, adopted at the European level legal standard that specifically
come to life two very fundamental principle of the protection of the environment,
the precautionary principle and the polluter pays principle. Introduction of the polluter
pays principle (PPP) causes that savings are involved and reduce public deficits. The main
objectives of article include not only analysis of the implementation of PPP in the Czech
legislative framework, but especially analysis of development of public environmental
protection expenditures at years 2006–2010.
Keywords: Polluter Pays Principle, environmental risks, environmental damage, public
environmental proteciton expenditures
JEL codes: G22, K32, Q58
1 Introduction
Advanced economies are trying to find the right policies, concepts and measures that
encourage businesses to prevent damage to the environment or at least trying to reduce
their negative impact. Currently, the much debated is environmental insurance
(insurance of environmental risks and environmental damage). Economic entity
producing good or providing a service may affect their activities to another entity specific
external effects that are unintended. This may be a positive external effect, but also
the negative effect (in the environment is a frequent effect). Its activity can cause
negative externalities (environmental damage). It is followed by reduction
of environmental quality.
Available data on the overall impact of environmental regulations on industrial
competitiveness, say it is not likely to cause a more pronounced negative impact
of externalities on the environment. As for the small and medium-sized enterprises
(SME), it must be said that often cause more environmental damage than would be due
to their size expected, which may be due to the lack of preventive measures, and is often
related resources. The amount of funding is determined by the financial health
of the company. Now in the post-crisis period, the company settled with their financial
problems, while still in this period had to prepare the force of law on the prevention
and remedying of environmental damage.
Given the fact that SME are causing more environmental damage than large ones, it can
be assumed that companies SME impacts could be significant. Undesirable side effects,
such as the increase in the proportion of damage caused by SME, it would be possible
to mitigate the targeted use of national support mechanisms or mechanisms
of the European community aimed at introducing environmentally friendly processes
in SMEs. The proposed approach to liability for damages to the environment ensures
the protection of economic entities in the financial sector, which should not bear liability
unless they have operational responsibility. For this reason, undesirable adverse effects
on the sector, is unlikely. Assuming that will ensure legal certainty due
182
to the accountability and transparency should be the impact on the insurance industry
especially in the longer-term positive as it gains experience in working in that mode
and will create a new market for new insurance products.
Polluter Pays Principle (PPP) has become an important component of environmental
policy at both the national and international level since its adoption by the Organisation
for Economic Cooperation and Development (OECD) in 1972 as one of the guiding
principles of environmental policies. By Smets (1993) the PPP is recognised worldwide
and is referred to in national legislation, as well as in many regional and international
declarations and agreements. The Principle was introduced in 1987 in the Single
European Act (1987). In 1992 by the Maastricht Treaty was confirmed the adoption of
this Principle by the twelve European Union Member States. Furthermore, this principle
expanded in the EU Member States in the context of the new guidelines and measures.
Among the most far-reaching measures include the introduction of liability for
environmental damage and the adoption of Directive 2004/35/CE of the European
Parliament and of the Council of 21 April 2004 on environmental liability with regard to
the prevention and remedying of environmental damage (hereinafter Directive
2004/35/CE) and its transposition into national legislation was in 2008.
The importance of the impact of the introduction of PPP report Tobey and Smets (1996),
followed by the PPP with regard to dealing with asymmetric information (Wirl and Huber,
1998). This paper compares the polluter pays principle with the PPP in a model
of one-sided externalities and asymmetric information. Even allowing for mitigation
of environmental impacts through arrangements and agreements, it will not result
in perfect information social optimum. Finally, it is important to mention article Mamlyuk
(2010) dealing with the critiques important conceptual and practical weaknesses
of the PPP.
2 Methodology and Data
In first part of this paper will be conducted searches of articles, studies, literature,
and last but not least, legislation, and not only at EU level but also in the Czech Republic.
Based on the findings using the search activities will be carried out comparative analysis
of the findings in the study area, then it will be a synthesis of partial knowledge.
Emphasis will be placed on in-depth understanding of the studied phenomenon.
Qualitative research is based on an inductive approach. In third part of this paper will be
analyzed the development of expenditures for protection of the environment from central
sources in years from 2006 to 2010. For this analysis will by used the following
methodology: basic characteristic development and elementary statistic analysis: first
and second difference, growth coefficient, growth rate, increase rate, average absolute
gain and average growth coefficient. Time series for the analysis are very short, so it
is not possible to perform such an analysis of time series with the prediction. Data
analyzed in the third part of the article will be taken from Statistical Environmental
Yearbook of the Czech Republic 2011.
3 Legislative Framework at EU and CR Level
During the development approach to environmental responsibility has been considered
a number of different measures and instruments. Among the most important of them
at EU level include:
• community accession to the Lugano Convention;
• mode optimized for the damage transboundary;
• action by Member States, which would follow the recommendation
of the Community;
• community Directive;
• liability relating to the sector, especially in the field of biotechnology, which
should be inserted provisions on environmental responsibility into existing
Directives in this area.
183
3.1 Legislative Framework at EU Level
In 2004, the EU issued a Directive 2004/35/CE. The Directive establishes a framework
of environmental liability based on the "polluter pays" principle in order to prevent
environmental damage and remedy. It should be emphasized that except for Directive
2004/35/CE, which implements the concept of PPP, exist International Convention
on Liability and Indemnification (outside the scope of the Directive 2004/35/CE):
 the International Convention of 27 November 1992 on Civil Liability for Oil
Pollution Damage;
 the International Convention of 27 November 1992 on the Establishment
of an International Fund for Compensation for Oil Pollution Damage;
 the International Convention of 23 March 2001 on Civil Liability for Bunker Oil
Pollution Damage;
 the International Convention of 3 May 1996 on Liability and Compensation
for Damage in Connection with the Carriage of Hazardous and Noxious
Substances by Sea;
 the Convention of 10 October 1989 on Civil Liability for Damage Caused
during Carriage of Dangerous Goods by Road, Rail and Inland Navigation
Vessels.
In 1992 it was adopted by the Council of Europe Convention on Civil Liability
for environmental damage hazardous activities. In April 2004, also were adopted
Directive 2004/35/CE with regard to the prevention and remedying of environmental
damage. The basic idea of this Directive is PPP. The economic entity, whose activity has
caused the environmental damage or the imminent threat of damage, is financially
responsible for the damage. It is the responsibility of the objective. Strict liability is
incurred, regardless of fault or absence of liability.
Liability for environmental damage is therefore governed by European Parliament
and Council Directive 2004/35/CE. EU Member States were required to transpose
the Directive into their national legal systems by 30th
April 2007. Insurance risk
to the environment is one of the fastest growing insurance sectors in Europe, which
largely contributed also Directive. On the basis of this Directive shall, with effect from 1st
May 2008 Czech Republic, Act on the prevention and remedying of environmental
damage. The Act also lays down a duty financial security through insurance or bank
guarantees for companies whose operations are defined by this Act.
Basic principles of Directive 2004/35/CE:
 The Precautionary Principle;
 PPP;
 The principle of restitution in kind.
3.2 Legislative Framework at CR Level
The legislative framework in the Czech Republic (CR) is composed of the following
legislative measures:
 Act No. 167/2008 Coll. on the prevention of environmental damage
and its correction and amendment of some laws;
 Decree 17/2009 Coll. Identify and remedy environmental damage to soil (risk
analysis);
 Government Regulation No. 295/2011 Coll., A method for assessing the risk
of environmental damage and further conditions of financial security;
 Guideline of the Ministry of Environment to implement basic risk assessment
of environmental damage (of 2nd
November 2012);
 Guideline of the Ministry of Environment to carry out a detailed assessment
of the risks of environmental damage (of 7th
May 2012);
 Law No. 17/1992 Coll., The environment, the Act No. 114/1992 Coll.,
On Nature and Landscape, Law No. 254/2001 Coll., On water (Water Act), Act
184
No. 289 / 1995 Coll., on forests and amending certain laws (the forest Act),
etc.
Below we focus on the Act No. 167/2008 Coll. and basic information about the Act:
 Efficiency 17th
August (delayed beyond its scheduled 30th
April 2007);
 The supervisory authority is the Ministry of Environment, CEI, the NP
and CHKO;
 The law regulates the rights and duties of persons for the prevention
of environmental damage and the remedy, if there has been, or is threatened
immediately to protected species of wild fauna and flora in natural habitats
as defined by this Act, on the water or soil;
 Fills the current legislative framework, especially Law No. 17/1992 Coll.,
The environment, the Act No. 114/1992 Coll., On Nature and Landscape, Law
No. 254/2001 Coll., On water (Water Act), Act No. 289/1995 Coll., on forests
and amending certain laws (the forest Act), etc.
The firms concerned by Act No. 167/2008 Coll.:
 operation of equipment subject to an integrated permit (IPPC);
 sewage treatment plants, water treatment plants, water plants;
 manufacturing (chemicals, etc.);
 agriculture;
 transport of hazardous substances;
 incineration of waste;
 storage and disposal of hazardous wastes, etc.
According to preliminary data, the total is about 40,000 companies in the Czech Republic
(some sources say up to 80,000 companies).
New Government Regulation No. 295/2011 Coll. regulates:
 the evaluation of risks of environmental damage;
 basic assessment (screening assessment including parts A to F, where
the total number of points achieved greater than 50, the operator must
perform a detailed evaluation);
 detailed evaluation (consuming and costly environmental risk studies),
 assessment criteria sufficient financial security (financial means to ensure
the implementation of preventive and corrective measures).
 Exception: remedy environmental damage is greater 20 million CZK or ISO
14001 or program EMAS.
The operator, which performs operational activity listed in Annex 1 to this Act, is obliged
to provide financial security for the reimbursement of costs under this Act.
The effectiveness of financial security is in force from 1st
January 2013.
4 Analysis of Public Environmental Protection Expenditures
In the fourth part of this paper will be analyzed the development of expenditures
for protection of the environment from central sources in years from 2006 to 2010 –
firstly basic characteristic development, secondly elementary statistic characteristic
of development state budget expenditure on environmental protection – namely: first
and second difference, growth coefficient, growth rate, increase rate, average absolute
gain and average growth coefficient.
The public environment protection expenditures in 2010 amounted to higher
than the CZK 28 billion. It is important to look at the evolution of these costs
in the context of the potential savings that evokes the introduction of PPP in the Czech
legislation. However, the potential savings from the introduction of PPP shall see, as from
2013 onwards.
185
4.1 Basic Characteristic Development of Public Environmental Protection
Expenditures
For calculation of basic characteristic development of time series it is necessary
to analyze data about development of public environmental protection expenditures (see
Table 1 and Figure 1).
Table 1 Development of Expenditures for Protection of the Environment from Central
Sources in years from 2006 to 2010
Year (t)
Source of expenditures
State
budget
(in mil. CZK,
current
prices)
State funds
(in mil. CZK,
current
prices)
NPF*
(in mil.
CZK,
current
prices)
Total
(in mil. CZK,
current
prices)
2006 16 252.8 2 406.0 5 172.0 23 830.8
2007 18 169.1 1 700.0 6 325.0 26 194.1
2008 11 759.2 2 049.8 5 728.0 19 537.0
2009 16 481.6 2 068.7 8 466.4 27 016.7
2010 18 473.3 4 444.7 5 921.1 28 839.1
Note:
*) The National Property Fund of the Czech Republic was abolished by Act No. 178/2005 Coll. as of 1st
January
2006. Both its competencies and the resources used to rehabilitate old ecological burdens are now administered
by the Ministry of Finance in addition to state budget.
Source: own elaboration from CENIA (2012)
The data in Table 1 shows how the amount of total public expenditure with the exception
of 2008 climb to nearly 29 billion CZK in 2010. For comparison it should be noted that
total public expenditure on environmental protection amounted to more than 2.43 %
of the total state budget for the year 2010 (CZECH, 2009). It would seem that this
is a small percentage, but in fact it is not.
Figure 1 Development of Expenditures for Protection of the Environment from Central
Sources in years from 2006 to 2010
0
1 000
2 000
3 000
4 000
5 000
6 000
7 000
8 000
9 000
0
5 000
10 000
15 000
20 000
25 000
30 000
35 000
2005 2006 2007 2008 2009 2010 2011
Statefunds,NPF(inmil.CZK,
currentprices)
StatebudgetandTotal(inmil.
CZK,currentprices)
Years
State budget (in mil. CZK, current prices) Total (in mil. CZK, current prices)
State funds (in mil. CZK, current prices) NPF (in mil. CZK, current prices)
186
From the data presented in Figure 1, it is clear, that the development level
of expenditure on environmental protection from the state budget stretches (copy it)
the total amount of expenditure on environmental protection from central sources.
4.2 Elementary Statistic Characteristic of Development of Expenditures
of State Budget
For elementary statistic characteristic of development of expenditures of state budget
in years  from  2006  to  2010  we  used  next  six  indicators  near  Hindls,  Hronová  and  Novák  
(2000) and Tsay (2005) the first difference (absolute gain, 1∆t), the second difference
(1):
    2 1 1 1t t t (1)
growth coefficient (2):


1
t
t
t
y
k
y
(2)
the growth rate (3):
   100
t ty yT (3)
increase rate (4):
 100ty tT k (4)
are given for development of state budget expenditure on environmental protection
in Table 2. In Figure 2, the development of values of first and second difference is shown.
The average absolute gain (5) and the average growth coefficient (6) are important
characteristics  Hindls,  Hronová  and  Novák  (2000)  and  Tsay  (2005):



  
 
 1
2 1
1 ,
1 1
n
t
t ny y
n n
(5)
where n is the number of values (in this paper n = 5).
The results of average absolute gain is 555 125 100 CZK.
 1
1
n
n
y
k
y
(5)
The results of average growth coefficient are 1.032533 (which corresponds to 3.25 %).
Table 2 Development of state budget expenditure on environmental protection in years
from 2006 to 2010
Year
(t)
State
budget (yt;
in mil. CZK,
current
prices)
1Δt 2Δt kt Tyt δyt
2006 16 252.8 × × × × ×
2007 18 169.1 1 916 × 1.117906 111.7906 11.79
2008 11 759.2 -6 410 -8 326 0.647209 64.72087 -35.28
2009 16 481.6 4 722 11 132 1.401592 140.1592 40.16
2010 18 473.3 1 992 -2 731 1.120844 112.0844 12.08
187
From Table 2 it is clearly shown decline in spending on environmental protection from
the state budget in 2008. In 2009, the value of expenditure on environmental protection
from the state budget almost back to 2006, and again in 2010, the value is only about
CZK 300 million higher than in 2007. In percentage terms, the greatest decrease was
monitored parameter values recorded in 2008 by more than 35 % over 2007.
The biggest increase in monitored variables was recorded in 2009 by more than 40 %
over 2008. Development of values of the first and second difference of these values
is illustrated in Figure 2.
Figure 2 Development of values of first and second difference of state budget
expenditure on environmental protection (from data of years from 2006 to 2010)
5 Conclusions
Implementation of PPP in the Czech legislative framework was in the context
of regulatory measures at EU level. Gradually, therefore, leads to the empowerment
of the consequences of environmental disasters from the state to the private sector.
The fourth part of this paper was focused on analysis of development of public
environmental protection expenditures.
The above analysis showed the evolution of public expenditure on environmental
protection, focusing on detailed analysis of the development of national budgets
for environmental protection. These analyzes showed that the state budget expenditure
on environmental protection is still in the period grew, except in 2008. Compared with
the total budget of the Czech Republic in 2010 these expenses were more than 2.43 %,
which is not insignificant amount. First of all, the introduction of the principle of PPP
(first-level EU, then the level of national legislation) states can save on expenditure
on environmental protection from central sources several million CZK. The introduction
of PPP in business practice by current legislation means for some companies mandatory
financial security from 1st
January 2013. The financial market in the Czech Republic deals
with a mandatory financial security especially with the creation of new insurance
products, commercial insurance aimed at environmental damage. For several years
shows the impact of the introduction of the principle of spending from the state budget
for environmental protection.
-10 000
-9 000
-8 000
-7 000
-6 000
-5 000
-4 000
-3 000
-2 000
-1 000
0
1 000
2 000
3 000
4 000
5 000
6 000
7 000
8 000
9 000
10 000
11 000
12 000
13 000
2007 2008 2009 2010
Firstandseconddifferenceinmil.
CZK
Years
1Δt 2Δt
188
Acknowledgments
This paper was created with the support of the Student Grant Competition 2013 (SGS
2013); grant Nr. 38010/2013: "The Comparison of Methods Solutions Environmental
Risks in the Global Insurance Market".
References
CENIA (2012).  Statistická  ročenka  životního  prostředí  české  republiky  2011.
Hindls,  R.,  Hronová,  S.  and    I.  Novák  (2000).  Metody  statistické  analýzy  pro  ekonomy.
2nd
Ed. Praha: Management Press.
Maastricht Treaty (1992). Available at: eur-lex.euroap.eu.
Mamlyuk, B. N. (2010). Analyzing the Polluter Pays Principle Through Law
and Economics, Southeastern Environmental Law Journal, 18, 1, p. 43-86.
Single European Act (1987). Available at: eur-lex.euroap.eu.
Tobey, J. A. and H. Smets (1996). The Polluter-Pays Principle in the Context
of Agriculture and the Environment. The World Economy, vol. 19, issue 1, pp. 63–87.
Tsay, S. R. (2005). Analysis of financial time series. 2nd
Ed. Chicago: John Wiley
and Sons.
Wirl, F. and C. Huber (1998). The Polluter Pays versus the Pollutee Pays Principle under
Asymmetric Information, Journal of Environmental Economics and Management, 35, 1,
pp. 69-87.
189
The fundamentals of accounting for business combinations
Oleksandra Lemeshko
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  602  00  Brno,  Czech  Republic
E-mail: lemeshko@mail.muni.cz
Abstract: Currently international accounting for business combinations is considered to
be the one of the predominant features of the 21st
century economy. In response to such
current challenges the aim of the paper consists in an attempt to introduce the
fundamentals of US GAAP and IAS/IFRS accounting for business combinations and
perform their qualitative comparison. The applied methodology is based on
multidisciplinary approach designed for accounting for business combinations. The
conducted research indicates that for today the accounting amounts reported under US
GAAP and IAS/IFRS in context of accounting for business combinations are considered to
be highly relevant and comparable. Generally under both sets of Standards accounting
for business combinations is realized in a sequence of four steps: first an acquirer and
acquisition date are determined, then acquired assets and assumed liabilities are
recognized and measured at fair value, after that a set of consolidated financial
statements for combining entities is compiled, and, finally, goodwill or bargain purchase
is identified and impaired. Although significant harmonization and unification have been
reached in this field still some differences in accounting policies continue to remain. As
the most disordered and debated topics there are considered to be accounting for
goodwill, fair value measurement of non-controlling interests and impairment of
intangible assets.
Key words: accounting for business combinations, US GAAP, IAS/IFRS, acquisition
method, fair value measurement, consolidation, goodwill and bargain purchase.
JEL codes: M41
1 Introduction
Currently accounting for business combinations has ultimately become one of
benchmarks of international accounting. Nowadays in times when world economy stroken
by global financial crises and subsequent recession approaches the edge of the 7th
wave
of business consolidation international accounting for business combinations has become
of crucial importance. For a rather long time in the financial reporting theory there has
been a longstanding debate about accounting for business combinations. Such debate
spread across a number of fundamental questions. The most essential ones deserve
some attention.
The first question accounting society was faced with was the one about the values in
terms of which the accounting for business combinations should be carried out. The
proposed variants spread from simple historical values net from accumulated
depreciation, through special carrying amounts and recoverable amounts, and up to fair
values.   The   second   question   was   about   the   way   of   presentation   of   combining   entities’  
financial statements in order to promote their highest relevance, representational
faithfulness and comparability. Likewise in previous case the relevant proposals varied
from case to case: for some deals it was more informative and meaningful to present
combining entities together as a single accounting entity with consolidated financial
statements, while for others the preparation of combined financial statements was more
desirable taking into account, for example, the overall unconsolidated nature preserved
despite such deal. The third question was about goodwill accounting. Since accounting for
goodwill emerged in a course of combination is a logical termination of fair value
measurement and consolidation, the corresponding problems can be defined as
derivative from the ones emerged within such primary procedures. The greatest difficulty
contains in abcense of unified approach to measurement of goodwill emerged within a
190
business combination: although the primary sources of accounting for business
combinations indicate fair value as the single amount in term of which all balance
components (including goodwill) should be measured and consolidated nonetheless the
original regulative framework designed for goodwill accounting indicates such values as
net amount, carrying amount and recoverable amount.
In strive to find the most appropriate answers to these questions three international
projects - Project on Business combinations, 1999-2008, Project on Fair value
measurement, 2005-2011, and Project on Consolidation, 2010-2011- have been
launched by FASB and IASB. Although Projects have substantially contributed to solution
of above presented questions nevertheless they did not prosper in elimination of all
existing discrepancies.
In response to current challenges for today there have been conducted numerous
studies. However despite the timeliness to study the above presented problems the
majority of existing studies are focused on more general issues such as the overall
harmonization and convergence of US GAAP and IAS/IFRS. The findings based on works
of Ampofo and Sellami (2009), Meulen et al. (2007), Rezaee et al. (2010), Zeff (2009)
indicate that US GAAP and IAS/IFRS accounting generally have high value of relevance
and comparability. Thus currently there is an evident lack of research dedicated to both
fundamentals of US GAAP and IAS/IFRS accounting for business combinations and
qualitative comparison of existing relevant authoritative pronauncements. The existing
lack of such research might be explained by evident complexity and highly sophisticated
nature of chosen topic.
2 Aim and Methodology
In response to current challenges the aim of the paper consists in an attempt to provide
brief insight into the fundamentals of US GAAP and IAS/IFRS accounting for business
combinations and perform their qualitative comparison.
The methodology for current paper is based on snowball approach comprising a number
of authoritative pronauncements designed for accounting for business combinations.
There are FAS 141 and IFRS 3 (R) at the core. These accounting Standards are results of
10 years of intensive collaboration between FASB and IASB and they are considered to
be the primary sources of accounting for business combinations. To promote relevance,
representational faithfulness and comparability of accounting information the combining
entities are required to apply accounting procedures collectively known as acquisition
method (FAS 141 (R), par. 1, p. 1 and IFRS 3 (R), par. 1, p. A136). Although such
procedures contain the major guidance in field of accounting for business combinations,
nevertheless they do not cover all aspects.
To extend and specify the basic guidance of FAS 141 (R) and IFRS 3 (R) another pair of
international Standards has been chosen – FAS 157 and IFRS 13. In regard of business
combinations both Standards establish complete framework for identifying and
measuring the fair value of acquired assets (FAS 157, par. 5, p. 2 and IFRS 13, par.
appendix A, p. 32-33). The proposed framework comprises a set of initial requirements,
basic guidance for initial recognition and measurement. Thus after FAS 141 (R) and IFRS
3 (R), FAS 157 and IFRS 13 are considered to be the second major sources of accounting
for business combinations.
Moving outward from the core of the imaginary snowball there can be found another pair
of Standards dealing with other aspects of accounting for business combinations. To
enhance the relevance, reliability and comparability of the information presented in
consolidated financial statements FAS 160 and IAS 27 have been introduced (FAS 160,
par. 1, p. 160-6, 160-8 and IAS 27, par. IN4, p. A705). These Standards provide basic
guidance for consolidation accounting of combining entities.
The last set of questions referring to accounting for business combinations and remaining
uncovered in above mentioned authoritative pronouncements constitute the scope of the
191
last two pair of Standards – FAS 142 and IAS 36. From the perspective of view of
business combinations the Standards are aimed to provide guidance for proper
accounting and reporting of goodwill emerged in a course of combination (FAS 142, par.
4, p. 8 and IAS 36, par. 2). Although opposite to previous pairs of Standards this last
pair differs significantly in some accounting procedures nevertheless the overall approach
for goodwill accounting is the same.
The presented above Standards are considered to be the major sources of accounting for
business combinations. Here it should be stated that depending on the sequence of
accounting procedures these four pair of Standards can be organized into two groups:
FAS 141 (R) supplemented with FAS 157 and IFRS 3 (R) suplemmented with IFRS 13
comprise primary sources of accounting for business combinations while FAS 160
supplemented with FAS 142 and IAS 27 supplemented with IAS 36 comprise the
secondary guiding notes. These pairs of authoritative pronouncements will constitute the
methodology of the current paper.
3 Results and Discussion
3.1 Acquisition method
As it has been stated earlier realization of a number of mutual Projects brought
substantial harmonization of corresponding accounting practices exisiting within both US
GAAP and IAS/IFRS. In particular, the procedures which constitute the fisrt stage of
accounting for business combinations are considered to be the best example of such
harmonization and convergance. Under both set of Standards acquisition method is
applied through acquisition approach, which in its turn is realized in a sequence of four
steps: (1) identification of acquirer; (2) determination of acquisition date; (3, 4)
recognition and subsequent measurement of identifiable assets acquired, liabilities
assumed, equity transferred and goodwill or gain from bargain purchase (FAS 141 (R), p.
ii-iii and IFRS 3 (R), par. IN6, IN8 p. A134).
Before these four accounting operations will take place first certain preliminary
requirements have to be met. The first thing to do is to define the consolidation deal of
combining entities as a business combination (FAS 141 (R), par. appendix A2-A9, p. 33-
36 and IFRS 3 (R), par. appendix B5-B12, p. A154-A156). Such identification is done
through meeting by both combining entities the criteria of being businesses. Other
requirements for a business combination to take place are: (1) existance of the principal
or the most advantageous market where an asset or a liability can be sold with the
greatest volume; (2) knowledgeable and willing market participants are able to transact
for the asset acquired and liabilities assumed; and (3) market price which is the most
fully and non-biased reflecting the fair value of individul asset or liability (FAS 157 par. 7-
11, p. 3-4 and IFRS 13, par. 8-14, p. 14-15).
Step 1. Identification of acquirer
For each business combination one of the combining entities shall be defined as an
acquirer (FAS 141 (R), par. 8, appendix A10-A15, p. 36-37 and IFRS 3 (R), par. 6,
appendix B14-B18, p. A156-A157). The identification of an acquirer is based on the way
and structure by which the deal is taking place. In a business combination effected
primarily by transferring cash or other assets or by incurring liabilities the acquirer
usually is the entity that transfers cash or other assets or incurs liabilities (FAS 141 (R),
par. 8, appendix A10-A15, p. 36-37 and IFRS 3 (R), par. 6, appendix B14-B18, p. A156A157).
In a business combination effected primarily by exchange of equity interests, the
acquirer usually is the entity that issues its equity interests. In case of formation of a
new entity to effect a business combination both Standards state that it is not necessary
the acquirer and one of the combining entities that existed before the deal shall be
identified as the acquirer (FAS 141 (R), par. 8, appendix A10-A15, p. 36-37 and IFRS 3
(R), par. 6, appendix B14-B18, p. A156-A157).
192
Additionally both Standards state that while identifying the acquirer it is necessary to
take into consideration the relative size of combining entities measured by assets,
revenues or earnings (FAS 141 (R), par. appendix A13, A15, p. 37 and IFRS 3 (R), par.
15a, p. A156-A157). Further the relative size of combining entities will affect the
distribution of controlling power in the board of directors of newly organized entity. Thus
it is another way how to determine the acquirer.
Taking into consideration above expressed approaches sometimes it is possible to define
two acquirers: legal and actual. Daimler-Chrysler AG is an example. In accordance with
primary legal prescriptions of both corresponding Standards the legal acquirer was
Daimler-Chrysler AG and legal acquires were Daimler-Benz AG and Chrysler Corp.
However, if we are talking about actual acquirer, the evaluation of the relative size of
combining entities and distribution of governing power, so the actual acquirer was
Daimler-Benz AG while Chrysler Corporation was the acquire and their merger turned out
to be a stock swap acquisition.
Step 2. Determination of acquisition date
Both sets of Standards require the acquirer to identify the acquisition date (FAS 141 (R),
par. 10-11, p. 4 and IFRS 3 (R), par. 8-9, p. A137). Generally it is considered to be the
date on which the acquirer obtains control over the acquire. Technically it is the date on
which the acquirer legally transfers the consideration, acquires the assets, and assumes
the liabilities of the acquire. It is the date of signing the deal or the closing date.
However further both Standards state that such transfer of control might be on a date
that is either earlier or later than the closing date. Thus the acquisition date and the
clossing date are not the same. Again Daimler-Chrysler AG might be an example. For
Daimler-Chrysler AG deal the date of May 6, 1998, when Daimler-Benz  AG  CEO  Jürgen  
Schremp and Chrysler Corp. CEO Bob Eaton officially signed the merger agreement to
build a new entity – Daimler-Chrysler AG, granting to it the whole controlling rights of
both acquires was the acquisition date. However it took another 200 days to complete
the deal in regard of legal settlement. That is why the closing date is assumed to be
November 12, 1998 when the first official consolidated financial report was completed on
occasion of Daimler-Chrysler AG placement on NYSE 5 days later i.e. on November 17,
1998.
Step 3. Recognition of the identifiable assets acquired, liabilities assumed, and
any non-controlling interest transferred
At the acquisition date according to both Standards the acquirer should separately from
goodwill recognize all identifiable acquired assets, assumed liabilities and any transferred
non-controlling interest in the acquire (FAS 141 (R), par. 12-15, p. 5 and IFRS 3 (R),
par. 10-13, p. A137-A138).
To qualify for recognition and subsequent fair value measurement under both sets of
Standards first the combining entities should meet the definitions of assets and liabilities
as they are used in complience with FASB Concepts Statement No. 6, Elements of
Financial Statements and IASB Framework for the Preparation and Presentation of
Financial Statements.
Usually the recognition principle comprises such groups of elements as: long-term and
short-term non-financial and financial assets, long-term and short-term financial
liabilities,  stakeholders’  equity,  derivative  instruments,  revenues  and  expenses  [14].  The  
recognition in regard of: (1) long-term and short-term non-financial assets – is being
compiled through depreciated and non-depreciated per asset classes in accordance with
relevant Standards; (2, 3) long-term and short-term financial assets and liabilities – is
being compiled through profit or loss, available-for-sale or held-to-maturity per security
classes in accordance with relevant   Standards;;   (4)   stakeholders’   equity   – is being
compiled through owner and non-owner change per equity class in accordance with
relevant Standards; (5) derivative instruments – is being compiled through asset and
liability per instrument class in accordance with relevant Standards; (6) revenues and
expenses - is being compiled through continuing operations, discontinued operations,
193
extraordinary items and comprehensive income classes in accordance with relevant
Standards.
Step 4. Measurement of the identifiable assets acquired, the liabilities assumed,
any non-controlling interests transferred and goodwill assumed
At the acquisition date according to both Standards the acquirer should measure the
identifiable assets acquired and assumed liabilities at their fair values (FAS 141 (R), par.
20-21, 59-60, p. 7, 18-19 and IFRS 3 (R), par. 18, B41-B45, p. A139, A163-A164). In
regard of fair value measurement of non-controlling interests between two sets of
Satndards there are some differences. US GAAP requires any non-controlling interest in
the acquiree to be measured at fair value while IAS/IFRS states that for each business
combination at the acquisition date the acquirer shall measure all components of noncontrolling
interest at either fair value or at proportionate share of its present ownership
instrument in net assets acquired.
For fair value measurement under both sets of Standards there are special valuation
techniques which consist with market approach, income approach and cost approach
(FAS 157, par. 18-20, p. 7-8 and IFRS 13, par. 38-40, p. 23-25). The acquirer should
use the most appropriate one taken into account the given circumstances and for which
sufficient data is available.
Foremost fair value measurement of: (1) long-term and short-term non-financial assets comprises
general adjustments (e.g. write-offs, advance payments to suppliers) and/or
adjustments referring to accumulated depreciation (e.g. amortization charges); (2, 3)
long-term and short-term financial assets and liabilities - comprises only general fair
value adjustments (e.g. restricted cash as asset with uncertain cash flow, long-term
deferred income tax as asset with uncertain cash flow, imputed interest and discounts,
customer  deposits);;  (4,  5)  stakeholders’   equity  and  derivative instruments - comprises
only general fair value adjustments (e.g. cumulative distributions to shareholders,
foreign currency translation adjustments); (6) revenues and expenses – comprises two
sorts of adjustments: there are general adjustments referring to restructuring of
statement of change in financial position by itself and fair value adjustments referring to
revaluation  at  fair  value  of  particular  combining  entities’  balance-sheet components [14].
3.2 Consolidation and Goodwill impairment
The second stage of accounting for business combinations is made up by compilation of
consolidated financial statements and goodwill impairment.
Step 1. Incorporation of consolidation adjustments
In consort with relevant Standards the consolidated financial statements for any business
combination are made up mainly of original assets, liabilities and business results, data
on   which   can   be   derived   from   combining   entities’   pre-combination financial statements
recognized and reevaluated at fair value [4, 12, 6, 11, 7, 9]. Thus such consolidated
financial statements contain a number of fair value and consolidation adjustments
referring mainly to: (1) intangible assets not previously recognized; (2) lease contracts
of combining entities; (3) cancellation of double accounts and minority interest; and (4)
restructuring adjustments.
Here it is necessary to say a couple of words about cancelation of double accounting. In
accordance with US GAAP and IAS/IFRS while consolidating financial statements for
business combination special attention should be paid to cancellation of double accounts.
The consolidation of financial statements for business combination basically involves
summing up the amounts for various financial statement items across the separate
financial statements of combining entities reevaluated at fair value with adjustment the
amounts resulting from such summation in order to eliminate double-counting resulting
from intercompany transactions such as movements of cash, revenues, assets, or
liabilities. The same cancelation should be done in regard of minority interest account of
both combining entities to share in capital of each other.
194
Step 2. Merger modeling
In consort with relevant Standards while accounting for any business combination the
merger modeling is applied. Generally it comprises: (1) determination of exchange ratio
and purchase price; and (2) allocation of purchase price and goodwill impairment [7, 9,
5, 10].
The   exchange   ratio   can   be   determined   according   to   the   combining   entities’   book   or  
market values, earnings or some other characteristic reflecting the market power of
entities. Also exchange ratio can be determined through the model made up by Larson
and Gonedes (1968), which considers ex ante and ex post earnings and P/E ratios of
combining entities [13].
The purchase price can be determined as the market value of capital shares of acquires
converted into shares issued by acquirer. Usually such price consideration is calculated as
a multiple of: (i) sales revenues; (ii) earnings before interest, taxes, depreciation and
amortization; (iii) earnings before interest and taxes; and (iv) net income [14]. After the
purchase price determination follows its allocation. It assumes the determination of
consideration transferred by combining entities with further comparison of its value with
estimated fair value of assets acquired in a course of combination and allocation to
particular cash-generating or reporting units (IAS 36, par. 80-90 and FAS 142, par. 34-
35, p. 16-17).
The residual value of such allocation is defined as either goodwill or bargain purchase. As
it has already been stated within existing Standards there is no standardized way how
goodwill emerged in a course of combination should be measured – at fair value, at net
value or in terms of carrying or recoverable amount [4, 12, 6, 11, 5, 10]. The same
disagreement is true for measurement and impairment of other intangibles.
Finally, the determined goodwill or bargain purchase should be further tested for
impairment. Goodwill impairment refers to the procedure under which goodwill (bargain
purchase) emerged in result of business combination is being tested for impairment. If
the outcome of such test proves the possibility of impairment than such impairment is
performed. Although testing procedures prescribed by two sets of Standards slightly
differ nonetheless under both sets of Standards bargain purchase can be both impaired
and amortized while goodwill can be only impaired and cannot be amortized (FAS 142,
par. 18-38, p. 12-17 and IAS 36, par. 80-100).
4 Conclusions
Currently international accounting for business combinations is considered to be one of
predominant features of the 21st
economy. The era when accounting for business
combinations was on some periphery of international accounting has gone into the past.
Nowadays the world economy is on the edge of the 7th
wave of business consolidation
which in circumstance of world financial crisis and subsequent recession has become of
crucial importance. So do the international accounting practices designed to facilitate this
busines phenomenon.
The conducted research indicates that for today the accounting amounts reported under
US GAAP and IAS/IFRS in context of accounting for business combinations are considered
to be highly relevant and comparable. Generally under both sets of Standards accounting
for business combinations is realized in a sequence of four steps: first an acquirer and
acquisition date are determined, then acquired assets and assumed liabilities are
recognized and measured at fair value, after that a set of consolidated financial
statements for combining entities is compiled, and, finally, goodwill or bargain purchase
is identified and impaired. Although significant harmonization and unification have been
reached in this field still some differences in accounting policies continue to remain. The
most disordered and debated topics are considered to be issues of goodwill accounting,
fair value measurement of non-controlling interests and impairment of intangibles.
195
References
Accounting Standards Update for FAS 142, Goodwill and Other Intangible Assets.
Financial Accounting Standards Board,  №  2011-08, 2011, p. 34.
Ampofo A. and R. Sellami (2009). Examining the differences between United States
Generally Accepted Accounting Principles (U.S. GAAP) and International Accounting
Standards (IAS): implications for the harmonization of accounting standards. Accounting
Forum, Elsevier, No. 29, p. 219-231.
FAS 6, Elements of Financial Statements. FASB, 1985, p. 91.
FAS 141 (R), Business combinations. FASB, 2007, p. 201.
FAS 142, Goodwill and Other Intangible Assets. FASB, 2001, p. 110.
FAS 157. Fair Value Measurement. FASB, 2007, p. 158.
FAS 160. Consolidation. FASB, 2007, p. 86.
IFRS Framework for the Preparation and Presentation of Financial Statements. IASB,
2001, p. 4.
IAS 27, Consolidated and Separate Financial Statements. IASB, 2001, p. 18.
IAS 36, Impairment of Assets. IASB, 2001, p. 147.
IFRS 13, Fair Value Measurement.. IASB, 2011, p. 46.
IFRS 3 (R), Business Combinations. IASB, 2008, p. 50.
Larson K.D. and N.J. Gonedes (1968). Business Combinations: An Exchange Ratio
Determination Model, The Accounting Review, Vol. 44, No. 4 (Oct., 1969), pp. 720-728.
Lemeshko O. (2013). Comparison of mergers and acquisitions in the view of IAS/IFRS
and US GAAP. Master thesis. Available at: <http://is.muni.cz/th/370756/esf_m/>.
Meulen S. et al. (2007). Attribute differences between U.S. GAAP and IFRS earnings: An
exploratory study, The International Journal of Accounting, No. 42, p. 123-142.
Rezaee Z. et al. (2010). Convergence in accounting standards: Insights from
academicians and practitioners, Incorporating Advances in International Accounting, No.
26, p. 142-154.
Zeff S. (2009). Some obstacles to global financial reporting comparability and
convergence at a high level of quality, The British Accounting Review, No. 39, p. 290-
302.
196
Investigation of factors affecting short interest
in the Australian Stock Exchange
Dagmar  Linnertová,  Oleg  Deev, Martin Cupal
Masaryk University
Faculty of Economics and Administrations, Department of Finance
Lipová  41a,  602  00  Brno,  Czech  Republic
E-mail: Dagmar.Linnertova@mail.muni.cz
Abstract: ETFs (Exchange Traded Funds) represent financial innovation that combines
characteristics of mutual funds with advantages provided by continual exchange trading.
ETFs shares can be also used for specific operations such as short selling or trading on
margin. Because of minimal regulation the short sell interest (SI) with ETFs is several
times higher than short sell interest with common stocks. There is a lack of research
analyses the factors affecting the level of short sell interest with ETFs, although, we could
find researches in that area focusing on common stocks. In that paper we investigate the
factors that influence the level of short sell interest with ETFs in Australian Stock
Exchange during the period of 2000 – the end of 2012 and if these factors change in
particulars sub-periods represented before, during and after the financial crisis periods.
Keywords: short interest, Australian Stock Exchange, short sell, ETFs, determinants
JEL codes: G10, G12, G14
1 Introduction
The ETFs represent the most active category in the family of new products called ETP or
Exchange Traded Products. These products in the simple words combine advantages of
continuous exchange trading with benefits that offer diversified underlying portfolio. The
first ETF was introduces by the company State Street in 1993 and was called Spider or
SPRD (underlying of this ETF is S&P 500 index). Nowadays ETFs are traded in all
important exchanges in the Europe, Asia, Middle East & Africa and North and Latin
America. State Street Company became together with iShares and Vanguard leaders in
the world ETFs market.
ETFs represent open-end mutual funds that are listed in particular exchange and
continuously trading during the day such as stocks. Although, the first ETFs represented
particular stock index during the years they have reacted to changing market conditions.
In the mid of the 2000 commodity boom emerged in markets and thus, first ETFs mimic
commodities (gold, oil, silver or commodity indexes) were introduced. ETFs are also
available for specific market operations like buying on margin or short sale. Short selling
of ETFs is a common way of hedging or speculation in the case of pessimistic
expectations. Because of lack or ETFs short selling regulation the short interest of ETFs is
more than 10 times higher than for individual stocks. (Madura and Ngo, 2008)
As previous mentioned the shorting of ETFs is popular, but there is a lack of researches
investigate this activity. The purpose of this paper is to identify the factors or
characteristics that are attractive for short sellers and if these characteristics change
during the time. The sample period is from 1st
January 2000 to 31st
December 2012 with
the sub-periodS: 2000-2006 such as pre-crisis period, 2007-2009 for crisis and 2010-
2012 such as post-crisis period. We empirically investigate these determinants in
Australian Stock Exchange because represents one of ten exchanges in the world that
makes information about short sale resp. short interest publicly available.
Although, there existed studies investigate why some stocks are more attractive for short
sale than others, there is a lack of this investigation for ETFs. It is also important to note
that ETFs must be examined in different manners because some determinants are
different. Some of them are usefull in investigation of stocks but are not available or
197
usefull for ETFs e.g. fundamentals-to-price ratios resp. company fundamentals and on
the other hand replication strategy or asset class focus must be taken into account.
Table 1 Determinants
Variable Name Description
SIRit
Short interest
ratio
The total number of shares an investor has sold short
divided by the average monthly trading volume for a
specific period
ASSit Assets in funds Asset value of the ETF per month
Priceit ETF close price Closing price
Voluit Trading volume Mean trading volume of the ETF per month
MarCapit
Market
capitalization of
ETF
Mean market capitalization of ETF per month
Shares_outit
Shares
outstanding
Total number of shares outstanding of ETF per month
Volait Volatility
The 90-day price volatility equals the annualized
standard deviation of the relative price change for the
90 most recent trading days closing price, expressed
as percentage
Betait Raw Beta of ETF
Volatility measured of the percentage price change of
the ETF given a one percentage change in a
representative index
Returnit
ETF rate of
return
Recent performance of ETF measured as a daily
return on ETF
Price-1Dit
Previous day ETF
close price
Previous day closing price of ETF
Domicilei
Country of
domicile
(Dummy
variable)
1 if Australia, 0 if North American
Replicationi
Replication
strategy of ETF
(Dummy
variable)
- rep_deri: 1 for derivative, 0 for others
- rep_fulli: 1 for full, 0 for other
- rep_opti: 1 for optimized, 0 for others
derivative: using derivatives
full: physically backed - full sample
optional: physically backed - partial sample
Creationi
Creation-
redemption
process of ETF
(Dummy
variable)
- inkindi: 1 for in-kind, 0 for others
- cashi: 1 for cash, 0 for others
- combinationi: 1 for in-kind/ cash, 0 for others
Fund_Asset_Focusi
Asset focus of
ETF
- equityi: 1 for equity, 0 for others
- debti: 1 for debt, 0 for others
- asset_allocationi: 1 for allocation, 0 for others
Fund_Cap_Focusi
Capitalization
focus of ETF
- largei: 1 for large, 0 for others
- midi: 1 for mid, 0 for others
- multii: 1 for multi, 0 for others
- smalli: 1 for small, 0 for others
Dechow et al. (2001) suggest that stocks with low fundamentals-to-price characteristics
are more attractive for short sellers. Angel et al. (2003) look at short selling on Nasdaq
and introduce 6 findings. For our research three of them are interesting: (1) short selling
is more common among stocks with high returns than stocks with weaker performance,
(2) actively traded stocks experience more short sales than stocks of limited trading
volume and (3) short selling varies directly with shares price volatility. Kot (2007) tests
explanations of short-sale trading in NYSE and Nasdaq stocks during the period 1988 –
2002 with results that short-selling activity is positively related to arbitrage opportunities
and hedging demand, and negatively related to previous short-term return. Further,
option listing is the most dominant variable in explaining the short-selling level and the
198
short-selling level is more positively related to convertible debt and option listing during
the bubble period because these was more space for arbitrage opportunity. McKenzie and
Henry (2012) focus on determinants of short selling during the trading day in Hong Kong
stock market and find out those dividend payments, company fundamentals, option
trading, interest rate spread and past return are significant determinants of short selling.
But not all of mentioned determinants can be taken into account in investigation of these
for ETFs. For the first time short sale with ETFs examine Madura and Ngo (2008) and set
determinants of short sale for ETFs in AMEX in the period 2001 - 2004. They determine
that sector-based ETFs have large short interest level and international ETFs have an
unusually small short interest level. This level is also larger for ETFs with higher trading
volume and lower market capitalization.
2 Methodology and Data
Using Bloomberg ETPs screen by Exchange, we choose Australian Securities Exchange
and limited all available ETPs only in ETFs. As of January 2013 there are 69 ETFs fulfilling
the imposed criteria. We chose the base our analysis on monthly observations from
January 2000 to December 2012 thus we collected 10.764 observations.
Model Specification
The investigation of determinants of ETFs short sale interest (activity) is based on the
multivariate model:
𝑺𝑰𝑹𝒊𝒕 = 𝜶 + 𝜷 𝟏 𝑨𝒔𝒔𝒊𝒕 + 𝜷 𝟐 𝑷𝒓𝒊𝒄𝒆𝒊𝒕 + 𝜷 𝟑 𝑽𝒐𝒍𝒖𝒊𝒕 + 𝜷 𝟒 𝑴𝒂𝒓𝑪𝒂𝒑𝒊𝒕 + 𝜷 𝟓 𝑺𝒉𝒂𝒓𝒆𝒔_𝑶𝒖𝒕𝒊𝒕 + 𝜷 𝟔 𝑽𝒐𝒍𝒂𝒊𝒕 +
𝜷 𝟕 𝑩𝒆𝒕𝒂𝒊𝒕 + 𝜷 𝟖 𝑹𝒆𝒕𝒖𝒓𝒏𝒊𝒕 + 𝜷 𝟗 𝑷𝒓𝒊𝒄𝒆 − 𝟏𝑫𝒊𝒕 + 𝜷 𝟏𝟎 𝑫𝒐𝒎𝒊𝒄𝒊𝒍𝒆𝒊 + 𝜷 𝟏𝟏 𝒓𝒆𝒑_𝒅𝒆𝒓𝒊 +
𝜷 𝟏𝟐 𝒓𝒆𝒑_𝒇𝒖𝒍𝒍𝒊+𝜷 𝟏𝟑 𝒓𝒆𝒑_𝒐𝒑𝒕𝒊 + 𝜷 𝟏𝟒 𝒊𝒏𝒌𝒊𝒏𝒅𝒊 + 𝜷 𝟏𝟓 𝒄 𝒔𝒉 + 𝜷 𝟏𝟔 𝒄𝒐𝒎𝒃𝒊𝒏𝒂𝒕𝒊𝒐𝒏 + 𝜷 𝟏𝟕 𝒆𝒒𝒖𝒊𝒕𝒚𝒊+𝜷 𝟏𝟖 𝒅𝒆𝒃𝒕𝒊 +
𝜷 𝟏𝟗 𝒂𝒔𝒔𝒆𝒕_𝒂𝒍𝒍𝒐𝒄𝒂𝒕𝒊𝒐𝒏𝒊 + 𝜷 𝟐𝟎 𝒍𝒂𝒓𝒈𝒆𝒊 + 𝜷 𝟐𝟏 𝒎𝒊𝒅𝒊 + 𝜷 𝟐𝟐 𝒎𝒖𝒍𝒕𝒊𝒊 + 𝜷 𝟐𝟑 𝒔𝒎𝒂𝒍𝒍𝒊
Investigation is applied for the whole period for the first and then for three sub-periods
corresponding with pre-, during and post-crisis periods. The models are tested using
OLS regression and Breusch-Pagan test for heteroscedasticty.
Results of investigation short interest determinants are summarized in the Table 2. The
main factors influencing short sale interest in the full sample represent variables: shares
outstanding, inkind or cash (variable about create/redeem process), debt (variable
concerns about ETFs asset focus) large and small (variable concerns about ETF
capitalization focus). The variable shares outstanding is positive and significant for the
full sample, short seller choose ETFs with high level of shares outstanding, in this case
short sellers do not deal with recall risk with ETFs or problem of short squeeze. We
founded out that that creation-redeem process is also significant. ETFs with in-kind
process are positively related to short interest and ETFs with cash creation redeem
process are negatively related to this dependent variable. The variable debt represents
ETFs focused on debt instruments is positive and significant. The variable concerns fund
capitalization focus is significant and negative for large and small capitalization. The
coefficient of determination is high (89,57 % and 88.92 %, respectively).
In the sub-periods 2000-2006 and 2007-2009 are missing some results because of
singularities. The coefficient beta that measures sensitivity of ETFs in a particular market
index is significant in both sub-periods with inverse effect. The coefficient of
determination is significant for both sub-periods (97,79% resp. 93,32% and 98,43%
resp. 97,97%).
For the period 2010 – 2012 (post-crisis period) we get results similar to the full sample
period analysis. Number of shares outstanding is significant and positive determinant.
Similar results for creation/redeem process where in-kind created ETFs are positive and
significantly related to short interest level compare to cash create/redeem process that is
negative and significant. According to ETFs asset focus debt focus ETFs are significant
and according to ETFs market capitalization focus large ETFs are positively related and
significant resp. small ETFs are negatively related and significant to short interest level.
199
Table 2 Results
Variable Full Sample 2000 – 2006 2007 – 2009 2010 - 2013
Constant
6.719e+03
(7.850e+03)
3.580e+04
(2.081e+03)
3.885e+04
(1.170e+02)
4.614e+03
(8.759e+03)
ASSit
4.048e+01
(5.540e+01)
-3.999e+01
(2.910e+01)
-3.427e+00
(9.720e+00)
7.280e+01
(6.139e+01)
Priceit
9.118e+01
(5.198e+02)
-6.307e+01*
(3.490e+02)
7.739e+00
(1.127e+02)
7.592e+02
(6.780e+02)
Voluit
-7.897e-05
(4.065e-04)
-3.141e-05
(1.872e-05)
-8.6008-07
(1.281e-05)
-1.747e-04
(5.331e-04)
MarCapit
-6.497e+01
(5.534e+01)
3.425e+01
(2.259e+02)
-4.161e-01
(9.908e+00)
-1.079e+02*
(6.152e+01)
Shares_outit
3.058e+02***
(5.671e+01)
6.393e+01
(2.559e+02)
5.163e+02***
(3.769e+00)
8.600e+02***
(1.123e+02)
Volait
6.407e+01
(4.441e+01)
-4.961e+00
(1.048e+01)
2.200e+00
(1.861e+00)
3.058e+01
(8.108e+01)
Betait
-1,406e+03
(1.467e+03)
-1.092e+03**
(4.027e+02)
4.684e+02***
(6.718e+01)
-1.068e+03
(1.815e+03)
Returnit
-6.041e+03
(1.409e+04)
1.328e+04*
(6.744e+03)
3.308e+02
(2.037e+02)
-2.188e+04
(2.290e+04)
Price-1Dit
2,106e+01
(5.198e+02)
8.333e+02**
(4.059e+02)
6.908e+00
(1.181e+01)
-2.657e+02
(6.705e+02)
Domicilei - - - -
Replicationi
rep_deri
rep_fulli
rep_opti
-
-3.559e+03
(7.567e+03)
-3.499e+03
(7.865e+03)
-
-
-
-
-
-
-
-8.502e+03
(8.347e+03)
-4.280e+03
(8.637e+03)
Creationi
inkindi
cashi
combinationi
3.204e+04***
(1.784e+03)
-6.304e+03**
(3.084e03)
-
-
-
-
-
-
-
1.829e+04***
(3.002e+03)
-7.433e+03**
(3.396e+03)
-
Fund_Asset_Focusi
equityi
debti
asset_allocationi
-
4,073e+04***
(3.921e+03)
-3,544e+04
(5.072e+03)
-
-
-
-
-
-
-
1.970e+04***
(4.118e+03)
-8.501e+03
(5.795e+03)
Fund_Cap_Focusi
largei
midi
multii
smalli
-2,297e+04***
(2.174e+03)
-
-
-4,258e+03**
(2.088e+03)
-
-
-
-
-
-
-
-
2.120e+04***
(2.406e+03)
-
-
-5.450e+03**
(2.303e+03)
R-squared
Adjusted R-squared
0.8957
0.8892
0.9479
0.9332
0.9843
0.9797
0.8909
0.8812
Note: *, **, and *** indicates significance of 90%, 95% and 99% level, respectively. Standard
errors in parentheses
200
3 Conclusions
In the paper we examined the short sale with ETFs and the factors that influence investor
activity with these products. Our analysis investigated main determinant of short interest
resp. short sell activity in the Australian Stock Exchange in the period of the 2000 –
20012 and in particular sub-periods correspond with (pre-crisis, crisis and post-crisis
development). We introduced basic determinants that influence the level of short
interest.
Acknowledgments
MUNI/A/0753/2012 - Evropské  finanční  systémy  2013,  Department  of  Finance,  Faculty  of  
Economics and Administration, Masaryk University in 2013.
References
Angel, J. et al. (2003). A close look at short selling on NASDAQ. Financial Analysis
Journal, vol. 59, pp. 66 – 74.
Dechow, P. M. et al. (2001). Short-sellers, fundamental analysis, and stock returns.
Journal of Financial Economics, vol. 61, pp. 77 – 106.
Kot, H., W. (2007). What Determines the Level of Short-Selling Activity? Financial
Management, vol. 36, pp. 123 – 141.
Madura, J., Ngo, T. (2008). Short interest in exchange-traded funds, Financial Markets
and Portfolio Management, vol. 2, pp. 381 – 402.
McKenzie, M., Henry, O. T. (2012). The determinants of short selling: evidence from the
Hong Kong equity market. Accounting and Finance, vol. 52, pp. 183 – 216.
201
Public Contracts in Czech Teaching Hospitals - efficiency issues
Alena  Maaytová, Stanislav Klazar
University of Economics Prague
Department of Public Finance
W. Churchill sq. 4, 13000, Prague 3
E-mail: maaytova@vse.cz
E-mail: klazar@vse.cz
Abstract: The average overall expenses spent on health-care services in the Czech
Republic is 7.5% of the GDP. The estimated degree of inefficiency of these expenses is
the serious issue in context of public finance. The goal of the text was to verify whether
the efficiency can be increased in the area of public procurements in teaching hospitals
by using the specific kind of public procurement procedures. Data revealed that the more
transparent and more open procedures enable the higher reduction of final prices. It
implicates some interesting economic and political consequences.
Keywords: teaching hospitals, efficiency, public expenditures, public procurement
JEL codes: I18, H57
1 Introduction
Health-care systems are currently trying to resolve two basic problems: fluctuations in
revenue and the continuous growth of expenses. The increasing expenses are caused by
medical procedures, new medicines, the ageing of the population, increasing
expectations on the part of the general public and also the ineffective spending of
resources. The degree of inefficiency of the expenses spent on health-care services in the
Czech Republic is estimated at 20% (Czech Ministry of Health, 2009). The average
overall expenses spent on health-care services in the Czech Republic amounts to 7.5% of
the GDP (it is slightly lower than the EU average), while the share of public expenses on
health-care services in the Czech Republic is higher in comparison with EU states and
amounts to 84% of the total expenses.
The goal of the text is to verify whether the efficiency can be increased in the area of
public procurement in teaching hospitals. The first step leading to an increase of
efficiency of the use of public resources is the identification of the factors that influence
the effectiveness of public contracts represents. If a certain type of public contract will
appear to be more effective and if there will be no other obstacles to its use on a more
massive scale, then a practical recommendation can be expressed in the area of the
structure of public contracts. Theory and the existing empirical research favour this
conclusion.
2 Public contracts in Hospitals
The volume of public resources spent in health care in the Czech Republic arouses the
interest of many economic subjects to participate in the use of these resources. In theory
this means an increase in competition in the given area and the opportunity to increase
efficiency. With regard to the aforementioned degree of inefficiency of the resources
spent in health care it is evident that for reasons specific to health-care services (Arrow,
2001; Stiglitz, 1997; Williams, 1997) and due to the unsuitably configured control
mechanisms, not even the considerable number of subjects in health-care services has a
positive effect on the growth of competition and thus the growth of efficiency.
One of the sources of the ineffective use of public resources in health care in the Czech
Republic is the public contracts implemented in hospitals. Newhouse (1970) for example,
deals with the possibilities of the effective management of hospitals, stating that the
management of a hospital is considered to be effective if it achieves the maximum
combination of quality and quantity with the fewest possible expenses. According to
Dlouhý   and   Strnad   (1999) it is possible to evaluate the effectiveness of hospitals
202
according to results and outputs. The results are monitored using the QALY, DALY and
ADL indicators while output can be evaluated according to performance indicators and
according to financial indicators (financial analyses). A standard financial analysis is also
used for evaluating hospitals, though the economic result of a hospital depends on the
effect of several factors that the hospital is not able to influence (the structure of the
sickness rates, i.e. the number and types of diagnoses, the structure of patients, the
finances allocated from health insurance, etc.). Hospitals can, however, influence the
public procurement method.
Attention has not yet been given to the theoretical reflection of public contracts in health
care in either Czech or foreign trade journals. There may be a number of reasons for
this:
1. The issue of public contracts in health-care services is part of the problem of
public contracts.
2. The volume of funds spent on public contracts in hospital is low compared to the
overall expenses in health-care services. The degree of inefficiency and the
related corruption in the area of public contracts in health-care services is small
due to the functioning of control mechanisms, legislation, etc. This reason does
not rule out the existence of corruption in health care services, though it does not
put it in direct relation to public contracts. The problem of corruption in health
care services is generally a worldwide problem, though it is connected more with
the preferential exploitation of health care in selected health-care facilities or with
drug policies (Vian 2008; Lewis 2006; Tanzi 2002).
The importance of hospitals in health-care services can be seen from their specific
function and from the fact that they consume a considerable part of the overall volume of
finances earmarked for health-care services. See table 1 in for more details.
Table 1 Spending On Health-Care Services (Billions Of CZK)
Year
Total
Spending
Public
Spending
Spending on Hospitals
from Total Spending on
Health-Care in %
2006 226.810 197.027 49.9
2007 241.935 206.563 50.9
2008 264.520 218.719 51.2
2009 291.640 243.692 45.3
2010 289.035 243.281 47.7
2011 288.572 242.410 47.3
Source: Information system of public procurements (2013)
Hospitals are usually defined as institutions that are authorised to provide a certain type
of health care and, that have a certain number of beds and an organised team of doctors
(Dlouhý  and  Strnad,  1999;;  Mc  Kee  and  Heally,  2003).  Thanks  to  the  absence  of  a  profit  
motive (in a certain part of health-care services) not enough pressure to lower spending
while increasing the quality of the services provided is placed on the management of the
hospitals. Hospitals have special needs for equipment, both technical and operational,
and staff. Teaching hospitals represent a specific important part of the health-care
system for reasons of medical research and for reasons of providing highly-specialised
health care and the high expenses related to this. There were 11 teaching hospitals in
the Czech Republic up to the year 2011. In 2012 two hospitals lost the status of teaching
hospital with specialised care even though they have clinics of exceptional standing. The
merging of two teaching hospitals in Brno is planned for 2013, which would lead to a
decrease in the number of teaching hospitals to eight. For overview see Table 2.
Health-care   facilities   in   the   Czech   Republic   usually   have   the   character   of   “contracting  
authority”  which  means  that  when  purchasing  goods,  services  and  fixed  assets they have
to adhere to the Act on Public contracts. Non-adherence to this Act, combined with the
insufficient use of the contracting method, usually means that the price for the public
203
contract will be higher (Pavel, 2007). Increasing the transparency of the process for
selecting subcontractors for public contracts can contribute to lowering the public
expenditures.
Table 2 Teaching Hospitals (TH) – Overview
Year
Total
Spending
on
Hospitals
(Billions
of CZK)
Spending on
Teaching
Hospitals
(Billions of CZK)
Share of
Spending on
TH (%)
Number of
Beds in
Teaching
Hospitals
Share of
Beds in
TH (%)
2006 113.18 39.46 34.86 16231 25.29
2007 123.14 42.74 34.71 16167 25.40
2008 135.43 46.09 34.03 16129 25.50
2009 132.11 50.33 38.10 15868 25.22
2010 138.74 52.08 37.54 15725 25.27
2011 136.49 52.79 38.68 15505 25.70
Source: Ekonomické  informace  ve  zdravotnictví  (2011)
3 Formulation of hypotheses
The objective of the analyses of public contracts realised in Czech teaching hospitals in
the years from 2006 to 2011 is to determine the factors that influence the size of the
final (explained variable) prices for the public contracts.
The  amount  of  the  resulting  price  is  partly  influenced  “ex  ante”  (e.g.  by  the  selection  of  
qualification criteria limiting entry to the tender for a greater number of subcontractors
or  by  choosing  the  type  of  proceedings)  and  partly  “ex  post”  (the  price  is  influenced  by  
supplements   to   the   concluded   contracts)   (Pavel   and   Sičáková-Beblavá,   2012).   Due   to  
accessibility  of  appropriate  data  the  only  the  factors  influencing  the  price  “ex  ante”  are  
the   subject   of   this   text’s   investigation.   The   following   hypotheses   were   inferred   on   the
basis of the aforementioned considerations:
1. The tender price realised in Czech hospitals depends on the number of contractors
applying for the public contract (and the type of proceedings).
2. The tender price in the expected price for the public contracts realised is influenced by
the type of the contract.
Under  some  reasonable  conditions  (Pavel  and  Sičáková-Beblavá,  2012)  the  ratio  of  final  
(after a negotiation) price to initial (before a negotiation) prices (r = P1/P0) of public
contracts is a good measure of efficiency of public contracts. There is some historical
evidence that high negotiation power on the supply side of markets (or lower market
competition level among suppliers) induces the higher final price (p1) and lower
efficiency   of   contract   (Ochrana   and   Maaytová,   2012).   On   the   other   hand   the   overall
process transparency can lead to lower final price (Varian, 1992).
Type of public procurement which is depending on the procedure applied is a good proxy
for the relevant market characteristics. Types of public procurement depending on the
procedure applied are classified as follows (EU, 2012):
a) Open procedure contracts (dummy OPEN in analysis later) - The contracting authority
invites an unrestricted number of applicants to submit tenders for the public contract. It
is supposed to be most transparent with high level of competition.
b) Some kind of restricted procedures. It consists of announced contracts, negotiation
without publication contracts, etc.). The contracting authority directly invites one or more
suppliers to negotiations. It is supposed to be least transparent, with the low level of
competition.
According to implication derived from previous literature it is suitable to divide the
contracts depending on what is to be supplied. The most common contracts are the
204
public contracts for supplies, where the items are demanded. The public contracts for
building works are those, where the performance of building work or the construction of
buildings is demanded. The residual category is labelled as contracts for services and is
covering everything else.
4 Description of data and basic statistical analysis
According to the Czech law all public contracts have to be recorded through the www
portal to the database. The individual data are then (after some minor manipulation)
ready for statistical or other analysis. The overview of analysed data is in Table 3. After
initial examination of data we decided to exclude observations which seemed to be very
untrustworthy. We must admit the database is not in a very good condition. It seems
there is nothing like preliminary data check (verification) and we find a considerable
amount of incomplete observations or observations with logically inconsistent values of
tracked variables. From the whole number of 2760 observations for the period 2006 –
2011 only 1641 observations were fully described. After the pilot verification of final (P1)
and initial prices (P0) we decided exclude observations with the index P1/P0 outside the
interval (0,3-3). Very high or on the opposite side very low value of index P1/P0 are
questionable. The final dataset consists of 1514 observations, see Table 3.
Table 3 Basic Overview of Dataset (Trustworthy Data)
Subject of
tender
Supplies Services Buildings Total
No. of Obs. 1231 148 135 1514
% of total 81 10 9 100
Sum
in billions CZK
10.964 2.628 9.809 23.4
% of total 47 11 42 100
P1/P0 in % 90 92 99 91
Note: Lower P1/P0 indicates the success in price reduction during the negotiation. (%) means the
relation  to  the  total.  “Sum”  means  the  total  value  of  contracts  in  subgroup.
Source: Information system of public procurements (2013)
Data revealed that the most often and most valuable contracts are supply contracts,
followed by building contracts. Concerning the efficiency the building contracts are least
efficient (measured by P1/P1). The probable reason is that the technical quality of
buildings as subjects of tenders is strictly regulated by special law so the initial price if
almost fixed from the beginning of negotiation. On the opposite the quality of supplies
and services is more variable and the characteristics of demanded items are vaguer
hence there is more place for price changes.
5 PivotTable analysis results and discussion
In  accordance  with  the  theory  the  variable  “number  of  offers  (applicants)”  (NofOF)  which  
is monitored in dataset should be the relevant factor. It reflects the openness of market
and partly also the transparency of procedures.
We can see (Table 4) that observations (particular public tender) with higher number of
offers are characterized by the lower P1/P0, hence higher number of offers the higher
level   of   efficiency,   c.   p.   Noticeable   increase   of   efficiency   is   for   “services”,   from  
P1/P0=103   %   to   92%,   which   is   average   P1/P0   in   the   subgroup   “services”.   Standard  
deviation is used to illustrate the variance of P1/P0 around its average. Note that the
variable P1/P0 is with percentage as unit, hence the standard deviation have percentage
points as unit too. For example – the  average  dispersion  of  P1/P0  for  “services”  with  only  
1  “offer”  is  21  percentage  points  around the average of P1/P0 which is 103 percents. The
level of variation is high so it is clear there are some other relevant variables influencing
the key efficiency.
205
Table 4 Openness Of Legal Procedure As A Factor Of Final Prices
Oppennes
and subject
of tender
No. of
Obs.
Obs.
(%*)
Sum
millions
of CZK
Sum
(%*)
P1/P0
(%)
St. Dev.
(%)
NONOPEN 337 100 4284 100 99 17
Supplies 165 49 1576 37 96 12
Services 79 23 647 15 100 24
Buildings 93 28 2061 48 102 16
OPEN 1177 100 19117 100 89 28
Supplies 1066 91 9389 49 89 28
Services 69 6 1981 10 84 28
Buildings 42 4 7747 41 94 27
Note:  (%*)  measure  relation  to  “subtotal”,  St.  Dev.  means  standard  deviation.
Source: Information system of public procurements (2013).
To illustrate the influence of legal procedure see Table 4.  There  are  contracts  with  “open”  
procedures where the contracting authority invites an unrestricted number of applicants
so it is supposed to be most transparent with high level of competition between
suppliers. On the other hand there are also contracts with procedures which impose
some restriction (EU, 2012).
We can see that (again in accordance with theory) the open procedures are more
efficient   (P1/P0=89%   in   comparison   with   99%   for   “non-open”   procedures),   so   the  
negotiation power of hospital management is significant and managers are able to
negotiate lower final price. It seems to be promising for future to manage the public
procurements in hospitals in a more efficient way and to share noticeable amount of
public finance.
The dispersion of results is unfortunately high (due to omitting of some other relevant
variables) so in the next text we employ more sophisticated methods to control for some
other variables.
Based on the analysis above we can conclude that the open public contracts are more
effective. However, this result must be taken very carefully – there are the logical
assumptions that this kind of procedures is connected with higher level of administrative
costs (Pavel, 2013) and that this production is usually characterised by only a limited
number of suppliers.
6 Conclusions
The average overall expenses spent on health-care services in the Czech Republic is
7.5% of the GDP and in connection with their high degree of inefficiency can constitute a
serious danger to public finance. For financially sustainable health-care services it is
necessary to focus on the problem of the efficiency of the financial resources that are
spent and on the partial elimination of the reasons for the inefficiency.
We find out that more transparent and open procedures (measured by the number of
contractors) enable the higher reduction of final prices. The explanatory variables
(number of offers) as a measure of transparency and openness of the whole process are
statistically significant for majority of observations. The negotiation power of hospital
management is significant and managers are able to negotiate lower final price in case
the open and more transparent procedure is applied.
It implicates some interesting economic and political consequences. The support or
motivation for usage of some kind of procedures can lead to significant decrease of public
expenditure.
The expected value of contract was revealed as another significant factor. More
expensive contracts are usually contracts, where the negotiation does not lead to the
lower final price.
206
Acknowledgments
This text was prepared under the institutional support of IP 100040 project. The both
authors wish to warmly acknowledge the highly relevant comments from the blind
referee chosen by the conference committee.
References
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Arrow, K. J. (2001). Uncertainty and the Welfare Economics of Medical Care (American
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DOI: 10.1215/03616878-26-5-851
Czech Ministry of Health. (2009). Efficiency of Czech health system. Study - Association
of Czech and Moravian Hospitals. Retrieved from http://www.acmn.cz/file.php?
nid=1140&oid=1209322
Dlouhý,  M.,  &  Strnad, L. (1999). Hospitals, quality, efficiency and finance. IGA and Czech
Ministry of Health Study.
Ekonomické   informace   ve   zdravotnictví.   (2011).   Retrieved   from   http://www.uzis.cz/
publikace/ekonomicke-informace-ve-zdravotnictvi-2011
EU. (2012). Public contracts. Retrieved from http://europa.eu/youreurope/business/
profiting-from-eu-market/benefiting-from-public-contracts/czech-republic/index_en.htm
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Czech Republic. Retrieved from http www.isvz.cz/isvz/Podpora/ISVZ.aspx
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Ochrana,   F.,   &   Maaytová,   A.   (2012).   Východiska   pro   vytváření   transparentního   a  
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and Practical Aspects of Public Finance Conference. Retrieved from http://kvf.vse.cz/wp-
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na Slovensku. Politická  ekonomie, 5.
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systému  veřejného  zdravotnictví  v  České  republice. Praha : Transparency international Česká
 republika.  
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Applied Econometrics and International Development, 2(1), 110–115.
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Vian, T. (2008). Review of corruption in the health sector: theory, methods and
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Edgar Publishing.
207
Contemporary crisis and strategic management
of the private banking sector in the Czech Republic
Jan  Matoušek1
,  Miroslav  Mareš2
1
Czech Banking Association
Vodičkova  30,  110  00,  Praha 1
E-mail: matousek@czech-ba.cz
2
Masaryk University
Faculty of Social Studies, Department of Political Science
Joštova  10,  602  00  Brno  
E-mail: email@email.cz
Abstract: This paper analyses reaction of the Czech private banking sector to the
financial crisis. The change of management strategies towards external as well as
internal financial market threats is described. Specific attention is paid to the role of the
Czech Banking Association, including its communication strategies with individual private
banks, with the governmental sector and with foreign partners. The impact of the CBA on
the stability of the finance market in the Czech Republic is assessed. The authors use the
approach of the crisis management, comparison on the time axis and explanation of
personal experience (one of the authors is Deputy Managing Director of the CBA). The
authors come to the preliminary conclusion that the reaction of the Czech private
banking sector was and is adequate, well timed and it leads to successful managing of
the crisis.
Keywords: financial crisis, banking
JEL codes: G32, H12, H63
1 Introduction
The world financial crisis, which started in 2007, affected many sectors of the world
economy and politics as well as national political sectors. It started huge financial political
countermeasures. One of the most important actors of the anti-crisis policy is the private
banking sector. In some countries, including the Czech Republic, it was able to eliminate
the serious negative impact of the crisis. The reaction of the private banking sector in the
CR is the object of the analysis of the paper. The role of  the  Czech  National  Bank  (ČNB)  
as well as of the Czech Banking Association (CBA) is included in this analysis.
2 Methodology and Data
This paper uses approaches of crisis management in combination with the approach of an
analysis of financial policy. The ideal type is compared with the real type in the analysis
of the process of crisis management in the private banking sector. Risk assessment
method and key assumption check technique are used in the context of identifying
important elements of the bank policy development (U.S. Government 2009: 7-8). The
descriptive method serves for characterizing the role of the Czech National Bank and the
Czech Banking Association. Data for this analysis were generated from internal sources of
the  CNB  and  the  CBA  (Bušek and Matoušek, 2013).
Strategic and crisis management and private banking sector
The basic goal of the strategic management in private banking sector is to outline clear
goals  (Kruliš  2009)  of  banks  in  relation  to  their  specific  role  in  the  financial  systems and
economy. In this context strategic management can be understood also as prevention of
a crisis. However, if the crisis comes, the crisis management must be used. According to
Antušák  five  main  functions  of  the  crisis  management  can  be  identified. They are divided
in two levels.
The world financial crisis in 2007 started outside the territory of the Czech Republic,
however, due to interconnection of world economy and financial markets, this fact was
208
reflected also in the policy and behavior of the Czech governmental as well as private
actors.
Table 1 Antušák’s levels and functions of crisis management
Levels Functions
Risk management (the goal is to
minimalize the creation of a crisis)
-------
Prevention
Correction
Counter-crisis interventions
Reduction
Recovery
Managing crisis (the goal is to reduce
damage and to minimalize duration of
the crisis)
Source:  Antušák  (2009)
From the point of view of crisis management several important assumptions and factors
can be identified within the Czech private banking sector: the development of
capitalization of the private banking sector (mostly in relation to capital adequacy), liquid
assets, bank deposits and bank loans.
2 Evaluation of important assumptions and factors within the Czech
private banking sector
Key assumptions and factors are described in the set of graphs in this chapter of this
paper. Their comprehensive evaluation is elaborated in this chapter, including the
assessment of the risk factors of the development.
Figure 2 Development of the capitalizaton of the banking sector in the Czech Republic
Source: Czech National  Bank,  quoted  from  Bušek  and Matoušek  (2013)
Vývoj  kapitalizace  bankovního  sektoru  ČR  
8,00
9,00
10,00
11,00
12,00
13,00
14,00
15,00
16,00
17,00
31.12.2008 31.12.2009 31.12.2010 31.12.2011 31.12.2012
%
Kapitálová  přiměřenost Tier  1  Kapitálová  přiměřenost
209
Figure 3 Capital adequacy – comparison 2008 and 2011 in selected European countries
Source: European Central Bank, quoted from Bušek  and  Matoušek  (2013)
Figure 4 Development of highly liquid assets to total assets
Source: Czech National Bank, quoted from Bušek  and  Matoušek  (2013)
Austria
Belgium
Cyprus
CzechRepublic
Germany
Spain
France
UnitedKingdom
Greece*
Hungary
Ireland
Italy
Netherlands
Poland
Slovenia
Slovakia
20080
2
4
6
8
10
12
14
16
18
Tier  1  Kapitálová  přiměřenost  -­  srovnání  2008  a  2011  ve  
vybraných  evropských  zemích  (zdroj:  ECB)
2008 2011
Podíl  rychle  likvidních  aktiv  k  celkovým  aktivům
0,00
5,00
10,00
15,00
20,00
25,00
30,00
35,00
31.12.2008 31.12.2009 31.12.2010 31.12.2011 31.12.2012
%
210
Figure 5 Development of deposits in banks 2007-2013
Source: Czech National Bank, quoted from Bušek  and  Matoušek  (2013)
Figure 6 Development of bank loans in years 2007-2013
Source: Bušek  and  Matoušek  (2013)
Figure 7 The NPL ratio to total loans of the segment
Source:  Czech  National  Bank,  quoted  from  Bušek  and  Matoušek  (2013)
Vývoj  klientských  vkladů  v  bankách  2007-­2013
400
900
1400
1900
2400
2900
I.07
V.07
IX.07
I.08
V.08
IX.08
I.09
V.09
IX.09
I.10
V.10
IX.10
I.11
V.11
IX.11
I.12
V.12
IX.12
I.13
mld.  Kč
Vklady celkem Vklady  nefinančních  podniků Vklady  domácností
Vývoj  bankovních  úvěrů  2007-­2013
400
900
1400
1900
2400
I.07
V.07
IX.07
I.08
V.08
IX.08
I.09
V.09
IX.09
I.10
V.10
IX.10
I.11
V.11
IX.11
I.12
V.12
IX.12
I.13
mld.  Kč
Úvěry  celkem Úvěry  nefinančním  podnikům Úvěry  domácnostem
Podíl  úvěrů  v  selhání  na  celkových  úvěrech  daného  segmentu
0
1
2
3
4
5
6
7
8
9
10
IV.08
VII.08
X.08
I.09
IV.09
VII.09
X.09
I.10
IV.10
VII.10
X.10
I.11
IV.11
VII.11
X.11
I.12
IV.12
VII.12
X.12
I.13
%
Domácnostem Nefinančním  podnikům
211
In the set of the above presented figures the following main findings can be identified:
1. The development of capital adequacy can be evaluated positively in national as well
as in comparative international framework and this fact strengthens the
preparedness of the Czech private banking sector towards external negative
excesses of the development;
2. The impact of liquid assets on stability is strong (such assets are intermediately
available and this flexibility makes the resistance against crisis development easier).
3. The rate of deposits and loans is growing (with the exception of the year 2008, the
uncertainty regarding the future development was the main assumption, because
financial sources were available at that time);
4. Negative development of the NPL which led  to  restrictive  banking  measures  (Bušek,  
Matoušek  2013).
The generally strong preparedness of the Czech private banking sector for the impacts of
financial crisis is weakened also by other factors, mostly by risk development of the
industrial production (expected decline 14%) and by unclear impacts of European
regulation (Laušmanová  2012).  The  high  ratio  of  governmental  bonds  (72,2%  in  March  
2013) could be evaluated as a risk in the case of wrong  governmental  policy  (Bušek  and
Matoušek, 2013).
Amortization of Greek bonds in the Czech Republic is assessed at 10 000 000 000 Czech
crowns, however, there are no clear statistics about this issue. It is not a significant
threat to economic security in the Czech Republic. The same is valid for bonds
amortization from other risk countries  in  Czech  banks  (Bušek  and Matoušek, 2013).
3 The role of the Czech National Bank and the Czech Banking Association
The Czech National Bank is a governmental institution with control powers, while the
Czech Banking Association is an interest group of the Czech private banking sector. Both
institutions  play  an  important  role  at  the  time  of  global  crisis.  The  CNB  is  a  “conservative  
regulator”  with  impact  on  the  security  of  the  banking  sector.  Its  long-term policy has led
to the fact that extraordinary financial measures are not needed (in contrast to many
other countries). The only exception (with very limited response in the private banking
sector) are the extraordinary liquidity-providing monetary measures introduced in
October 2008 to support domestic financial market during the global financial crisis
(Czech National Bank 2011).
The Czech Banking Association is carrying out a persuasive policy aimed to the public. By
informing about the positive development of deposits and (mostly) loans. The spread of
information within financial markets was and is very important. Monitoring and lobbing of
the CBA (with the help of the European Banking Federation) at European level are helpful
for private banking sectors during the time of global crisis, as well transfer of knowledge
from countries with high impact of the crisis (a. o. from negotiations with Hungarian
partners at the level of non-formal meeting of banking representatives from the Visegrad
countries).
Conclusions
Czech private financing was and is well managed at the strategic level. From the point of
view of functions and levels of the crisis management only the first level – risk
management – was used in the Czech banking sector. Prevention measures and several
corrections have been applied up to now. Key assumption checks of the well managed
development of the banking sector are still included in this sector. The role of the Czech
National Bank and the Czech Banking Association can be assessed as an important factor
of the counter-crisis financial policy in the Czech Republic. However challenges of the
NPL-development, unclear European regulation and the development of governmental
politics and budget policy must in the future be respected in the strategic management
and in the prevention anti-crisis policy of the Czech private banking sectors.
212
Acknowledgments
This   contribution   was   prepared   as   part   of   the   research   project   “Methods   of   Predicting  
Long-term Geopolitical Development in Central Europe - VF20102015005“,  funded  by  the  
Ministry of Interior of the Czech Republic.
References
Antušák,   E. (2009). Krizový   management.   Hrozby.   Krize.   Příležitosti. 1st
ed. Praha:
Wolters Kluwer.
Bušek, J.,  Matoušek,   J. (2013). České   banky   – reakce na krizi. Praha:   Česká   bankovní  
asociace (internal non published material), archive of the authors.
Claessens, S., Ayhan, M. (2013). Financial Crises: Explanations, Types, and Implications.
Available at: http://www.imf.org/ external/ pubs/ft/wp/2013/wp1328.pdf
Czech National Bank (2011). Parameters of the liquidity-providing repo operations.
Available at: http://www.cnb.cz/en/financial_markets/money_market/param_liquidity-
providing_repo.html
Kruliš, J. (2011). Jak   vítězit   nad   riziky?   Aktivní   management   rizik. 1st
Edition. Praha:
Linde.
Laušmanová,   M.   (2012). Dopady   nové   regulace   na   financování. Praha: Asociace pro
rozvoj  trhu  nemovistostí/Česká  spořitelna.  Available  at:  http://www.artn.cz/
Mareš,   M. (2002).   Ekonomická   bezpečnost. In   Zeman,   Petr   (ed.):   Česká   bezpečnostní  
terminologie. 1st   Edition.   Brno:   Masarykova   univerzita/Ústav   strategických   studií  
Vojenské  akademie  v  Brně,  pp.  28-31.
U. S. Government (2009). Tradecraft Primer. Structured Analytic Techniques for
Improving Intelligence Analysis. Washington, D. C.: US Government.
213
Financial consequences linked with investments in current assets:
Polish firms case
Grzegorz Michalski
Wroclaw University of Economics
Institute of Financial Management,
Department of Corporate Finance and Value Management
Komandorska 118 / 120, PL53345 Wroclaw, Poland
E-mail: Grzegorz.Michalski@gmail.com
Abstract: Investments in financial liquidity of the firm are done thru investing money in
current assets. The goal of the paper is to find if efficiency measure ROA is linked with
current ratio, liquidity ratio and collection period. Paper presents a classification of
financial consequences of managerial decisions in current assets investments level. There
are two kinds of current assets investment influences: positive, linked with cash revenues
stimulation and decreasing level of risk, and negative, linked with increasing cash
expenses. Financial liquidity decisions could be considered from three perspectives: from
intrinsic value of financial liquidity compared with market value of liquidity point of view,
from risk sensitivity on current assets investments consequences point of view and from
behavioral finance approach to financial liquidity investments point of view. The paper
concerns with risk sensitivity on current assets investments consequences point of view.
Discussion and classification is illustrated by empirical evidence from Polish firms data.
Keywords: current assets, financial liquidity, risk management
JEL codes: G31, G32, G17, G02
1 Introduction
The goal of the paper is to check relation between efficiency measure ROA with current
ratio, liquidity ratio and collection period. Financial liquidity investments in enterprises
are made for safety purposes. Without them there is no possibility to safely manage
operational cycle of the enterprise. Operational cycle is realized as main process in
enterprise value creation, during which there is added value thru collecting raw
materials, next thanks using enterprise fixed assets, with energy and intellectual capital
of enterprise, raw materials change into final production. Finished goods are offered to
an enterprise clients, and the way they are offered is also opportunity to create additional
value.
Pinkowitz (2006) and Dittmar (2003) present in their study relation, which according to
our point of view, is a proof for individual firm risk sensitivity on liquid assets policy
(comp. Zmeskal 2010). According to Dittmar (2003) enterprises in environment with lack
of protection of investors keep more liquid assets than do enterprises in protected
environment. In language of our paper, we agree that it is a proof of individual firm risk
sensitivity on liquid assets policy. The higher is individual firm risk sensitivity on liquid
assets policy resulting from lack of investors protection, the higher level of liquid assets
hold in firm should be, to balance higher risk absorption. In 2004 Kalcheva presented the
study in which enterprises are painted as higher level of liquid assets holders in higher
individual firm risk sensitivity on liquid assets policy resulting from discrepancy between
a controlling shareholder holdings of cash flow rights against voting rights. Dittmar
(2005) claims the liquid asset holdings in U.S. firms are valued more in firms with a low
value of the Gompers (2003) index. That index (Gompers 2003) is lower for enterprises
with higher individual firm risk sensitivity on liquid assets policy, what is an companion of
fewer anti-takeover protections. That results are for Pinkowitz (2006) the proof that near
the true is the hypothesis that cost for the controlling shareholders consumption is higher
when the private benefits attached to liquid assets holdings and, that shareholders value
liquid assets holdings less, when investors are better protected. And vice versa,
214
individual firm risk sensitivity on liquid assets policy is linked with liquid assets holding
negatively as the protector against higher risk consequences in more sensitive case.
Current assets are important part of all steps in operational cycle. The lack of current
assets in any time of operational cycle realization could stop value creation and is linked
with possible long term consequences. The word possible is intentionally here and is
linked with idiosyncratic risk of liquid assets investment level. The idiosyncratic risk of
liquid assets investment level, is a probability that the returns on assets and firm value
creation may decline due to an operating cycle event that could specifically affect return
on assets and firm value creation process but not the branch in which firm operates as a
whole.
Figure 1 IRSLAP relation to ROA creation
2 Methodology and Data
Pinkowitz (2006) claim that agency costs of free cash flow are an source of fact that
liquid assets levels should be discounted as they are partly spent to increase the welfare
of people controlling the enterprise and not maximize the benefits of all investors (comp.
Michalski 2009 and Soltes 2013). Idiosyncratic sensitivity on results of policy of liquid
assets management risk change, is an explanation of that phenomena. In the light of
other authors examination the value of liquid assets holdings, the idea of idiosyncratic
sensitivity on results of policy in liquid assets management risk. Pinkowitz (2005) uses
the Fama (1998) results to explain the sources of value of liquid assets investments for
U.S. enterprises. That is provided with Michalski (2012a, 2012b, 2012c, 2012d) using
Zmeskal (2009), Jajuga (1986), Soltes (2013) and Polak (2011) results. Faulkender
(2005) shows an approach which uses returns from assets rather than assets prices. In
Paradox of Liquidity, Myers (1998) shows the paradox of fact, that liquid assets
maintained by enterprise can be used privately by managements at lower cost than less
liquid assets. That is case which confirms an idea that idiosyncratic sensitivity on results
of policy of liquid assets management risk, works different for different firms. Some of
them are no sensitivity on such results (comp. Dluhosova 2012 and Kulhanek 2012b).
Some of them are strongly sensitive. To illustrate relation between current assets policy
with efficiency measures, I use multiple regression analysis. Collected data for 2010 and
2011 is taken from 288 beverage Polish producers.
Model Specification
Risk sensitivity on risk is various in each enterprise. When we use firm from one branch,
we notice that the level of the idiosyncratic sensitivity on risk resulting from liquid assets
policy (ISRLAP) vary from sensitivity indicator (ISRLAP) for enterprise from other branch.
Inside the branch, there are also differences between sensitivity risk indicator levels
(ISRLAP). Figure 1 shows the idea, how the (ISRLAP) depends on liquid assets level.
215
Figure 2 Risk sensitivity level depending on liquid assets level and individual firm
sensitivity on risk of operating cycle changes
Table 1 Liquidity ratios and ROA 2010-2011
Mean
Standard
deviation
Numer of
observations
CA11 Current assets 2011 5152,278 20348,90 288
CA10 Current assets 2010 5628,582 21975,27 288
CSH11
Cash and equivalents
2011
767,704 4144,92 288
CSH10
Cash and equivalents
2010
749,649 3860,94 288
WC11 Working capital 2011 2626,574 13523,25 288
WC10 Working capital 2010 2221,850 8545,63 288
NCA11
Net Current assets
2011
-331,512 10543,26 288
NCA10
Net Current assets
2010
50,509 10609,25 288
COPE11 Collection period 2011 87,810 52,89 288
COPE10 Collection period 2010 82,052 36,02 288
CUR11 Current ratio 2011 61,398 48,69 288
CUR10 Current ratio 2010 56,895 49,27 288
LIRT11 Liquidity ratio 2011 61,191 48,94 288
LIRT10 Liquidity ratio 2010 57,587 49,39 288
WCpE11
Working capital per
employee 2011
91,693 37,58 288
WCpE10
Working capital per
employee 2010
89,560 95,30 288
ROA11 ROA 2011 62,186 48,72 288
Source: Amadeus database
Level of liquid assets is linked with the consequences of agency theories which claim that
natural is expectation of owners to avoid overinvest in liquid assets (Pinkowitz 2006,
Pinkowitz 2005, Fama 1998, Dluhosova 2004, Kulhanek 2012a) and are used for
analyzing the determinants of the value of cash for U.S. firms (Pinkowitz 2005). Bates
(2009) shows that industrial enterprises have large, higher than doubled their cash-toassets
ratios in 1980–2006 years. That is an confirmation that such kind of the firm have
higher level of sensitivity on operating cycle risk than other firms, and it is basis of our
216
choice to use Polish beverage producers as the illustration of that phenomenon. Paper of
Kalcheva (2007) presents that insufficient protection capital owners in conditions of
ineffective governance could be connected with large liquid assets maintain. Dittmar
(2007) shows that decreasing of risk sensitivity (ISRLAP) by higher governing quality
could reduce liquid assets levels. Work of Iskandar-Datta (2012) documents liquid assets
levels as important for French and Japanese enterprises. Dittmar (2003) documented
that high sensitive on risk firms operating in places with insufficient small shareholder
protection, holds doubled liquid assets holdings in comparison with places in which that
protection is sufficient (comp. Jajuga 2000, claims that multivariate observations
precedes the determination of dissimilarities in multidimensional scaling and Jajuga 2006
findings with use of classification likelihood approach in case of each observation comes
from one of several populations).
That mechanism of not-fully protected owner of the capital, works similarly with
operating cycle period risk and is represented by relation between risk sensitivity
(ISRLAP) and liquid assets levels.
Table 2 Liquidity ratios and ROA for 2010-2011
CA11
CA10
CSH11
CSH10
WC11
WC10
NCA11
NCA10
COPE11
COPE10
CUR11
CUR10
LIRT11
LIRT10
WCpE11
WCpE10
CA10 0,87 1,00
CSH11 0,55 0,42 1,00
CSH10 0,35 0,53 0,76 1,00
WC11 0,82 0,74 0,29 0,17 1,00
WC10 0,54 0,70 0,30 0,50 0,39 1,00
NCA11 -0,35 -0,43 0,14 0,11 -0,15 -0,01 1,00
NCA10 -0,36 -0,28 0,15 0,33 -0,36 0,28 0,84 1,00
COPE11 -0,12 -0,10 -0,04 -0,07 -0,08 -0,04 0,05 0,04 1,00
COPE10 -0,13 -0,10 -0,08 -0,06 -0,07 -0,05 0,06 0,09 0,38 1,00
CUR11 -0,30 -0,23 -0,19 -0,10 -0,23 -0,20 0,06 0,06 0,31 0,55 1,00
CUR10 -0,24 -0,28 -0,14 -0,18 -0,17 -0,28 0,06 0,01 0,30 0,58 0,81 1,00
LIRT11 -0,30 -0,23 -0,19 -0,10 -0,23 -0,20 0,06 0,06 0,31 0,55 1,00 0,81 1,00
LIRT10 -0,24 -0,29 -0,15 -0,18 -0,18 -0,28 0,06 0,01 0,30 0,58 0,82 0,98 0,82 1,00
WCpE11 0,05 0,04 -0,02 0,01 0,07 0,07 0,04 0,04 0,04 0,20 0,31 0,27 0,30 0,25 1,00
WCpE10 0,04 0,14 0,04 0,23 0,04 0,20 0,00 0,01 0,10 0,11 0,21 0,13 0,21 0,14 0,33 1,00
ROA11 -0,29 -0,22 -0,20 -0,11 -0,22 -0,20 0,05 0,05 0,28 0,53 0,98 0,81 0,98 0,82 0,30 0,21
Source: Amadeus database
Using 2010-2011 data for 288 Polish firms producing beverages, we find that among
liquidity strategy indicators, ROA using P/L before tax % 2011 depends on current ratio
2011, liquidity ratio 2010, collection period 2011 and current ratio 2010.
R= 0,98033871; R^2= 0,96106399; Corrected R^2= 0,96051366; F(4,283)= 1746;
p<0,0000 ; Std. error of estimation: 9,6812.
ROA (2011) = 3,13 + 0,94 CUR11 + 0,137 LIRT10 – 0,0216 COPE11 – 0,0836 CUR10
(1,197) (0,02) (0,056) (0,0114) (0,0554)
217
Figure 3 Box plot
Table 3 ISRLAP relation between liquidity ratios and ROA
Beta std err beta b std err b t(283) p
3,13145 1,19699 2,61611 0,00937
CUR11 Current ratio 2011 0,94159 0,020821 0,94208 0,02083 45,2228 0,00000
LIRT10
Liquidity ratio
2010
0,13851 0,057128 0,13662 0,05635 2,42462 0,01595
COPE11
Collection period
2011
-0,0235 0,012372 -0,0216 0,0114 -1,895 0,05911
CUR10 Current ratio 2010 -0,0846 0,055974 -0,0836 0,05535 -1,5111 0,13189
Figure 4 Model of relation between liquid assets indicators and ROA
ROA (2011) = 3,13 + 0,94 CUR11 + 0,137 LIRT10 – 0,0216 COPE11 – 0,0836 CUR10
(1,197) (0,02) (0,056) (0,0114) (0,0554)
In our results we have found that efficiency measure ROA is linked (with one period lag)
with current ratio, liquidity ratio and (without lag) with current ratio and collection
period.
That model confirms that firm efficiency depends on liquid assets investments.
218
3 Conclusions
The main goal of the paper was reached. We find an argument that risk sensitivity is an
answer of the firm on risk. Empirical illustration collected from Polish firms shows that
efficiency measure ROA is linked (with one period lag) with current ratio, liquidity ratio
and (without lag) with current ratio and collection period. That confirms that individual
risk sensitivity of the firm is an idiosyncratic answer of the firm on risk that is specific to
a firm. In that context firm is understand as "portfolio of assets". When idiosyncratic risk
of the firm has almost no correlation with market risk influence on firms results, the
same is with idiosyncratic sensitivity on risk of liquid assets investment. Firms with
higher idiosyncratic sensitivity on that kind of risk, should use adequate diversification of
liquid assets or simply higher level of liquid assets, to hedge their position and efficiency
of long term operational investments. That is important in context of the way
idiosyncratic risk influence the firm results. When idiosyncratic risk more than systematic
risk, describe most of the risk of an firm value and firm revenues over time, the
individual sensitivity on risk of financial liquidity management accounts for most firm
efficiency changes resulted from financial liquidity strategy.
Acknowledgments
The research is financed from the Polish science budget resources in the years 2011–
2014 as the research project financed by National Science Centre granted according
decision nr DEC-2011/01/B/HS4/04744.
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221
Influence of volatility on the value
of the modern structured products
Peter  Mokrička
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  602  00  Brno, Czech Republic
E-mail: mokricka@econ.muni.cz
Abstract: For investors, volatility may be one of the investment criteria. Changes in
implied volatility along with the changes in the value of the underlying assets have
cardinal influence on the value development of modern structured products during their
duration. This paper therefore examines the influence of volatility on the value of modern
structured products and his result is a systematization of this group of investment
instruments according to mentioned criteria.
Keywords: volatility, certificates, structured products
JEL codes: G11, G12
1 Introduction
In the last decade, the modern structured products as one of the financial innovations
have established themselves on the financial markets. Most often they are a compound
investment instruments consisting in addition to the basic instruments such as shares or
bonds of a derivative components, which are often option. Due to the number of possible
combinations of basic instruments and derivative components, structured products can
offer by a chosen investment horizon, by defined propensity to risk and by defined
expectations of development of the value of the underlying an optimal product for each
investor. The value of structured product is derived from value of underlying assets. The
added value of structured products is also the ability to offer optimization of risk-return
profile and their availability for the retail investor and thus made accessible investment
strategies using derivative components also for this group of investors.
Structured products guarantee presented risk-return profile only at maturity. However, if
an investor does not want to hold product until maturity or he intends trade in structured
products, he must also consider the development of parameters that affect the value of
structured products during their duration.
These parameters include the volatility. This paper also deals with volatility and examines
its effect on the value of structured products during their duration.
To be able to assess the impact of volatility on the value of structured products, they
must first analyze and determine the components from which each of the products
consists of. Then, using deduction and the basic Black-Scholes option pricing model
programmed in Visual Basic in MS Excel we want to determine the effect of increasing
volatility in the value of structured products under the influence of volatility on individual
components from which the product is composed.
It will be systematize the segment of structured products according to this influence – it
will be create basic groups of products - products positively correlated with the increase
in volatility, products negatively correlated with the increase in volatility and products
uncorrelated with the change of volatility.
For structured products will herein be regarded as products marketed in the publication
of doc. Svoboda (Svoboda et Rozumek, 2005), which correspond to the Derivate League
- German concept of systematization of structured products (Deutscher Derivate
Verband, 2011) and in which did not incorporated any exotic option.
222
2 Analysis of modern structured products and influence of implied
volatility on value of these products
Structured products are divided according to Deutscher Derivate Verband (Deutscher
Derivate Verband, 2011) and EUSIPA derivative map (European structured investment
products association, 2012) into two major segments – investment and leverage
products.
For investment products that do not contain exotic option will be for the purposes of this
study considered index certificate, basket certificate, sprint certificate, outperformance
certificate, guarantee certificate, discount certificate and reverse convertibles.
For leverages products that do not contain an exotic option will be considered plain
vanilla warrants and knock-out products.
Particular structured products are certainly possible to construct by more possible ways
(for example discount certificate is possible to construct as a purchase of the underlying
assets – (long position) - and the simultaneous sale of a call option (short position),
which value depend on the value development of the same underlying asset. Due to put
call parity of option, it is clear that the same risk-return profile could be reached by
investment in bond (long position) and the simultaneous sale of a put option (short
position)). In the following text will be analyzed and presented structure (components) of
particular structured products, which will be used in next work.
Index and basket certificates have similar and symmetrical risk-return profile. They can
be designed in buying the underlying asset itself, that mean also buying stocks in the
weight corresponding with weighting of individual titles in the index or basket itself. The
product may theoretically not include a derivative component (an option). Their benefit
for investors lies in availability for retail investors because these products are issued in
ratio. The value of index certificate is derived from value of any index but due to ratio is
lower then underlying asset. Investor can also with a low amount diversify his
investment. By a basket certificate a benefit for retail investors lies in availability of
specific actively managed investment strategies.
Outperformance certificate consists of buying the underlying asset (or other component
that guarantees symmetric and fully participation in the performance of the underlying
asset, which may for example be option called as LEPO (options with low or zero strike
price). A second component of this product, financed by dividend yield of the underlying
asset, which renounces investor to the issuer and design of this product, is long position
in call option (usually only part of one call option). Due to the purchased call option then
the investor can participate in the growth of the value of the underlying assets
disproportionately (assuming the exercise price of purchased call option corresponds to
the current value of the underlying assets by issue of the certificate).
From basics of option theory it is obvious that increasing volatility increases price of the
option and it does not matter whether it is a call option or a put option. To assess the
impact of volatility on the value of structured products during their duration is important
rather our position in this option (long or short).
By outperformance certificate it is a long position in a call option, therefore, increased
volatility will mean growth of value of contained options and thus then also increase the
value of the structured product (the first component is not affected of volatility such as
the just described component).
There is another situation by a sprint certificate with which the investor has the
opportunity to participate on the growth in the value of the underlying assets twice, but
only in a predefined range. This product is called also double chance certificate and this
opportunity is possible again due to contained long position in call option on the
underlying asset. Unlike outperformance certificate is buying of call option financed by
selling of two call options with a strike price higher than strike price of the purchased
option. A double participation in the growth of the value of underlying asset is allowed
223
only just in this range of exercise prices of purchased and sold call options. Here we
enter into both long and short positions in the option components. The influence of two
sold call options is slightly higher than that of a purchased call option; therefore with
growth of volatility decreases the value of the structured product.
Guarantee certificate consists of two ground components. One of them is a zero coupon
bond and this component is responsible for the guarantee of this products. A zero-coupon
bond (also called a discount bond or deep discount bond) is a bond bought at a price
lower than its face value, with the face value repaid at the time of maturity (Mishkin,
2006). From difference of these two amounts is financed buying of call option on some
underlying asset. The long position in call option is also second component of this
product. Similar like by outperformance certificate means the growth of implied volatility
the increase of value of guarantee certificate due to long position in call option.
The payment profile for reverse convertible and discount certificate is identical. Both
products offer a positive yield by sideways markets. While reverse convertibles come
with a guaranteed coupon, discount certificates can be purchased at a price lower than
that of the underlying security. The maximum returns with reverse convertibles and
discount certificates are equally high if both products are based on the same underlying
security and have identical strike prices and expirations. Depending on how the price of
the underlying security develops, at the expiration date there is a repayment of the
invested   amount   or   a   delivery   of   the   underlying   security   (Julius   Bär   website,   2013).  
Reverse convertible and discount certificates differ in their structure. In comparison to
reverse convertible which consists of bond and sale of a put option discount certificate
contain underlying security and sale of call option. In both cases there is a short position
in option, so increase in implied volatility results in a decrease in the value of the
product.
Warrants are broadly speaking securitized option. The influence of volatility on value of
the warrant is the same as the influence of volatility on value of the option. The essential
difference between options and warrants, however, is the fact that the investor may by
warrants enter only into a long position. Similar to options is then distinguished long
position in a put warrant and long position in a call warrant.
Table 1 Structure of chosen structured products
Chosen
structured
product
Underlying/
Zerobond
Long
position
in call
Long
position
in put
Short
position
in call
Short
position
in put
Index/basket
certificate
Yes No No No No
Sprint certificate Yes Yes No Yes No
Outperformance
certificate
Yes Yes No No No
Discount
certificate
Yes No No Yes No
Reverse
convertible
Yes No No No Yes
Guaranteed
certificate
Yes Yes No No No
Call warrant No Yes No No No
Put warrant No No Yes No No
Knock-out
products
No No No No No
Knock out products were created as an alternative product to warrants in times of high
volatility. Because of this it is also known one of the first names of these products
“WAVE´s”  (Warrant  Alternative  Vehicles).  These  products  basically  work  on  the  principle  
224
of credit; their value is determined primarily by differences between spot price of
underlying asset and strike price. Volatility has no influence on value of these products.
The following table summarizes the structure of examined structured products. Based on
this table it is possible to identify positive and negative correlation (alternatively any
correlation) between growth of implied volatility and change in the price of the structured
product.
Figure 1 The rate of change in price of option (at the money) by growth of implied
volatility by 1 percentage point for different interest rates
Based on the above mentioned information, we are able to identify the impact of changes
in implied volatility to changes in the value of the structured product. But we are not yet
able to say anything about the intensity of this influence. In this paper is shown just only
an example for the potential effect of changes in implied volatility (change by one
percent) on value of at-the-money call option. Such option is used by construction of
outperformance certificate or guarantee certificate.
Figure 2 The rate of change in price of option (at the money) by growth of implied
volatility by 1 percentage point for different times to maturity
Figure 1 illustrates the degree of impact of the increase in implied volatility of one
percent from different default level of implied volatility. As already mentioned, it is an atthe-money
call option one year before the maturity and it is examined the influence for
0
1
2
3
4
5
6
7
8
9
10
0,1 0,15 0,2 0,25 0,3 0,35 0,4 0,45 0,5 0,55 0,6 0,65
Volatility
Changeinpriceofoptionin%
By interest rate 0,005
By interest rate 0,02
By interest rate 0,05
0
1
2
3
4
5
6
7
8
9
0,1 0,15 0,2 0,25 0,3 0,35 0,4 0,45 0,5 0,55 0,6 0,65
Volatility
Changeinoptionpricein%
Time to maturity 0,5 years
Time to maturity 1 year
Time to maturity 3 years
225
different levels of interest rates. For calculation is used basic Black-Scholes option pricing
model.
Figure 2 illustrates the degree of impact of the increase in implied volatility of one
percent from different default level of implied volatility. It is an at-the-money call option
and it is considered interest rate of 2 %. In this figure is examined the influence for
different times before the maturity.
3 Conclusions
Changes in implied volatility affect the time value of the options, which are a common
building element for financial innovation called modern structured products. Depending
on how the changes in implied volatility affect the value of structured products it is
possible divided these segment of investment instruments into three basic groups:
products positively correlated to changes in implied volatility (increase in implied
volatility causes increase in value of structured product and vice versa), products
negatively correlated to changes in implied volatility (increase in implied volatility causes
decrease in value of structured product and vice versa) and products which are not
correlated to changes in implied volatility.
Due to the fact that growth in implied volatility means growth in option price is decisive
for structured products, whether in the product structure is an option purchased or sold.
On this basis, it is possible to systematize structured products according to criteria
influence of volatility on the value of the product as follows:
 products, which are positively correlated to changes in implied volatility
o guarantee certificate, outperformance certificate, call warrant, put warrant
 products, which are negatively correlated to changes in implied volatility
o discount certificate, reverse convertibles, sprint certificate
 products, which are not correlated to changes in implied volatility
o index certificates, basket certificates, knock-out products
Among the products with negative correlation to changes in implied volatility, we can find
exactly products with limited return potential. These products are characterized by
parameter which  is  called  “cap”.  In  the  structure  of  these  products  it  is  a  short  position  in  
option. Finally it must be stressed that the influence of the changes in implied volatility
on value of structured products is relevant only during the lifetime of the product. The
volatility does not affect the value of structured product at maturity.
Acknowledgments
This paper is part of the outputs of the specific research project MUNI/A/0753/2012 European
financial systems 2013, solved on the Department of Finance, Faculty of
Economics and Administration, Masaryk University in 2013.
References
Deutscher Derivate Verband (2011). Die Derivate-Liga, Deutscher Derivate Verband
Available at: <http://www.deutscher-derivate-verband.de/DE/MediaLibrary/Document/
brosch%C3%BCren/Brosch%C3%BCre%20Derivate-Liga.pdf>
European structured investment products association (2012). EUSIPA Derivate Map
Available at: <http://www.eusipa.org/images/grafiken/european_map_web.pdf>
Julius   Bär   (2013).   Yield enhancement – Swiss private bank. Available at:
<http://www.juliusbaer.ch/htm/1004/en_CH/Yield-enhancement.htm>
Mishkin, F. S. (2007). The Economics of Money, Banking, and Financial Markets
(Alternate Edition). New York: Addison Wesley, pp. 70.
Svoboda, M., Rozumek, D. (2005). Investiční   certifikáty.   Praha:   Komise   pro   cenné  
papíry.
226
History of Financial Crises and its impact
to current regulatory development of Basel Committee
Vladimir Novotny
University of Economics, Prague
Faculty of Finance and Accounting, Department of Banking and Insurance
W. Churchill Sq. 4, 130 67 Prague 3, Czech Republic
E-mail: vlado.novotny@gmail.com
Abstract: This paper is summarizing all major crises and is trying to give an answer to
few questions: a) what are possible mitigants of financial crises if any; b) does the new
regulation help us to do that; c) what is potential impact of new regulation to the sector.
In more detail this paper is summarizing the history of financial crises from 17th
century
to the present. Furthermore this paper is also discussing current views on the proposed
regulation plus putting in place few potential outcomes how to deal with current crisis.
Keywords: financial crises, regulation, Basel 3
JEL codes: G21, G28
1 Introduction and literature survey
Despite of the fact that it seems that there is huge amount of good literature on the topic
of recent financial turmoil, and that is only partly true, because there is huge amount of
literature but mostly is not good one. We are focusing on few authors one of which is
Kindleberger et al. with his insight into the several market panics and manias. Obviously
this is becoming a never ending story as his bestseller is in its actual sixth edition. First
edition was issued in 1978 and last and current one was published in 2011. The next
authors, which we bring in this paper is George Cooper and Hyman Minsky. We are also
bringing view of Basel, which becomes more active on the field of the more academic
papers. So at the end we are trying to utilise this two streams of literature a) more
experience based literature and b) rather complicated highly technical papers from Basel
and European Commission. Goal of this paper is also summarize currently known impact
of regulation to the economy.
2 From Tulip mania to recent turmoil on the financial markets
In this chapter we would like to present little bit from the history of market bubbles and
would like to then find common denominator.
In times of pre crisis/pre bubble bursting, countries coming into question experiencing
huge inflow of capital followed by unusually high increase in the prices of the assets (ie
equities/shares) and houses. Bubbles implode due to the unsustainable increase of the
indebtedness. This may last for several periods ie one to few years, but not for the
extended period of time. Such a increase of the flow of the money is usually connected
with appreciation of the currency and increase of the assets prices mostly equities but
also connected with increase of the prices of houses. (Kindleberger, Charles P. et al.,
2011)
The big ten financial bubbles (Kindleberger, Charles P. et al., 2011):
1. The Dutch Tulip Bulb Bubble 1636,
2. The South Sea Bubble 1720,
3. The Mississippi Bubble 1720,
4. The late 1920s stock price bubble 1927– 29,
5. The surge in bank loans to Mexico and other developing countries in the 1970s,
6. The bubble in real estate and stocks in Japan 1985– 89,
7. The 1985– 89 bubble in real estate and stocks in Finland, Norway, and Sweden,
227
8. The bubble in real estate and stocks in Thailand, Malaysia, Indonesia, and
several other Asian countries 1992– 97 and the surge in foreign investment in
Mexico 1990– 99,
9. The bubble in over-the-counter stocks in the United States 1995– 2000,
10.The bubble in real estate in the United States, Britain, Spain, Ireland, and
Iceland between 2002 and 2007 – and the debt of the government of Greece.
Just for an illustration short before the tulip mania has burst there is an example of one
farmer who for one Viceroy Bulb paid following (Shama, Simon; (1992). The
Embarrassment of Riches: An Interpretation of Dutch Culture in the Golden Age (pp.
358)):
 Two lasts of wheat,
 Four lasts of rye,
 Four fat oxen,
 Eight pigs,
 Twelve sheep,
 Two oxheads of wine,
 Thousands pounds of cheese,
 Bed,
 Some clothing,
 Silver beaker.
And we could be continuing listing in way how such a extraordinary values can
exchanged  in  any  type  of  crisis  which  I’ve  listed  above.  
Based on illustration above we can generalise that a mania or panics involve transactions
where the prices of underlying assets (i.e. stock, house etc.) are having on actual market
price much higher than it can be achieved anytime in the future. It is very natural that
experiencing bubble bursting usually has deep roots in changing the policy of particular
segment of economy. Virtually every country has established  the  central  bank  as  „lender  
of  last  resort“  so  potential  shortage  of  the  liquidity  can  be  managed  in  such  a  way  that  
potential abrupt movement in currency value can be under control plus number of
insolvencies due to lack of liquidity is under control too.
„Theories   based   on   uncertainty   of   the   market,   on   speculation   in   commodities,   on  
‘overtrading,’   on   the   excesses   of   bank   credit,   on   the   psychology   of   traders   and  
merchants,   did   indeed   reasonably   fit   the   early   ‘mercantile’   or   commercial   phase   of  
modern capitalism. But as the nineteenth century wore on, captains of industry ...
became the main outlets for funds seeking a profitable return through savings and
investments.“  (Hansen,  1957)
From above mentioned it seems that the financial crisis are inherent to the system. But
now what we would like, is to move to see how last crisis is being seen by the Basel
Committee and European Commission.
3  European  Commission’s  view
European commission based on their recent memo, which was published on March 21st
,
2013(European Commission, CRDIV/CRR – Frequently Asked Questions, MEMO/13/272
(2013) pp.1) believes that these are 3 major points which they learned from recent
crisis:
1) They believe that there is necessity to enforce cooperation between monetary,
fiscally and supervisory politics globally. Cross border developments were observed
too late, cross border impacts were difficult to analyse
2) Capital levels seem to be inadequate. Some financial institutions appeared to be
resilient to external shocks and some with similar capital levels seems to be not
resilient to external shocks.
3) Cross border scope of many financial institutions became insurmountable to many
national  accountable  authorities.  European  Commission  puts  it  in  these  words:  “The  
228
knowledge that banks could have been resolved, also in a cross border context,
would have changed the balance of power between public authorities and banks, with
the former having more tools at their disposal than just the public purse and the
bail-out option, and the latter not being able to enjoy the best of all worlds: privatize
gains,  socialize  losses.  This  would  have  put  a  dent  on  bank's  risk  appetite.  “
Furthermore these drawbacks were identified to regulatory framework valid in times of
recent crisis:
 Capital was actually not loss absorbing
 Failing liquidity management
 Inadequate group wide risk management
 Insufficient governance
G-20 in its declaration from April 2nd
2009 conveyed the commitments of the global
leaders to address the crisis with internationally consistent efforts to, among others:
 Improve the quantity and quality of the capital in the banking system
 Introduce supplementary non-risk based measure to contain the build-up of
leverage
 Develop a framework for stronger liquidity buffers at financial institutions
 And implement the recommendations of the Financial Stability Board to mitigate
the pro-cyclicality.
4 How Basel III is addressing shortcomings identified?
Current Basel III is addressing shortcomings identified these actions:
 Higher quality and more of the capital
 Capital Buffers
 Clear definition on liquidity required
 Leverage back stop
 Capital requirement for derivatives
Figure 1 Composition of capital
Source: European Commission MEMO on CRD IV/CRR Frequently asked questions from March 21st
,
2013, MEMO/13/272
229
4.1 Higher quality and more of the capital plus capital buffers
Very briefly today, banks and investment firms are required to hold the capital at level at
least of 8% of risk weighted assets. Tomorrow, while the total capital an institution will
need to hold remains at 8% share that has to be of the highest quality, common equity
tier 1, will increase from 2% to 4.5%. Short visual description of new capital requirement
is described in Figure 1.
Newly introduced Capital Buffers:
 Capital conservation buffer
 Countercyclical buffer
 Global systemic institution buffer
 Other systemic Institution buffer
 Systemic Risk Buffer
4.2 Clear Definition on liquidity required
There are two means how to improve the liquidity
1) Improve short term liquidity by the introduction short term stress test like scenario
over upcoming 30 days this is so called Liquidity Coverage ratio
2) To avoid fall of the cliff behaviour is necessity also create a longer ie medium term
ratio which in Basel 3 is called Net Stable Funding Requirement this ratio is having
outlook for over one year period.
Both ratios in current language of Basel 3 include the exact expectation of behavior of
assets and liabilities split into the products.
4.3 Leverage
Leverage is an inherent part of banking activity; as soon as an entity's assets exceed its
capital base it is levered. The Commission does not propose to eliminate leverage, but to
reduce excessive leverage. The financial crisis highlighted that credit institutions and
investment firms were highly levered, i.e. they took on more and more assets on the basis
of an increasingly thin capital base. The leverage ratio is proposed as described in formula
(1)
Assets
Capital
Leverage  (1)
where Capital – Tier 1 Capital,
Assets – Non-risk weighted assets.
Leverage ratio should be very simple indicator that offers a safeguard against the risk
associated primarily with model risk.
4.4 Capital requirement for derivatives
The  new  rules  increase  the  own  funds  requirements  associated  with  credit  institutions’  and  
investment firms’   derivatives   that   are   traded   over-the-counter and securities financing
transactions (e.g. repurchase agreements).
5 Cost of recent crisis (European Commission, CRDIV/CRR – Frequently
Asked Questions, MEMO/13/272 (2013) pp 35.)
 Crisis-related losses incurred  by  European  banks  between  2007  and  2010:  almost  €1  
trillion or 8% of EU GDP (IMF)
 EU GDP contraction in 2009 due to the economic recession induced by the financial
crisis: 6% (Eurostat)
 Approved state aid measures between October 2008 and October 2010:  €4.6  trillion  
or 39% of EU GDP (Commission)
230
 State   aid   measures   effectively   used   in   2008   and   2009:   more   than   €2   trillion  
(Commission)
 Examples of companies where inadequate management of liquidity risk largely
contributed to their failure: Northern Rock (UK), HBOS (UK), Bradford and Bingley
(UK), Bear Sterns (US), Lehman Brothers (US)
 Examples of companies whose (mostly hybrid) capital instruments did not live up to
the expectations as regards their loss absorption, permanence and flexibility of
payments capacity (which had to be reinforced through Commission state aid
decisions): RBS (UK), Bradford and Bingley (UK), KBC Group (BE), Bayern LB (DE),
Commerzbank (DE), Lloyds (UK), Allied Irish Banks (IR), Bank of Ireland (IR), Cajasur
(ES)
6 Basel III impact analysis
Basel Committee on Banking Supervision (Hereinafter Basel Committee) on regular basis
assesses the impact of implementation on banking system. In March 2013 there was
produced  update  on  that  topic  under  name  “Results  of  the  Basel  III  monitoring  exercise as
of  30  June  2012”.  This  Basel  Committee’s  paper  focuses  on  the  210  banks  divided  into  2  
groups. Group 1 banks are those that have  Tier  1  capital  in  excess  of  €3  billion  and  are  
internationally active. All other contributing banks are in Group 2. Group 1 includes 101
banks and Group 2 includes 109 banks.
Table 1 Aggregate capital ratios and capital shortfalls
Aggregate capital ratios and capital shortfalls*
Fully implemented
requirement, in per cent
Capital ratios,
in per cent
Capital shortfalls,
in bln of euros
Minimum
Minimum
plus capital
conservation
buffer
Current Basel III Minimum
Minimum
plus capital
conservation
buffer**
Group 1
CET1 4.5 7.0 10.8 8.5 3.7 208.2
Tier 1 6.0 8.5 12.0 8.7 16.2 198.5
Total 8.0 10.5 14.4 9.9 61.8 222.2
Group 2
CET1 4.5 7.0 10.9 9.0 4.8 16.0
Tier 1 6.0 8.5 11.4 9.5 1.6 7.3
Total 8.0 10.5 14.7 11.3 5.0 12.0
Notes:
*The shortfall is calculated as the sum across individual banks where a shortfall is observed. The
calculation includes all changes to risk weighted assets (e.g. definition of capital, counterparty
credit risk, trading book and securitisation in the banking book). The Tier 1 and total capital
shortfalls are incremental assuming the higher tier capital requirements are fully met
**The shortfalls including the capital conservation buffer also include the capital surcharges for 28
G-SIBs as applicable.
Source: Results of the Basel III monitoring excercise, Bank Committee on Banking Supervision,
March 2013, MEMO/13/272 Page 8
7 Potential Impacts (European Commission, CRDIV/CRR – Frequently
Asked Questions, MEMO/13/272 (2013) pp.36)
Short term impact, ignoring benefits of the new measures:
 Estimated Common Equity Tier 1 (CET1) shortfall to meet the new minimum
requirements and the conservation capital buffer, based on 2009 capitalisation levels:
231
 Immaterial in 2013 (binding only for 3 banks in the EU QIS sample) – due to
transitional provisions;
 €84  billion  in  2015  – due to transitional provisions;
 €460  billion  in  2019  – full implementation.
 Limited impacts till 2019( after this period an superior increase in EU GDP is expected,
please refer to the figures below):
 Decrease of only 0.14%-0.17% in EU GDP for each percentage point increase in CET1
capital ratio (Basel Committee, Commission services)
 Decrease of only 0.42%-0.49%   in   EU   GDP   for   the   above   €460   billion   CET1   capital  
shortfall (Basel Committee, Commission services)
Long-term economic impact (after 2019), considering both benefits and costs of the new
measures:
 Average decrease of only 1.8% in stock of loans for 2020-2030 (Commission services)
 On Average an increase of only 0.29 percentage points in loan rates for 2020-2030
(Commission services)
 Net economic benefits (i.e., benefits less costs) increase of 0.3% - 2% in EU GDP,
stemming from reduction in expected frequency of systemic banking crises (Basel
Committee, Commission services)
 Reduction in the probability of systemic banking crises: within the range of 29% to
89% when banks recapitalise to a total capital ratio (including buffers) of at least
10.5% (Commission services, Basel)
 Additional systemic benefits in terms of stemmed pro-cyclicality and reduced severity
of any future banking crises not captured in the above estimates.
Conclusions
Based on above mentioned it seems that understanding of the crisis is fairly good, and
we personally believe that Basel Committee is doing good job to address the issues and
monitor impact of proposed changes to the system. Where we feel little bit unsure is
involvement of the politics into the creation of a robust control system and cooperation
on global level. In that sense we never had an ambition to assess these two points.
Purely  from  view  of  the  Basel  Committee’s  paper  it  seems  that  banking  sector  is  really  
moving to stronger and more stable system, but only future will answer this question.
References
Basel Committee on Banking Supervision (2013). Results of the Basel III monitoring
Excercise as of June 2012.
Bruner R. F., Carr, D.R. (2007). The Panic of 1907 Lessons Learned  from  Market’s  Perfect  
Storm, Wiley, 296 p.
Cooper G. (2008).The Origin of Financial Crises: Central Banks, Credit Bubbles, and the
Efficient Market Fallacy, Vintage, 208 p.
European Commission (2013), CRDIV/CRR – Frequently Asked Questions, MEMO/13/272.
Hansen A. (1957). Business Cycles and National Income, New Tork: Norton, 746 p.
Kindleberger, C. P. et al. (2011). Manias, Panics and Crashes: A History of Financial
Crises, Sixth Edition, Palgrave Macmillan, 368 p.
Minsky, H. P. (1986). Stabilizing an unstable economy, McGraw-Hill, 350 p.
Shama, S. (1992). The Embarrassment of Riches: An Interpretation of Dutch Culture in
the Golden Age, Vintage, 720 p.
Tarullo, D. K. (2008). Banking on Basel: The Future of International Financial Regulation,
Peterson Institute, 324 p.
232
Asset Pricing with Conditional Value at Risk
Mihály  Ormos,  Dusán  Timotity
1
Budapest University of Technology and Economics
Department of Finance
Magyar  tudósok  krt.  2.,  1117  Budapest,  Hungary
E-mail: ormos@finance.bme.hu and timotity@finance.bme.hu
Abstract: We introduce a novel equilibrium asset-pricing model, which we build on the
relationship between Conditional Value-at-Risk (CVaR) and the expected return. This
approach of risk measure allows us to get rid of the normality condition of returns.
Combining the CVaR risk measure method with our regression model, nonrealistic
assumptions – such as rational and risk–averse investors, unlimited leverage opportunity
and price-taker investors – of the most commonly used models can be almost entirely
omitted. In our model we can define the optimal choice for every single investor.
Aggregating the required returns, allows us including different leverage constraints and
margins, thus we do not assume unlimited borrowing for risk-free interest rate.
Furthermore, based on the anchoring effect in Prospect Theory risk-seeking behavior can
be explained and implemented in our model. On the other hand, the aggregation method
to calculate expected returns allows price-maker investors having great influence on
price movements (in case of a block transaction or in a non-liquid market segment).
Keywords: Behavioral Finance; Asset Pricing; Prospect Theory; Anchoring; Conditional
Value-at-Risk
JEL codes: G12, G32
1 Introduction
We construct an equilibrium asset pricing model that, contrasting the standard models
using Expected Utility Theory (EUT), is based on loss-averse investors described by the
Prospect Theory (Kahneman–Tversky, 1979). We approximate loss-aversion by Value-atRisk
(Campbell et al., 2001; Jorion, 2007) and Conditional–Value–at–Risk (Rockafellar,
Uryasev, 2000), by which we can define the expected loss weighted by its probability.
We apply the assumption that in some cases investors define a reference point on their
utility curve, resulting in an anchoring, thus they do not refuse risk, moreover they start
to follow risk-seeking behavior to an extent (Ariely et al., 2003), since their expected
utility can be maximized in this behavior. Furthermore, our regression is more realistic
than standard asset pricing models from another point of view too: we do not assume
the returns to be normally distributed, which is crucial in standard asset pricing, however
is not true. The results we present in this paper are somewhat different from the well
known models approximating expected return by standard deviation – such as the
Modern Portfolio Theory (Markowitz, 1959), or the Capital Asset Pricing Model (CAPM)
(Sharpe, 1964; Lintner, 1965; Mossin, 1966) –, as we change the risk parameter from
standard deviation to Conditional–Value–at–Risk (CVaR). Our model omits the
assumption of price–taker investors (included in the perfect market assumption of the
CAPM) and allows significant price–making activity by block transactions or in non–liquid
market segments. We also allow limited borrowings (the opposite of the initial
assumption of Sharpe), different leverage constraints and interest rates for every single
investor. Therefore, we define a more realistic and precise way to explain individual
optimization method, and by approximating the expected return through the aggregation
of investors' required returns, we create a model that describes expected return without
using the most significant unrealistic assumptions of standard asset pricing models.
In the following chapter we introduce "rational" risk-seeking behavior based on the
anchoring effect of Prospect Theory (Kahneman-Tversky, 1979) and implement it in asset
pricing models, the third chapter describes the use of Conditional Value-at-Risk in our
regression, in the fourth section we solve the problem of limited leverage constraints in
our model, the fifth section describes the method of calculating the expected return of
233
assets through aggregating individual required returns and finally we summarize the
most important results of this paper.
2 Risk–seeking and its implementation
With the Prospect Theory Kahneman and Tversky (1979) created a model that allows
analyzing single period choices, since according to them the investor makes its choice
from the zero reference point on its utility curve every time. However, in reality this is
not the case every time. Proven by their research on the heuristic–driven biases (Shefrin,
2002), for different reasons – such as the anchoring effect (adjusting to a reference
situation), the inclusion of sunken costs or the sticking effect (investors give up to
properly manage their portfolios after a massive loss and do not sell) – investors can be
motivated to fix a reference point on their utility curve and keep it at their next decision,
which places them to non–zero starting point on their utility curve. This way, one can
easily see that there are mathematically fair investments that result in utility growth.
Therefore, investors maximizing the variance of the chosen fair investment – until an
optimal value – can increase their expected utility gain for fixed expected return. Hence,
this is by definition risk–seeking behavior. This situation can be seen on the Figure 1
where we used the exponential utility function for Constant Absolute Risk-Aversion
(CARA, Pratt, 1964), the (1 − 𝑒 ). The calculation of the function is based on the
following equation since the slope of the utility curve for losses is in average -2.25 times
the slope for gains:
)1(25.2)1(25.2)1(25.21 )05.005.0(5)05.0(5)05.0(55 xx
eeee 
 (1)
where a=5 is an average measure of constant absolute risk–aversion and x = -5% is the
negative return as reference point after 5% loss. Based on the results, in this case
investors are risk–seeking until they are able to reach 7.72% return on the positive side.
Figure 1 Fair investment after previous loss
Since if x < 0, than U(x) = - 2.25U(-x) in average and the investor maximize the risk
(here the variance) until the utility growth due to positive wealth change y is greater
than utility loss due to negative wealth change where he/she becomes risk–neutral, the
risk–neutral curve can be defined using the following equation. Here, we simulate a
mathematically fair investment consisting of (x + y) gain and (-x - y) loss both with 50%
probability. The reference point is (-x). The investor keeps following risk–seeking
behavior until the utility growth and loss get to be equal, therefore:
)()()2()( xUyUyxUxU  (2)
Through defining an alternative version of this equation we get:
234
  )(
25.1
)2()()(225.2
yU
yxUyUxU


(3)
From this equation it can be seen that due to the law of diminishing marginal utility
[2U(x)+U(y)-U(2x+y)]>0, therefore, U(y) and y have to be positive in order to have
solution for the equation. This means that a mathematically fair investment with zero
expected return and (x+y)2
variance produces greater utility than the same one with
zero variance. According to the detailed description of risk-seeking phenomena (Timotity,
2012) the variance decreases as the expected return of the fair investment increases
until that point, where the expected return equals the initial loss reference point and the
variance is zero. The transition function is calculated by using the parameters of the
utility curve and in case of CRRA and CARA it has negative slope. We illustrate this
phenomenon in variance-expected return based coordinate system on Figure 2, where
the investor having negative reference point after -x loss chooses investment A' instead
of A, although A has the same expected return and lower variance.
Figure 2 Risk-seeking until risk-neutrality
By combining the Expected Utility Theory based Markowitz model with the Prospect
Theory based risk-seeking, it is clear that we can implement this behavior into standard
asset pricing models too through the following method: we defined that risk–seeking has
always a limit where it reaches risk–neutrality and this converges to zero variance with
the growth of expected return (since it decreases monotonically in variance – expected
return system).
Figure 3 Implementing risk-seeking
(x+y)2
2
x
A A’
E(r)
The risk–neutral curve (RNC) consists of points where investors are risk–neutral, where
their utility depends only on the expected return of investments and it is not influenced
by risk (here variance). Therefore, they choose the portfolio with the highest possible
return on their RNC. Since this curve crosses the MPT defined curve of efficient portfolios,
the efficiency frontier (EF) – given the definition of EF – the portfolio with the highest
expected return – also the optimal choice – is exactly the cross of these two curves (see
235
Figure 3. This means that risk–seeking described above has no effect on the efficient
portfolios of standard equilibrium models and this type of behavior can be implemented
in these regressions.
3 CVaR risk measure in our model
One of the most compromising risk measures today is the Conditional Value-at-Risk.
Although its predecessor, the VaR was able to measure the risk of non normal
distributions used in standard asset pricing model, only the CVaR has developed the
important characteristics of sub-additivity and convexity (Rockafellar-Uryasev, 2000).
These characteristics and its possibility to measure the risk of any investment have made
it the most precise risk-measuring technique today (Krokhmal-Palmquist-Uryasev, 2002),
and that is why we decided to base our model on it. Furthermore, through this method
we can describe the risk-seeking phenomenon with a measure applicable for any type of
distribution function.
Although CVaRα is usually given by the probability weighted average of the loss function
of  an  investment  maximized  with  zero,  we  define  this  as  the  average  of  the  worst  α  cases
of the return distribution. Therefore, in our model it shows true value (that is negative)
of the loss and we do not maximize its value at zero, hence, positive values of the return
distribution also play an important role. This modification does not affect optimization
methods, however, in our opinion it is easier to understand its role in our model and
highlights more our results this way. For example, due to positive time preference
investments with almost no risk at all may have positive CVaR at high levels  of  α  since  
returns are not capped with zero. We have made Monte Carlo simulation for randomly
weighted portfolios of four assets, namely the 3-month T-bill, 10-year T-bond, S&P500
index and the Coca-Cola shares. Figure 4 shows the results of the simulation where it can
be clearly seen that for portfolios with the same expected return CVaR (also for 50%) is
lower using historical distribution than using normal distribution.
Figure 4 Difference from normal (Gaussian) distribution in CVaR0.5 - E(r) system
3.1 Simulating individual choice with CVaR
According to Arrow and Pratt (Pratt, 1964) the level of risk–aversion of an investor can
be defined by a unique parameter, the "A" measure of risk–aversion. Since we assume
that the Kahneman–Tversky utility function has the same convexity on the right side as
the expected utility function in EUT, the behavior of an individual can be described in
both risk–averse and risk–seeking cases with the aid of this measure, therefore, our
model can define the optimal choice for every investor with the constraints that the goal
is utility maximizing and the available portfolios have an efficiency frontier.
In case of risk–aversion the approximation of expected utility (Markowitz, 1959) could be
used in our model too in the following method:
2
5.0)()( AFEFU  (4)
236
By solving this approximation for CVaR instead of the variance (Timotity, 2012), we get a
monotonically decreasing function. This means that the convex utility function and the
concave efficiency frontier of the variance-expected return models stands in CVaR based
models too – which can be seen on Figure 4 too –, although, the slopes are different due
to the fact that variance increases and CVaR (in our definition) decreases for riskier
investments.
As for the risk-seeking behavior described in Section 2 we found solution in CVaR based
asset pricing modeling also. Since the variance-based risk-seeking is applicable only in
case of symmetric distributions, we had to find a risk measure that describes the true
expected value of gains and losses (the true risk) for any type of return distribution. With
the use of the variable Prospect (Timotity, 2012) – the opposite of CVaR – for 1-α  level  
as the expected gain above the expected return and Conditional Value-at-Risk  for  α  level  
as the expected loss below the E(r), we can define the risk-seeking phenomenon for
every distribution function. Solving the risk-neutral equation (the same in equation 3 for
CVaR) we get a monotonically increasing function. This means that the same situation
applies for CVaR-E(r) modeling as it is in variance-E(r) based system, hence, investors
choose the portfolio with the highest expected return on the risk-neutral curve, which will
be the section of this curve and the efficiency frontier. Therefore, the CVaR-E(r) based
efficiency frontier and the asset pricing method stays intact if we implement risk-seeking
behavior too. This solution can be seen on Figure 5, where investor would choose
investment A' instead of A, however, since he or she wants to maximize the expected
return – being risk-neutral – the optimal portfolio will be on the efficiency frontier.
Figure 5 Risk–neutral curve and efficiency frontier
4 The effect of limited borrowings
In this section we omit one of the main, although very unrealistic assumptions of
standard asset pricing models, the unlimited borrowings. Equilibrium models define the
capital market line (CML) as the set of efficient investment opportunities including risk–
free and risky assets that goes to infinity. However, in reality this is not true. In most of
the cases there is no opportunity to invest in such positions. These assumptions cause
investors gaining other advantages in exchange for paying interest rate: on one hand
they get insurance "for free" due to liquidation at margin call. In this case the investor
cannot lose more than its own invested money, however it would be possible through a
leveraged portfolio without marginal requirements. This reduction of risk has no excess
cost for him or her, however, he or she gets some of the negative risk eliminated, and
thus gets higher expected return. This is described on Figure 6 and in the following
equation.
)1)(1()1()1()()( )1(, ffrpCVaRfrpxrxrErE QfrpQQCPL Q
  (5)
where E(r) is the expected return of L leveraged portfolio with margin requirements,
E(r) is the expected return of P unleveraged portfolio, (1 + x) is the leverage, r x is the
interest rate for borrowing multiplied by the borrowed quantity (the total cost of
237
borrowing), p r < −1 + f is the probability of Q leveraged portfolio without margin
requirements producing return below (-1+f), CVaR , ( ) is the Conditional–Value–at–
Risk of Q portfolio at p probability.
Figure 6 Effect of leverage with margin requirements
Since CVaR is always less than or equal to (-1+f) (the VaR) investors get increasing
marginal expected return in case of margin requirements instead of constant marginal
expected return. This means that the relation between the leverage of the portfolio and
the expected return is not linear, therefore, in the function describing CVaR-E(r)
leveraged portfolios
( )
is not constant.
According to this situation, it is clear that asset pricing models have to specify the
leverage limit and the interest rate for every investor first, than calculate the set of
efficient portfolios for each of them under these conditions. Hence, individuals are
optimizing their choice for different efficiency frontiers, and thus, models have to take
into consideration investors' leverage limit and interest rate (that can be quite different in
some cases). Figure 7 shows this optimization method, where A and B being efficient and
C being inefficient for the second leverage limit (Efficiency frontier 2) may not be efficient
under different conditions which produce Efficiency frontier 1.
Combining the isoutility curve (depending on the "A" measure of risk-aversion) with each
efficiency frontier, the optimal choice for every investor can be identified. On Figure 7
this is represented by point O for rc interest rate and Efficiency frontier 1. This way, our
model can determine the optimal choice and so the required return for each investor. In
the next section we use this individual optimization and the required returns to obtain the
expected return of capital assets.
Figure 7 Individual optimization with leverage constraints
5 Defining the expected return
As mentioned above, by using three parameters– namely the CARA or CRRA, the interest
rate paid and the leverage limit – optimal choice and required return can be defined for
each investor. Since different conditions apply to individuals, their required returns will
238
not be the same. Through aggregating these choices institutions having all the necessary
information can estimate the aggregate expected return of the market (which moves the
prices). However, in order to have this aggregation these institutions have to have a
model to describe the relation between different required returns assigned to a fixed
CVaR risk. Since every publicly traded asset has a unique price, these required returns
have to sum up to a unique expected return based on some function. In our opinion, this
function could be a value–weighted aggregating function. This means that we sum up
each investor's required return with weights equal to their invested value, and thus we
get a unique expected return. This way, investors become price-maker as they all have
effect on the market price of assets.
The weighted aggregation function is based on macroeconomic demand and supply
functions. The price assigned to a portfolio is defined by the current aggregated supply
and demand on the investment opportunity. Since databases allow us analyzing who
wants to make transactions on the current price with what volume and what is the
required return of the investor, one can define the future price (current price multiplied
by the required return) and the future volume of each investor. This way the aggregated
supply (AS) function can be defined. According to behavioral financial studies (Fama,
1991) the investors insist on smoothing their consumption over time, therefore, it seems
to be realistic to assume that their incoming cash–flows are balanced and continuous due
to diversification. Furthermore, if the future cash–flows are assumed to be fixed (which
seems to be true since the price always reflects all the information about them), the
aggregated demand (AD) grows in the same pace as AS, which is the required return of
investors. This means that in the discounted cash–flow pricing method the exponent of
the cost of alternative choice is getting smaller over time. Combining the functions
mentioned above we create the AS–AD system in our model, hence one can approximate
the future prices of assets and their expected returns. The illustration of this
approximation is shown on Figure 8, where the initial (t0) AS and AD functions are
increased by continuous return over time (represented on axis "t"). The functions used
are PS=2Q and PD=5-2Q. We fixed the continuous return at fixed 20% in order to be
expressive, hence the exponential growth over time can be clearly seen.
We underline that this type of approximation of the expected return is very sensitive to
the input data, therefore, the bigger the used database is the more precise the
regression will be. This necessary data can be extracted by analyzing the actions of
clients in financial institutions or brokerage services, however, using our model at
national level (such as under the supervision of the Securities and Exchange
Commission) or at international level (for example with the administration of the
International Monetary Fund (IMF) or the European Central Bank (ECB)) could produce
fairly precise approximations of future prices of capital markets.
Figure 8 The formation of expected return
Conclusions
The use of Conditional–Value–at–Risk in asset pricing modeling seems to be more and
more important since it is a much more precise measure than volatility or standard
239
Value–at–Risk. This can be seen in many recent papers and in statements of
international financial institutions (for example the recommendation on CVaR by the
Bank for International Settlements, 2012). Although there have been some models based
on this risk measure before our research (Meng et al, 2011), they all have used the
unrealistic assumptions of the exclusion of risk–seeking or price–maker investors and the
infinite borrowing opportunity. Our model has achieved to omit these assumptions too,
therefore, we could create a more general and realistic regression. The implementation of
main utility functions (the ones from EUT and Prospect Theory) in CVaR–E(r) system
allows defining the optimal choice of each individual in both risk–averse and risk–seeking
situations. The inclusion of risk–seeking investors through the Kahneman–Tversky utility
function has allowed omitting the first unrealistic assumption of standard models. The
price–maker activity described by aggregated demand and supply functions has made
the second assumption unnecessary. Although these functions have not been analyzed in
this paper, there have numerous attempts to describe them with market microstructural
or behavioral financial approaches; however, these theories usually focus on the short–
term changes of prices, and on long–term our approximation seems to be a good solution
too. Finally, the third unrealistic assumption (unlimited borrowing for fixed risk–free rate)
has become unnecessary too since in our model we define different leverage limit and
interest rate for every individual. Hence, our asset pricing method can define the
required return and the optimal choice for each investor and under proper infrastructural
conditions it can aid raising capital for companies through choosing the adequate
investors, it supports individuals in trading, makes corporate financial analysis more
precise (Andor-Dülk,   2013)   and   approximates   the   expected   returns   and asset prices
without using unrealistic assumptions and limitations.
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Rockafellar, R.T., Uryasev, S. (2000). Conditional value-at-risk for general loss
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241
Asset Pricing and Entropy
Mihály  Ormos,  Dávid  Zibriczky
Budapest University of Technology and Economics
Department of Finance
Magyar  tudósok  krt. 2., 1117 Budapest, Hungary
email: ormos@finance.bme.hu
zibriczky@finance.bme.hu
Abstract: We investigate entropy as a novel risk measure which explains the equity
premium of securities and portfolios in a simpler way and at the same time with higher
explanatory power than the beta parameter of the capital asset pricing. To measure the
risk of an investment opportunity the portfolio theory applies the variance of the return,
and show that the risk can be reduced by diversification and the systematic risk (beta) is
applied as the risk measure. Entropy represents a measure of the uncertainty of a
probability variable. Analogously, for asset pricing we define the continuous entropy as
an alternative measure of risk. Our results show that the entropy is decreasing in the
function of the number of securities involved into a portfolio similarly to the variance. In
this empirical study we use the daily returns of 150 randomly selected securities for a
period of 27 years. Our regression results show that the entropy has a higher
explanatory power for the expected return than the CAPM beta.
Keywords: Asset Pricing; Entropy; Risk measure
JEL codes: G12, G32
1 Introduction
We build an equilibrium model applying a novel risk measure, the entropy. Entropy
characterizes the uncertainty or a measure of dispersion in a random variable, in our
certain case, the uncertainty of stock and portfolio returns. In modern Markowitz (1952)
portfolio theory and equilibrium asset pricing models we apply linear regressions which
methodology suppose that the returns are stationary and normally distributed; however
this  is  not  the  case  (see  Erdős  and  Ormos,  2009).  Entropy  on  the  other  hand  does  not  
have these kind of boundary conditions, even as a starting point the density function
itself has to be estimated. In the traditional asset pricing model there is equilibrium
between the beta parameter, which is covariance – ratio variance between the market
portfolio and the investigated investment opportunity. If the random variable is normally
distributed than the entropy follows its standard deviation, thus in the ideal case there is
no difference between the two risk measure. However our results show, that there is a
significant difference between the standard deviation or beta and the entropy of a given
security or portfolio. In this paper we show that entropy offers an ideal alternative to
capture the risk of an investment opportunity, as if we explain the return of a wide
sample of securities and portfolios with different risk measures; on an OLS setting the
explanatory power is much higher in the case of the entropy measure of risk than the
traditional ones.
2 Entropy
Entropy is a mathematically-defined quantity that is generally used for characterizing the
probability of outcomes in a system through a process. Originally, it was introduced in
thermodynamics by Rudolf Clausius (1867) to measure the ratio of transferred heat
through a reversible process in an isolated system. In statistical mechanics the
interpretation of entropy is the measure of uncertainty that remains about the system
after observing its macroscopic properties (pressure, temperature or volume). The
application of entropy in this perspective was introduced by Ludwig Boltzmann (1872).
He defined the configuration entropy as the diversity of specific ways in which the
components of the system may be arranged. He found strong relationship between the
242
thermodynamic and statistical aspect of entropy, the formula of thermodynamic- and
configuration entropy differs in the so-called Boltzmann constant only.
There is an important application of entropy in information theory as well, which is often
called Shannon (1948) entropy. The information provider system operates as a stochastic
cybernetic system, in which the message can be considered as a random variable. The
entropy quantifies of the expected value of the information in a message, in other words
the amount of information that is missing before the reception. The more unpredictable
(uncertain) messages are provided by the system, the more expected information it
contains in a message. Consequently, the higher uncertainty in the messages of the
system means higher entropy. Because the entropy equals to the amount of expected
information in a message, it measures the maximum compression ratio without losing
information.
In financial applications Philippatos and Wilson (1972) find that entropy is more general
and has some advantages relatively to the standard deviation, in their paper they
compare the behavior of standard-deviation and entropy in portfolio management.
Kirchner and Zunckel (2011) argue that in financial economics entropy is a better tool to
capture the risk reduction by diversification; however in their study they suppose
Gaussian assets. Dionisio et al. (2006) argues that the entropy observes the effect of
diversification and is a more general measure of uncertainty than the variance, since it
uses more information about the probability distribution. The mutual information and the
conditional entropy show a good performance when compared with the systematic risk
and the specific risk estimated through the linear equilibrium model. Nawrocki and
Harding (1986) propose to apply state-value weighted entropy as a measure of
investment risk; however they are dealing with the discrete case. In all the above
scholarly papers one can recognize that entropy could be a good measure of risk;
however, it seems to be difficult to use it. Our main motivation is to show, that entropy
based risk measure, on the one hand, more precise, and on the other hand, it is not
more complicated than the use of the variance covariance equilibrium models.
3 Entropy function
3.1 Discrete entropy function
Let

X be a discrete random variable. The possible outcomes of this variable is denoted
by nooo ,..,, 21 , and the corresponding probabilities by  ii oXp  
Pr , 0ip and 1
1

n
i
ip
The  Generalized  discrete  entropy  function  (Rényi,  1961)  for  variable

X is defined as
  






 

n
i
ipXH
1
log
1
1 


(1)
where  is the order of entropy, 0 and 1 , and the base of the logarithm is 2.
The order of entropy expresses the weight taken into account in each outcome, by lower
order of entropy the more likely outcomes are underweighted, and vice versa. The most
widely used orders are 1 and 2 .
1 is a special case of generalized entropy. However the substitution of  to (1)
results  a  division  by  zero,  it  can  be  shown  by  using  L'Hôpital's  rule  in  the  limit  of   1 ,
that H converges to the Shannon entropy:
   


n
i
ii ppXH
1
1 log (2)
The case of 2 is  called  Collision  entropy  or  „Rényi  entropy”:
243
  





 

n
i
ipXH
1
2
2 log (3)
 
XH is non-increasing function in  , and both entropy measures are greater than
zero assuming finite number of possible outcomes:
   
 XHXH 120 (4)
3.2 Continuous or differential entropy function
Let X be a continuous random variable taking values from ℝ with a probability density
function  xf . Analogously to (1), the continuous entropy is defined as:
   
 dxxfXH



ln
1
1
(5)
One can see that the base of the logarithm in (1) and (5) are different. However the
entropy depends on the base, it can be shown that the value of entropy changes only in
a constant coefficient by different bases. We use natural logarithm for all differential
entropy functions. The formulas for special cases ( 1 and 2 ) are the following:
      dxxfxfXH ln1 (6)
    dxxfXH
2
2 ln (7)
An important difference between the discrete- and continuous entropy is that while
discrete entropy takes only non-negative values, continuous entropy can take negative
values as well
 XH ℝ (8)
4 Entropy estimation
Let nxxx ,...,, 21 be the observation of continuous random variable X , and  XH n, the
sample-based estimation of  XH . The plug-in estimations of entropy are calculated on
the basis of density function estimation  xf . Assuming the probability density function
is estimated as  xfn the integral estimate of entropy is the following:
    dxxfXH
nA
nn 




ln
1
1
, (9)
where nA is the range of integration which may exclude small and tail values of  xfn .
We propose to select     iin xxA max,min  ni ,...,2,1: .
One of the simplest methods of density estimation is the histogram based density
estimation. Let    ii xxb minmax   ni ,...,2,1: be the range of sample values. Partition
the range into k equal width bins, and denote the cutting points by kt . The width of a bin
is constant: ii tt
k
b
h  1 . The density function is estimated by using the following
formula:
244
 
nh
xf i
n

 (10)
if  1,  ii ttx where i is the number of data points falling in the i-th bin. Based on the
properties of histogram, a more simple estimation formula can be deducted for
Generalized- and Shannon entropy using (9) and (10):
  








k
i
i
n
nh
hXH
1
, ln
1
1



 (11)
  







k
i
i
in
nh
v
n
XH
1
,1 ln
1 
(12)
The   formula   for   Rényi   entropy   can   be   derived   by   substituting   2 to (11). The
parameter of this method is the number of equal width bins (k). However, there are
several methods to choose this parameter (e.g square-root   choice,   Scott’s   normal  
reference rule (1979), Freedman-Diaconis rule (1981)), the detailed description of these
are out of scope of this paper. Beirlant et. al (1997) overviewes several other entropy
estimation methods like kernel density estimation-, resubstitution-, splitting-data- and
cross-validation methods, we focuses on the application of histogram based estimation.
5 Risk estimation
Let be given a set of data  FM RRRSD ,,,: . The elements are the set of securities
 lSSSS ,...,,: 21 corresponding observations for each,  lRRRR ,...,,: 21 ,  iniii rrrR ,...,, 21
the observations for the market return  MnMMM rrrR ,...,, 21 and the observations for the
risk free return  FnFFF rrrR ,...,, 21 where l is the number of securities, and n is the
number of samples. The main goal of this paper is to apply entropy as a novel risk
measure. In order to handle risk measure uniformly, we introduce  as a unified
property for securities. Let  iS be the risk estimate for the i-th security. In the
economic literature the most widely used risk measures are Capital Asset Pricing Model
(CAPM by Sharpe, 1964) beta, and the standard deviation. Denote them with  and  ,
respectively. The estimation for this risk measures for security i are the following
   
 FM
FMFi
ii
RR
RRRR
S


 2
,cov
ˆ

 (13)
   Fii RRS 
ˆ (14)
where  is the CAPM beta, cov() is the covariance of the arguments and  is the
standard deviation. Our hypothesis is that uncertainity on the observation values can be
interpreted as a risk of the security, so we introduce entropy as a risk measure. Because
the differential entropy function can take negative values as well ( H ℝ), for a better
interpretability we apply inverse natural logarithm and define the entropic risk measure
by the following formula:
 iH Sˆ  Fin RRH
e 
(15)
One can see that H takes values from the non-negative real numbers,   ,0H
245
6 Explanatory power
Let T be a target variable, with sample  ltttt ,...,, 21 and let E a single explanatory
variable with sample  leeee ,...,, 21 . To estimate the explanatory power of variable E
for variable T , we use the following method. The linear relationship between the two
variables can be described with linear regression model: EaaT 10  .
The parameters of model ( 0a and 1a ) are estimated by ordinary least squares (OLS), and
the estimation for a target value is the following: ii eaat 10
ˆˆˆ  where 0
ˆa and 1
ˆa are the
estimation of 0a and 1a , respectively. One of the most often used estimation of the
explanatory power is the R-squared (the coefficient of determination) of the linear
regression:
 
  
 





 n
i
i
n
i
ii
tt
eaat
etR
1
2
1
2
10
2
ˆˆ
1, (16)
We are curios how efficiently the risk measures describe the expected risk premium of a
security, denote this measure by   . Let the explanatory variable E be the risk
measure of the securities, where the sample is
      lSSSe 
ˆ,...,ˆ,ˆ 21 (17)
and the target variable T is the expected risk premium of the securities, where the
sample is
      FlFF RRERRERREt  ,...,, 21 (18)
where  is the unified risk measure function, and  E is the expected value of the
argument. We define the estimation of the in sample explanation efficiency as the Rsquared
of the previously defined variables (17) (18):
    etR ,ˆ 2
 (19)
7 Empirical results
In our empirical analysis we apply daily returns from the Center for Research in Security
Prices (CRSP) database in a period from 1985 to the end of 2011. We randomly selected
150 securities from the S&P500 index components, which were available for the full
period. The market return CRSP value weighted return. The index tracks the return of the
New York Stock Exchange (NYSE), the American Stock Exchange (AMEX) and NASDAQ
stocks. The risk-free rate is the return of the one-month Treasury bill from the CRSP.
7.1 Characterizing the diversification effect
We investigate whether the entropy is able to measure the reduction of risk by
diversification. We generate 10 million random equally weighted portfolios with different
number of securities involved (at most 100,000 for each size) and we estimate the risk of
each   portfolio   with   standard   deviation,   Shannon   and   Rényi   entropy   by   using   daily   risk  
premiums. Both types of entropy functions are calculated by histogram based density
function estimation, with 175 bins for Shannon entropy and 50 bins for Renyi entropy.
246
Figure 1 Average value of risk and risk reduction vs. number of securities in portfolio
Figure 2 Explaining power of risk measures in long period
0
1
2
3
4
5
6
7
8
1 10 100
Averageriskmeasure
Number of securities in portfolio
Shannon Rényi StDev
0%
10%
20%
30%
40%
50%
1 10 100
Averageriskreduction
Number of securities in portfolio
Shannon Rényi StDev
-0,02
0
0,02
0,04
0,06
0,08
0,1
0,12
0,14
0 1 2 3 4 5
E(ri-rF)
Risk (std)
Standard deviation
E(ri-rF) = 0.0170 + 0.0085*std
R²  =  0.0783
-0,02
0
0,02
0,04
0,06
0,08
0,1
0,12
0,14
0 0,5 1 1,5 2
E(ri-rF)
Risk (beta)
Beta
E(ri-rF) = 0.0209 + 0.0151*beta
R²  =  0.0617
-0,02
0
0,02
0,04
0,06
0,08
0,1
0,12
0,14
0 2,5 5 7,5 10 12,5 15
E(ri-rF)
Risk (H1)
Shannon entropy
E(ri-rF) = 0.0091 + 0.0034*H1
R²  =  0.1298
-0,02
0
0,02
0,04
0,06
0,08
0,1
0,12
0,14
0 2,5 5 7,5 10 12,5
E(ri-rF)
Risk (H2)
Rényi  entropy
E(ri-rF) = 0.0059 + 0.0049*H2
R²  =  0.1571
247
Figure 1 shows the average value of risk measures for different elements in portfolio. The
diversification effect is characterized by the entropic risk measures as well as the
standard deviation. By 10 random securities involved in the portfolio approximately 40%
of risk reduction can be achieved comparing to a random single security portfolio based
on all of the three investigated risk estimators. These results suggest that entropy shows
similar but not the same behavior to standard deviation, so it can serves as a good
measure of risk.
7.2 Long term explanatory power
In order to evaluate how efficiently the risk measures explain the expected risk premium
in a long period, we estimate the risk for each security as standard deviation, CAPM beta,
Shannon- and  Rényi  entropy  based  on  the  full  period  (denoted  by  P1). While the single
explanatory variable is the risk measure, the target variable is the expected risk
premium of the security. We apply the explanatory power estimation by calculating  ˆ
(R-squared) for each risk measures.
Figure 2 shows the efficiency of explaining the expected risk premium by risk measure,
where X-axis is risk measure and the Y-axis measures the expected daily risk premium.
The CAPM beta performs the poorest with 6.17% efficiency. However the explanatory
power of standard deviation (7.83%) is higher than CAPM beta, both of entropies
perform significantly better, 12.98% for Shannon entropy and 15.71%  for  Rényi  entropy.  
Based on the equation of linear regressions, the average unexplained risk premium
(intersect on axis Y)  for  entropies  (0.0091,  0.0059)  is  lower  than  the  standard  methods’  
(0.0170 for standard deviation and 0.0209 for CAPM beta).
7.3 Short term explanatory power
However, attractive results are achieved in-sample,  it  doesn’t  not  necessary  result  high  
efficiency out-of-sample. Therefore we took several 10-year periods, shifting the starting
years by one year from period (1985-1994) to period (2002-2011). Because the full data
set consists of 27 complete years, we applied 18 10-year periods. We split each 10-year
period into two 5-year short periods (P2i and P2o), where the risk measures are
estimated based on the first period and the efficiency of forecasting is measured in the
second period. We also examined the risk measures in-sample to compare the results
with the long term ones.
Table 1 Efficiency of explaining risk premium in different samples
Risk measure iP1
ˆ iP2
ˆ oP2
ˆ  iPR 2
ˆ  oPR 2
ˆ
Standard deviation 7.83% 7.94% 9.70% 0.73 0.63
Beta 6.17% 13.31% 6.45% 0.95 0.99
Shannon entropy 12.98% 13.38% 10.15% 0.67 0.62
Rényi  entropy 15.71% 12.82% 9.34% 0.62 0.60
Table 1 summarizes the explanatory power of the investigated risk measures in different
samples. The first column ( iP1
ˆ ) shows the result of the long term analysis, the second
and third rows ( iP2
ˆ , oP2
ˆ ) contains the average efficiency measured in and out of short
term samples, respectively, and the last two columns (  iPR 2
ˆ ,  oPR 2
ˆ ) measures the
relative standard deviation of the efficiency applying in and out of sample test for short
periods. While the standard deviation risk measure performs almost the same in long and
short  run  (7.83%  vs.  7.94),  it’s  forecasting  efficiency  is  surprisingly  good  (9.70%).  While  
the explanatory power of CAPM beta in long period is low (6.17%) the average efficiency
in short periods is more than twice as much higher (13.31%). Comparing the results in
and out of sample, the forecasting power is relatively low (6.45%) which suggest that
248
the model may be over fitted for training sample. The Shannon entropy performs better
in  each  sample  versus  the  standard  deviation  and  the  CAPM  beta.  Rényi  entropy  shows  
the  highest  explanatory  power  in  the  long  run;;  however  in  short  periods  Rényi  performs  
worse than Shannon entropy. Comparing the reliability of risk estimators, the standard
deviation of the in and out of sample results is the lowest for entropy risk measures, and
the highest for CAPM beta.
8 Conclusions
Entropy combines the advantages of CAPM beta and standard deviation. It captures risk
without using any information about the market; it is capable to measure the risk
reduction effect of diversification. The explanatory power for expected return in sample is
better than CAPM beta, especially in the long run which contains bullish and bearish
periods; the predictive power for expected return is higher than standard deviation. Both
Shannon  and  Rényi  entropy  gives  more  reliable  risk  estimation;;  their  explanatory  power  
exhibits significantly lower variance compared to CAPM beta or to the standard deviation.
References
Beirlant,   J.,   Dudewicz,   E.J.,   Györfi,   L.   Meulen,   E.C.   (1997).   Nonparametric Entropy
Estimation: An Overview. International Journal of the Mathematical Statistics Sciences, 6,
pp. 17-39
Boltzmann, L. (1872). Weitere   studien   über   das   wärmegleichgewicht   unter  
Gasmolekülen.  Wiener Berichte, 66, pp. 275–370; in WA I, paper 23.
Clausius, R. (1867). The mechanical theory of heat: with its applications to the steamengine
and to the physical properties of bodies. J. van Voorst.
Dionisio, A., Menezes, R. & Mendes, D.A. (2006). An econophysics approach to analyse
uncertainty in financial markets: an application to the Portuguese stock market.
European Physical Journal B, 50, pp. 161-164.
Erdős,   P.,   Ormos   M.   (2009).   Return   Calculation   Methodology: Evidence from the
Hungarian Mutual Fund Industry. Acta Oeconomica, 59(4), pp. 391–409.
Freedman, D., & Diaconis, P. (1981). On the histogram as a density estimator: L2 theory.
Probability Theory and Related Fields, 57(4), pp. 453-476.
Kirchner, U., Zunckel C. (2011). Measuring Portfolio Diversification. arXiv:1102.4722 [q-
fin.PM]
Markowitz, H.M. (1952). Portfolio Selection. Journal of Finance, 7(1), pp. 77–91.
Nawrocki, D.N., & Harding, W.H. (1986). State-value weighted entropy as a measure of
investment risk. Applied Economics, 18(4), pp. 411-419.
Rényi,   A.   (1961).   On   measures   of   information   and   entropy.   Proceedings of the 4th
Berkeley Symposium on Mathematics, Statistics and Probability 1960. pp. 547–561.
Philippatos, G.C., & Wilson, C.J. (1972). Entropy, market risk, and the selection of
efficient portfolios. Applied Economics, 4(3), pp. 209-220.
Scott, D.W. (1979). On optimal and data-based histograms. Biometrika 66(3): 605-610.
Shannon, C.E. (1948). A mathematical theory of communication. The Bell System
Technical Journal, 27, pp. 379423.
Sharpe, W.F. (1964). Capital Asset Prices - A Theory of Market Equilibrium Under
Conditions of Risk. Journal of Finance, 19(3), pp. 425–42.
249
Analysis of phishing in the e-banking field
and protection techniques against this type of fraud
Gabriela  Oškrdalová
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  602 00 Brno, Czech Republic
E-mail: oskrdalo@econ.muni.cz
Abstract: The   subject   of   the   paper   “Analysis   of   phishing   in   the   e-banking field and
protection   techniques   against   this   type   of   fraud”   is   an   analysis   of   phishing   in   the  
e-banking (internet banking, phone banking, payment cards etc.) field, an identification
of warning signs of phishing, a creating of models of this type of fraud and
an identification of the basic recommendations for users of e-banking services to reduce
security risks connected with using of e-banking services.
Keywords: e-banking, security, risk, fraud, phishing
JEL codes: G020, G210, G290
1 Introduction
Phishing is one of basic types of frauds in the e-banking we can meet with in the Czech
Republic and all over the world. For the precision we can meet with the phishing not only
in the e-banking field, but for example in the e-shopping field too (generally we can meet
with the phishing by all institutions and systems that can be abused, for example we can
mention PayPal). In this paper we will analyse this type of frauds in the e-banking field,
it means the phishing fraud connected with internet banking, phone banking, payments
cards etc. Except the phishing fraud, so called Lebanon loops, hidden cameras, touch
sensors, counterfeit payment cards, skimming, pharming, spoofing, trashing and thefts
of payment cards belong to the basic types of frauds in the e-banking field1
. Relatively
often particular types of mentioned frauds are combined together to obtain more
sensitive information about an e-banking services user or e-banking services by
a defrauder which he/she uses to increase the success probability of the fraud for
a defrauder, to extend possibilities of using obtained (sensitive) information. For
example, the phishing fraud can be combined with producing and using a counterfeit
payment card. The ground of phishing frauds has been known for a long time and we can
recognize that generally the effort to use or abuse the confidence of people, in this case
of users of e-banking services, connects these frauds together. Despite the fact that the
phishing fraud has been described in many scientific and unscientific publications,
defrauders are successful always2
.
The aim of this paper is to analyze phishing in the e-banking field, identify warning signs
of phishing, create models of this type of fraud and define the basic recommendations for
users of e-banking services to reduce security risks connected with using of e-banking
services. The mentioned aim is reached by using of generally science methods, primarily
analysis, description, synthesis, induction, deduction and modeling. Normative and
positivist methodologies have been employed to reach the aim.
2 Results
As it was recognized, generally the effort to use or abuse the confidence of people
connects individual types of phishing frauds together. Anti-Phishing Working Group
1
Basic types of these frauds in the Czech Republic are described for example in Klufa et al. (2012)
2
More information about phishing attacks in the Czech Republic and in the world (including statistical
information) are published for example on Anti-Phishing Working Group’s  WWW pages in APWG Phishing Attack
Trends Reports.
250
defines phishing as “...a   criminal mechanism employing both social engineering and
technical   subterfuge   to   steal   consumers’   personal   identity   data   and   financial   account  
credentials. Social engineering schemes use spoofed e-mails purporting to be from
legitimate businesses and agencies, designed to lead consumers to counterfeit websites
that trick recipients into divulging financial data such as usernames and passwords.
Technical subterfuge schemes plant crimeware onto PCs to steal credentials directly,
often using systems to intercept consumers online account user names and passwords –
and to corrupt local navigational infrastructures to misdirect consumers to counterfeit
websites (or authentic websites through phisher-controlled proxies used to monitor and
intercept  consumers’  keystrokes).“.
The common ground of phishing frauds in the e-banking field is that a defrauder
addresses a payment card user, an internet banking or phone banking user etc.
(generally an e-banking services user) under somebody else’s   identity   and   requests  
notification of sensitive information (personal data, passwords, logins, PIN codes,
account numbers, payment card numbers, date of payment card expiry etc.). The
defrauder gives reason for this request for example with an effort to increase safety,
software updating, verifying of access functionality after a failure of the system, a failure
of a required transaction, an user’s   account   blocking   or   with   a   possibility   of   more  
advantageous conditions for using of services. In these cases the defrauder often passes
off as someone else to increase the success probability of the phishing fraud. Banks,
supervisory authorities, e-shops, server operators are favourites.
The phishing defrauder uses different ways to contact a potential user of e-banking
services, a potential victim of the phishing fraud, sends e-mails, letters, SMS messages,
calls a potential victim etc. There are many different ways of obtaining sensitive
information too. Nowadays the internet belongs to the standard ways of obtaining
sensitive information of the phishing fraud victim. In this case the victim logs on
fraudulent WWW pages according to defrauder’s   instructions. These fraudulent WWW
pages have been created by the phishing defrauder and they look as original WWW
pages of the subject that has contacted the user of e-banking services (bank, supervisory
authority, e-shop, server operator etc.). The victim fills in a form by requested
(sensitive) information on these WWW pages. The phishing defrauder can obtain
sensitive information about user of the e-banking services by e-mail, letter, phone call,
SMS message, etc. All these ways of obtaining sensitive information about the victimized
user of e-banking services have in common together that the victimized user of
e-banking services believes that has been contacted by real, legitimate bank, the real
supervisory authority, the e-shop etc., so the real, legitimate subject, not by a defrauder.
These victimized users of e-banking do not realize that the real, legitimate subjects do
not communicate with users of their services by this way and they do not ever want the
sensitive information about their users of services by this way. So victimized users of
e-banking services believe it is all right and they do not suspect that they have become
a victim of a phishing fraud.
To increase the credibility of a phishing attack and to increase the number of victimized
users of e-banking services, the phishing defrauders use some tricks. Except the
mentioned look of fraudulent WWW pages which are same or very similar to the original
WWW pages of the user’s   bank,   e-shop, real supervisory authority etc., phishing
defrauders use similar contact e-mail addresses (for example e-mail addresses with
a transposed letter order, reduced e-mail addresses, e-mail addresses with using
a  supplemented  symbol  as  “-“).  By  links  on  fraudulent  WWW  pages,  phishing  defrauders  
sometimes hide the WWW address line or its part to reduce the risk that a potential
victimized user of e-banking services uncovers an attempt on a phishing fraud.
If the phishing defrauder is successful and the addressed user of e-banking services
believes the defrauder and tells or sends him some sensitive information, it is only
a question when the phishing defrauder abuses obtained sensitive information in his
favor. He can use obtained information by oneself or can sell it to another person.
251
The gravity of phishing frauds can be seen in the fact that it is relatively easy to address
many chosen (potential) users of e-banking services for a defrauder and some of them
believe the defrauder and tell or send some sensitive information. If only 5 % of
addressed   users   believe   the   defrauder   (as   Polanský   and   Řeháčková (2008) mention in
Practical financial handbook for everyday for example), so by a great number of
addresses potential victims, phishing is an important threat for all subjects in the ebanking
field. In this connection we should not forget to mention nowadays favored
social networks (Facebook, Twitter, LinkedIn etc.) when it is possible to gain a lot of
information about their users. If the social network user believes the defrauder and gives
some entering data as the user name and password, the defrauder can use them to gain
easily much more (additional) information and the identity of this victimized, trustful user
can be stolen by the phishing defrauder. As early as in year 2006, Kirk (2012) warned of
the dangerous that is associated with social networks.
Figure 1 Abuse of internet/phone banking with phishing
The phishing defrauder creates
fraudulent WWW pages with a form
to enter sensitive information
The phinshing defrauder sends
an e-mail to user of internet/phone
banking with the reference
to the created fraudulent WWW
pages with a request for filling
of requested information
The user of internet/phone banking
opens the fraudulent WWW pages
through the sent reference
The user of internet/phone banking
fills the form with sensitive
information about his account,
safety elements (including entry
information as a user name or a
password), etc. – the defrauder
obtains sensitive information
The defrauder knows necessary
sensitive information
The defrauder transfers money from
the  deceived  user’s  account
The defrauder passes off as the
user, logs  in  to  the  user’s  
internet/phone banking
252
2.1 Models of phishing frauds
As we have noted there are many ways for realizing of phishing frauds. One of possible
ways of this type of fraud in the internet/phone banking field is represented in figure 1
“Abuse of internet/phone banking with phishing”.  Figure  2  “The  fraud  with  a  counterfeit
payment  card  and  with  phishing”  represents  one  of  possible  ways  of  phishing  fraud  in  the  
payment card field. As we can see the ground of phishing frauds is similar.
Figure 2 The fraud with a counterfeit payment card and with phishing
2.2 Recommendations to reduce the phishing risk
Recommendations to reduce phishing risk are variable. As the first we should mention
that users of e-banking services should not respond to e-mails, letters, SMS messages,
phone calls which content a request to tell or send sensitive information. Banks as other
financial institutions or payment system keepers, supervisory authorities, e-shops, server
operators etc. do not communicate with their users (clients) in security questions in this
way. In these cases it is very probable that it is an attempt to obtain some sensitive
The phishing defrauder creates
fraudulent WWW pages with a form
to enter sensitive information
The phinshing defrauder sends
an e-mail to user of payment card
with the reference to the created
fraudulent WWW pages with a
request for filling of requested
information
The user of payment card opens the
fraudulent WWW pages through the
sent reference
The user of payment card fills the
form with sensitive information
about the payment card, safety
elements (as a PIN code), etc. – the
defrauder obtains sensitive
information
The defrauder produces a
counterfeit payment card
The defrauder uses the counterfeit
payment card to withdraw cash
from  the  deceived  user’s  account,  
pays for goods and services etc.
The defrauder knows necessary
sensitive information about the
payment card and its user
253
information about users of e-banking services. For mentioned reason the addressed
person should not visit the WWW pages by the link that the sent e-mail or SMS message
contain.  The  user’s  curiosity  can  be dangerous because in these cases it is not possible to
rule out another type of an attack in the e-banking field and the user’s   personal
computer can be attacked. Users should always respect the recommendation that writing
WWW address in the address line of an internet browser by the user is better and safer
than using a link to enter WWW pages (it is valid for communication with a bank, for
entering a payment system, a social network etc.). When writing WWW address, the user
should be careful and should control possible mistakes because he could enter fraudulent
WWW pages created by a phishing defrauder and then he could provide sensitive
information to the phishing defrauder.
We can recommend to users of e-banking services not making an intended transaction or
break realized transaction if the user discovers that the system does not behave
standardly, as usual, it asks entering sensitive information which does not need or which
it has not wanted to enter sooner or which it should not want to enter. In these cases the
user should contact the bank (or another subject, according the particular case) and
describe the situation. It is possible to contact the police too.
To identify the attempt of a phishing fraud the low quality level of an attack can help (we
can mention in a foreign language written e-mails, SMS messages or WWW pages, with
grammatical or stylistic mistakes, WWW pages with mistakes in format, used colours,
contents spreading etc.). But it is true that nowadays phishing attacks are often on very
high quality level which increases the success probability of this attack. To discover
a phishing attack using unprotected communication of an internet browser with the
server  (a  WWW  address  begins  “http://”  instead  of  “https://”  in  the  address  line).  The
user of e-banking services should pay attention to the beginning of the WWW address
stated in the address line of the internet browser which signs the used communication
regime  type  (protected  with  “https://”  and  unprotected  with  “http://”).      
To reduce phishing risk we can recommend a control of the WWW pages authenticity by
way of the certification verifying. In this case the user of e-banking services can verify
that the visited WWW pages have a certificate which has been issued for the subject
whose WWW pages the user of e-banking services wants to visit and for these WWW
pages. Preferably the certificate should be issued by a reputable company. The user can
verify the WWW pages authenticity for example by a click on a padlock symbol (this
symbol can be situated in the right bottom corner on the lath or in the address line of the
used internet browser).
To reduce phishing risk it contributes when user of e-banking services knows and
observes basic security rules for safe using of personal computer and internet (as using
only trustworthy computers with periodically updated software (an operating programme,
an antiviral and an antispyware programme etc.), using powerful firewalls, computers
which the user controls, where he does not visit suspicious WWW pages, where he does
not use suspicious programmes etc.). In connection with phishing we should remind
common e-banking   services   security   recommendations   as   “use   supplementary   safety  
elements  (as  sending  authorisation  SMS  messages  etc.)”,  “use  day,  week  or month limits
for e-banking  (internet  banking,  phone  banking,  payment  card  etc.)  transaction”,  “check  
carefully  your  bank  account  and  payment  card  statements,  when  you  get  them”,  “if  you  
find a discrepancy in your bank account and payment card statements (for example
a transaction you have not made by your payment card or a cashless transfer you have
not assigned, a cashless transfer on an unknown account number, the amount of
a transaction is higher than it really was), you should contact your bank, payment card
issuer  and  complain  about  this  transaction”  too.    
We can summarize that users of e-banking services have the key role to reduce phishing
risk.
254
3 Conclusions
Phishing is one of basic types of frauds in the e-banking we can meet with in the Czech
Republic and all over the world. The ground of phishing frauds has been known for a long
time and we can recognize that generally the effort to use or abuse the confidence of
people connects these frauds together. Usually a defrauder addresses to user of
e-banking services under somebody else’s  identity  and  requests notification of sensitive
information (personal data, passwords, logins, PIN codes, account numbers, payment
card numbers, payment card date of expiry etc.). Despite the fact that the phishing fraud
has been described in many scientific and unscientific publications, defrauders are
successful always.
The gravity of phishing frauds can be seen in the fact that it is relatively easy for
a defrauder to address many chosen (potential) users of e-banking services and some of
them believe the defrauder and tell or send some sensitive information.
Recommendations to reduce phishing risk are variable. We can highlight that users of
e-banking services have the key role to reduce phishing risk. So it is very important to
reduce phishing risk so that users of e-banking services know and observe basic security
rules for safe using of personal computer and internet as common e-banking services
security recommendations too.
Acknowledgments
The paper was created with the support of institutional support of Ministry of Education,
Youth and Sports.
References
ANTI-PHISHING WORKING GROUP: APWG Phishing Attack Trends Reports. Available at:
<http://www.antiphishing.org/resources/apwg-reports/>.
ANTI-PHISHING WORKING GROUP: Phishing Activity Trends Report: 4th Quarter
2012.Available at: <http://docs.apwg.org/reports/apwg_trends_report_Q4_2012.pdf>.
Kirk, J. (2012). Phishing Scam Takes Aim at MySpace.com : Social networking site is
increasingly a target for identity thieves. PCWorld [on-line]. [18 January 2012]. Available
at: <http://www.pcworld.com/article/125956/phishing_scam_takes_aim_at_myspace
com.html>.
Klufa, F., Scholz, P., Kozlova, M. (2012). Cashless payment frauds in the Czech Republic
(in Czech). Czech Consumer Association [on-line]. [10 January 2012]. Available at:
<http://www.konzument.cz/publikace/soubory/pruvodce_spotrebitele/A5_bezhotovostni
_podvody.pdf>.
Polanský, I., Křeháčková, Z. (2008). Practical financial handbook for everyday (in Czech).
Prague: SOS – Association of consumers’defence, 39 p.
255
Single supervisory mechanism bank
as a first step to the banking union
Dalibor Panek
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  602  00  Brno,  Czech  Republic
E-mail: panek@econ.muni.cz
Abstract: The text focuses on the reasons for the establishment, structure, and issues
with banking union in the European Union. Describes the basic element and the first step
of implementation, a single supervisory mechanism bank. Includes banking entities under
the direct supervision of the European Central Bank and supervision of national
supervisors. Contain some problems when creating a banking union and a single bank
supervisory mechanism. In final assessment provides selected for successful
implementation of the project and its benefits.
Key words: Regulation and supervision, banking sector, union bank, single supervisory
mechanism, financial system stability
JEL codes: G28
1 Introduction
The first step towards a system known as Union Bank was made by the European
Commission proposal for the uniform performance monitoring mechanism in banks in
september 2012. Supervision of banks domiciled in countries eur area takes the
European Central Bank (ECB). Countries outside the euro area may enter this system of
supervision voluntarily enter. The proposal came force by agreeing to the European
Parliament and all EU member states. In December 2012 was a proposal to create a
banking union approved by the President and Government of the EU Member States. In
march 2013 agreed MEPs emergence of banking union - create a system of banking
supervision the euro area.
2 The single bank supervisory mechanism
When processing the issue of building a single bank supervision and bank Union EU in the
introduction using method description, in the section describing the current processes in
this area and conclusion using the methods of analysis and comparison.
Creating   a   systém   single   bank   supervisory mechanism (SSM) represents the first step
towards a banking union European Union. During the year 2013, the extension of
supervision by the European Central Bank to Bank decisive in the financial system of the
European Union, since the beginning of 2014, the supervision of banks held by the
European Central Bank has at all banks, savings banks and credit cooperatives operating
in euro area countries (6000 banks).
The supervision shall be exercised by the European Central Bank and the national
supervisory authorities. The European Central Bank will be able to exercise supervision if
your decision. Above the big banks will exercise direct supervision ECB. Oversight bodies
are major banks according to their importance for the economy of the EU countries (on a
consolidated basis, 150 banks):
 total assets of the bank at least 30 billion EUR;
 share of total bank assets to GDP EU countries more than 20%;
 as determined by the ECB for consultation with national surveillance;
 as determined by the ECB according to the extent of cross-border activities in the
EU;
 bank requesting financial support from the European reserves;
 outside these criteria three largest banks in each Member State.
256
System of consolidated supervision of banks (SSM) pays mandatory for banks of the euro
area, other EU Member States have the option of voluntary input. The European Central
Bank will take over by Supervisors Board fundamental powers of national supervisors,
the granting and withdrawal of licenses, supervision of transparency of financial
operations, assessment of mergers and acquisitions and compliance capital requirements
under the CRD (Capital Requi rement Directive). National supervisory authorities shall
exercise supervision in consumer protection and regular supervision of banking
activities.
Uniform system of bank supervision mechanism is only a first step towards a banking
union of anticipated four pillars:
 single bank supervisory mechanism (SSM);
 single resolution mechanism (SRM) with a set of tools and powers in relation to
banks;
 common system of deposit protection (EDIRA);
 financial reserves to cover losses of deposits and the possibility of bank
recapitalization.
The single bank supervisory mechanism can be seen as a tool to achieve financial
stability of the EU financial market, financial integration and maintenance of national
financial policies of EU member states. The reason is the existence of a high degree of
interconnectedness of financial markets and institutions European Union in need of
identifying and solving problems in the financial market.
A very important reason for the creation of a single supervisory system had the
possibility of recapitalization of banks without state interference. These options make
assumptions decoupling between states and banks associated with their growing debt.
Central banks are contemporary theory and practice of a recognized financial institutions
of an integrated financial sector supervision. Therefore, you can also mark the European
Central Bank as a trusted financial institution for the location of the single supervisory
mechanism in European Union.
As arguments to support this solution can also provide generally accepted reasons:
 the ECB's monetary policy requires continued support financial system stability
 system macro and micro prudential supervision requires continuous interaction
 exercise effective supervision is conditional information synergies
An open question to discussion and resolution of discrepancies remain between the
institutional power of supervision and responsibility of the national supervisory
authorities for the financial sector.
Another problem of a single bank supervisory mechanism is the specification of the rules
of their transition and supervision of major banks and banks supervised by national
supervisors. Methodology division of bank supervision between EU and national
supervision does not involve real risks of banks for the stability of the financial sector.
Through the open problems, the new system of consolidated supervision of banks in EU
system mostly positively accept.
System comes with a gradual solution still missing national mechanisms in the event of
crisis situations in the financial sector, supports the creation of standard mechanisms
rescue strategies in relation to financial market participants, including the protection of
deposits. Elements of debilitating creation of a single bank supervisory mechanism is to
be seen in the negative attitude of some countries to participation in this system, denial
of national supervisory authorities and government delegation of supervisory powers to
the European Supervisory Authority.
3 Conclusions
Creating a banking union is a major strategic objective gradually realized through
successive step. The first one is a single bank supervisory mechanism, whose current
development and addressing open issues to be considered in this context.
257
The stability of the financial system is fundamentally influenced by the stability of the
banking sector as a major player in the financial market. This fact corresponds to the
structure of consolidated supervision of banks for the stability of banks, transparency of
financial operations and creating a ready and effective rescue systems in case of failure
of financial markets. A single supervisory system creates a shared responsibility of the
EU for financial stability, reduces problematic linking banks and the state bank
recapitalization.
A fundamental prerequisite for the effectiveness and success of supervision is expertise
and professionalism of its setting and implementation of the possibility of effective tools
for observation of the rules of financial institutions. Continued integration of the EU
financial sector towards enhance its stability is conditional upon a general understanding
of these processes, their significance and broad political leaders.
References
Pavlát, V.,   Kubíček, A. (2010). The regulation and supervision of financial markets.
Prague:  VŠFS.
Constancio, V. (2012). Establishment of the Single Supervisory Mechanism – the first
pillar of the Banking Union. Available at: <http://www.bis.org/review/r130201a.
pdf?frames=0>.
Constancio, V. (2013). Establishment of the Single Supervisory Mechanism, Available at:
<http://www.bis.org/review/r130130e.pdf?frames=0>.
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Utilization  of  Benford’s Law by Testing Government
Macroeconomics Data
Michal  Plaček
SVŠE  (Private  College  of  Economic  Studies  in  Znojmo)
Department of Finance and Accounting
Loucká  21, 669 02 Znojmo, Czech Republic
E-mail: placek@svse.cz
Abstract: This article builds on research published in the article "Fact and Fiction in EU
Govermnental   Economics   Data"   (Rauch,   Göttsche,   Brähler,   2011).      By   exploring   the  
possibilities   of   applying         Benford´s   Law   as   a   useful   instrument   for   detecting   data  
manipulation, this instrument is applied to the Czech Republic, especially on monthly
data of exports and imports for the period 1996-2012, which make up the trade balance.
We use the Z test as the test criterion and the first and second digits are tested.
Keywords:  Benford´s  law,  Z  test,  Government  data,  manipulation
JEL codes: C16, E01,M42
1 Introduction
The article "Fact and Fiction in EU Governmental Economics Data" (Rauch et al., 2011)
which was published in the German Economics Revue was one of the first applications
Benford’s Law on government economic data. Before aforementioned article, the articles
"On  the  Application  of  Benford´s  Law  of  the  International  Macroeconomic  Statistics" (Nye
and Moul, 2007) and from (Gonzales-Garcia and Pastor, 2009), which confirmed the
theory (Nye and Moul, 2007). Within these, the application is focused mainly on
accounting and tax issues e.g. (Carslaw, 1998); (Nigrini, 1997), mathematics (Hill,
1998), (Morrow, 2009), and examining the results of elections (Decker et al., 2011)
and last but not least, the credibility of scientific results such as regression coefficients
(Diekmann and Jann, 2010).
In the article "Fact and Fiction in EU Governmental Economic Data," the authors focused
on the economic data of the EU-27 from 1999 to 2009. Instead of the convergence test
with   Benford’s distribution, they evaluate data of states according to the extent of the
deviation  of  their  data  from  Benford’s distribution. Within this context, the following data
were tested:
• Government deficit and debt
• Government revenue, expenditure and main aggregates
• GDP and its main components
• Balance sheet, consolidated assets and liabilities
The resulting findings were that EU countries, especially Greece, show a significant
deviation   from   Benford´s   data distribution. The problems of Greek national accounting
confirmed further development. Among the states collectively known as PIGS, test data
revealed significant  deviations  from  Benford’s  distribution only in Ireland. If we focus on
the Czech Republic, the authors showed the smallest means of the chi-square test.
2 Theory of Benford’s  Law
The  first  mention  of  Benford’s Law, originally also called the "First digit law" was made by
the American astronomer Simon Newcomb in 1881 in an article entitled "Note on the
Frequency of Use of the Different Digits in Natural Numbers" in The American Journal of
Mathematics. The article says that the probability that the first digit being the number 1
is 0,111 (1:9 = 0,1111), as we might expect, but 0,301. This thesis was rediscovered by
Frank Benford in the article "The Law of Anomalous Numbers", who dealt with this
problem more systematically and surveyed more than 20,000 data samples (Barrow,
2011).
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The mathematical applications of Benford's law are mainly dealt with by Theodore Hill
(For further details refer to Berhger and Hill, 2011). We can consider the following
fundamental theorem as being essential: "If it is chosen as a random distribution and if
from any such distribution given a random sample, significant numbers from this
distribution converge to the logarithmic distribution  alias  Benford’s distribution."
Probability of the first  digit  D1  according  to  Benford’s distribution:
𝑃(𝐷1 = d1) =  log(1 + ) 𝑑1 ∈ {1 … 9} (1)
Probability of the second  digit  D2  according  to  Benford’s distribution:
𝑃(𝐷1 = d1) =  log(1 + ) 𝑑1 = 1 𝑑2 ∈ {1 … 9} (2)
If we extend our analysis to the occurrence the number on n the positon, we will use this
general formula:
𝑃 {𝑑} = ( )
∑ ln 1 + (3)
Table 1 Benford’s distribution
Number
First
Digit
Second
Digit
Third
Digit
0 - 0,120 0,102
1 0,301 0,114 0,101
2 0,176 0,109 0,101
3 0,125 0,104 0,101
4 0,097 0,100 0,100
5 0,079 0,097 0,100
6 0,067 0,093 0,099
7 0,058 0,090 0,099
8 0,051 0,088 0,099
9 0,046 0,085 0,098
Source: Waltrin, 2008
Other mathematical properties of Benford’s law are as follows:
 Multiplication   of   Benford´s   distribution   by      any   constant   results   in   the   same  
distribution
 Can be applied to all numerical systems
 Multiplication, division, squaring, addition and subtraction   of   Benford´s  
distribution  results  again  in  the  Benford´s  distribution  (Watrin,  2008)
Benford´s  law  can  be  used  to  detect  these  data  manipulation:
 Rounding financial performance by managers, for example roundings profit from
789,000 to 800,000
 Rounding up net income and EPS (earnings per share)
 Rounding losses down (Nigrini, 1997)
 Duplication of financial figures such as invoices
 Deleting data
 Rewriting values
In  order  to  use  of  Benford´s  law,  data  should  meet  the  following  conditions:
 All the data in the file must be in the same units
 There must be a data limited the maximum and minimum values
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 Data can not be numbers used for identification,and numbers and numbers which
have been generated randomly
 Data should include a rather small number. "(TPA Horwath, 2011)
 It is desirable to have more data files
 Data should not be influenced by psychology, such as prices ending in the number
99
It is appropriate to use data that have a mean greater than the median, and a positive
kurtosis. In general, the larger the ratio of mean and median, the more suitable the data
is  for  Benford´s  test  (Durtschi et al., 2004)
In  the  following  table  are  types  of  data,  which  can  be  use  for  application  of  Benford´s  
test
Table 2 Suitability  of  Data  for  Benford’s test
Suitable data Unsuitable data
Accounts payble Numbers of checks
Accounts receivable Numbers of invoices
Wages ZIP Codes
Sales Psychological prices (e.g. 999)
Expendictures ATM withdrawals
Whole year transaction Thefts
Source: Durtshi et al., 2004
Another limitation   of  Benford’s  law  is   the types of fraud which can not be detected by
utilizing this procedure.   Very   simply   Benford’s   law will help us determine whether the
overall data set of observations were added, removed, or vice versa. Concerning
transactions, which were not recorded, such as a bribe, stealing assets, or duplicate
transactions, such   as   an   invoice   number,   Benford’s test can   not   be   applied.   Benford´s  
test also does not help us to detect fictitious employees or the same bank account
numbers (Durtschi et al., 2004).
3 Practical part
In   this   section   we   will   apply   Benford´s   law   to   data   published   by   the   Czech   Statistical  
Office on foreign trade during the period 1996 to 2012, specifically to the export and
import of goods. Their difference defines the trade balance, which can be active or
passive, and explains whether the state imports or exports more. The trade balance is
often mentioned by the media as one of the indicators of economic performance.
The methodology for data collection CSO is as follows:
– Export: The export value of goods sent abroad, and crossed the state border for the
purpose of permanent or temporary retention abroad. Total exports consist of
exports to the EU and exports to countries outside the EU.
– Import: Import value of goods received from abroad and crossed the state border
for the purpose of permanent or temporary retention at home. Total imports consist
of imports from the EU and imports from countries outside the EU.
The Czech Statistical Office publishes data for individual months, so the sample test data
for export and import will contain 204 individual items, and a total of 408 items will be
tested.
For   testing   convergence   with   Benford´s   law   we   will   use   the   Z   test,   which   can   be  
calculated as following:
Z = (4)
where: p0 – is the observed proportion in the dataset;
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pe – is  the  expected  proportion  based  on  Benford´s  law;
si – is the standard deviation of particular digit;
n - is the number of observations (the term 1/(2n) is a continuity correction factor
and is used only when it is a smaller than absolute value term).
Z-Stat shows the statistical significance of the difference between the two proportions.
The significance takes into account the size of the difference (over or under), the
expected proportion, and the sample size. Scores above 1,96 are significant at the 0.05
level, and above 2,57 are significant at the 0,01 level.
3.1 Results for export
a) At the 0,05 level of significance, we test the following hypothesis: H0 = occurrence
of  each  digit  in  the  first  place  converges  to  the  Benford´s  distribution
H1 =  occurrence  of  each  digit  in  the  first  place  does  not  converge  to  the  Benford´s  
distribution.
Table 2 Results for the first digit
Digit Empirical Benford Diff. Abs. Diff Z stat
1 0,431 0,301 0,130 0,130 3,982
2 0,245 0,176 0,069 0,0649 2,496
3 0,00 0,125 -0,125 0,125 5,291
4 0,039 0,097 -0,058 0,058 2,667
5 0,064 0,079 -0,015 0,015 0,688
6 0,044 0,067 -0,023 0,023 1,165
7 0,069 0,058 0,011 0,011 0,500
8 0,049 0,051 -0,002 0,002 0,138
9 0,059 0,046 0,013 0,013 0,725
The test showed high levels of Z statistics in particular with the numbers 1, 2, 3, 4, which
means that we must reject H0 at this level of significance.
b) At the 0,05 level of significance, we test the following hypothesis:
H0 = occurrence of all individual digits on the second position converges to the
Benford´s  distribution
H1 = occurrence of each digit in the second place do not converge to the
Benford´s  distribution
Table 3 Results for the second digit
Digit Empirical Benford Diff. Abs. Diff Z stat
0 0,147 0,120 0,027 0,027 1,097
1 0,152 0,114 0,038 0,038 1,601
2 0,103 0,109 0,006 0,006 0,157
3 0,069 0,104 -0,036 0,036 1,554
4 0,074 0,100 -0,027 0,027 1,157
5 0,103 0,097 0,006 0,006 0,184
6 0,093 0,093 0,000 0,000 0,011
7 0,078 0,090 -0,012 0,012 0,405
8 0,098 0,088 0,010 0,010 0,405
9 0,083 0,085 -0,002 0,002 0,085
Z test did not show any value above 1,96. We must accept H0.
3.2 Results for import
a) At the 0,05 level of significance, we test the following hypothesis:
H0 =   occurrence   of   each   digit   in   the   first   place   converges   to   the   Benford´s  
distribution
H1 =  occurrence  of  each  digit  in  the  first  place  does  not  converge  to  the  Benford´s  
distribution.
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Table 4 Results for the first digit
Digit Empirical Benford Diff. Abs. Diff Z stat
1 0,529 0,301 0,228 0,228 7,035
2 0,216 0,176 0,040 0,040 1,393
3 0,000 0,125 -0,125 0,125 5,291
4 0,000 0,097 -0,097 0,097 2,761
5 0,025 0,079 -0,055 0,055 2,762
6 0,074 0,067 0,007 0,007 0,236
7 0,078 0,058 0,020 0,020 1,099
8 0,059 0,051 0,008 0,008 0,339
9 0,020 0,046 -0,026 0,026 1,620
The test showed high levels of Z statistics in particular with the numbers 1,3,4,5, which
means that we must reject H0 at the level of significance.
b) At the 0.05 level of significance, we test the following hypothesis:
H0 = occurrence of all individual digits on the second position converges to the
Benford´s  distribution
H1 = occurrence of each digit in the second place do not converge to the
Benford´s  distribution
Table 5 Results for the second digit
Digit Empirical Benford Diff. Abs. Diff Z stat
0 0,172 0,120 0,052 0,052 2,175
1 0,137 0,114 0,023 0,023 0,940
2 0,137 0,109 0,028 0,028 1,197
3 0,083 0,104 -0,021 0,021 0,867
4 0,054 0,100 -0,046 0,046 2,089
5 0,132 0,097 0,036 0,036 1,606
6 0,088 0,093 -0,005 0,005 0,132
7 0,074 0,090 -0,017 0,017 0,716
8 0,044 0,088 -0,043 0,043 2,072
9 0,078 0,085 -0,007 0,007 0,211
The test showed high levels of Z statistics in particular with the numbers 0,4,8 which
means that we must reject H0 at the level of significance.
4 Conclusions
The test results show that the first digit demonstrates a significant deviation from
Beford's distribution. These findings do not automatically equate to the suspicion of the
manipulation of governmental macroeconomic data. When interpreting the results of the
Czech case, the author inclines towards theses published in the following articles "On the
Application of Benford's Law to International Macroeconomics Statistics" (Nye and Moul,
2007) and "Benford's Law and Data Quality Macroeconomics (Gonzales-Garcia and
Pastor, 2010). Deviations in data from Benford's distribution may be caused by a
structural economic shift during the period 1996-2012, and not by poor quality of data.
It should be also noted that the quality of data affects all macroeconomic transformations
as well as seasonal adjustments. Benford's test is also recommended to be applied to
larger data samples.
References
Barrow D. J. (2011). Benford´s   very   strange   law.   Lecture. Gresham College [online].
Available at: <http://www.youtube.com/watch?v=4iz4EHriYz0&feature=related>
Berhger A., Hill P.T. (2011). A   Basic   Theory   of   Benford´s   Law. [online]. Probability
Surveys MIT, Avaible at:<www.ijournals.org/ps/include/getdoc.php?id=696&article>
263
Carslaw C. A. P. (1988). Anomalies in Income Numbers: Evidence of Goal Oriented
Behavior, Accounting Review 63, 321–327.
Decker J., Myagkov M., Ordeshook C. P. (2011).The   irrelevance   of   Benford´s   law   for  
detecting fraud in election. Capotech/ MIT Voting Technology Project Working Paper.
[online]. Available at: <http://www.vote.caltech.edu/drupal/files/rpeavt_paper/benford
_pdf_4b97cc5b5b.pdf>
Durtschi C., Hillison W., Pacini C. (2004).  The  effective  use  of  Benford’s law to assist in
detecting fraud in accounting data, Journal of Forensic accounting, vol 5, pp.17-34.
Gonzales J.,Pastor G. (2009). Benford’s   Law   and Macroeconomic Data Quality,
International Monetary Fund Working Paper.
Hill, T. P. (1998). The First Digit Phenomenon, American Scientist, 86, 358–363.
Hill, T. P. (1995a). A Statistical Derivation of the Significant-Digit Law, Statistical Science
10, 354–363.
Hill, T. P. (1995b), Base-Invariance  Implies  Benford’s  Law,  Proceedings of the American
Mathematical Society, 123, 887–895.
Nigrini M. (1993). Can Benford's Law be used in Forensic Accounting? Balance Sheet,
June 1993, [online]. Available at:<http://www.nigrini.com/benfordslaw.htm>
Nigrini M., Mittermaier L. (1997). The Use of Benford’s   Law   as   an   Aid   in   Analytical  
Procedures, Auditing A Journal of Practice and Theory. 16, 52–67.
Nye J., Moul C. (2007). The Political Economy of Numbers: On the Application of
Benford’s  Law  to  International  Macroeconomic  Statistics,  B.E. Journal of Macroeconomics,
7. Available at: <http://www.bepress.com/bejm/vol7/iss1/art17>
Rauch,   Göttsche,  Brähler  (2011)  Fact  and  Fiction  in  EU   Governmental  Economics  Data.  
German Economics Revue, 12 (3), 243-255.
TPA HORWATH (2011). Nové   způsoby   odhalování   manipulace   s účetními   daty. [online].
Available at: < http://www.tpa-horwath.cz/upload/files/PDF/Manipulace_s_ucetnimi_
daty_JSK_10_08.pdf>
Watrin C.H. (2008). Benfords Law: An Instrument for Selection Tax Audit Targets? Review
of Managerial Science, 2-3, 219-237.
264
Availability of Information about the Company Insurance
against Natural Hazards on the Czech Insurance Market
Lenka  Přečková
Silesian University in Opava
School  of  Business  Administration  in  Karviná,  Department  of  Finance
Univerzitní  nám.  1934/3,  733  40  Karviná,  Czech  Republic
E-mail: preckova@opf.slul.cz
Abstract: The paper deals with on one of the possible event of information asymmetry
on the insurance market in the Czech Republic. The event is the comprehensiveness
of information about an insurance product. The information about the company insurance
against   natural   hazards   on   the   insurers´   website   is   the   subject   of   the   research.   The  
valuation was carried out till the date 1. 3. 2013. The valuation is focused on the
possibility of looking up the insurance conditions on the website and on the formal
elaboration of insurance conditions by the criterion the number of the extrinsic
information of the insurance product and by the criterion the number of insurance
conditions documents. The goal of the paper is to evaluate the level of the information
publication of insurers about the company insurance against natural hazards. The
qualitative research based on the methods of description, analysis and comparison is the
substance of the paper. The result of paper is the level of information disposal, i.e. the
source of information asymmetry, on the supply side of insurance market.
Keywords: asymmetry of information, information unawareness risk, insurance market
supply, formal presentation of insurance terms and conditions, company property
insurance
JEL codes: D01, G22
1 Introduction
The article elaborates on common practices by insurers in regard to public information
sources of particular insurance products, in this case company insurance against natural
disasters. The area of information on the insurance market relates with the problem
of information asymmetry. Information asymmetry is a widely discussed phenomenon
of contemporary economics. It is demonstrated by accessibility and completeness
of information.   Hořejší   et   al.   (2006)   states   that   information   is   asymmetric, when one
participant in a market knows more than other participants. One side of the market, no
matter if it is the supply or demand one, has a better knowledge of something, whereas
the other is not informed completely. Asymmetrical information is a consequence
of undisclosed activities or information.
Why is information so important in economics? Information is crucial for every economic
activity and studying it represents an effort to draw economic models to actual economic
behavior and decision-making   (Liška,   2012).   Whenever   we   make   decisions,   we   use  
information that we receive and then process. This means information must always be
properly analyzed.
Information which is either incomplete or not supplied at all causes information
asymmetry. The insurance market is one of the most prominent places where
information  asymmetry  occurs.  There  are  many  ways  to  conceal  the  truth.  Daňhel  et  al.  
(2007) points out that the insurance market can be characterized as a market with
mutual information imperfection considering that neither of parties can predict the future
which may bring negative financial consequences to both parties of the insurance
contract, predominantly on the supply side. In the insurance market, there is no
information   asymmetry   on   the   demand   side,   i.e.   on   the   client’s   side   (Daňhel,   2002).  
According to Tříska  (2002),  the  customer  knows  his  or  her  own  risk  level  better  but  it  is  
the insurer who masters insurance terms and conditions, e.g. circumstances under which
the insurance company is entitled to refuse to pay out a claim.
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Why is information in the insurance market so important for clients? Although they
usually do not realize this, information is priceless for clients. Every piece of information
which   is   either   incomplete   or   which   is   not   supplied   at   all   can   lead   to   both   client’s  
and insurer’s   loss.   The   risk   of   being   uninformed   can   be   especially   serious   when  
concluding an insurance contract and also in the event of a claim itself. Complete
information is essential for decision making when concluding an insurance contract, the
risk of financial loss can then be minimized.
The goal of the paper is to evaluate the level of the information publication of insurers
about the company insurance against natural hazards. The evaluation was conducted
on March 1, 2013 and was based on one particular insurance product: company property
insurance against natural disasters. This article evaluates insurers offering this product.
The insurers themselves were selected on the basis of an up-to-date (Dec 31, 2011)
analysis of the Czech insurance market. Information is assessed according to its sources,
on the particular insurance product on the Internet. The article also presents qualitative
research and asks this research question: Does an insurer with high percentage
of written insurance on the insurance market provide total and well-arranged information
about insurance against natural disasters to clients? The research question suggests itself
because it can be supposed that an insurer with a large insurance market share will
maintain a trustworthy attitude to its customers. Clients can assume, not relying on their
own relevant information analysis, that an insurance company with a large market share
is trustworthy and it properly informs its clients and thus minimizes the risks of being
uninformed.
2 Methodology and Data
The goal of the paper is to evaluate the level of the information publication of insurers
about the company insurance against natural hazards. The subject of this research is
relevant information on insurance against natural disasters published on websites
of analyzed Czech insurers. This sort of information can often be found in insurance
“terms  and  conditions”  (TaC).
In order to reach the aim of this research, analysis of the company insurance market
must be conducted. Up-to-date data was collected from individual insurers as published
on Dec 31, 2011 on the website of Czech Insurance Association, in particular: total
written premium, written premium of non-life insurance and written premium of property
insurance. On the basis of this analysis, insurers providing company insurance against
natural disasters were selected.
The first stage of research: Using the method of description, we have to compile an
algorithm with relevant TaC of company insurance against natural disasters. It is also
important to verify whether or not these TaC are accessible on the Internet. This means
that insurers who do not publish their TaC on their websites, must be excluded from the
second stage of this research.
The second stage of research: Using the method of analysis and comparison, we evaluate
the algorithm. Insurers are subsequently divided into three groups:
- Insurers offering one product of company insurance against natural disasters
- Insurers offering one product of company insurance against natural disasters with
an ambiguous search for TaC.
- Insurers offering more products of company insurance against natural disasters.
Criteria for dividing insurers into these groups are: the algorithm of search for relevant
information on company insurance against natural disasters and the number of company
insurance against natural disasters products which are offered by the insurer. The larger
number of insurance products,  the  more  difficult  it  is  to  find  relevant  TaC  on  the  insurer’s  
complex website. Also, this research was conducted on condition that the business
organization is interested merely in the company insurance against natural disasters.
The third stage of research: TaC are evaluated according to their formal presentation.
The criteria for this step are in the number of irrelevant information found and the
266
number of documents representing the TaC. TaC may sometimes include information
on other property insurance products which can lead to the delusion that the insurance
coverage is much larger. This criterion is thus of high importance. The other criterion
(number of documents representing the TaC) can make clients overlook some documents
so they may also overlook some important relevant information.
3 Analysis of the company insurance against natural disasters market
In order to evaluate availability of information, an analysis of the market with company
insurance against natural disasters must be conducted. In this analysis, we use data
published in the Individual results of Czech Insurance Association (ČAP)   members  
in 2011.   The   insurance   market   of   ČAP   members   in   non-life insurance got to
71 447 131 000 Czech crowns in 2011. According to the statistics published by Czech
National   Bank   (ČNB),   the   insurance   market   of   all   insurers   in   non-life insurance got to
83 083 478 000 Czech crowns in 2011.  The  share  of  ČAP  members  in  the  written  benefit  
of the Czech Republic in 2011 was 86 %. Company insurance against natural disasters is
marketed by 13 insurers as shown in the summary below (Table 1).
Table 1 Summary of insurers offering company insurance against natural disasters
# Insurer Abbreviation
1. Allianz  pojišťovna,  a.s. ALLIANZ
2. Česká  pojišťovna  a.s. CP
3. Česká  podnikatelská  pojišťovna,  a.s.,  Viena  Insurance  Group CPP
4. ČSOB  Pojišťovna,  a.  s.,  a  member  of  the  ČSOB  holding CSOBP
5. Generali  Pojišťovna  a.s. GP
6. Hasičská  vzájemná  pojišťovna,  a.s. HVP
7. HDI Versicherung AG, affiliate branch HDI
8. Kooperativa  pojišťovna,  a.s.,  Vienna  Insurance  Group KOOP
9. MAXIMA  pojišťovna,  a.s. MAXIMA
10. Slavia  pojišťovna  a.s. SLAVIA
11. Triglav  pojišťovna,  a.s. TRIGLAV
12. UNIQA  pojišťovna,  a.s. UNIQA
13. Wűstenrot  pojišťovna  a.s. WUST
Source:  Data  processed  according  to  ČAP’s  results  (www.cap.cz,  2013)
Total written premium, non-life written premium and company property written premium
in 2011 (the most recent data refer to March 1, 2013) are presented in Table 2. The
chart arranges subjects according to their percentage in the insurance market (with
company property insurance) in 2011 (PIM). It is obvious that arranging subjects
according to their total written premium (in insurance against natural disasters) shows
different results than arranging them by total written insurance. This difference is
however  not  substantial.  ČP  ranks  first  in  all  categories  of  written  insurance,  KOOP  ranks  
second. The insurance company GP ranks third with its company insurance but not with
its general insurance and non-life insurance. In that case, GP ranks fourth before
ALLIANZ.
4 Results
This part elaborates on research results based on the method described above. Firstly,
insurers with TaC unavailable on their website are excluded from further research.
Remaining insurers are divided into three groups. Within these groups, formal
presentation   of   their   TaC   is   evaluated.   The   term   “TaC”   refers   to   TaC   of   business  
insurance against natural disasters.
267
Table 2 Written insurance in 2011
Insurer
Total
written
insurance
(thou CZK)
Non-life
written
insurance
(thou CZK)
Company
property written
insurance
(thou CZK)
Percentage in the
market with
company property
insurance
(%)
CP 31 331 985 20 161 703 3 864 165 33.09
KOOP 22 754 328 16 755 494 3 055 741 26.17
GP 8 438 854 5 859 411 1 402 659 12.01
ALLIANZ 9 444 750 7 307 604 1 165 961 9.99
CSOBP 7 382 076 4 026 397 692 386 5.93
UNIQA 5 180 134 3 994 092 618 861 5.30
CPP 5 841 549 4 678 864 402 120 3.44
HVP 491 942 482 809 181 257 1.55
HDI 316 154 316 154 159 827 1.37
SLAVIA 762 063 762 063 49 588 0.42
TRIGLAV 674 546 674 546 41 772 0.36
MAXIMA 316 849 316 849 40 470 0.35
WUST 323 112 323 112 1 791 0.02
Total 93 258 342 65 659 098 11 676 598 100.00
Source:  Data  processed  according  to  ČAP’s  results  (www.cap.cz,  2013)
Table 3 Evaluation of accessibility of TaC
Insurer
Can TaC be
found?
Path to relevant TaC
Number
of products
ČP YES www.ceskapojistovna.cz → firmy → majetek
→pojištění  majetku
1
KOOP Will be dealt with in the following section. More than 1
GP Will be dealt with in the following section. More than 1
ALLIANZ Will be dealt with in the following section. More than 1
ČSOBP YES www.csobpoj.cz → pojištění   firmy   → pojištění  
podnikatelských  rizik  → živelní  pojištění
1
UNIQA YES www.uniqa.cz → podnikatelé   → podnikání   I   →
živelní  pojištění
1
ČPP Only
partially
1
HVP YES First path: www.hvp.cz → podnikatelé  → pojištění  
staveb
Second path: www.hvp.cz → podnikatelé   →
pojištění  movitého  majetku
1
HDI NO
SLAVIA NO
TRIGLAV YES www.triglav.cz → pojištění   podnikatelů   → živelní  
pojištění
1
MAXIMA YES First path: www.maximapojistovna.cz → pojištění  
majetku → pojištění   podnikatelů   a   průmyslu   →
pojištění  firmy
Second path: www.maximapojistovna.cz →
pojištění   majetku   → pojištění   podnikatelů   a  
průmyslu  → pojištění  živelní
1
WUST NO
Source:  Data  collected  from  insurers’ websites
268
4.1  Availability  of  TaC  on  insurers’  websites
When searching for TaC, clients search for terms as: business insurance, property
insurance, insurance against natural disasters. The following chart describes steps clients
have to take in order to find relevant information. Also, the chart presents numbers
of insurance products or insurance programs against natural disasters. The number
of insurance products (or programs including insurance against natural disasters) is
closely connected with how difficult it actually is to find relevant TaC. This could
represent a problem for decision making clients when searching for complete
information.
Table 3 shows that TaC of insurance against natural disasters cannot be found on
websites of CPP, HDI, SLAVIA and WUST. These insurers were excluded from further
research.
Remaining insurers are, for the sake of our research, divided into three groups:
- Insurers CP, CSOBP, UNIQA and TRIGLAV offer one insurance product against natural
disasters
- Insurers HVP and MAXIMA do not provide unambiguous path to such a product
- Insurers KOOP, GP and ALLIANZ offer more insurance products against natural
disasters
4.2 Evaluation of insurers CP, CSOBP, UNIQA and TRIGLAV
The chart below presents results based on given criteria. CSOBP and TRIGLAV are
insurers whose TaC include information on insurance against natural disasters only.
UNIQA and TRIGLAV compile all information into a single TaC document.
Table 4 Evaluation of CP, CSOBP, UNIQA and TRIGLAV
Insurer
Criterion: number of irrelevant
products
Criterion: number
of documents
CP 1 3
CSOBP 0 2
UNIQA 5 1
TRIGLAV 0 1
Source:  Data  collected  from  insurers’  websites
CP presents three TaC documents of insurance against natural disasters. TaC include
information which is irrelevant and fall under other products. For the sake
of completeness it is important to say that the insurer also offers special insurance
programs to business organizations. These programs are presented on a separated
clearly arranged website. For this reason, these insurance programs were not evaluated.
CSOBP’s insurance against natural disasters is presented in two TaC documents. These
do not include information about other insurance products. Clients might not realize that
there are also general TaC which are located elsewhere than TaC of insurance against
natural disasters itself. UNIQA presents one TaC document with insurance against
natural disasters. These TaC contain five insurance products which do not fall under
insurance against natural disasters. UNIQA divides business insurance into two parts:
“Business   I”   and   “Business   II.”   The   “Business   I”   insurance   contains   also   insurance  
against natural disasters. This scheme is however very well arranged and searching for
information is unambiguous. TRIGLAV presents only one TaC document with information
on insurance against natural disasters. This information is related only to insurance
against natural disasters. On the basis of our analysis it is possible to summarize that the
process of searching for relevant information on TaC is simplest on the website of
TRIGLAV.   This   insurance   company’s   formal   presentation   of   TaC   ranks   first   as   well.  
TRIGLAV presents only one document with TaC and it includes merely information on
insurance against natural disasters. CSOBP ranks second, CP third and UNIQA fourth.
269
4.3 Evaluation of insurers HVP and MAXIMA
The process of searching for TaC was ambiguous and misleading in case of HVP
and MAXIMA. For this reason, these insurance companies are evaluated separately.
These insurers do not offer other insurance programs for business organizations. HVP
presents three individual documents with TaC of insurance against natural disasters.
These documents contain merely information on insurance against natural disasters.
There are two ways to find information on TaC of insurance against natural disasters. TaC
for  immovable  property  are  to  be  found  in  the  section  “stavby”  (buildings).  There  are  two  
documents in this section, TaC of damage insurance and TaC of buildings. TaC of
movables  are  to  be  found  in  the  section  “movité  věci”  (movables).  Again,  there  are  two  
documents, TaC of damage insurance (identical with the building insurance) and TaC of
movables. Clients have to search for information using two different links on the website
and might overlook some documents. TaC of MAXIMA are presented in two separate
documents related merely to insurance against natural disasters. The process of
searching for relevant information does not seem very logical. The path is mentioned in
the  chart  3.  When  clients  open  the  section  “Pojištění  podnikatelů  a  průmyslu”  (Business  
and   Industrial   Insurance),   they   have   to   choose   from   four   options:   “Pojištění   firmy”  
(Business   Insurance),   “Pojištění   odpovědnosti   za   škodu   podnikatele”   (Business   Liability  
Insurance),   “Pojištění   strojů   a elektroniky”   (Machinery   and   Electronics   Insurance)   and  
“Živelní   pojištění”   (Insurance   against   natural   disasters).   To   be   able   to   open   this   last  
option, clients need to click on the link   “Živelní   pojištění”   (Insurance   against   natural  
disasters) where TaC of insurance against natural disasters can be found. However, these
TaC are not the only valid TaC ofinsurance against natural disasters. Other TaC must be
searched for in the section   “Pojištění   firmy”   (Business   Insurance)   where   another   TaC  
document is located. This document contains general TaC for all property insurance
policies. This seems disorganized and poorly arranged. Also, clients might overlook one
of these documents.
Table 5 Evaluation of HVP and MAXIMA
Insurer
Criterion: number of irrelevant
products
Criterion: number
of documents
HVP 0 3
MAXIMA 0 2
Source:  Data  collected  from  insurers’  websites
Based on our analysis, it is more difficult to find relevant TaC on websites of insurers HVP
and MAXIMA. Both insurers present only relevant information in their TaC. It can be
stated that both insurers, with respect to formal presentation of their TaC, rank equally.
4.4 Evaluation of KOOP, GP and ALLIANZ
The chart below shows the number of insurance products offered within insurance
against natural disasters and the process of searching for TaC.
KOOP offers   two   products   for   business   organizations,   namely:   “Pojištění   malých   a  
středních  podnikatelů”  (Small  and  Medium  Business  Insurance)  and  “Pojištění  průmyslu  a  
podnikatelů”  (Industrial  and  Business  Insurance)  (as  shown  in  the  chart  above).  Both  of  
these include insurance against natural disasters. TaC of industrial and business
insurance are not available. Considering that the insurance company KOOP does not
publish TaC of industrial and business insurance, our further research will analyze the
insurance product TREND because this is suitable for small and medium businesses.
There are six irrelevant products which fall under TaC of the product TREND. GP offers
two   products   to   their   business   clients:   “Pojištění   podnikání”   (Business   Insurance)   and  
“Pojištění   individuálních   rizik”   (Individual   Risks   Insurance).   The   Business   Insurance  
program contains three products (as shown in the chart above). Individual risks
insurance is intended directly for industrial organizations. It is possible to find TaC here
but there are also some irrelevant pieces of information included. Our research will
further analyze TaC belonging to the individual risks insurance program because it is
270
more general. ALLIANZ offers   two   insurance   options   to   their   clients:   “Pojištění  
individuálních  rizik  a  průmyslu”  (Individual  risks  and  Industry  Insurance)  and “Majetek  a  
odpovědnost”   (Property   and   Liability).   Within   the   Property   and   Liability   program,   the  
insurer offers also some other insurance products (as shown in the chart above).
“Pojištění   individuálních   rizik   a   průmyslu”   (Individual   risks   and   Industry   Insurance)   is  
aimed at industrial organizations. It is possible to find TaC here but there are also some
irrelevant pieces of information included in them. These TaC will be further analyzed.
Table 6 Evaluation of KOOP, GP and ALLIANZ
Source:  Data  collected  from  insurers’  websites
Table 7 Evaluation of KOOP, GP and ALLIANZ
Insurer
Criterion: number of irrelevant
products
Criterion: number of
documents
KOOP 6 1
GP 1 1
ALLIANZ 1 1
Source:  Data  collected  from  insurers’  websites
On the basis of our analysis, it can be summarized that the insurance companies GP
and ALLIANZ rank equally as far as the formal presentation of their TaC is concerned.
Both of them included an irrelevant product in their TaC. In case of GP and ALLIANZ,
a product suitable directly for industrial insurance was selected. The situation is different
with other products because these are more general. KOOP presents all information
in one TaC document but there are six irrelevant products. Another problem is that the
insurer KOOP does not publish TaC of Industrial Business Insurance. This research
analyzed  the  product  TREND.  All  of  these  insurers’  websites  are  distinguished  by  rather  
complicated navigation.
5 Conclusions
This article dealt with the nature of information on business insurance against natural
disasters which is connected with demonstrations of information asymmetry on the
supply side of the insurance market. This article is based on qualitative research using
the method of description, analysis and comparison. It evaluated sources of information,
i.e.   terms   and   condition   available   on   insurers’   websites.   The goal of the paper is to
evaluate the level of the information publication of insurers about the company insurance
against natural hazards. In connection with the target which had to be reached, this
research asked this question: Does an insurer with high percentage of written insurance
on the insurance market provide total and well-arranged information about insurance
against natural disasters to clients? The article assessed the accessibility of TaC
Insurer
Can TaC be
found?
Path to relevant TaC
Number
of products
KOOP
YES
www.koop.cz → Pojištění   malých   a   středních  
podnikatelů  → TREND or STARD PLUS or ORDINACE
3
NO
www.koop.cz → Pojištění  průmyslu  a  podnikatelů  →
pojištění  majetku  → pojištění  živelní
1
GP
YES
www.generali.cz → individuální   rizika   → pojištění  
průmyslových  rizik    → požární  pojištění  majetku
1
YES/
NO in case
of MENU
www.generali.cz → majetek → pojištění   podnikání  
→ TOP  GEN  or  DROBNÝ  PODNIKATEL  or  MENU 3
ALLIANZ
YES
www.allianz.cz → Firemní   klienti   → majetek a
odpovědnost  → Pojištění  PRO  PODNIKATELE  or  NOE  
PLUS  or  SPECIÁLNÍ  NABÍKDY    
3
YES
www.allianz.cz → Individuální   rizika   a   průmysl   →
Pojištění  majetku  a  přerušení  provozu
1
271
and formal presentation of TaC based on two criteria. The first criterion was the amount
of irrelevant information (products) within TaC. The other criterion was the number
of TaC. Insurers were, on the basis of algorithm analysis of the process of searching
for TaC, divided into three groups and then evaluated according to the given criteria. The
research did not include insurance companies which do not publish TaC on their website
at all. These are CPP, HDI, SLAVIA and WUST.
The first group of insurers is represented by CP (percentage in the insurance market
/PIM/: 33.09%), CSOBP (PIM 5.93%), UNIQA (PIM 5.3%) and TRIGLAV (PIM 0.36%).
These insurers offer one insurance product within business insurance against natural
disasters and the process of searching for TaC is clearly arranged. CP has the largest
percentage in the insurance market and ranks third within this group. The reason for this
position is: irrelevant information within one insurance product and three TaC
documents. The highest ranking company is TRIGLAV which has, however, a relatively
small share in the market.
The second group of insurers is represented by HVP (PIM 1.55%) and MAXIMA (PIM
0.35%). These insurers should improve the arrangement of their websites as far
as searching for TaC is concerned. Formal presentation of TaC is considered excellent.
TaC do not contain any irrelevant information.
The third group of insurers is represented by KOOP (PIM 26.17%), GP (PIM 12.01%)
and ALLIANZ (PIM 9.99%). These insurers have the largest percentage in the insurance
market. Their websites, however, are not very well-arranged, especially because they
offer more insurance programs. The insurance company KOOP ranks last in this group
because it does not publish TaC of industrial and business insurance. It also ranks last
with regard to formal presentation of its TaC because these contain six insurance
products which are irrelevant for business insurance against natural disasters.
The summary, based on methods and criteria which were described, is as follows: CP
ranks very high. On the other hand, KOOP provides information which is not complete. It
is important to mention TRIGLAV, whose market share does not even reach 0.5%
but which ranks very high because of the simplicity of navigation when searching
for information and because of formal presentation of its TaC. We cannot summarize that
an insurance company with a significant percentage in the insurance market always
provides complete transparent relevant information. Clients cannot rely on the fact that
a large share in the insurance market guarantees complete and well-arranged
information  on  the  insurer’s  website.
References
Daňhel,   J.   et   al.   (2007). Analýza   globálních   trendů   ve světovém   a českém   komerčním  
pojišťovnictví.  Praha:  VŠE.
Daňhel,   J.   (2002).   Problém   asymetrie   informací   v pojišťovnictví. Ekonomika,   právo,  
politika, Issue 19/2002, pp. 51 - 63.
Hořejší,  B.  et  al.  (2006).  Mikroekonomie. 4th Ed.. Praha: Management Press.
Liška,  V.  (2012).  Asymetrie  informací  a  rozhodovací  proces.  Asymetrie  informací  – teorie
a praxe. pp. 5-13.
Tříska,   D.   (2002).  Nedokonalosti   trhu   a  jejich   řešení.   Ekonomika,   právo,   politika, issue
19/2002, pp. 51 - 63.
272
Commodity Markets Financialization
and its Impact on the World Economy
Oldřich  Rejnuš1
, Kamil Smolík2
1
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  601  00  Brno,  Czech  Republic
E-mail: rejnus@econ.muni.cz
2
Brno University of Technology
Faculty of Business and Management, Department of Economics
Kolejní  4,  612  00  Brno,  Czech  Republic
E-mail: smolik@fbm.vutbr.cz
Abstract: This paper deals with the financialization of contemporary world commodity
markets and its influence on the economy. Firstly, it defines the ways in which this
current trend shows itself and then analyzes its impact on the economy. Concurrently the
paper tries to find out in what way is the effect positive for the economy as well as in
what is negative. The aim of the analysis is also to confirm or refuse the hypothesis that
financialization of commodity markets is one of the factors which has contributed to the
emergence of modern "financial" crisis and helps its prolongation. The closing part of the
paper focuses on the question if this process is beneficial or harmful for the further
development  of  the  world’s  economy  or  whether  is  appropriate  to  matter  in  this  process  
or rather waits for the result of the spontaneous development itself.
Keywords: financialization, commodity markets, world economy
JEL codes: G01, G11, G12, G15
1 Introduction
Important changes are currently being realized at commodity markets. Especially after
the appearance of technological bubble in the year 2000 and its subsequent burst,
investors started to seek further possibilities of investment to ensure wider diversification
of investment portfolios and new ways of speculation. Formation of so called commodity
boom that has been influencing not only commodity markets, but financial markets as
well, is dated to this time; it has been strongly influencing world economy functioning,
too.
The aim of the paper is to define and characterize the process of financialization of
commodity markets theoretically, and subsequently to analyze its outcomes and impacts
on world economy functioning. And last but not least, it enables to evaluate, based on
acquired knowledge, whether this process is beneficial or harmful, what the benefits or
harms are and whether it would be suitable to commence more strict regulation.
2 Definition  of  „financialization”  of  commodity  markets
In the past, commodity investment instruments were differentiated from financial
investment tools by the fact, that, due to their weight, they could not be traded promptly
in bigger extent, or in another words, the deals could not be settled immediately.
Financialization of commodity markets has significantly changed this fact. As it has
become an important worldwide development trend, it seems to be unnecessary to
analyze it in more details.
Financialization of commodity markets can be, before all, characterized as extreme
growth of volume of commodity market, that is not settled physically, but they are
settled financially. This way of trading is performed, before all, by speculators, for whom
financial settling in commodity markets is suitable. It is simpler for them and, on top of
that, they are not threatened by the fact that they would have to deliver or take
particular commodity physically. Another characteristic of commodity markets
273
financialization is the fact that commodities are more and more often used as a part of
portfolio of investment and share funds or, they become, together with commodity
market index, foundation assets of many various sorts of newly constructed synthetic so
called   “structured   products”.   This   includes   structured   term   deposits,   various   sorts   of  
structured bonds, collective investment structured funds securities or various sorts of so
called  securitized  lever  derivatives  (Rejnuš,  2011). Factors, leading investors to this way
of commodities trading include diversification of their investment portfolios, existence of
(often very high) financial lever in case of speculation. As far as issuers of structured
products are concerned, foundation commodities or commodities trading indices enable
further developing of variety of constructions of synthetic products, created by them.
3 Analysis of impact of commodity financialization on economy
Financialization of commodity markets and its progressive propagation show a number of
different ways that need to be analyzed in details.
3.1 Analysis of influence of commodity financialization of price lever and
volatility on price of commodities
In connection with financialization of commodities market, the influence of increased
share  of  so  called  “financial  investors”  and  their  business  strategy  on  contemporary  level  
of commodities price in comparison with historical prices and contemporary extremely
high volatility of commodities are being discussed. These topics are based on two basic
theories.
The   first   one,   so   called   “fundamental   theory”   assumes   that   the   prices   of   commodity  
assets are determined almost solely by fundamental factors and that all commodity
market users, making their business and investment decisions, assume development of
offer and demand on spot markets. This theory is based on prerequisite of effective
markets theory that commodities markets instantly and correctly absorb all available
fundamental information and expectations. Provided that uninformed users of commodity
markets assess the price of commodity incorrectly, well informed businessmen would
take advantage of it and the price of respective commodity would get back to balance of
“fundamental  values”  (Staritz,  2012).  It  shows  that prices of commodities should not be
influenced   by   “financialization”   but   they   should   be   determined   by   real   fundamental  
factors that are the cause of increased volatility of commodities prices. This opinion is
advocated for example by Pirrong (2008), Hong and Yogo (2010), Power and Turvey
(2011) or Singleton (2011).
Second,   so   called   “financial   theory”   assumes   that   volatility   of   commodities   prices   is  
influenced not only by fundamental factors, but that it is significantly influenced by
expectations, behaviours and interaction of individual market users; they include not only
so   called   “informed   investors”,   but   so   called   “uninformed   investors”   as   well.   The  
difference between behaviours of these two groups of investors is based in the fact that
“uninformed   investors”   follow   trends,   excessively   respond   to   good   and   bad   news   and  
utilize   various   methods   of   technical   analysis.   This   may   launch   so   called   “herd   effect”  
(UNCTAD,   2009).   And   vice   versa,   the   second   group   of   “informed   investors”   utilizes  
statistical techniques for their decision making. Both these groups of market users can,
due to their behaviours, considerably deflect the price of commodities from their
fundamental values (Staritz, 2012).
3.2. Analysis of development of traded volumes of commodity terminated
derivative instruments
Commodity derivatives are highly volatile instruments; some authors consider them to be
one of the most risky forms of investment ever (Kline, 2001) and they can be traded in
different ways. Basic form of stock exchange trading is traditional trading with
commodity futures or, as the case may be, with stock exchange options (usually stock
exchange options on commodity futures).
274
Figure 1 shows the development of the number of traded commodity derivative contracts
at world commodity stock exchanges from 1993 to 2011. These data, although individual
sorts of contracts are not specified in details, unambiguously show the importance and
consequences of financialization of commodity markets. And what is especially important,
their rapid growth can be seen even in the pre-crisis time, since the end of 2005. Beside
this, there are contractually concluded (mostly via OTC market) forward, swaps and
contractually concluded options.
Figure 1 Derivative transactions at world commodity stock exchanges
Source: BIS, Quarterly Review, December 2011 /millions of contracts/
3.3. Analysis of mutual interconnection of commodity and financial
markets
Investments into commodity indices that are generally considered to be overall indicators
of price  development  at  commodity  markets  are  a  very  popular  form  of  “investments  into  
commodities”.  These  are  investments  that  can  be  done  in  many  different  ways,  not  only  
by means of term derivative instruments, but by means of collective investment funds or
by means of various sorts of structured products. Various commodity indices that differ
from each other by their construction, covered commodities or by weights that are
matched to them, terms of rolling of individual contracts (this means replacing current
contract with approaching date due by a contract with more distant date due) etc. can be
used for these purposes. Most important commodity indices used as underlying assets
include  for  example  Standard  &  Poor’s  – Goldman Sachs Commodity Index (S&P-GSCI),
Dow Jones - UBS Commodity Index (DJ-UBSCI), Deutsche Bank Liquidity Commodity
Index (DBLCI), etc.
Using commodity indices as underlying assets is often discussed by professional public.
Some authors consider them to be a source of fundamental imbalance of commodity
markets (Tang and Xiong, 2010; Gilbert 2010). When investing into futures derived from
commodity indices, the investors in most cases speculate on growth of rate, which means
they enter long positions. This causes growth of commodities prices unless they are
physically settled.
Tang and Xiong (2010) pay attention to commodities indices with respect to more and
more tight fitting correlation with revenues from shares. In their book Index Investment
and Financialization of Commodities the authors say that financialization of commodity
markets in the US is caused by considerable number of, before all, institutional investors.
This leads to increased volatility of commodity markets and to tighter correlation of
revenues with those of share indices – see below Figure 2. It shows the fact that the
trend of approximation of the correlation of revenues between commodity index S&P
GSCI and share index S&P 500 is the most considerable after the Lehman Brothers
275
bankruptcy in 2008, which concurrently proves the tendency to moving commodity and
financial markets closer to each other.
Figure 2 Correlation of revenues S&P 500 and S&P GSCI
Source: Tang, K., Xiong, W. Index Investment and Financialization of Commodities (August 2010)
3.4 Analysis of diversification of investment portfolio as a stimulus to
financialization of commodity markets
In 1950's, Harry Markowitz established basic principles of portfolio theory and defined
the advantages of mutual diversification of financial and real assets, based on such
allocation of individual assets that brings maximal expected revenue to the investor,
while the level of risks is on the same level. In this case, the investor considers
characteristics and correlation of individual assets (investment instruments) that
influence the revenue and the risk of the whole investment portfolio. As the prices of
commodity assets influence different factors than those that are influenced by financial
investment instruments, it appears to be highly suitable for diversification of investment
portfolio. And last but not least, they are adequately liquid.
Many authors have dealt with the problems of the importance of commodities in the
investment portfolio. The most important of them include publications by Gorton and
Rouwenhorst (2005), who find commodity assets to be an attractive class of assets
suitable for diversification of investment portfolio and investment into commodity assets
move, from the point of view of modern portfolio theory, closer to so called effective
border. Except for above mentioned ones, investment into commodity assets were dealt
with by many other authors (Gordon, 2006; Stoll a Whaley, 2009).
In connection with modern portfolio theory, two basic variables of this theory are
subsequently assessed: historical revenue and volatility (decisive deviation that
represents the risk of the investment) in the interval from Jan. 1st
1998 to Nov. 19th
2012. These variables are assessed for various sorts of commodity assets and compared
with investment instruments – shares, bonds, and financial market tools. Data used in
this analysis show total monthly revenues of individual commodity indices provided by
S&P Dow Jones Indices (indices S&P GSCI). Apart from composite commodity index S&P
GSCI Total Return (including collateralized revenues and revenues or losses caused by
positions rolling) similarly constructed indexes of energies, precious metals, industrial
metals, agricultural products and livestock are analysed. Share S&P 500 Price Return
Index, Barclays US Aggregate Bond Index and saving account with the interest rate of
2/12 % per month – which is considered to be riskless asset for this purpose, are
276
compared. The initial investment of 1 US dollar was used for this calculation and
transaction costs and taxation were abstracted from. In case of saving account, cash
deposit was added monthly with the assumption of composed interest. Results of the
analysis made by software Smartfolio are shown in Figure 3 and Table 1.
Figure 3 Graphic assessment of revenue appreciation into various sorts of investment
assets
Source:  Processed  by  K.  Smolík,  based  on  data  available  at  S&P  Dow  Jones  Indices,  Barclays  
Capital and finance.yahoo.com
Table 1 Historical yearly profitability and volatility of chosen sorts of investment assets
Asset type Average return Volatility
Agriculture 5,33 21,98
Barclay US Aggregate Bond 5,72 3,55
Energy 12,37 32,80
S&P GSCI 6,20 24,67
Industrial Metals 7,41 22,40
Livestock 0,18 15,94
Precious Metals 13,48 18,02
S&P 500 3,83 16,49
Source: Processed by: S&P Dow Jones Indices, Barclays Capital and Yahoo Finance
Achieved results of graphic assessment of investment revenue and values of historical
average yearly revenue, shown in a chart show that all commodity indices (except for
livestock index) were more profitable than stock exchange index S&P 500 and riskless
saving account in the monitored period. Volatility values of commodity indices are more
distinct as well.
It is necessary to point out that, within mentioned analysis, test of normality of division
of determinant deviation; this would be necessary to do when evaluating the risk by
means of this variable within the framework of investment portfolio compiling. In this
given  case,  this  represents  so  called  “Annual  Returns”  in  the  monitored  period from 1998
to 2012.
277
3.5 Analysis of existence of financial lever as a stimulus of commodity
markets financialization
As already ascertained, financialization of commodity markets brings financial and
commodity markets closer together. This finding has one more important dimension. As
the prices of term derivative instruments (in this particular case, before all, stock
exchange futures) relate to their underlying assets, it is apparent that term and prompt
prices influence each other.
This is connected with   the   fact   that   so   called   “paper   commodities”,   or   more   precisely  
manipulation with prices of contracts of futures, derived from commodities of commodity
indices, can influence their own prompt prices. This is done e.g. by shorting, entering so
called   “intelligent   stock   exchange   orders”   ,   by   trading   based   on   technical   analysis   etc.  
Furthermore, big institutional investors, e.g. hedge funds or Exchange traded funds /ETF/
buy, within business, large amounts of commodities. As they only pay margin in this
case,  they  can  control  large  amounts  of  fundamental  commodities  with  “little  money”  and  
in case of financial settling, they do not even need to close their positions. This artificially
increases demand for commodities that often represent production inputs, but in this
case this does not mean traditional fundamental demand, when relevant commodity is
used or stored on a short-time basis within reserves. The whole development trend is,
according to many economists, (e.g. Gensler, 2010) enabled by insufficient regulation of
commodity markets.
4 Conclusions
This analysis shows that the process of financialization of commodity markets has both
its pros and cons.
The   positives   include   effortless   settling   of   term   trading   of   commodities¨;;   this   makes  
trading easier. Furthermore, it enables to trade commodities in smaller lots; this makes
accessible trading of commodities for less important (and smaller) investors. This
subsequently improves liquidity of commodities market as well as total traded volumes.
Facilitating of diversification of investment portfolios of not only big investors, but those
of the smaller ones brings additional positive factors.
On the other side, there is a number of negative influences of financialization of
commodities market. First of all it is possible purpose-built manipulation with prompt
commodities prices; this is connected with the growth of their volatility, and, last but not
least, prevailing impact on growth of commodities prices. This is rather dangerous
especially in case of food commodities or production input commodities. An as it is
possible to manipulate with prompt prices of commodities, it can purposely influence the
values of all synthetic structured products derived from them, which can seriously violate
functioning of the worldwide financial market as well as the development of world
economy.
Above mentioned facts show that regulation of commodity markets proves to be
necessary and the representatives of most economically developed countries are aware
of it. Nevertheless it is clear that due to existing close interconnection of commodity and
financial markets, it is necessary to perform all regulatory measures jointly for both
markets and it is impossible to divide them strictly from each other.
References
Gensler, G. (2010). History of derivatives regulation, culprit OTCs, Commodity Online, 2
July 2010, Available at: <http://www.commodityonline.com/news/history-of-derivatives-
regulation-culprit-otcs-29636-3-29637.html>.
Gilbert, C.L. (2010). Speculative Influences on Commodity Futures Prices 2006-2008,
UNCTAD Discussion Paper, No. 197.
Gordon, R. N. (2006). Commodities in an Asset Allocation Context, Journal of Taxation of
Investments, vol. 23.
278
Gorton, G. and Rouwenhorst, K.G. (2005). Facts and Fantasies about commodities
Futures, Yale ICF Working Paper, No. 04-20.
Hong, H. and Yogo, M. (2010). Commodity Market Interest and Asset Return
Predictability, Working Paper, Available at: < http://www.princeton.edu/~hhong/
Commodities.pdf>
Kline, D. (2001). Fundamentals of the futures market. New York: McGraf-Hill.
Pirrong, C. (1994). Squeezes, Corpses, and the Anti-Manipulation Provisions of the
Commodity Exchange Act, Regulation, Vol. 17 No. 4.
Power, G., Turvey, C. (2010). Long-range dependence in the volatility of commodity
futures prices: Wavelet-based evidence, Physica A, 389(1), 79-90.
Rejnuš,  O.  (2011). Finanční  trhy, Ostrava: Key Publishing.
Staritz, C. (2012). Financial markets and the commodity price boom: Causes and
implications for developing countries, Austrian Research Foundation for International
Development Working Paper, 30.
Singleton, K. J. (2011). Investor Flows and the 2008 Boom/Bust in Oil Prices, Stanford
University Working paper.
Stoll, H.R. and Whaley, R.E (2009). Commodity Index Investing and Commodity Futures
Prices, Vanderbilt University Nashville Working Paper.
Tang K., Xiong W. (2011). Index Investment and Financialization of Commodities, NBER
Working Paper.
UNCTAD (2009). Trade and Development Report 2009, New York and Geneva.
279
The Impacts of Basel III onto the European Banking Sector
Svend Reuse1
, Martin Svoboda2
1
Head of Controlling Department,  Sparkasse  Mülheim
FOM University of Applied Science, Essen
DIPS – Deutsches  Institut  für  Portfoliostrategien
Luxemburger  Allee  121,  45481  Mülheim, Germany
E-mail: svend.reuse@fom-dips.de
2
Masaryk University
Faculty of Economics and Administration, Department of Finance
Institute for Financial Market
Lipová  41a,  Brno  60200, Czech Republic
E-mail: svoboda@econ.muni.cz
Abstract: The European Commission and the European Parliament finalized the
implementation of Basel III. From 01.01.2014, the relevant rules of CRR and CRD have
to be fulfilled. But what effects do these rules have onto the banks, their strategies and
profitability? This article discusses the main new aspects, their impacts onto the banks –
and offers a strategy, how banks should deal with Basel III. Finally, the question if Basel
III leads to a more stable banking system is answered.
Keywords: Basel III, LCR, NSFR, Solvency
JEL codes: E58, G21, G24
1 Introduction
Basel III was created as a direct reaction onto the Financial Crisis in 2008. On September
12th
, 2010, the Basel Committee on Banking Supervision published first ideas according
to Basel III (see BCBS 2010.09). Nearly three years later, on April 16th
, 2013, the
European Parliament published the final rules for Europe (CRR 2013).
After a short presentation of the main aspects of Basel III, this article analyses the
impacts of Basel III onto the European Banking sector on the basis of existing literature.
In the final conclusion, some hints for a strategic repositioning are presented and a
conclusion, whether Basel III makes the banking system more stable, is given.
2 Description of Basel III
2.1 Main Aims of Basel III
Basel III offers several main new aims that have to be fulfilled. These aims are
structured and shown in Table 1.
Table 1 Main aims of Basel III
Aspect
Capital
ratios
Liquidity
ratios
Leverage
Ratio
Parts of
equity
Modifying
Risk
Weighted
Activa
Classification Higher New New
More
restrictive
More detailed
Valuation at
a first glance     
Source: based on Hartig (2012)
At a first glance, most of the requirements of Basel III seem to be positive, but the
European implementation process and the impulses for the banking sector lead to several
difficulties.
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2.2 Implementation Process
Basel III contains several aspects that might influence the Banking sector. Derived from
the main BCBS publications (see BCBS 2010.08, BCBS 2010.09, BCBS 2010.12, BCBS
2011.06, BCBS 2013.01), the European Parliament finalized the CRR (Capital
Requirements Regulation). This regulation (CRR) and the resulting CRD IV (Capital
Requirements Directive) can be defined as the implementation of Basel III into European
law, the BCBS papers do not have a binding character. So the main focus of this article is
laid on the CRR and the CRD. While the CRR is obligatory after publishing it into the
Official Journal of the European Union, the CRD IV has to be transformed into e.g. Czech
or German law. All banks have to fulfill these requirements from January 1st
, 2014, even
though some transitional rules are offered.
Table 2 Main aspects of Basel III in European implementation
Aspect Description Date CRR CRD IV
Quality of
equity
As a consequence of the financial crisis,
the equity of banks has to consist of
more stable components. The definition
of Tier I and Tier II equity as well as the
required quality and deductions from
the equity parts are described.
The basic 8% of total capital ratio
remains constant compared to Basel II,
but the Common Equity Tier 1 has to be
at to be at least 4.5% and the Tier 1
capital ratio has to have a level of 6%
(For further explanation see Deutsche
Bundesbank 2011, p. 19).
01.2014 –
12.2017
Art. 23-86
Art. 87-89
Art. 451-
461
---
Quantity of
equity:
buffers
An additional countercyclical capital
buffer has to be implemented. Further,
banks have to fulfill a Capital
Conservation Buffer of Common Equity
Tier 1. Both buffers can increase up to
2.5%. Even system risk buffers might
occur.
01.2014 –
01.2019
--- Art. 122-
132, 149
CCP and CVA
underlying
According to EMIR (European Market
Infrastructure Regulation, described in
detail in Reuse/Svoboda 2013, pp.
583), central counterparty risk has to
be covered with equity as well. Further,
the credit value adjustment risk (CVA)
is added.
01.2014 Art. 294-
300
Art. 371-
375
---
Large
exposures
Requirements concerning large
exposures become stricter. In practice,
the aggregation of creditors to a large
exposure will lead to more and/or
higher large exposures.
01.2014 Art. 376-
392
---
Liquity
Banks have to fulfill liquidity coverage
requirements (Liquidity Coverage Ratio,
explained   in   Bučková/Reuse   2011,   pp.  
7) and stable funding (NSFR, Net Stable
Funding Ratio).
LCR:
60%:
2015
100%:
01.2018
Art. 400-
415
Art. 444
---
Leverage
Ratio
In addition to the risk weighted activa
(RWA), a leverage ratio is introduced.
This number has to exceed 3%.
01.2014 – Art. 416-
481
---
Remuneration
policy
Banks have to fulfill several
remuneration policies, they have to be
published as well.
01.2014 – --- Art. 88-91a
Source: based on CRR (2013) and CRD IV (2013)
281
2.2 Main Aspects of the European Implementation
Based onto the CRR publication, Table 2 defines and describes the main aspects of Basel
III, mentions dates of implementation and offers sources of the CRR/CRD.
In addition, the EBA (European Banking Authority) has to work out several binding
technical standards that make some of the abstract rules of CRR and CRD more concrete.
In addition, the local implementation of the CRD IV has to be considered. This leads to
the consequence that banks have to fulfill several rules, hoping that these rules will be
consistent on January 1st
, 2013.
In addition, CRR and CRD IV will lead to several changes in the banking sector. These
changes are discussed in section 3.
3 Impacts onto European Banks
3.1 Impacts onto Customer Business
Customers will feel the impacts of Basel III as borrower and investor. First, the borrower
side has to be discussed. A higher capital conservation buffer leads to higher regulatory
equity costs. They increase from 8 to 10.5%. Even though capital reservation for classical
small- and medium sized companies (SME) especially in Germany remains constant (see
CRR 2013, Art. 26a; DSGV 2013), the implementation of the countercyclical buffer leads
and the fact that the 8% of Art. 77 CRR must consist of higher quality leads to additional
equity costs as well. This effect will occur especially in banks where the leverage is
relatively high. In addition to the costs of a credit, some banks have to delever. An
example is the Commerzbank (see Commerzbank 2012). This bank reduces RWA, often
concerning classical customer business. Classical savings institutions e.g. in Germany will
not have to do this, their equity is c.p high enough. Nevertheless, a partial scarcity of
loans especially at high levered big banks might be a consequence.
In  the  other  side  of  a  bank’s  balance  sheet,  the  investors  might  get  a  price  advantage.  As  
the retail deposits of private customers are only weighted with 5% according to Art. 409
CRR. As a consequence, this will lead to a price battle for these retail deposits (see Reuse
2012). Customers will get a higher interest rate for their deposits.
To the opinion of the authors, the effect on the liability side of the banks will be much
higher than the additional equity costs on the asset side. Banks have to care and
optimize their risk management and strategy.
3.2 Impacts onto Risk Management
Looking into the future and concerning the low interest rate structure especially in
Germany banks normally will simulate a lower interest rate margin. Combined with the
margin effects of section 3.1, classical earnings will decrease. In addition, the battle for
the so called high liquid assets according to Art. 404 CRR will increase. Looking at
German Sovereign bonds – they fulfill the requirements – it becomes clear that the yield
resulting from such an investment is quite low.
Banks have to face the problem, lower earnings lead to a lower risk bearing capacity.
Banks should decrease their risks in a calm way in order to prevent having too much
risks in the next years. But they have to be carefully, no impacts onto their core business
model must occur.
Further it might help to increase quality of the risk management. The better the methods
and models are, the more valid the results become, the more risk bearing capacity might
be quantified. Further, a good risk management might lead to a better quality of the
RWA which might be reduced as a consequence of data quality improvement (See
Frère/Reuse  2010).
Mid-sized banks should again analyse, whether an IRB approach (Art. 137 CRR) is better
than the standard approach and leads to lower RWA. In this case it has to be considered
that internal ratings have to be implemented in a very extensive way.
282
3.3 Impacts onto the Strategy
The last but most important field of impacts is the strategy of a bank. Since 2008 it
becomes clear that banks primary have a strategic problem. What is their core business
model?   Frère/Reuse/Svoboda   (2008,   pp.   25)   have   shown   that   banks   often   do   not
concentrate onto their core business but invest into every field where additional earnings
seem to be possible. This was one of the main mistakes that lead to the financial crisis in
2007/2008.
Deleveraging, reducing risks and focusing on the core competences of a bank must be
the consequence. Outsourcing, cost reduction and an optimized but concentrated
portfolio   mix   (Frère/Reuse/Svoboda   2008,   pp.   29)   combined   with   a   focus   onto   a  
sustainable customer relation with stable earnings is a solution to survive the next years.
As the market for banks is completely distributed, a crowding out might occur. To the
opinion of the authors, the number of banks will decrease in the future.
Banks have to face these strategic risks early in order to have enough time to react.
Basel III becomes completely valid in the beginning of 2019 – changing a strategic model
takes at least this time.
4 Conclusions
4.1 Summing up the Main Results
Basel III has several influences onto the banks. Table 2 sums up the main aspects and
values them according to their relevance in the European banking sector.
Table 3 Main results and impact of Basel III in Europe
Hypothesis Valuation Relevance
1. Basel III makes loans more expensive for
companies and communes
true mid
2. Decreases possibility to grant loans discussable low
3. Increases to demand for AAA sovereign bonds true high
4. Supports short-term culture according the loan
side
true mid
5. Discriminates SME-financing false low
6. Increases battle for retail deposits true high
Source: based on Hartig (2012)
Table 3 shows the main risks and problems of Basel III. It becomes clear that retail
deposits   and   the   investment   into   “risk-free”   sovereign   bonds   are   the   most   important  
impacts of Basel III. But how can a bank solve the problems resulting from Basel III?
To the opinion of the authors, the best way for big banks to react onto Basel III is a
deleveraging strategy combined with a risk reduction and an optimization of the RWA,
perhaps doing a capital increase in order to generate additional Tier 1 capital.
Small and medium sized banks shall focus onto the core competences and disestablish
peripheral areas as e.g. the investment banking. They should generate synergies by
concentrating back office capacities in an outsourcing project. Only economies of scale in
these areas help to solve the cost problem all banks face nowadays.
4.2 Does Basel III make the Banking System more stable?
Finally, the question if Basel III makes the banking system more stable has to be
analyzed critically. First, Basel II has to be analyzed. It is often was stated that it was not
able to prevent the crisis. This is partially right – but Basel II was introduced too late, it
became valid in 2007/2008, but the mistakes were made before the crisis (see Lippert
2012).
283
Coming back to Basel III, the argumentation is as follows. On the one hand, Basel III
offers positive aspects, e.g. the countercyclical capital buffer in order to prevent higher
volatility the market. The idea to focus onto liquidity – the crisis has shown the problems
here – has to be judged positive as well. Increasing equity ratios might help to stabilize
the banking sector – if it is implemented worldwide and not only in Europe (See Haasis,
2011). Especially the USA often fail to implement Basel rules in time.
On the other hand, several disadvantages of Basel III have to be stated. It took about 4
years to complete this work – from 2010 to 2014. But what is the result? CRR and CRD
have about 1,600 pages (it might be up to 60,000, see Zeranski 2013, p. 10), the banks
in Europe are regulated very hard and the requirements become very high and cost a lot.
The danger exists that banks retire from too much business areas. This will have an
impact onto the economy.
Last, the primary quantitative-based approaches of Basel III are not enough. The critical
success factors are a high qualitative risk management and a good qualitative banking
supervision. To the opinion of the authors, many of the problems in the financial crisis
might have been prevented, if the supervisors would have been critical enough. Even
Basel I/II offered a strong regulatory background to uncover these mistakes. Only a
critical but not disproportionate supervision which considers regional circumstances in the
EU combined with banks that have a clear strategy and a good risk management have a
chance to prevent a further crisis. The future will show if EBA and ECB will be able to
solve this complex task.
References
BCBS. (2010.08). An assessment of the long-term economic impact of stronger capital
and liquidity requirements. Retrieved from: http://www.bis.org/publ/bcbs173.pdf.
BCBS. (2010.09). Group of Governors and Heads of Supervision announces higher global
minimum capital standards. Retrieved from: http://www.bis.org/press/p100912.pdf.
BCBS. (2010.12). Basel III: International framework for liquidity risk measurement,
standards and monitoring. Retrieved from: http://www.bis.org/publ/bcbs188.pd.
BCBS (2011.06). Basel III: A global regulatory framework for more resilient banks and
banking systems. Retrieved from: http://www.bis.org/publ/bcbs189.pdf.
BCBS (2013.01). Basel III: The Liquidity Coverage Ratio and liquidity risk monitoring
tools. Retrieved from: http://www.bis.org/publ/bcbs238.pdf.
Bučková,  V., Reuse, S. (2011). Basel III Global Liquidity Standards: Critical Discussion
and Impact onto the European Banking Sector, in: FAI – Financial Assets and Investing,
03/2010, pp. 7–18.
Commerzbank (2012). EBA-Kapitalziel vorzeitig erreicht - Operatives Ergebnis im ersten
Quartal 2012 bei 584 Mio. Euro, May 9th
, 2012, retrieved from:
https://www.commerzbank.de/de/hauptnavigation/presse/pressemitteilungen/archiv1/20
12/quartal_12_02/presse_archiv_detail_12_02_9363.html,.
CRD IV (2013). Directive of the European Parliament and of the Council on the access to
the activity of credit institutions and the prudential supervision of credit institutions and
investment firms (COM(2011)0453 – C7-0210/2011 – 2011/0203(COD)). Retrieved
from:http://gesetzgebung.beck.de/sites/gesetzgebung.beck.de/files/rsw/upload/Beck_Ak
tuell/CRD%20IV%20-%201.%20Lesung.pdf.
CRR (2013). Regulation on prudential requirements for credit institutions and investment
firms, COM(2011)0452–C70417/2011–2011/0202(COD), A70171/2012. Retrieved from:
http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//NONSGML+AMD+A7-
2012-0171+002-002+DOC+PDF+V0//EN.
DSGV (2013). Pressemitteilung 43/2013,   Fahrenschon:   „Verabschiedung   von   Basel   III  
durch EU-Parlament   wichtiger   Schritt   zu   mehr   Stabilität   und   Sicherheit“,   Berlin,   April  
16th
, 2013, available from: http://www.dsgv.de/_download_gallery /Pressemitteilungen
_2013/130416_PM_Basel_III_EU_Parlament_43.pdf.
284
Deutsche Bundesbank (2011). Basel III – Leitfaden zu den neuen Eigenkapital- und
Liquiditätsregeln   für   Banken, Bonn 2011, available from: http://www.bundesbank.de/
Redaktion/DE/Downloads/Veroeffentlichungen/Buch_Broschuere_Flyer/bankenaufsicht_b
asel3_leitfaden.pdf?__blob=publicationFile.
Frère,  E.,  Reuse,  S.,  Svoboda,  M. (2008). Aktuelle Probleme im Deutschen Bankensektor,
FOM Schriftreihe Band 13, Essen.
Frère,   E.,   Reuse,   S. (2010). Basel III – Auswirkungen der Neuregelungen auf die
deutsche Kreditwirtschaft, in: Banken Times, December 2010 / January 2011, pp. 3–4.
Haasis, H. (2011). Basel-III-Regelungen   müssen   in USA und Europa in gleicher Weise
angewendet werden, Pressemitteilung Nr. 49, May 11, 2011. Retrieved from:
http://www.dsgv.de/de/presse/pressemitteilungen/110511_PM_Basel_III_49.pdf.
Hartig, Johannes (2012). Finanzierungsperspektive im Nordwesten-Erwartungen der
Kreditinstitute, 9. Osnabrücker   Finanztag,   November   16th
, 2012, available:
http://www.wiso.hs-osnabrueck.de/uploads/media/Vortrag_Hartig.pdf, accessed on May,
1st
, 2013.
Lippert,   M.   (2012).   Bankgeschäft   wieder   langweilig   genug?,   in:   Betriebswirtschaftliche
Blätter, July 18th
, 2012, available from: https://www.sparkassenzeitung.de/index.cfm?
event=cmp.cst.documents.print&cid=46&documents.id=11201&token=0.
Reuse, S. (2012). Kapitalstrukturierung eines Kreditinstitutes vor dem Hintergrund von
Basel  III.  In:  Frère,  E.,  Reuse,  S.,  Rojahn, J. (ed.): Kapitalstrukturpolitik, 3. FOM-Master
Forschungsforum – Finance, Essen, November 19th
, 2012, pp. 55 – 86.
Reuse, S., Svoboda, M. (2013). Interest Rate Swaps – Modelling and Usage in the
Context of Basel III and EMIR, In: 6th
International Scientific Conference Managing and
Modelling of Financial Risks, Ostrava: VŠB-TU Ostrava, pp. 583 – 592.
Zeranski, S. (2013).  Auflagen  müssen  die  Richtigen  treffen,  in:  Braunschweiger Zeitung,
April 25th
, 2013, p. 10.
285
Outsourcing in Insurance
František  Řezáč1
, Martin  Řezáč2
1
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  602  00  Brno, Czech Republic
E-mail: lrezac@econ.muni.cz
2
Masaryk University
Faculty of Science, Department of Mathematics and Statistics
Kotlářská  267/2,  611  37  Brno, Czech Republic
E-mail: mrezac@math.muni.cz
Abstract: In relation to the impact of the financial and debt crisis, commercial insurance
companies operating not only at the insurance market of the Czech Republic but also in
global market conditions are looking for possible ways to make their activities more
efficient as well as the ways to decrease the costs of insurance service provision. One of
the effective ways to increase efficiency and decrease costs is outsourcing. The aim of
this paper is to evaluate the current state of outsourcing in the insurance practice in the
insurance market in the Czech Republic. This paper presents the theoretical background
of the issue of outsourcing. It is based on the study of literature and the findings of
research by PricewaterhouseCoopers. At the same time, it shows the outsourcing in the
current insurance practice at the insurance market of the Czech Republic with an
emphasis on human resources and payroll, accounting, business, claims management
and assistance services.
Keywords: outsourcing, insurance industry, insurance market, commercial insurance
company, costs.
JEL codes: G22
1 Introduction
The development of each society is dependent on not only internal but mainly external
influences. Commercial insurance companies operating at the insurance market of the
Czech Republic reflect the development of the Czech society brought about by the
transition from the totalitarian system to the market system. The acceptance of Act on
Insurance, in 1990, provided basic conditions for the development of the insurance
industry. The further development has responded to the changes in the society and the
insurance companies themselves. The first half of the 1990s can by simply characterised
as a period of development and higher costs, the second half is typically looking for
savings and decreasing costs. The turn of the century and the Czech Republic accession
to the European Union brought another optimistic initiative for the development and a
smaller attention to some cost items. However, the financial and debt crisis has forced
the commercial insurance company managements to find the ways to make their
activities more efficient and decrease costs. First attempts at using outsourcing came in
financial groups, i.e. also insurance companies, at the beginning of the new century, i.e.
in 2000. The tendencies have been even more pronounced since 2010, in response to the
financial crisis.
The usage of outsourcing also brings some negative experience in the insurance practice.
This is mainly manifested in the commercial activities – insurance companies released
some of their former employees and transferred them to mediation activities in
compliance with the Act on Insurance, the Act on Insurance Mediators, and the Act on
Trade, which meant a loss of direct influence on these persons.
The consequences include a decrease in the quality of provided insurance services and a
chase for commissions at the expense of the quality of advisory and mediating services.
A special phenomenon is a creation of ‘independent   networks’,   who   demand   special  
insurance products from the insurance companies and unusually high commissions. They
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threaten to draw their clients to other insurance companies otherwise. As a result, the
managements of the insurance companies are at a loss and do not have a solution.
2 Methodology and Data
The subject of this paper is an analysis of the use of outsourcing in the insurance practice
of selected commercial insurance companies operating at the insurance market of the
Czech Republic. The description method was used to process the theoretical approach to
this issue. The content analysis method was used to clarify the use of outsourcing in the
insurance practice. The theoretical part was created using resources gained from the
study of relevant literature. The analytical part was created based on annual reports and
web pages of the insurance companies. Moreover, presentations of an insurance practice
expert given within the subject Economics and Insurance Company Management,
discussions  with  experts  from  insurance  companies  and  the  author’s  personal  experience  
were used for this latter part.
3 Results
The term outsourcing involves all the processes a company decides to contract out to an
external service provider. These are serious strategic decisions related to expectations of
substantial cost savings or a higher quality of the selected business activities in the
hands of specialised companies, providing a competitive advantage of concentrated
know-how in the field.
According to Rydvalova and Rydval (2007), we can see outsourcing as a project in which
a company gains complex services from the supplier. Outsourcing is a long-term
relationship, exclusively with an external supplier, concerning providing services in one or
more fields of the company activities. To put it simply, outsourcing includes those cases
when the suppliers are able to provide several related services at a time, or they
concentrate on  provided  complex  service  ‘packages’.
Greaver (1999) defines outsourcing as an act in which some repeated internal activities
and decision rights are transferred to an external contractor on a contractual basis. He
emphasizes that the transfer concerns not only activities but often also factors of
production and decision rights. By factors of production he means people, equipment,
facilities, technologies and other assets that enable the activities to proceed. By decision
rights he means responsibility for decisions within some fields of the conducted activities.
PWC (2007) experts define outsourcing as a transfer of trade activities or functions to a
third party, usually together with people or even know-how.
Although the reasons for outsourcing differ, according to opinions of representatives of
various functional positions in a company, the most significant reasons related to
advantages of outsourcing can be derived from professional literature (Greaver, 1999):
Organizationally driven reasons:
– Effectiveness enhancement by focusing on what the company is the best at (the
core business).
– Flexibility increase and thus increased adaptability to the changing business
conditions, demand for products and services, and technologies.
– Organization transformation.
– Increase in the value of products and services, customer satisfaction and
shareholder value.
Improvement driven reasons:
– Improvement of operating performance.
– Gaining expertise, skills and technologies that would not be available otherwise.
– Improvement of management and control.
– Improvement of risk management.
– Gaining innovative ideas.
287
– Improvement of credibility and image by cooperation with excellent providers.
Financially driven reasons:
– Reduction of investment in assets and release of these resources for other
purposes.
– Generation of cash by transferring assets to the provider.
Revenue driven reasons:
– Gaining  market  access  and  business  opportunities  through  the  provider’s  network.
– Expansion  acceleration  by  using  the  provider’s  developed  capacity,  processes and
systems.
– Expansion of production and sales capacity during periods when such expansion
would not be possible otherwise.
– Commercial exploitation of the current skills.
Cost driven reasons:
– Reduction  of  costs  through  the  provider’s  performance  and  the  provider’s  lower  
cost structure.
– Transformation of fixed costs into variable costs (service fees depend on the
changing range of the provided service).
Employee driven reasons:
– Giving employees more opportunities for career development.
– Increase in loyalty and energy in the field auxiliary to the core business.
Based on the latest survey of PWC (2007), the most frequently stated reasons for
outsourcing are:
– Lower costs.
– Gaining access to talent.
– Selecting activities that others are able to do better.
– Flexibility increase.
– Improvement of customer relationships.
– Development of new products.
– Expansion within the market development.
– Geographic expansion.
The  impulse  for  outsourcing  is  seen  as  strategic  when  it  corresponds  with  the  company’s  
long-term strategy (Greaver, 1999). It means typical benefits related to outsourcing are
expected within several years and the results, both positive and negative, will be
significant for the organization.
Strategic outsourcing moves outsourcing into higher levels by asking questions about the
importance of outsourcing for the organization from the perspectives of:
– Vision of its future.
– Current and future core competencies.
– Current and future structures.
– Current and future costs.
– Current and future performance.
– Current and future competitive advantages.
A price for a wrong understanding or implementation of outsourcing can considerable
affect the future existence of an organization (Greaver, 1999). For example, a premature
contract termination (worse if it is long-term and strategic) together with a refusing
attitude of the original employees and technical specialists of the original organization
will lead to substantial problems as the employee count has to be regained or a new
provider has to be found. Both of these represent substantial costs. The problem will
escalate if the provider has been transferred the necessary assets in which the
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organization will have to invest again. Similarly, an incorrect identification of the core
company competencies will lead to a loss of future opportunity to increase the
competitive edge and the related profits.
Figure 1 The most frequently outsourced fields
Source:  Mochnáč (2008) based on PWC (2007)
The attention is currently devoted to outsourcing within the insurance practice at the
insurance market of the Czech Republic in relation to the implementation of European
guideline Solvency II. According to Art. 6, section 4, of Act no. 277/2009 Coll., on
insurance, a domestic insurance company, a third party insurance company, a domestic
reinsurance company or a third party reinsurance company is entitled to conclude
contracts with the purpose of permanent or temporary transferring or dividing of some
activities with exception of assuming insurance risks into insurance or reinsurance risks
into reinsurance, either within the entire range or partially, to another party (hereinafter
“contract   on   activity   selection”)   unless   accounting   is   distorted   or   stability   of   the  
company’s   economy   is   endangered.   This   is   also   valid   for   contracts   concluded   with  
another person in the group, its subject being sharing of costs of activities common for
all   these   persons   (hereinafter   “contract   on   cost   sharing”),   and   transfer   of   activities  
outside   the   Czech   Republic   (hereinafter   “activity   transfer”).   The   contract   on   activity
selection, the contract on cost sharing, or the activity transfer do not interfere in the
insurance   or   reinsurance   company’s   reliability   for   its   activities   and   duties   stipulated   by  
this act towards the Czech National Bank.
The basic fields where outsourcing can be used by commercial insurance companies are
accounting, human resources and payroll administration (Brown and Wilson, 2007).
Advantages:
– Decrease in costs related to activities of a payroll accountant (elimination of costs
of trainings on the continuous updating of norms, guidelines and laws),
professional growth provision and know-how development.
– Elimination of investments in specific applications and hardware, and their
updates.
– Distribution of risks related to correct calculations and their administration.
– Timely provision of necessary activities (e.g.   accountant’s   sick   leave   is  
compensated for, especially in the time of the closing dates, etc.). The problem of
their replaceability solved.
– Standard in the quality of provided services.
– Gaining complex services, including tax consultancy, audit, communication with
the financial authority, accounting reports (also in foreign languages), etc.
289
Disadvantages:
– Separation   of   the   supplier   from   the   organization’s   operations,   the   risk   of   time  
discrepancy between the acceptance of vital operational information and its
reflectance in the system.
– Relocation of relevant materials and documents between the organization and the
supplier.
– The supplier does not follow the organization in the context of its internal
development and market development.
– The provided service cannot be measured clearly and it is thus complicated to
analyze the corresponding price.
– The related field is highly sensitive as regards the processed data; there is a high
risk of their misuse.
The experience from the insurance practice confirms that in the second half of the 1990s,
insurance companies started moving a part of their human resources and payroll
administrative work to external contractors, which processed e.g. payrolls based on the
delivered documents. When finding potential employees, insurance companies have used
the services of employment agencies, which help them organize selection procedures and
interviews. However, the responsibility for the employee selection itself lies with the
insurance department manager in charge.
Another field insurance companies outsource is accounting. Currently, there are a high
number of companies focused on accounting and reporting. The formation of this kind of
specialized services has been motivated by the dissatisfying and unstable legal system
characterized by constant changes in acts and regulations. As soon as at a time of an act
publishing, usually other partial changes of the act are being proposed. Students of the
Faculty of Economics and Administration prefer to find work in companies concentrated
on audit or accounting and taxes to work in banks and insurance companies – they react
to the development of the labour market in the Czech Republic where financial
institutions dismiss their employees, while auditing, accounting and tax companies
search and hire them.
Figure 2 The fields of outsourcing in the close future
Source:  Mochnáč (2008) based on Brown and Wilson (2007)
Although releasing of trade service employees occurred in insurance companies at the
insurance market of the Czech Republic as soon as in the second half of the 1990s, this
practice remarkably expanded after 2004, when the act on insurance mediators and loss
adjusters became valid. At present, a substantial part of the trade activities and loss
adjusting is provided by external co-operators of the insurance companies. Some of them
62%
48%
48%
37%
23%
19%
18%
18%
12%
10%
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7
Research, Development, Innovation (KPO)
Document Services (DPO)
Legal Services (LPO)
Logistics, Supply Chain, Procurement (SLO)
Compliance, Finance Accounting (FAO)
Lead Generation, Marketing & Sales (MSO)
Back Office, General Admin (BPO)
Technology (ITO)
Engineering (ESO)
Learning, Training, Recruitment & HR (HRO)
290
work as freelance insurance agents (entrepreneurs), some of them work within
independent networks, still some, mainly those focusing on insurance for entrepreneurs
and industries, work as insurance brokers. The disorganization and heterogeneity of the
“traders”   at   the   insurance   market   and   the   large   amount   of   entities   bring   about  
considerable errors in the provided services.
A separate chapter within the effort at better quality of insurance services by means of
outsourcing is provision of assistance and auxiliary services. In this field, the insurance
companies cannot do without a close cooperation with external companies who offer
specific services. However, the risk of wrong selection of the assistance service provider
may lead to dissatisfaction with the insurance company itself, which is responsible for the
quality of the provided services, regardless of the actual supplier.
According to experts from Capgemini (Capgemini, 2008), the top global consulting
agency in the field of outsourcing, the period of classical outsourcing is over. Usage of
outsourcing is not only a matter of lowering costs; it has become a tool to harmonize
individual activities with the business goal. It is necessary to focus on the solution of the
current problems and ensure a future growth. Multisourcing, outsourcing model for the
future, can shift the competitiveness to a higher level.
4 Conclusions
Which insurance activities can be provided by outsourcing and which should remain
within the hands of the insurance company management - this is a subject of theoretical
explorations and discussions in the insurance practice. There is no clear and guaranteed
guide ensuring effectiveness of the provided services and lower costs.
Based on the study of relevant literature, the analysis of the insurance practice, and the
above mentioned facts, we can conclude that the decision about the range of outsourcing
usage in each particular commercial insurance company has to be made by the
management of the company themselves. The experience with the insurance practice
shows that the most frequently outsourced fields used by commercial insurance
companies are accounting and taxation, human resources and payroll work, trading
activities, loss adjusting, and providing of assistance and auxiliary services.
The most troublesome field seems to be the trading activity. There is a high amount of
entities providing this. Their expertise can be very low. Tests or examinations in
compliance with law conducted in the past were inefficient as the examiners’  demands
were very low. As a result, the provided services are of low quality, motivated by the
commission mainly and there is a race for concluding as many insurance contracts as
possible. Cancelling of insurance contracts or problems with payments of the prescribed
premiums are not penalized sufficiently. A possible solution of these negative phenomena
can be harder professional examinations leading to a more significant selection of
insurance mediators at the insurance market of the Czech Republic as well as the
insurance companies’  and  inspection  authorities’  control.
Acknowledgments
The presented paper is a partial output of Specific research project MUNI/A/0743/2012,
implemented at the Department of Finance, Faculty of Economics and Administration,
Masaryk University, of 2013.
References
Brown, D., Wilson, S. (2007). Black Book of Outsourcing [online], Available at WWW:
<http://www.theblackbookofoutsourcing.com/2007-vendors.aspx>.
CAPGEMINI (2008). The Keys to Successful Multisourcing [online], Available at: <http://
www.capgemini.com/resources/thought_leadership/the_keys_to_successful_multisourcin
g/>
291
Greaver, M. F. (1999). Strategic outsourcing: a structured approach to outsourcing
decisions and initiatives. New York: AMACOM, 314 p.
Mochnáč, M. (2008). Projekt outsourcing. Diploma thesis, Masaryk University.
PWC [PriceWaterhouseCoopers] (2007). Outsourcing Comes of Age [online]. Available at:
<http://www.pwc.com/extweb/pwcpublications.nsf/docid/DF993563025DFA0F852572D6
0067ACA3/$File/OutsourcingComesofAge.pdf>
Rzdvalová, P., Rydval, J. (2007). Outsourcing  ve  firmě. Brno: Computer Press, 102 p.
Zákon  č.  277/2009  Sb.,  o  pojišťovnictví.
292
Restructuring of the Czech Banking Sector –
Influence of Mergers on Selected Financial Indicators
Jaroslav  Sedláček,  Petr  Valouch,  Zuzana  Křížová,  Eva  Hýblová,  Alois  Konečný
Masaryk University
Faculty of Economics and Administration, Department Finance
Lipová  41a,  602  00  Brno, Czech Republic
E-mail: sedl@econ.muni.cz, valouch@econ.muni.cz, krizovaz@econ.muni.cz,
hyblova@econ.muni.cz, alois@econ.muni.cz
Abstract: Transformation of the Czech banking sector to market conditions was finished
by a privatization of large banks in years 1998–2001. Banks with problems stopped their
activities and new banks quickly adapted to the new conditions. The banking sector
stabilized and a gradual restructuring of the sector followed, adequate to the needs of
the developing economy. A typical feature of the restructuring was the increasing share
of foreign companies in the Czech banking market by means of acquisitions and mergers.
The structural changes were manifested not only in the fast growth of the group of
foreign bank branches, but also in connections of banks into larger units as well as
transformations of subsidiaries into branches and organization components of foreign
banks. Besides financial stability, restructuring should bring economic benefits to owners.
To prove a positive effect of mergers, we analysed the development of selected economic
indicators in the period before and after the changes in the Czech banking sector in
2003–2012. The regression models of indicator development curves of merging banks
were compared with the values reported by the entire banking sector. The results of the
regression analysis show a positive effect of mergers on the financial stability of the
transformed banks in two years after the merger as well as the value created for the
owners.
Keywords: banking sector, restructuring, mergers and acquisition, regression and
correlation analyses
JEL codes: G21, G28, M21
1 Introduction
The transformation of Czech banks to a form of stock trading companies was related to
the transformation of the Czech economy from centrally controlled to market-oriented.
After the dissolution of Czechoslovakia in January 1, 1993, it was necessary to constitute
a new banking sector, able to provide financing to companies in the conditions of market
economy. The existing one-level banking system was changed to the two-level banking
system with separate issuing and trade banking. The newly emerging commercial banks
did not have sufficient staff, technical facilities or network of branches to provide services
in the market economy, the suitable legal environment was missing. Moreover, they were
loaded with old bad loans that were transferred to them during the transition to the twolevel
banking system as well as new non-valuation assets in consequence of support for
businesses in a transforming economy, with a low level of their own capital. New bad
loans appeared in balance sheets of even newly emerging banks, increasing the risk of
moral hazard of banks when providing loans, and budget limitations were created in
economy   for   many   entities   (Horvátová   2008,   Kocmanová   and   Šimberová   2011,  
Stunguriene  2010,  Steker  and  Otrusinova  2012,  Beranová  and  Martinovičová  2010).  An  
increase in new bad loans led to a deterioration of the financial situation of large banks
and the stability of small banks was endangered. It was necessary to consolidate and
recover the banking sector to support the on-going economic transformation, e.g. by
purchase of non-valuation liabilities, increase in their basic capital by the current
stockholders or investors, or by installation of receivership, withdrawal of banking
licence, and ending their activities or selling problematic banks. The transformation of the
banking sector finished in 1998–2001 by a privatization of large banks. The development
of the bank structure in this transformation period is presented in Table 1.
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Table 1 Ownership structure of banks in the Czech Republic (1993–2002)
Form of ownership 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
State monetary institutions 1 1 1 1 1 1 1 1 1 1
Banks with state
participation
4 4 6 6 6 5 4 4 2 2
Banks with a decisive Czech
participation
28 28 25 18 15 14 10 8 10 9
Banks with a decisive
foreign participation
12 13 13 14 15 15 17 16 17 17
Branches of foreign banks 6 8 10 9 9 10 10 10 9 9
Banks in receivership 1 1 0 5 4 0 0 1 0 0
Active banks in total 52 55 55 53 50 45 42 40 39 38
Banks with cancelled
licences
0 1 3 2 4 8 3 2 3 1
Source: Revenda 2001, CNB – reports on bank supervision 1993-2002
After state shares of large banks were purchased by strategic foreign investors, the
Czech banking sector stabilized and in 2002 only one bank ended its activities.
Restructuring of the banking sector took place in the group of small banks mainly. The
withdrawal of a large number of small banks from the Czech banking market was gradual
– in contrast to the fast transformation, this had the advantage that it did not cause a
more considerable decrease in economy or employment rate. Problematic banks were
continually replaced with new ones, financed predominantly by foreign capital. The
capital was increased by new investors or by selling the banks with the assumption of a
future inclusion of the acquisition in the structure of the purchasing bank. The increase in
the basic capital had a form of an even participation of the current owners in new issues
of stocks. Especially the group of foreign subsidiaries fully owned by renowned foreign
banks was strengthened. Foreign capital, mainly from the EU countries (Germany,
France, Austria and Netherlands), was in the background of newly emerging branches or
organization components of foreign banks. As follows from Tab. 1, 38 banks and
branches remained at the Czech market after acquisitions and mergers. These were
financially sound and sufficiently equipped with foreign capital mainly. We can assume
that a further development of the Czech banking sector will proceed adequately to the
needs of the economic development in the field of providing financial sources and in
dependence on the behaviour of financial markets. We are mainly interested in economic
consequences (Brealey and Myers and Allen 2006, Režňáková  and  Karas  2012,  Bohušová  
and   Svoboda   and   Nerudová   2012, Malíková   and   Brabec   2012)   of   the   following  
restructuring in the Czech banking sector. The aim of our research is therefore to
quantify the economic effects of mergers and acquisitions (Bruner 2004, Sedláček   and  
Hýblová  and  Křížová  2011)  implemented at the Czech banking market in 2003–2012.
2 Problem Formulation
Strengthening of the influence of foreign owners is a typical feature of the development
of the Czech banking sector also in the period of the past ten years. Acquisitions and
mergers in this period brought a considerable increase in the number of branches and
organization components of foreign banks, while the number of banks in Czech hands
decreased. 14 new banks came to the banking sector and 9 banks left, as their licence
ended and they finished their activities. The development trend of the total number of
banks active in the Czech banking sector was reversed: from a dropping trend (see Tab.
1) to a growing one, as shown in Table 2.
A closer analysis of the processes and motivations of new banks entering the market
shows that they are direct investments of foreign banks (except for the Czech Fio banka)
aiming at gaining a larger share of the Czech banking market. The selected form is
branches or organization components, which are subject to regulations and the Czech
294
bank supervision to a limited extent only. For this reason, two banks transformed the
existing subsidiaries (Caylon Bank Czech Republic, a.s. a Citibank, a.s.) to branches. In
total, 5 mergers were implemented in the monitored period (out of which one in the
group of large banks and one in the group of small banks), one takeover of assets and
one change of legal form (see Table 3).
Table 2 Development of the Czech banking sector and the ownership structure 2003–
2012
Form of ownership 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
State monetary institutions 1 1 1 1 1 1 1 1 1 1
Banks with a decisive Czech
participation
11 9 9 8 9 8 7 8 8 8
Banks with state
participation
2 2 2 2 2 2 2 2 2 2
Banks with a decisive
foreign participation
17 17 15 16 15 13 14 14 15 15
Branches of foreign banks 9 9 12 13 14 16 18 19 21 21
Active banks in total 37 35 38 39 39 37 39 41 44 44
Number of new banks in the
period
0 0 2 1 2 1 3 2 3 0
Number of dissolved banks
in the period
2 0 1 0 2 3 1 0 0 0
Source: CNB – reports on bank supervision 2003-2012
Table 3 Ownership transformations at the Czech banking market in 2003–2012
Transformation type Dissolved companies Successor companies Year
Merger while no new company is
formed
BAWAG International
Bank CZ
BAWAG Bank CZ, a.s. 2005
Merger while no new company is
formed
Živnostenská  banka,  
a.s.
HVB Bank Czech
Republic, a.s.
2006
Merger while no new company is
formed
IC Banka, a.s.
Banco Popolare Czech
Republic, a.s.
2007
Takeover of assets HVB Reality CZ, s.r.o.
UniCredit Bank Czech
Republic, a.s.
2008
Merger while a new company is
formed
eBanka,  HYPO  stavební  
spořitelna
Raiffeisenbank a. s. 2008
Merger while no new company is
formed
BAWAG Bank CZ a. s. LBBW Bank CZ a. s. 2008
Change of legal form Fio,  družstevní  záložna Fio banka, a.s. 2010
Source: CNB – reports on bank supervision 2003-2012
To be able to assess the economic effects of mergers, we use the method of financial
analysis – ratio indicators out of which temporal series are created. This concerns the
following indicators:
– return on assets (ROA)
– return on equity (ROE)
– equity share (ES)
– retained earnings to assets (REA)
We assume growth for all the indicators. The temporal series capture the development of
indicators of the banks participating in mergers one year before the decisive day of
merger and during five years after the merger (t-1 till t+5). The methods of correlation
and regression analyses are used (Valouch   and   Konečný   2011). A specific regression
295
function suitable to describe the dependence of both quantities was selected based on
empirical analysis. The high quality of the agreement with data is shown by the linear
function (1).
y = b0 + b1x (1)
where: b0, b1 - parameters of the theoretical regression function
x - independent variables
Regression functions of indicators of merging companies are compared with the temporal
development of the banking sector as a whole (indicators indexed s - sector). The mutual
dependence between the development trends described by both regression curves is
measured by the Pearson correlation coefficient.
𝑟    =  
∑   –   ( )
∑ ( )   ∑ ( )   
(2)
where: rxy - selected correlation coefficient (Pearson)
X, Y - random quantities
i - number of random quantities i ∈⟨1, n⟩
Our hypotheses assume a growing character of the regression graphs of indicators in the
period after the decisive day in the banks that implemented a merger. If the mergers
were successful, the indicators of the participating banks should reach the values at the
level of sector after the merger.
3 Problem Solution
The temporal development of the indicators calculated for the banks that participated in
a merger and for the entire Czech banking sector in the period before and after the
merger is expressed in graphs, Figures 1–4. The character of the development of
indicators is described by regression equations, which confirm or deny the hypotheses.
The agreement of the regression model with the data is expressed by the value of R2
.
The equations of regression lines of the ROA indicator show a growing character.
y = 0.0371x  +  1.1486;;  R²  =  0.0338  (ROA) (3)
y  =  0.0018x  +  1.2614;;  R²  =  0.0012  (ROAS) (4)
Figure 1 Development Graph of Indicators ROA before and after the Mergers
The slope of the regression line of a merging company is steeper than that of the entire
sector, which could be considered positive. However, Fig. 1 shows that the value of the
y = 0.0371x + 1.1486
y = 0.0018x + 1.2614
ROA ROAs Linear (ROA) Linear (ROAs)
296
indicator dropped fast after the culmination in the second year after the merger until it
fell below the sector value in the fifth year after the merger. The development graphs of
both indicators ROA and ROA S differ substantially, as proved by the low value of the
Pearson correlation coefficient rXY = 0.079509.
The negative slope of both the regression lines of the ROE indicator (5 and 6) indicates a
decreasing character of the development. As Figure 2 shows, the value of the indicator
of companies after the merger approached the sector value at its highest point but did
not exceed it. Our hypothesis of a growing character of both graphs and their contact has
not been confirmed. The mutual dependence measured by the correlation coefficient rXY
= 0.293949 is weak.
y = -0.6296x  +  12.483;;  R²  =  0.2237  (ROE) (5)
y = -0.1643x  +  17.2;;  R²  =  0.0565 (ROES) (6)
Figure 2 Development Graph of Indicators ROE before and after the Mergers
The development of bank financial stability indicators calculated as the share of capital in
total liabilities is presented in Figure 3. Both curves have a changeable progress similar
to the previous indicators. In the banks that implemented a merger there is a two-year
drop followed by growth. The equations of regression lines (7, 8) confirm the growing
character of both functions but the merging group has a steeper slope. There is a direct
mutual dependence between the curves, very high, correlation coefficient rXY =
0.781629. The regression analysis confirmed the hypothesis of a positive development of
the ES indicator after the merger.
y = 0.1736x + 8.7357;;  R²  =  0.4824  (ES) (7)
y  =  0.0975x  +  7.2886;;  R²  =  0.1922  (ESS) (8)
The positive effect of mergers has also been confirmed for the REA indicator, expressing
the level of the preservation of assets in the form of undistributed earnings which
increase the internal value of the bank. The development graphs of the REA indicator
presented in Figure 4 and their regression functions described by equations (9) and (10)
confirm a growing character. Although there is a very high correlation rXY = 0.865614,
the growth is more dramatic in the group of merging banks. This result means that banks
that participated in a merger are more careful and postpone capital consumption until
later.
y  =  0.2521x  +  2.9257;;  R²  =  0.7006 (REA) (9)
y  =  0.1921x  +  1.6943;;  R²  =  0.9477 (REAS) (10)
y = -0.6296x + 12.483
y = -0.1643x + 17.2
ROE ROEs Linear (ROE) Linear (ROEs)
297
Figure 3 Development Graph of Indicators ES before and after the Mergers
Figure 4 Development Graph of Indicators REA before and after the Mergers
4 Conclusion
Restructuring of the Czech banking sector in the past decade had a form of acquisitions,
mergers, takeovers of assets, changes of legal forms or only changes of positions within
a consolidation unit. Acquisitions and mergers represent external forms of company
development and they should bring a faster growth. Analyses performed for banks that
have participated in mergers confirmed a positive effect of mergers on the profits of the
banks (ROA and ROE indicators) in the period of two years after the merger. This
indicates a fast integration of the participating companies and implementation of new
technologies, organization, management and culture. In the following years, these
indicators dropped below the sector level, which is probably related to the (in)ability of
the transformed banks to increase the value of their assets and change them into new,
more profitable ones. An important factor is also the influence of the external
environment as the development tendencies of profitability in both groups were
manifested negatively. As regards indicators of stability (ES and REA), our hypothesis
about the growing character of the graphs after the merger has been confirmed.
Moreover, it has been proved that successor companies achieve higher equity amenities
and preservation of their assets than the entire banking sector. Although studies of
business entities conducted by auditing and counselling companies have shown that a
y = 0.2521x + 2.9257
y = 0.1921x + 1.6943
REA REAs Linear (REA) Linear (REAs)
298
majority of mergers do not achieve the expected effects and often end in failure, this has
not been confirmed for mergers in the banking sector.
The paper contains   first   results   of   project   of   Grant   Agency   CR   no.   403/11/0447   “The  
Analysis  of  Taxation  and  Accounting  Procedures  during  Mergers”.
References
Beranová,   M.,   Martinovičová,   D.   (2010).   Application   of   the   theory   of   decision   making  
under risk and uncertainty at modeling of costs. Agricultural Economics, vol. 56, issue 5,
pp. 201–208.
Bohušová,   H.,   Svoboda,   P.,   Nerudová,   D.      (2012).   Biological   assets   reporting:   is   the  
increase in value caused by the biological transformation revenue? Agricultural
Economics, vol. 18, issue 11, pp. 520–532.
Brealey, R. A., Myers, S. C., Allen, F. (2006). Principles of corporate finance. 8th ed.
Boston: McGraw-Hill.
Bruner, R. F. (2004). Applied mergers and acquisitions. Hoboken: John Wiley & Sons Inc.
CNB (2013). Zprávy   o   výkonu   bankovního   dohledu   nad   finančním   trhem. Available at:
<http://www.cnb.cz/cs/dohled_financni_trh/souhrnne_informace_fin_trhy/zpravy_o_vyk
onu_dohledu/>.
Horvátová,  E. (2008).  Koncepcia  kapitálovej  primeranosti  bánk  a  investičných  podnikov  v  
podmienkach  Európskej  únie.  Journal of Economics, vol. 56, issue 6, pp. 582 - 597.
Kocmanová,  A., Šimberová,  I.  (2011).  Modelling of Corporate Governance Performance
Indicators. Inzinerine Ekonomika–Engineering Economics, vol. 23, issue 5, pp. 485-495.
Malíková,   O.,   Brabec,   Z.   (2012).   The   Influence   of   a   Different   Accounting   System   on  
Informative Value of Selected Financial Ratios. Technological and Economic Development
of Economy, vol. 18, issue 1, pp. 149-163.
Revenda Z. (2001). Centrální  bankovnictví. 2nd
ed. Praha: Management Press.
Režňáková,   M.,   Karas,   M.   (2012).   The   Effects   of   a   Change   in   the   Environment   on  
Business Valuation Using the Income Capitalization Approach. Equilibrium, vol. 7, issue 2,
pp. 119-137.
Sedláček,   J.,   Hýblová,   E.,   Křížová,   Z.   (2011).   The Analysis of the Development of
Ownership Structure in the Czech Merger Market. In Proceedings of the 13th
International Conference on Finance and Banking. Karviná:  OPF  SLU,  pp.  566-574.
Steker, K., Otrusinova, M. (2012). Current problems of consolidation in the Czech
Republic. In Proceedings of at the 5th International Scientific Conference on Finance and
the Performance of Firms in Science, Education and Practice.   Zlín:   UTB   FME,   pp.   542-
550.
Stunguriene, S. (2010). Optimization of the Ratio of the Bank Payment Cards Price and
Change. Inzinerine Ekonomika–Engineering Economics, vol. 21, issue 1, pp. 12-18.
Valouch,  P.,  Konečný,  A.  (2011).  The Development of the Number and the Structure of
Mergers of Trading Companies in the Czech Republic in 2001–2010. Paper presented at
the 6th IASME/WSEAS International Conference on Economy and Management
Transformation  (EMT´11). Angers, France: WSEAS Press, pp. 25 – 30.
299
Perspectives of the Monetary Development of EU
Miroslav Sponer
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  602  00  Brno,  Czech  Republic
E-mail: sponer@econ.muni.cz
Abstract: The article analyses the consequences of the monetary integration project
especially from the perspectives of convergence and divergence within the EMU
countries. The development background in EMU, present arrangements of the ECB and
governments of EU countries are assessed. The main aims of this article are to estimate
the possible monetary development in the future and to propose arrangements in the
area of monetary policy and other EU policies.
Keywords: Monetary integration, Convergence, Divergence, Economic and Monetary
Union (EMU), Monetary policy.
JEL codes: E42, E52, F33
1 Introduction
The idea of a monetary union is obviously a conflicting matter. Its conflicting character
has two aspects. The first one is of a scientific nature: a creation of a monetary union is
a completely unique and new issue, which means it lacks a background of either
economic theory or experience. The other aspect concerns political reality and interests.
However, the first aspect, theoretical or scientific, of the process is of more relevance.
There is no historical precedent for the European monetary integration and a single
currency project in its current form of Economic and Monetary Union (EMU). This project,
as  a  “currency  without  a  state”,  in  which  a  group  of  fiscally  sovereign  countries  share  a  
single currency and a single central bank, is a very different arrangement in many
respects. This article sets out to show that, however innovative the Eurozone set-up is, it
was not and will not be able to escape economic reality and the principles of economics.
The article generally analyses the consequences of the monetary integration project,
especially from the perspectives of convergence and divergence within the EMU
countries. The article aims to estimate the possible monetary development in the future
and to propose arrangements in the area of monetary policy and other EU policies.
The article has a nature of applied research focused on the field of integration processes.
The subject of research is the monetary concept of European integration. This issue is
explored at two basic levels. The first can be defined as informative and documentary
and its main aim is to map and explain the content and phenomena of the processes that
have progressed in the field of integration in West European countries so far. The other
dimension contains analytical views of partial issues of monetary cooperation and
formulates synthetic viewpoints based on multi-criteria exploration.
2 Legitimacy of EMU creation
A monetary union can be characterized as an area where national currencies of member
countries are replaced with a single currency and where responsibility for the monetary
policy is transferred to a supranational institution. This step can bring many benefits to
the involved countries on the one hand; on the other hand, it can bring an increase in
costs. A transfer to a single currency is economically legitimate if the total benefits
exceed possible losses.
From an economic point of view, the legitimacy of the monetary union is first of all based
on the ever closer connection of European financial markets. This is a phenomenon that
has started developing dynamically after the irreversible removal of all controls of capital
300
movement in Europe (July 1990) and finishing of the internal market of financial services
(January 1993).
Advocates   of   the   monetary   union   refer   to   Walter   Eucken’s   text   (Eucken,   1952)  
concerning  ‘the  interdependence  of  orders’.  Eucken  emphasizes  that  in  an  ideal  case  the  
area of monetary policy effect is identical with the limits of the financial space. If there
are independent national central banks (each with one national currency), it is not
possible to have an effective monetary policy in the financial space integrated in this
way. The existing Europe-oriented arrangement of financial markets can thus be only
matched to the united European monetary space that will be created by the monetary
union. Flexible as well as regional fixed exchange rates represent an inconsistent
solution, which leads to the existence of the united financial space and independent
spaces of national currencies. This creates a potential tension, which was mainly evident
during the 1990s. The tension can only be removed by a monetary union. With fixed
exchange rates, since 2002 the single European currency, financial and capital flows will
only be dependent on the advantages of the individual investment options. Expectations
of changes in exchange rates will cease to be determinants of international portfolio
decisions. According to the theory of interdependence of orders, the economic necessity
of the monetary union is in a close direct relation with the strengthening integration of
financial markets in Europe.
The advantage of the monetary union when compared to the system of regional fixed
exchange rates is the fixation of exchange rates, or the single currency, which prevents
all disturbances that could occur due to speculations at foreign-exchange markets. By
contrast, in the case of the European monetary system, we would have to expect
speculative attacks testing the reliability of the established parity values. Restrictive
monetary measures then have to be taken to defend this system.
In the case of an internal market with flexible exchange rates, we would have to consider
that speculative waves can come accompanied by strong deviations of exchange rates,
considerably exceeding the national differences in the development of salaries and prices.
Local companies would be disadvantaged within the international economic competition
in the country with the appreciated currency although their technological performance or
labour costs would remain. By contrast, the international competitiveness of local
producers would be enhanced in a country with the depreciated currency although their
productivity would not increase or their labour costs decrease. It means a wrong control
of the flexible exchange rate may lead to considerable changes in international
competitiveness. These disturbances would be highly significant at the European internal
market as it has a high degree of foreign trade interconnectedness of economies of EU
member countries. The main advantage of the monetary union is therefore the fact that
it would remove business risks related to the place of production and those conditioned
by changes in exchange rates within the EU. From this perspective, only the monetary
union will allow Europe to use the advantages of the internal market fully. In the case of
serious disturbances of the economic competition caused by wrong fluctuations of
exchange rates, we could not rule out possible protectionist tendencies in individual
countries. Moreover, due to the strong pressure on currency appreciation, there could be
efforts to limit capital movement, which would also put the existence of a united internal
market at risk. While currency shocks in the system of regional fixed exchange rates
mean negative macroeconomic effects, in the system of flexible rates they mainly affect
the international distribution of work and are of microeconomic nature. Therefore, the
internal market can be accepted only if the functionality of market mechanism is not put
in doubt at goods markets by speculative movements of the capital (Sponer, 2000).
3 (Non)Functioning of EMU
The first decades of EMU created an impression that it is a functional and successful
project. The exchange rate risk between Eurozone member countries was eliminated and
the other benefits were not doubted. Transaction costs decreased and international trade
grew within EMU. Interest rates of the member countries converged significantly and
301
loans crossed borders in growing volumes. The monetary integration went in a fast rate.
This meant a fast growth for the countries of the southern periphery.
The strength of any economic project naturally becomes clear not in good times, but in
bad times. Only bad times will reveal the strength of the structure that is meant to bear
the project and carry it into future better times. The effort put into laying the foundations
of any project is therefore certainly a sensible investment.
EMU was not an optimum monetary area when created neither did it head towards one,
unfortunately. Differences among individual countries remained. Capital flowed into
economies that offered higher evaluation, but at a price of a higher risk. The easy access
to relatively cheap loans motivated a fast growth of private and public expenditures,
which led to increasing deficits of current accounts. The influx of foreign capital at low
real interest rates, in the countries with higher inflation, caused overheating of these
economies.
The belief that EMU membership will lead to a better discipline of economic policies
proved to be an illusion. Most of the member countries did not use the favourable
conditions of economic growth for consolidation of public finances. EMU contributed to
overheating of weaker economies, which has covered structural differences between the
member countries, insufficiencies of the institutional framework and insufficient fiscal
discipline.
Besides the rules defined in the Stability and Growth Pact, the convergence criteria for
joining the single currency area, the prohibition of monetary financing of governments
and the seeking or taking of improper instructions by the ECB, a key safeguard and
deterrent against free riding is the current Article 125 of the Lisbon Treaty, which forbids
governments of EMU member states from bailing each other out. Apart from the
overemphasis on nominal criteria and the overlooking of key real convergence criteria at
the birth of the Euro, it must have been quite surprising for inside and outside observers
to see how weak all these safeguards subsequently proved to be in preventing free riding
and how quickly the no bail-out clause in primary EU law turned out to be dynamically
inconsistent.
This clause was supposed to prohibit fiscal transfers in any form, and until 2010, this was
formally indeed the case. However, during the first financial crisis in the EMU, such
transfers became possible and admissible via bilateral loans and the temporary European
Financial Stability Facility (EFSF). After the launch of the permanent European Stability
Mechanism (ESM), such transfers will become the norm. And if any sort of common bond
is introduced in the EMU, they will in fact become obligatory within EMU. What an
incredible change to have happened in just two years.
The Eurobond idea is deeply flawed, as it increases moral hazard and reduces the
willingness to reform the policies that gave rise to this burden, which the weak want to
put on the shoulders of the strong. What is more, it reduces the future incentive for even
the strongest to behave in a financially responsible way. But this does not mean that it
will not be introduced, because in a union where such issues are put to a vote and the
stability camp is in a minority, such a decision can be postponed, but not prevented.
As summarized by Issing (2008), a prerequisite for the success of the Euro was that it
had  to  be  established  and  maintained  as  “depoliticised  and  hence  stable  money”  with  a  
central bank enjoying genuinely independent status like the German Bundesbank. The
question is, however, whether this idea is or ever could have been sustainable. As recent
and not-so-recent history shows, politically generated rules are very often dynamically
inconsistent. And the more economically important they are, the more inconsistent they
often get. In contrast, the only strong rules are those that remain dynamically consistent
over time.
The   ECB’s   biggest   advantage   over   the   US   Federal   Reserve   System   is   that   it   does   not  
have  a  government.  This  should  be  the  best  safeguard  of  the  ECB’s  independence.  The  
302
ECB at times has to stand against all 17 governments of the EMU member states. This,
paradoxically, can put it in a more difficult situation than other central banks.
4 The Debt Crisis and its Consequences
The crisis of the Eurozone is usually perceived as a currency crisis. However, it is mainly
a crisis of the state debt and a banking crisis. European banks hold over a billion EUR of
the Spanish debt. This shows that the European crisis of state bonds is closely related to
a banking crisis.
Introduction of Euro in 1999 brought a radical constriction of the interest rate differential.
This generated a real estate bubble in countries such as Spain, Greece, and Ireland.
Instead of economic convergence stipulated by the Maastricht Treaty, the economies of
these countries grew faster as well as their trading deficits. By contrast, Germany, which
controlled the increase in labour costs, became more competitive, the proof being the
chronic surplus of the German trade balance. Some countries of the southern periphery
created state budget deficits exceeding the limits set by Maastricht. In spite of this, the
discount ability of ECB allowed them to make loans under practically the same conditions
as Germany, which exempted them from any pressure to remedy their excesses.
When the financial crisis started in 2008, financial markets began distinguishing between
state bonds of individual countries and their revenues grew rapidly. Financing of public
debts at the financial market became too expensive, or even impossible, for some
countries, and the debt increase threatened to get out of control. In some EMU countries
the fiscal policy became an additional source of shocks, instead of contributing to their
dampening.
5 Risks Related to Further Development
Although the state budget deficits started to decrease after 2009, government
indebtedness is still increasing and will be increasing in the future. Public finances of
some EMU member states are so bad that governments of these countries are forced to
prioritize their consolidation over the anti-cycling effect. Further budget cuts support the
negative impact on the economic development, they decrease tax incomes, increase the
unemployment rate and exacerbate the debt/GDP ratio. This development indicates one
of the deficiencies in EMU. It is expected that member countries will meet convergence
criteria without an adequate coercive mechanism in operation. In the present, when a
majority of the countries have abandoned the Maastricht criteria, it is obvious that there
is no regulation or escape mechanism. It is only expected that these countries will start
obeying the criteria again, although this step will bring about a deflationary spiral. The
pressure leading to the decreasing of budget deficits can throw the Eurozone or the
entire EU into a long recession, which may have significant political consequences. The
crisis of Euro may endanger the political cohesion of the European Union. Therefore, very
high risks of negative feedback between indebted public finances, economic stagnation
and vulnerable financial system remain in the Eurozone.
6 Possible Solutions
Anyone who believed that monetary integration as a political decision would in itself
automatically cause different economies to converge, enforce structural reforms, and
bring the EMU economies closer together through a single monetary policy alone, was
bound to be disappointed. It is clear that monetary policy cannot create wealth in the
long term or substitute for structural reforms in any economy – it could not and cannot
do so in the Eurozone either. A country can exist in any currency and exchange rate
system and not form imbalances as long as its other economic policies and remaining
adjustment mechanisms adjust to that monetary system.
The single monetary policy increases the likelihood of excessively low risk premium and a
pro-cyclical behaviour of real interest rates, which might temporarily slow down or even
destabilize the structural adjustment process. This is precisely what has happened.
303
During good times, the Euro has not enforced structural reforms. On the contrary, it has
enabled the structurally weakest to live for a time off a transfusion of credibility from the
strongest economies. This has amplified and prolonged the growth phase of the cycle and
deferred adjustment. The single monetary policy has not only petrified certain long-term
structural problems, it has even started to exacerbate them, because it has in fact
reduced the cost of financing unsustainable policies in some countries. All this has
implications for competitiveness and ensuing further imbalances as well as for the
indebtedness of individual sectors of the economy (Hampl, 2012).
No complex solution of the Eurozone crisis has been submitted. However, it is evident
that the debts of some member states will become unsustainable without economic
growth, just as EMU itself. Therefore, for the nearest future it will be essential that the
dilemma of adequate phasing of fiscal consolidation is solved so that at least a slight
economic growth is maintained with low expenses of the public debt administration.
Deficit countries need to be allowed to refinance their debts under adequate conditions
and with some rules. The rules need to make a gradual decrease in indebtedness
possible as well as allow the countries with a high unemployment rate to administer their
own budget deficits. Thanks to this, both aims and cyclically adapted deficits can be
achieved. On the other hand, if the rules are not observed, the economy will be left to its
fate. Together with the gradual fiscal consolidation, it is necessary to introduce structural
reforms. Only the fiscal consolidation and structural reforms of individual economies are
the real solution of the Eurozone crisis causes. The purchase of state bonds by the
European Central Bank (Outright Monetary Transactions) only means a decomposition of
the crisis impact through the ECB balance and the creation of stabilization funds (EFSF
and ESM) represents a decomposition of the debt crisis impacts on other countries
through fiscal transfers.
Further, bank deposits need to be protected in order to solve the Eurozone crisis and
prevent collapse of its financial system. If savers lose their Euros deposited in Cyprian or
Greek banks, the Euros deposited in Italian banks will be of a lower value than Euros in
German and Dutch banks. Significant banks in deficit countries need to be protected so
that these economies are protected against collapse. The European banking system
should then be recapitalized gradually. This is the only way out of the current crisis of the
Eurozone. The effort to develop  European  integration  “at  all  costs”  by  means  of  a  created  
political union and signing a new EU Treaty that would make a new single budget
possible bears the risk of the European Union disintegration.
7 Conclusions
Although the hottest issue now is the debt crisis, the main trouble of EMU is its structural
inconsistency, unsatisfactory institutional arrangement and insufficient respect for rules.
The common monetary policy now contributes to the deepening of differences between
the member countries, especially the countries of the southern periphery and the core of
EMU. Preservation of EMU in its current form is currently based on non-standard policies
of ECB, loans and fiscal transfers, as well as solidarity of member countries (not only
EMU but the entire EU). The EMU project with its ambition to speed up its growth to the
level ensuring global competitiveness has not brought the growth. Moreover, there is the
risk of its collapse with potentially disastrous consequences, not only for the economies
of EMU countries. The question whether it is an advantage to have the single currency is
not topical nowadays. EMU and Euro exist and their fall would cause immense damage.
The current EMU crisis does not consist in the opportunity of some members to part with
it; it consists in the preservation of the current way of its (non)functioning.
References
Eucken, W. (1952). Grundsatze der Wirtschaftspolitik. Tübingen.
Hampl, M. (2012). The Case of European Monetary Integration and its Former Hegemon,
German Economic Review 14(1), p. 2–14.
304
Issing, O. (2008). The Birth of the Euro. Cambridge: Cambridge University Press.
Sponer, M. (2000). Ekonomické   zhodnocení   Evropské   měnové   unie. Brno: Masaryk
Univerzity Brno, p. 175–183.
305
The choice of portfolio based on the theory of cooperative games
Anna  Sroczyńska  - Baron
University of Economics in Katowice
Department of Applied Mathematics
Ul.  Pułaskiego  25,  40 276 Katowice, Poland
E-mail: kozak@caprisoft.com.pl
Abstract: The theory of games as a domain of mathematics is one of the methods
proper for making decisions in the world of economics when we do not know how the
other subjects are going to act. It seems to be a suitable tool for gambling on the stock
exchange. During gambling on the stock exchange the problem of the choice of proper
portfolio appears. The player wants both great profit and low risk. It is reasonable to limit
the choice only to portfolios which belong to the effective set. Then the decision of choice
of a particular portfolio is individual and depends on the player and his aversion to the
risk. In this article this problem is presented as the game that is, the inner conflict of the
player. On the one hand he is expecting a great profit, yet on the other hand he is
expecting a low risk. Which portfolio should be pointed out to give the satisfaction to the
player? The solution of this problem presented in this work is based on the theory of
games, which treats the search for a proper portfolio as two - person game. The suitable
game was formulated and described. The analysis of the game as a cooperative one was
performed. There is also provided an example of explaining the way of acting with data
coming from the stock exchange in Warsaw.
Keywords: portfolio, cooperative games, player
JEL codes: C70, C71, G11
1 Introduction
The theory of games as a domain of mathematics is one of the methods for proper
decisions making in the world of economics especially when we are not certain of how the
other subjects are going to act. It seems to be a suitable tool to gamble on the stock
exchange, for example it is possible to use infinite games for strategy of shares purchase
or  sale  (Sroczyńska  – Baron, 2008). So it can also help us to construct a portfolio. One of
the most important tasks of a market player is the proper choice of shares for portfolio.
When the expected profit and the level of the risk are known, the market player has to
choose one portfolio which appears to be the best for him. He wants to find a portfolio
with the expected profit as high as possible and concurrently the risk as low as possible.
The problem lies in finding such a portfolio which gives satisfaction in respect to both
profit and risk. One can met with this problem   in   previous   works   (Sroczyńska–Baron,
2009). The solution of this problem presented in this work is based on the theory of
cooperative games, which treats the search for a proper portfolio as two - person game.
Standard deviation and expected rate are taken as measures of the risk and profit. The
analysis of the game as a cooperative one is also performed.
The main aim of this paper is to show how it is possible to use the theory of cooperative
games for the construction of a portfolio. The method of acting of the market player
based on the theory of games will be exemplified in this work. Data will come from the
stock exchange in Warsaw. The portfolio which consists of shares of three different
companies will be built. It will be shown that the method of the construction of portfolio
is proper irrespective of taken measure for both profit and risk by market player.
Nowadays, when economic crisis affects more and more people – including market
players - it is reasonable to use every possible tool to limit unnecessary risk and raise
profit. So the method for optimal portfolio based on the cooperative games seems to be
worth considering and helpful for people who wants to invest on the stock exchange.
306
2 Methodology
In this work Markowitz model is the base for later consideration. Standard deviation is
taken as a measure of risk and expected rate of return is taken as a measure of profit by
a market player. Cooperative games are used to solve the examined problem. A special
game   called   “portfolio   – game”   is   constructed and solved by one of the tools of
cooperative games – Zeuthen’s  and  Harsanyi’s  method.  
2.1 Markowitz model - some remarks
In Markowitz model the expected rate of return is taken for a measure of expected profit
and standard deviation is taken for a measure of risk for shares (Markowitz, 1966). When
the n historical rates of return for shares are known, one can calculate the expected rate
for  share  “ i ”  as
n
r
Er
n
k
ik
i

 1
(1)
where kr – rate  of  return  in  period  “ k ”, n – number of historical observation.
The standard deviation can be calculated according to the formula
 
n
Err
s
n
k
iik
i


 1
2
(2)
When the expected rates and standard deviation for shares are known, one can calculate
the expected rate for portfolio as


m
i
iip ErxrE
1
)( (3)
where ix – weight of share ”i ” in portfolio, m – number of shares in portfolio.
The standard deviation is described as a root extracted from the sum of the following
products
  jiji xxrr ,cov , i, j =  1,…,  m, (4)
where  ji rr ,cov means covariance
 
  
n
ErrErr
rr
n
k
jjkiik
ji


 1
,cov (5)
When the expected rate of return and standard deviation for portfolios are known, the
player is obliged to make a decision and choose one. The best portfolios satisfy the
effective set of Markowitz model, where the effective portfolio has the highest possible
expected rate of given risk and the lowest possible risk of given expected rate (Haugen,
1993). When it is suggested to choose portfolio which does not belong to this set, it is
always possible to propose a portfolio which is better in respect of the risk or profit. So,
where the set of portfolios is limited to the effective set, the decision of the choice of one
particular portfolio belongs to the player and depends on his aversion to risk. One of the
common method is to determine the curves of indifference and find the point of contact
between them and the effective set. It gives us the portfolio of maximal expected utility marked
as portfolio Z on picture 1 (Jajuga, 1997).
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Figure 1 The choice of portfolio of maximal expected utility
Source: Jajuga (1997)
Unfortunately, a big problem lies in constructing the curves of indifference for the player
in reality, so the described method poses many troubles.
2.2 Cooperative games
Two person cooperative games are the model of game where players can communicate
before the game and commonly choose one solution. The traditional area of payment can
enlarge by associative randomized strategies (it means every randomized strategy fixed
by two players together). When the players cooperate they can reach every point of the
created set. However, they limit this area to the set of jointly undominated outcomes,
where point  , is jointly dominated by  11, if  1 and  1 . This set is called
a joint maximum set. At this moment the preferences of the two players become
completely inverse and the players can no longer cooperate to gain common benefits.
Neumann and Morgenstern noticed that it was hard to demand from players to agree and
choose the strategy which gave them worse payment then this one which they could
guarantee themselves. When we limit the joint maximum set in this way we are given
the area of negotiation of the game. It is just the solution by von Neumann and
Morgenstern of cooperative game. However, in some particular cases it is reasonable to
assume that the players are ready to give up a part of their profits to get a solution
satisfying the both of them. Therefore it can be assumed that the solution has to belong
to the joint maximum set and we should find an arbitrage solution there. One of the
methods is the way of acting showed by Zeuthen and Harsanyi (Luce, Raiffa 1958). The
player I pursues  11, , and the player II pursues  22, . The relative losses of the
players are the following:
2
12
1
21
,



 (6)
The player, whose relative loss is smaller, gives in. The first player should resign if
2
12
1
21



 


(7)
The second player should resign if
2
12
1
21



 


(8)
The loosing player can only offer the point without further concessions. The obtained
solution  is  equal  to  Nash’s  solution  of  the  market  problem.
308
2.3 The game – portfolio
When we know the expected rates for shares and risk level connected with them we can
try to construct an optimal portfolio for the player. The player is obliged to make a
decision. Knowing the particular value of the risk, he knows how big the expected
winning can be. Yet, it can be stated, that the task of the market player is to find a
portfolio with the highest possible profit at the lowest risk. It is commonly known that
each player dreams about great profit yet, with the lowest possible risk not want to take
large risk. And so the question arises of how to point to a portfolio, which is the best?
Let us try to find a solution, based on the theory of games, in which we treat the search
for a proper portfolio as a two - person game. The first player will be the part of our
nature  responsible  for  “caution  and  deliberation”.  The  second player will be the part of
our  nature  responsible  for  the  so  called  “lust  for  profit”.  The  game  payments  of  player  I  
(“caution   and   deliberation”)   are   different   values   of   the   risk   for   portfolio   and   the   game  
payments   of   player   II   (“lust   for   profit”)   are   different values of expected profit for
portfolio. The first player desires a low risk, while the second one desires a big profit.
This situation is not an antagonistic game because a strategy good for player I is not
always a bad one for player II. It is possible to find a portfolio with a high risk and a low
profit which none of the players finds worth going for. Of course, it is very unnatural to
treat the game of inner conflicts as a non cooperative game so we will try to find the
solution based on cooperative game. Additionally, the order of negotiation is imposed. It
is justifiable to assume that the players are ready to give up a part of their profits, to get
a solution satisfying the both of them, when it is the inner conflict of the market player.
Therefore it can be assumed that the solution has to belong to the joint maximum set
and we should find an arbitrage solution in this set.
3 The researches
3.1 Data
Data used for researches come from Polish Stock Exchange in Warsaw. Let us take a
standard deviation as a measure of a risk and an expected rate of return as a measure of
profit. The task of the player is to construct a portfolio, which consist of three types of
shares. The companies: KGHM Polish Copper SA, PKN ORLEN SA and KREDYT BANK SA
were chosen – as three companies from index WIG 20 from different branches to ensure
the risk diversification. One can use any method to choose companies for consideration,
for example the synthetic measure of development in the stock selection (Węgrzyn,  
2007, 2008). The necessary values for calculating are the following (based on data
coming from Stock Exchange in Warsaw from 31.01.2010 till 30.06.2011 as monthly
rates)
Table 1 Market data KGHM Polish Copper SA, PKN ORLEN SA and KREDYT BANK SA
(01.2010 – 06.2011)
Company Expected Rate Standard Deviation
KGHM SA 0,0468 0,0988
PKN ORLEN 0,0277 0,0712
KREDYT BANK 0,014 0,0424
3.1 Run of the researches
First, the expected rates and standard deviations for different portfolios depending on the
participation of three kinds of shares in portfolio were calculated and the results in
portfolios are presented on Figure 2.
Of course, it is reasonable to limit this set only to portfolios which satisfy the efficient set
of Markowitz model (the periphery of the set from A to B on the picture 1). This set
consists of the best portfolios. When it is suggested to choose portfolio which does not
belong to this set, it is always possible to propose a portfolio which is better in respect of
309
the risk or profit. For example, it is pointless to pursue the portfolio: 0489,0pS and
  026,0prE (it means 10% of KGHM, 65% of PKN Orlen and 25% of Kredit Bank
shares) because of the possibility of portfolio: 0489,0pS and   032,0prE (it means
42% of KGHM, 31% of PKN Orlen and 27% of Kredit Bank shares). At this moment, the
decision of the choice of one particular portfolio belongs to the player and depends on his
aversion to risk. One of the common method recalled earlier is to determine the curves of
indifference and find the point of contact between them and the effective set. Because of
a big problem to determine them for a real player, we will try to find the solution of the
problem how to find the best portfolio for the market player who desires on the one hand
big profit, but on the other hand small risk with the use of the theory of games.
Figure 2 The set of portfolios of KGHM Polish Copper SA, PKN ORLEN SA and KREDYT
BANK SA, 30th
June 2011
Figure 3 The set of all possible payments of portfolio - game (KGHM Polish Copper SA,
PKN ORLEN SA and KREDYT BANK SA, 30th
June 2011)
3.2 Solution
Let us look at the Figure 2 as the set of all possible payments of one game. Of course,
one transformation should be done to obtain a normal form of a game (till now, the first
player desires as low as possible payment – standard deviation). On the figure 3, there is
shown the set of all possible payments of the portfolio game after the transformation.
Now, both players desire as high as possible payment. The first player desires a high
transformed risk, while the second one desires a big profit.There is just shown the
The set of portfolios of KGHM, PKN ORLEN
and KREDYT BANK SA 30.06.2011
0,01
0,015
0,02
0,025
0,03
0,035
0,04
0,045
0,05
0,02 0,03 0,04 0,05 0,06 0,07 0,08 0,09 0,1 0,11
Sp
E(Rp)
The opportunity set of portfolio - game
0,01
0,015
0,02
0,025
0,03
0,035
0,04
0,045
0,05
0,02 0,03 0,04 0,05 0,06 0,07 0,08 0,09 0,1 0,11
player I
playerII
C
D
A
B
310
opportunity set on the picture 3. When the players cooperate they can reach every point
of the presented set. However, they limit this area to the set of jointly undominated
outcomes and obtain a joint maximum set. It is showed on the figure 3 as a periphery of
the set from C to D.
The portfolios from the set of jointly undominated outcomes satisfy the effective set of
Markowitz model. One can say that a joint maximum set in the terminology of the theory
of games is just the effective set in the terminology of Markowitz model. Both of them
consist of the same portfolios. At this moment the preferences of the two players become
completely inverse and the players can no longer cooperate to gain common benefits.
The player I pursues the solution  0217,0;0987,0 whereas the player II pursues the
solution  0468,0;0345,0 .   Let   us   use   the   Zeuthen’s   and   Harsany’s   method   to   find   the
solution of this problem. So, player I proposes the point  0217,0;0987,0 and player II
proposes the point  0468,0;0345,0 . The relative loss of the player I is
65,0
0987,0
0345,00987,0
1
21






(9)
The relative loss of the player II is
54,0
0468,0
0217,00468,0
2
12






(10)
The relative loss of the player II is smaller so he must give in. He should resign but he
can offer the point without further concessions, for example  0379,0;07,0 . Now, the
relative loss of the player I is
29,0
0987,0
07,00987,0
1
21






(11)
The relative loss of the player II is
43,0
0379,0
0217,00379,0
2
12






(12)
Figure 4 Optimal portfolio in a sense of cooperative games of KGHM, PKN Orlen and
Kredit Bank 30th
June 2011
The relative loss of the player I is smaller so he must give in. Continuing in this way,
after calculations with the use of computer program, the players will gain the point
 034,0;08,0 in the end. Then both players will have to agree. It is the solution of the
game – portfolio  by  Zeuthen’s and  Harsany’s  method.    Thus,  the  real  solution  is  the  point  
(before the transformation): 0532,0pS and   034,0prE . It means that the portfolio
The sollution of portfolio - game
KGHM SA
45%
PKN ORLEN SA
38%
KREDIT BANK
SA
17%
311
should consists of 45% of KGHM shares, 38% of PKN Orlen and 17% of Kredit Bank
shares (Fig. 4). It could be called – universal portfolio of maximum utility.
Of course, it is possible to use any other method of cooperative games, for example
Raiffa’s  method  or  the  boundary  average  of  payments.  
4 Conclusions
When the market player knows sufficient description of the strategies (possible risk and
profit), he is able to gamble with the aid of the theory of games. The method of the
choice of portfolio of shares based on the theory of games is presented and exemplified
in this work. The Markowitz model was determined as one game (the inner conflict of the
player) solved with the tools of the theory of games. The measures of expected profit
and the level of risk used in this work are expected rate and standard deviation.
However, it is visible that one can act in the same way when other measures of profit
and risk will be taken. The method is irrespective of taken measure for both profit and
risk by market player. What is more, it is irrespective of number of taken companies for
consideration. Market player acts in the same way when he wants to construct portfolio
which consists of two, three or as many as he wants companies. Calculations would not
become harder at any case.
The presented method could help market players to make decision and find portfolio
which gives satisfaction in respect to both profit and risk. Every tool helpful for market
player seems to be important nowadays, when one must exist during the crisis all over
the world.
Of course, it seems to be impossible to describe the stock exchange as one big game
because of too many relations and connected with them questions, problems and doubts.
However, it is a dead certainty that one can use some tools of the theory of games to
solve partial tasks of the market player. The example of how to do it, was presented in
this work.
References
Haugen, R. (1993). Modern Investment Theory, Prentice Hall Inc.
Jajuga K., Jajuga T. (1997). Inwestycje. Instrumenty finansowe; ryzyko finansowe,
inżynieria  finansowa,  Warszawa: PWN.
Luce, R., Raiffa H. (1958). Games and decisions. Introduction and Critical Survey, New
York: John Wiley&Sons Inc.
Markowitz H. (1966). Portfolio Selection, Journal of Finance, 7, pp. 77–91.
Sroczyńska–Baron A. (2009). The application of the theory of games for purpose of
making a choice of portfolio, FindEcon Monograph Series: Advences in financial market
analysis, no 7, p. 173 – 183.
Sroczyńska–Baron A. (2008). The application of model of shares purchase based on the
theory of games in investing on market. Studia  i  prace  Wydziału  Nauk  Ekonomicznych  i  
Zarządzania  Uniwersytetu  Szczecińskiego, in    Tarczyński  W.  ed.,  no  10,  p.  650  – 662.
Węgrzyn   T.   (2007).   TMAI - wpływ   grupowania   wskaźników   finansowych   na   uzyskiwane  
stopy  zwrotu  z  budowanego  portfela  papierów  wartościowych, In Adamkiewicz-Drwiłło  H.  
ed., Uwarunkowania   rozwoju   przedsiębiorczości.   Determinanty   i   narzędzia   zdobywania  
przewagi konkurencyjnej, Warszawa: PWN, p. 394.
Węgrzyn  T.  (2008).  TMAI – wartości  nominalne  wskaźników  finansowych  czy  tempo  ich  
przyrostu?  Na  przykładzie  banków  notowanych  na  GPW  w  Warszawie, In Chrzan P. ed.
Modelowanie matematyczne i ekonometryczne na polskim rynku finansowym, Katowice:
Wyd. AE, p. 43.
312
Lessons Learned from the 2013 Banking Crisis in Cyprus
Daniel  Stavárek
Silesian University in Opava
School  of  Business  Administration  in  Karviná, Department of Finance
Univerzitní  nám.  1934/3,  733  40  Karviná,  Czech  Republic  
E-mail: stavarek@opf.slu.cz
Abstract: The Cypriot banking crisis in 2013 led to closing commercial banks, capital
controls and economic as well as political instability. The Troika has agreed with the
Cypriot government on a plan that should resolve the problems in banking sector and
stabilize the economic and financial environment. This paper overviews the specifics of
the Cypriot banking system and puts them into relation with the crisis. The rescue
program and its individual components are examined and adequacy of the bail-in
approach is discussed. The paper concludes that Cyprus suffers from the status of being
too small to be equal while having banking sector too big to save. Moreover, the way of
bank resolution approved for Cyprus could be a wrong example how banking crises in the
euro area should be sorted out in future as the bail-in approach increases systemic risk.
The appropriate strategic solution for the euro area would be a proper banking union with
common supervision, deposit insurance scheme and bank resolution proceedings.
Keywords: banking crisis, Cyprus, bailing-in, crisis resolution
JEL codes: F34, F36, G01, G21
1 Introduction
Based on the recent data on borrowing conditions for governments, ratings on sovereign
debt or internal financial balances in the Eurosystem, the euro area has been gradually
returning to stability. Unfortunately, these positive signs could be outweighed by the
crisis in Cyprus and difficulties to sort it out. Cyprus is a small economy (only 26th
largest
in the EU) with a huge banking sector whose assets are eight times higher than the
country’s  GDP.  Hence,  serious  problems  in  banking  sector  automatically  lead  to  economic  
instability and intensive pressures on Cypriot public finance. Total financial needs to
resolve the Cypriot crisis are estimated to 17 billion euro, which equals to an annual
GDP. Financing the solution through a loan from the European Stability Mechanism
and/or other creditors would push government debt to unsustainable level of around
200% of GDP.
If the problems in Cyprus are not sorted out properly, one could only hope that it will not
have a systemic impact on the euro area, given the small relative size of Cyprus (5% of
the financial support provided to Greece). Nevertheless, design of the rescue program
and character of the measures adopted can be considered as a groundbreaking in a way
how banking crises have been resolved in the euro area so far. Therefore, it is worth to
analyze the Cypriot banking system and the entire rescue plan in more details. The aim
of the paper is to define key aspects of the banking system in Cyprus, examine the
agreed process of bank resolution and highlight potential effects of the current
development in Cyprus on national economy and the euro area as a whole.
2 Stylized Facts on Banking System in Cyprus
The size of the banking system in relation to the economy distinguishes Cyprus from
most members of the EU and the euro area. Assets of the entire banking sector exceeded
the Cypriot GDP more than eight times in 2011. Figure 1 depicts size of banking system
in selected EU countries and Switzerland (SUI) measured as the ratio of total banking
assets on GDP. It is evident that the Cypriot banking system has gained its extended size
over the last five years when the annual rate of growth was highest in the EU. Fairly
behind Luxembourg (almost 2200% of GDP), Cyprus stood in 2011 on the second place
followed by Ireland (720% GDP), UK (515% GDP), Netherlands (415% GDP) and the EU
313
average of 360% GDP. Stephanou (2011) argues that the expansion of the Cypriot banks
and banking system has been part of the broader push to promote the island as an
international business centre with efficient financial infrastructure, attractive tax policy
and favorable business climate.
Figure 1 Size of banking system (total banking assets on GDP, in %)
Source: Helgi Library (http://www.helgilibrary.com/indicators/index/bank-assets-as-of-gdp)
Note: Luxembourg is on the right axis.
Behind these aggregate figures one shall search for other aspects that make the Cyprus’s  
banking sector special. First, domestic financial institutions (commercial and cooperative
banks) play a dominant role in the system. Domestic commercial banks own 61% and
cooperatives 12% of total assets. The rest of assets is in balances of subsidiaries of
foreign banks (23%) and branches of foreign banks (4%). Furthermore, commercial
banks’  sector  is  highly  concentrated  as  the  two  largest  banks  (Marfin  Laiki  Popular  Bank  
and Bank of Cyprus) comprised nearly 88% and the three largest banks (plus Hellenic
Bank)   almost   98%   of   commercial   banks’   assets.   It   is   not   common   in   the   EU   that   the  
three  largest  banks  manage  assets  that  account  for  500%  of  the  country’s  GDP.
Figure 2 Structure and volume of deposits in Cypriot banking system (mln EUR)
Source: European Central Bank (http://sdw.ecb.europa.eu/browse.do?node=bbn3193)
Note: Total deposits are on the right axis.
0
500
1000
1500
2000
2500
3000
3500
0
200
400
600
800
1000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
CYP CZE DEN EU GER GRE
IRL ITA NED SUI UK LUX
0
50 000
100 000
150 000
200 000
250 000
300 000
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2005Nov
2006Apr
2006Sep
2007Feb
2007Jul
2007Dec
2008May
2008Oct
2009Mar
2009Aug
2010Jan
2010Jun
2010Nov
2011Apr
2011Sep
2012Feb
2012Jul
2012Dec
Euro area Domestic Other Total
314
A next important characteristic of the Cypriot banking system is that the resources of
Cypriot banks consisted to a very large extent of deposits. Figure 2 shows geographical
composition of deposits along with development of the total volume. It is evident that
bank deposits were substantially rising principally in the period of 2008Q2 – 2009Q2.
One short-term and temporary upsurge also occurred in summer 2010. Geographical
composition of deposits reveals that any increase of total deposits was usually powered
by inflow of foreign deposits from other euro area countries as well as non-EU countries
(Saiyid et al., 2011). Deposits raised in Greece represent the core of funds from other
euro area countries. On the other hand, the most deposits from non-EU countries came
to Cyprus from Russia and other countries from the Commonwealth of Independent
States.
Interests paid on deposits in Cyprus were constantly significantly higher than in the rest
of the euro area. Enjalbert and Vincent (2013) illustrate this on deposits from households
with agreed maturity up to one year. While the average interest rate in Cyprus was 4.5%
in January 2013, the euro area average was 2.6%. Figure 3 shows that customer
deposits constituted almost sufficient pool of funds for lending activities of Cypriot banks.
The loans/customer deposits ratio has been close to 100% until the end of 2010.
Deteriorating macroeconomic conditions have led to a pickup in arrears on loan portfolios
and increasing share of non-performing loans (NPL) that was above 15% in 2012Q3
(Figure 4). Since the macroeconomic conditions in Cyprus and Greece are not likely to
improve in near future the share of NPL may skyrocket considerably above 20%. At the
same   time,   the   country’s   two   largest   banks   exhibited   the   share   of   NPL   of   27.4%   and  
17.1% respectively.
Figure 3 Customer deposits and total
loans in Cypriot banking system (mln EUR)
Figure 4 Non-performing loans and capital
adequacy in Cypriot banking system (%)
Source: International Monetary Fund (http://elibrary-
data.imf.org/Report.aspx?Report=4160276&Country=423)
Cypriot banks have a high exposure to Greece. Substantial portion of assets are allocated
in Greek government and bank bonds and direct loans to Greek enterprises. In October
2011, Cypriot banks held Greek government bonds in volume that amounted to 27% of
GDP. According to Saiyid et al. (2011) it is the highest exposure to Greek government
bonds  of  any   country’s   banking   system.   The   only   part   of   the   Cyprus’s   banking   system  
which is primarily exposed to the local economy is the segment of cooperative banks.
These institutions have been traditionally serving particularly Cypriot residents offering
them both deposit and loan products. Enormous exposure to Greece can be considered
as   the   major   risk   hidden   in   Cypriot   banks’   balances.   The   Private-Sector-Involvement
(PSI) debt restructuring process in Greece shook the Cypriot banking system to its roots.
In October 2011, the EU council decided to wipe out about 80% of the Greek debt held
by private sector. For Cyprus, the write-down of Greek debt resulted in a loss between
4.5 and 5 billion euro, which is substantial burden for a country with a 17 billion euro
GDP.
100%
105%
110%
115%
120%
125%
130%
135%
140%
60000
65000
70000
75000
80000
85000
90000
95000
2008Q4
2009Q1
2009Q2
2009Q3
2009Q4
2010Q1
2010Q2
2010Q3
2010Q4
2011Q1
2011Q2
2011Q3
2011Q4
2012Q1
2012Q2
2012Q3
Customer deposits Total loans Loans/Deposits (right)
0
2
4
6
8
10
12
14
16
18
2008Q4
2009Q1
2009Q2
2009Q3
2009Q4
2010Q1
2010Q2
2010Q3
2010Q4
2011Q1
2011Q2
2011Q3
2011Q4
2012Q1
2012Q2
2012Q3
Capital adequacy (Tier 1) NPLs/ Total loans
315
All developments and structural aspects jointly affected profitability in the Cypriot
banking system. In 2011, six of seven largest Cypriot banks recorded loss. Most notably,
Laiki Bank lost more than 5 billion euro (12.2% of assets, 162% of equity) and Bank of
Cyprus lost 1.7 billion euro (3.6% of assets, 67% of equity). Profitability of the entire
banking system measured by the Return on Assets (RoA) and Return on Equity (RoE) is
portrayed in Figure. A sharp decline at the end of 2011 was caused by the Greek haircut.
Although the development has stabilized, profitability is still negative and the prospects
are gloomy.
Figure 5 Profitability in Cypriot banking system (%)
Source: International Monetary Fund (http://elibrary-
data.imf.org/Report.aspx?Report=4160276&Country=423)
3 Rescue Program for Cyprus
When it comes to banking crisis there is always serious discussion on how the problems
should be sorted out and how the rescue plan should be designed. One of the key
questions that must be addressed is whether a bail-out or bail-in kind of program shall
be implemented. This issue has attracted considerable attention in literature (see e.g.
Öther-Robe et al. (2011), Zhou et al. (2012) or De Grauwe (2013)). However, there is no
consensus on the possibility of bailing-in bank shareholders and lenders of troubled
banks and forcing investors to take losses, or relying on public finance and tax payers to
take the hit. Legal rules along with elementary economic principles such as idea that
claims should be priced according to their risk and expected repayment in case of failure
should be reflected in creditor ranking applied in bank resolution. Principally, equity
holders take a hit before junior debt holders that are followed by senior creditors and
uninsured depositors. Last, if at all, insured depositors should take a hit (Beck, 2013).
In June 2012, all three major rating agencies downgraded Cypriot sovereign bonds below
investment grade. This automatically made the government debt not eligible as collateral
for borrowing from the eurosystem. The ECB decided not to suspend this rule as it had
done in case of Greece, Portugal and Ireland. Although the Cypriot government formally
asked the Troika for assistance immediately, the government did not actively negotiate
until November when the ECB warned that if Cyprus did not engage in negotiations, the
ECB would consider cutting off liquidity. A structural program consisting of major
reductions in pension benefits, wages and salaries for broader public sector, privatization
of state-owned enterprises and suspension of living adjustments was negotiated and
agreed with the Troika in December. However, this program did not include any action
needed in banking sector. The new Cypriot president took over 1 March 2013 and wanted
to complete the structural adjustment program that had been delayed. He participated
on the European council on 15-16 March 2013 and expected program revisions similar to
those faced by other countries in trouble.
-80
-60
-40
-20
0
20
-5
-4
-3
-2
-1
0
1
2
2008Q4
2009Q1
2009Q2
2009Q3
2009Q4
2010Q1
2010Q2
2010Q3
2010Q4
2011Q1
2011Q2
2011Q3
2011Q4
2012Q1
2012Q2
2012Q3
RoA (left axis) RoE (right axis)
316
However, the meeting took place in time when it was evident that recapitalizing the
Cypriot banks will cost 10 billion euro (56% of GDP). Furthermore, the public sector
needs 7 billion euro to cover its financial needs over the next three years. If the amount
of 17 billion euro were   lent,   Cyprus’s   public   debt   would   be   set   on   an   unsustainable  
trajectory. Hence, a group spearheaded by the IMF and creditor countries called for a
more radical plan to put forward. The final version of the rescue plan considered the
following sources of financing: (i) 10 billion euro from the European Stability Mechanism,
(ii) 1 billion euro from the IMF, (iii) 5.8 billion euro from a tax on deposits. In addition,
the following measures were also considered: (i) corporate tax rate to be raised from
10% to 12%, (ii) bail-in of junior debt, (iii) commitment to downsize the local banking
sector to around 350% of GDP, (iv) adoption of fiscal-consolidation measures amounting
to 4.5% of GDP, (v) privatization program expected to contribute 1.4 billion euro.
This first version of the rescue deal was very in favor of bail-in approach. Unfortunately,
it violated in many aspects the creditor ranking mentioned above and, hence, a spirit, if
not a letter of law. It was decided to impose a one-time wealth tax on deposits in Cypriot
banks: 6.75% on deposits below 100.000 euro and 9.9% on deposits above this
threshold. Involving insured depositors into rescue package was an extraordinary
decision that was not necessary and could undermine a trust in deposit insurance
schemes across the euro area. It is definitely unfair that insured depositors would suffer
a haircut while uninsured depositors still maintain a large share of their claims (Beck,
2013). As Darvas (2013) points out, the second major drawback of the plan was the
silence about senior bank bondholders. Although issued bonds are just fraction of the
banks’  sources  taxing  small  depositors  while  exempting  senior  bondholders  is  unjust.
Shortly after the rescue program was announced, a massive wave of indignation and
rejection broke over the whole EU. The Troika has to bring forward a revised version of
the plan. An agreement between European and Cypriot authorities was finally reached on
25 March 2013. The two irrational defects of the initial version have been eliminated. The
new deal fully protects insured deposits in all banks and it bails-in  all  banks’  bondholders  
and shareholders. The complete rescue program consists of the following measures:
I. Marfin Laiki Popular Bank (second largest bank) will be resolved immediately with
full contribution from shareholders, bondholders and uninsured depositors. The
implementation will rely on the Bank Resolution Framework. Losses for uninsured
depositors could range from 20% to 40%.
II. Laiki  Bank  will  be  split  into  a  “good  bank”  (performing loans and insured deposits)
and  a  “bad  bank”  (non-performing loans and uninsured deposits). The bad bank
will be run down over time. The good bank will be folded into Bank of Cyprus
(largest bank). Uninsured deposits in Bank of Cyprus will remain frozen until
recapitalization has been effected, any may subsequently be subject to haircuts.
III. Bank of Cyprus will be recapitalized through a full contribution from shareholders,
bondholders and a deposit/equity conversion of uninsured deposits.
IV. All insured depositors in all banks will be fully protected.
V. The  Troika’s  program  funds  (10  billion  euro)  will  not  be  used  to  recapitalize  Laiki  
Bank and Bank of Cyprus.
4 Effects of Cypriot Crisis and Bank Resolution
The recent crisis in Cyprus and the manner how the crisis is going to be resolved will
affect Cypriot economy as well as measures and solutions that will be taken in the euro
area to sort out banking crises.
Cyprus is a small economy that has built up the prosperity and economic growth on being
an offshore centre in Europe, and, lately, in the EU and the euro area. Cyprus has
attracted money from Russia and elsewhere offering exceptionally high interest rates.
Furthermore, foreign companies pay a flat tax rate of just 10%. Such a strategy has led
to creation of an extremely sizeable financial and banking industry. The island has also a
flourishing tourism sector and serves as an international shipping hub. On the other
hand, the Cypriot economy is the least industrialized in the euro area. Manufacturing
317
industries generate less than 6% of GDP, compared to the euro area average of 16%.
The banking crisis and ending of its tax-haven and offshore centre status will force
Cyprus to revamp its economic strategy and to rebuild the economy on a new basis.
With no doubt that, after such a severe shock, the Cypriot economy will be pushed into a
harsh recession. Experience from other countries that have recently gone through a
banking crisis (Iceland, Ireland, Latvia) tells that a GDP fall of about 10% should be
taken for granted. Latvia and Ireland also suffered from massive employment losses,
which is likely to happen in Cyprus, too (Iceland could proceed with substantial currency
depreciation, which helped to alleviate unemployment pressures). Most probably,
economic recession in Cyprus will take more than couple of quarters. It is so because
replacing the banking sector as the main growth engine by other economic activities will
not be an easy task for Cypriot government and enterprises.
Although the extent of Cyprus’s   problems   is   relatively   small   from   the   European  
perspective, the crisis in Cyprus offers a good guide to understanding how future banking
crisis  would  be  resolved  in  the  euro  area.  The  statement   of  the   Eurogroup’s  president,  
the Dutch finance minister Jeroen Dijsselbloem, is unambiguous. He declared that the
case  of  Cyprus  would  be  the  new  “template”  for  the  future.  It  means  that  existing  rules  
and procedures of bank resolution have been altered and a line has been crossed.
The new bail-in approach assumes that future bail-out operations will involve depositors.
Yet, of the 147 banking crises since 1970 tracked by the IMF, none inflicted losses on all
deposit holders, irrespective of the amounts they hold and the banks they are with. Now
depositors in weak banks in weak countries have every reason to worry about sudden
raids on their savings. Having in mind serious problems in Italy, Spain or Slovenia, every
time the fear arises in a country, a bank run will occur because depositors will want to
prevent the loss of their deposits by withdrawing them from the bank. Hence, this new
bail-in doctrine enforced by creditor countries like Germany and Netherlands will increase
the systemic risk in the euro area and will contribute to a more frequent occurrence of
banking crises.
Proponents of the bail-in approach often argue that it is victory of common sense (see
e.g. Darvas, 2013). They emphasize that tax payers are protected against paying for
private-sector losses. Another argument in favor of bailing-in is that uninsured depositor
simply took a risk that has been in Cyprus awarded by high interest revenues and low
taxes. An ex post taxing of deposits is only one of possible manifestations of higher risk.
On the other hand, it must be stated that the benefit for taxpayers is only temporary and
goes exclusively to taxpayers in Germany and other creditor countries. Taxpayers in
Cyprus now and taxpayers in other countries in future will pay the full price of deep
economic depression that will follow the banking crisis and collapse of banks (De Grauwe,
2013). The argument on legitimacy of bailing-in uninsured depositors is also wrong.
Deposits are not like other investment such as shares, bonds or CDOs. Therefore, a
traditional investment dilemma between return and risk cannot be applied. Bank deposits
constitute the basis for payment system and source for financing a day-to-day business
activities and living of households. An abrupt taxation (confiscation) of deposits wipes out
large segments of business and household sectors and creates a huge damage of
economy.
The Cypriot crisis could not only be an important precedent for thinking and decisions of
European authorities how to deal with future banking problems but is also has relevant
implications for a banking union. A banking union should contain three elements: a single
supervisor, a common resolution authority and a credible system of deposit insurance.
The case of Cyprus holds lessons on all three accounts. The ECB as a single supervisor
would never have allowed Cypriot banks to attract huge amounts of deposits by paying
above-market interest rates and then to invest substantial portion of assets to Greece.
The ECB would also take over the role of common resolving authority. Even now a bank
in difficulties cannot survive if the ECB does not grant or renew emergency liquidity
assistance (ELA). Moreover, the ECB is independent institution with enough financial
318
resources to be used for an orderly resolution of even large banks. The revolt of small
Cypriot depositors and economists across the Europe when the rescue program initially
included a special tax on all deposits indicated that leaving deposit insurance scheme
exclusively at the national level is no longer an option. A credible system common for the
entire euro area shall be proposed and implemented.
5 Conclusions
The aim of the paper was to define key aspects of the banking system in Cyprus,
examine the agreed process of bank resolution and highlight potential effects of the
current development in Cyprus on national economy and the euro area as a whole. The
Cypriot banking significantly differs from systems in other euro area member countries.
It is extremely large in relation to the national economy as the total bank assets amount
to 850% of GDP. The banking sector is highly concentrated, which makes the banks too
big to fail and too big to save from public resources. Deposits attracted in domestic
market   as   well   as   abroad   represent   most   of   the   banks’   liabilities.   Strong   business   and  
political relations to Greece are reflected in dangerously high share of Greek assets in
banks’  portfolios.  When  Greece  imploded,  these  investments  went  sour  and  the  banks  in  
Cyprus were insolvent.
Banking crisis will definitely slow down the Cypriot economy and increase unemployment.
In addition, Cyprus must necessarily reshape its economic model and find new sectors
and industries that will restart the economy and ensure sustainable growth.
These specifics in combination with extent of problems and financial needs led the Troika
to propose a new type of rescue plan, which bails-in bank shareholders, bondholders and
depositors. This decision completely rewrote the rules that had been followed in the euro
area to resolve banking crises. We argue that involving depositors to the bail-out
operations is not optimal and this approach would increase systemic risk in banking
systems across the euro area and particularly in countries where symptoms of potential
crisis can be perceived.
The   Cyprus’s   banking   crisis   also   showed   how   far   the euro area is from creating the
banking union and how much the banking union is needed. It is evident that the troubles
would have never been so intensive and the rescue program so surprising and
controversial if the banking union had been put into practice. There was no euro area
supervision of large Cypriot banks, no transnational approach to put them into controlled
bankruptcy, no common deposit insurance and no flow of bank rescue funds from
abroad. If anything is good on Cypriot crisis it is the possibility of being an impulse to
resume negotiations about banking union in the euro area.
Acknowledgments
Publication of this paper was supported by the Student Grant System of Silesian
University (project SGS/7/2013). The support is gratefully acknowledged.
References
Beck, T. (2013). Cyprus: What are the alternatives. VoxEU.org, 22 March. [cited 12 April
2013]. Access from: <http://www.voxeu.org/article/cyprus-what-are-alternatives>.
Darvas, Z. (2013). Cyprus deal: the right intentions but major flaws. Bruegel.org, 18
March. [cited 12 April 2013]. Access from: <http://www.bruegel.org/nc/blog/
detail/article/1045-cyprus-deal-the-right-intentions-but-major-flaws/>
De Grauwe, P. (2013). The New Bail-in Doctrine: A recipe for banking crises and
depression in the eurozone. CEPS Commentary, 4 April 2013. Brussels: Centre for
European Policy Studies.
Enjalbert, J., Vincent, A. (2013). Cyprus: last-minute deal. BNP Paribas Eco Week, 29
March, s. 7-9. Paris: BNP Paribas.
319
Ötker-Robe, I., Narain, A., Ilyina, A., Surti, J. (2011). The Too-Important-to-Fail
Conundrum: Impossible to Ignore and Difficult to Resolve. IMF Staff Discussion Note
SDN/11/02.
Saiyid, M., Simone, A., Weber, A., Dao, M.C. (2011). Cyprus: Selected Issues Paper. IMF
Country Report No. 11/332.
Stephonou, C. (2011). The Banking System in Cyprus: Time to Rethink the Business
Model? Cyprus Economic Policy Review, vol. 5, issue 2, pp. 123-130.
Zhou, J., Rutledge, V., Bossu, W., Dobler, M., Jassaud, N., Moore, M. (2012). From Bailout
to Bail-in: Mandatory Debt Restructuring of Systemic Financial Institutions. IMF Staff
Discussion Note SDN/12/03.
320
Microeconomic factors influencing impaired loans
and credit derivatives prices
Boris  Šturc, Natália  Žoldáková
University of Economics
Faculty of National Economy, Department of Banking and International Finance
Dolnozemska 1, 85235 Bratislava, Slovak Republic
E-mail address: sturc@dec.euba.sk
natalia.zoldakova@euba.sk
Abstract: The article analyses relationship of microeconomic factors with the portion of
impaired loans in selected commercial banks. There is multiple regression used for
quantification of relationship. For the analysis have been used banks of key euro area
countries. Our first hypothesis is based on the presumption of strong correlation among
main microeconomic indicators in EU17. We focused on correlations within EU. It focuses
on searching for correlation between factors and the ratio. Final result should be
econometric model that quantifies this relationship and selects significant factors among
less important factors.
Keywords: microeconomic factors, impaired loans, multiple regression, derivatives
JEL codes: G32
1 Introduction
Microeconomic factors depend on the nature of banking operations. Bank has mainly one
basic objective – to generate profit, so that it tends to use credit derivatives in cause of
its non-financial indicators.
The first important distinguishing sign is a reason why banks use the credit derivatives.
According to Fitch Ratings study from 2010 the banks use derivatives in 43% for trading,
38% for hedging (credit risk management) and 32% for brokering or market-making
activities. European banks use derivatives according to ECB for trading and hedging.
A bank has two reasons for selling credit protection according to Dias and Mroczkowski
(2010).Key motivation is risk diversification and the second reason is a need to find more
profitable investments. Furthermore, these products contributed to extend the products
offered to clients and to optimize economic capital. Credit derivatives have been
important instrument for risk protection according to ECB (2009) because the European
banks have been clear buyers of credit derivatives unlike US banks.
According to Ashraf et al. (2006) who is interested in bank motivation of using
derivatives, there are determinants that have bank in common. He found out, after
observation of 56 banks using them in 1997-2004, that the probability that a bank
participates in credit derivatives market is positively influenced by size of a bank, using
other types of derivatives (mainly currency) and the existence of general strategies for
derivatives risk management and negative barriers of entry management. The most
significant determinants are according to them the size and portfolio diversification.
Geographic diversification is not considered to be a positive determinant according to
them. Outside these considerations is possible to involve also negative determinants like
profitability (interest margin), deposits and loan commitments.
According to Minton study, the banks use credit derivatives mainly for trading. From this
point of view there are instruments for credit risk management the most significant. They
mean securitisation and selling the loans, size, foreign currency loans, commercial and
industrial loans for big clients, using other derivatives, stronger transparency of
derivative information, the existence of relationship between creditor and debtor (that
can partially can limit using credit risk Instruments use). There are also negative
determinants like loans secured by real estates or other assets, equity, profitability (net
321
interest margin) and from accounting point of view the unability to apply accounting
methods for hedging (they are specific in cause of high volatility of hedging operations).
Mahieu and Xu (2007) dealt with determinants of credit derivatives use in relation to
hedging (not trading). There are size, loan trading engagement and securitisation
distinguished from negative factors like interest margin and total deposits indicator.
2 Methodology and Data
We created a relationship of selected factors and portion of impaired loans for analysis of
microeconomic factors impact on use of credit derivatives. The factors have been
selected according to literature and own opinion.
– Cash & Near Cash Items
– Total Loans
– Total Assets
– Total Equity
– Tier 1 Risk-Based Capital Ratio Total Risk-Based Capital Ratio
– Net Interest Margin
– Net Revenue
Quantitative data was drawn up from Bloomberg for observed bank – Deutsche Bank. For
modeling the relationship was used stepwise regression. It includes regression models in
which the choice of predictive variables is carried out by an automatic procedure. I used
one of the often used approaches – backward selection – because it involves starting
with all variables in the model, trying out the variables one by one and including them if
they are statistically significant.
3 Results
Results of observation of microeconomic factors and impaired loans
Table 1 Deutsche Bank - Microeconomic factors and impaired loans
Parameter Estimate
CONSTANT -0,0103117
Total Loans -1,18457E-7
Net Interest Margin 0,0825636
Table 2 Analysis of Variance
Source
Sum of
Squares
Df Mean Square F-Ratio P-Value
Model 0,00135974 2 0,000679871 12,69 0,0070
Residual 0,00032144 6 0,0000535733
Total (Corr.) 0,00168118 8
Portion of impaired loans:
DB = -0,0103117 - 1,18457E-7*Total Loans + 0,0825636*Net Interest Margin
Results of observation of microeconomic factors and CDS prices
Table 3 Deutsche Bank - Microeconomic factors and CDS prices
Parameter Estimate
CONSTANT -42,8469
Total Equity 0,00883867
Net Revenue -0,00773047
322
Table 4 Analysis of Variance
Source
Sum of
Squares
Df
Mean
Square
F-Ratio P-Value
Model 29127,0 2 14563,5 22,03 0,0010
Residual 4627,98 7 661,14
Total (Corr.) 33755,0 9
Prices CDS:
DB = -42,8469 + 0,00883867*Total Equity - 0,00773047*Net Revenue
As the observation of the microeconomic factors is a part of wider study, we can
conclude that microeconomic factors have at least stronger impact on impaired loans and
selected credit derivatives prices in comparison with macroeconomic factors.
CDS prices have been selected in cause of high amount of this credit derivative being
traded. It has a long history and almost all the banks disposes of some amount of them
in the portfolios.
References
Ashraf, D., Altunbas, Y., Goddard, J. (2005). Determinants of the use of credit
derivatives by large US banks, Working paper.
Bernadette A., Minton, R. S. and Williamson, R. (2006). How much do banks use credit
derivatives to reduce risk? NBER Working Paper, Avaialable at: <http://www.fdic.gov
/bank/analytical/cfr/2006/sept/StulzR.pdf>.
Blake, D. (1995). Analýza  finančních  trhu, Praha: Grada Publishing.
Dias, R, Mroczkowski,N. (2010). Review of the use of credit derivatives: implications for
risk management in banking. Available at: http://researchbank.swinburne.edu.au/vital/
access/manager/Repository/swin:27468
Markovič,   P.,   Ponecová,   G. (2007). Finančné   nástroje   ako   prostriedok   transferu  
finančných   rizík   podniku [online], Avaialable at: http://semafor.euke.sk/
zbornik2007/pdf/markovic2.pdf
Martin, M.R.W., Reitz, S.,Wehn, C.S. (2006). Kreditderivate und Kreditrisikomodelle Eine
mathematische Einfuhrung. Wiesbaden: Friedr. Vieweg & Sohn Verlag |GMW Fachverlage
GmbH.
Mahieu, R. J. and Xu, Ying. (2007). Hedging with Interest Rate and Credit Derivatives by
Banks, RSM Erasmus University Working Paper, Avaialable at: <http://ssrn.com/
abstract=971805 or http://dx.doi.org/10.2139/ssrn.971805>.
323
Can tax policy co-cause the crisis?
Irena  Szarowská
Silesian University
School of Business Administration, Department of Finance
Univerzitni nam, 1934, 733 40 Karvina, Czech Republic
E-mail: szarowskal@opf.slu.cz
Abstract: Although taxes have not generated the crisis, some aspects of tax policy may
have led to increased risk-taking and indebtedness of banks, households and companies.
Tax incentives may indeed the behavior of economic agents, leading them to wrong
economic decisions. The aim of the paper is to review main channels through which the
tax policy can affect financial markets and financial stability. Attention is focused on last
and current development of tax reliefs for housing and capital gains, tax benefits for
corporate debt financing and taxation of financial institutions Conventional scientific
methods such as analysis, induction, comparison and synthesis are used in the paper.
Keywords: crisis, corporate debt financing, housing, taxation of financial institutions
JEL codes: G01, G10, G20, G30, H20
1 Introduction
There is a general consensus that the 2008 financial crisis is the worst economic crisis
since the Great Depression of 1929. As Hemmelgarn and Nicodeme (2010) mention, it
has been characterized by a housing bubble in a context of rapid credit expansion, high
risk-taking and exacerbated financial leverage, leading to deleveraging and credit crunch
when the bubble burst. Economists and researchers have tried to identify causes which
have caused or co-caused the crisis. This effort has a very simple and logical reason –
identifying and understanding problems can help to prevent future crises. Lloyd (2009)
emphasizes that root causes of the financial crisis can be identified at two levels: global
liquidity policies (especially low interest rates in particular nurturing a strong credit
expansion with cheap leverage and bubble tendencies in asset prices); plus a poor
regulatory framework, which not only failed to prevent the growth of asset bubbles but
actually contributed to their growth and concentration into the specific areas, such as
mortgage securitization, where the credit bubble eventually burst with such damaging
consequences. More immediate causes of the crisis can be categorized as follows:
• a high appetite for yield and a high tolerance of risk;
• lack of transparency;
• insufficient regulation;
• lack of adequate corporate and fiscal governance.
The discussion has focused also on the tax policy and measures. The complexities of
national tax codes, and the international interaction between them encouraged the use of
complicated financial instruments and international tax planning, reducing transparency.
Slemrod (2009), Keen et al. (2010), Guily (2010), Ceriani et al. (2011) and others have
tried to answer the question whether taxation and tax policy can play any role in
precipitating the financial crisis. They have considered as the most important elements of
the tax system affecting financial crisis: the tax preference for corporate debt financing,
the taxation of financial institutions, tax competition, tax reliefs for housing and for
capital gains, the incoherence of capital income taxation (tax arbitrage, tax clienteles,
and derivative securities), the use of tax havens for creating tax efficient securitization
instruments and the tax preference of the performance-based remuneration.
The aim of the paper is to review main channels through which the tax policy can
contribute to the crisis. Attention of the paper was focused (due to the length of the
article) only on tax reliefs for housing and capital gains, tax benefits for corporate debt
324
financing and taxation of financial institutions. The paper examines development and also
possible tax measures and regulation.
The paper employs standard methods of scientific paper. In the theoretical part, mainly
the methods of description and induction are used; next the methods of comparative
analysis, deduction and synthesis will be applied.
2 The Tax Treatment of Housing
We start with the tax treatment of housing as housing, and in particular the collapse of
the housing price bubble, has been singled out as a triggering cause of the crisis. Tax
policy   can   affect   two   key   aspects   of   housing   markets:   house   prices   and   households’  
leverage. These are interrelated, as high house prices encourage removing equity
through increased borrowing, the availability of cheap loans drives up house prices, and
the expectation of price increases raises the expected return on borrowing to acquire
housing. There is a considerable diversity of housing tax regimes across countries.
Definite international comparison is difficult due to the complexity of tax codes and
differences in terms of deductions, exceptions or threshold limits.
Study of IMF (2009) points out the risks in distorting a market so central to financial
stability reinforce long-standing efficiency and equity arguments for more neutral
taxation. Taxation of capital gains is needed to match the capital gains tax liability on
other financial assets; and deductibility of mortgage interest is needed to match the
taxation of the interest available from investing in other assets. From a theoretical point
of view, the non-taxation of the rental return to owner-occupied housing, coupled with
the deductibility of mortgage interest and home equity loan interest for itemizers and
especially favorable statement of capital gains on housing, adds up to a substantial tax
preference. Unfortunately, tax systems are not neutral. Owner-occupation is tax-favored
with respect to renting in many countries, and with respect to most forms of return on
personal savings.
In practice, imputed rents and capital gains on primary residences are rarely taxed,
creating a general bias towards housing that mortgage interest relief is likely to reinforce.
Very few countries bring imputed rents into the income tax (the Netherlands, Belgium
and Switzerland being exceptions). Some tax capital gains on owner-occupied housing,
but typically more lightly than other income or only beyond a high threshold (or both).
Even in the absence of distortions on the financing side, these features would tax-favor
owner-occupation relative to renting. And mortgage interest costs attract tax relief,
subject to limits, in a number of countries (e.g. Denmark, France, Italy, Spain, Italy,
France, U.S., UK, Ireland, Netherlands, Czech Republic). Since borrowing to acquire other
assets is generally not deductible, this makes investment in housing even more favored.
Look at Ceriani et al. (2011) or Taxes in Europe database for details.
Mortgage interest relief would not tax-favor mortgage finance if the alternative to
borrowing were investing less in fully-taxed assets and other interest were also
deductible. If alternative investments were fully taxed, mortgage interest deductibility
would mean that the opportunity costs of acquiring housing by borrowing and by running
down other assets would in each case be the after-tax interest rate—so mortgage finance
would not be tax -favored. Many countries, however, tax other forms of saving (such as
pensions) at reduced rates. In that case, if the return on those assets matches the pretax
interest rate on mortgage debt, there is an arbitrage gain from leveraging against
housing and investing own-funds in the non-housing asset. And while interest on loans
used to finance consumption is generally not deductible, home equity loans have
provided (within limits) just such a tax-favored way to borrow and spend.
The distributional impact of mortgage interest relief can be complex, but deductibility
likely favors the better off. Higher income individuals may be more likely to face
constraints on their access to tax-favored assets (since this is often subject to caps), so
that their opportunity cost of investing in housing is the after-tax return. This creates an
argument for some tax relief to ensure that the less well-off also pay an after-tax rate.
325
Against this, however, deductions are worth more to the better-off as they take them
against a higher marginal rate of tax. The second effect would be avoided if relief were
provided—as many countries do—not as a deduction but as a credit.
Noord and Heady (2001) and next Noord (2005) have stated that the deductibility of
mortgage interest, by reducing the user cost of ownership, decreases the price elasticity
of demand; it therefore increases the volatility of the housing market. In general, the
price sensitivity of demand for housing is inversely related to the extent of preferential
tax treatment for housing and to the expected rate of house price appreciation.
Moreover, Keen et al. (2008) note that mortgage interest tax relief encourages the buildup
of gross housing debt and there is evidence that countries offering more favourable
tax treatment for home ownership do indeed have higher ratios of mortgage debt. There
is also evidence that mortgages fell significantly relative to home value (in UK and U.S.)
after reforms reduced the value of mortgage interest relief (e.g. in Scandinavian
countries).
Ownership or occupation and transaction taxes also play an important role. Many
countries charge substantial recurrent taxes based on ownership or occupation. These
have potential appeal both in serving as user charges reflecting the value of local public
services and, to the extent that these and other features are location-specific, as being
less vulnerable to interjurisdictional tax competition than the corporate income tax and
other taxes on more mobile bases. Johansson et al. (2008) have presented evidence that
such taxes (along with consumption taxes) have significantly less adverse effects on
growth than income taxation. Also Hilbers et al. (2008) have highlighted that economic
activity somehow related to housing accounts for an important share of GDP. In Europe,
this share is estimated at between 5 and 10 per cent. Next, Lutz et al. (2011) have
identified five main channels through which the housing market affects public finances:
property tax revenues, transfer tax revenues, sales tax revenues (including a direct
effect through construction materials and an indirect effect through the link between
housing wealth and consumption), and personal income tax revenues. They found that
property tax revenues do not tend to decrease following house price declines and
concluded that the resilience of property tax receipts is due to significant lags between
market values and assessed values of housing and the tendency of policy makers to
offset declines in the tax base with higher tax rates. The other four channels have had a
relatively modest effect on state tax revenues and public finances. It is necessary to have
on mind that a shortfall in public revenue may cause a debt problem.
Moreover, investors paying tax on interest income at a rate higher than that at which
they can offset capital losses benefit by pooling assets to pay interest at a rate which
reflects the expected losses. Investors facing the same rate on both, on the other hand,
do not care about the mix of interest and gains. Bringing the two types together creates
scope for tax arbitrage from which both can benefit. Eddins (2009) developed this
argument.
The search for new ways to allocate risk has encouraged to the development of new
financial instruments, in particular the technique of securitization. The most common
securitizations (in relations to the housing) are mortgage-based securities (MBSs)
whereby the claims of thousands of mortgages are pooled together in a Special Purpose
Vehicle (SPV), which is a legal entity outside of the balance-sheet of the financial
institution, allowing them to bypass capital ratios regulations. Tax issues raised by
securitization include: whether any gains on assets placed in the SPV by the originator
are taxable; whether the SPV itself is taxable; and whether payments to holders of the
securitized assets will be taxed as interest or dividends. MBSs can be divided between
commercial MBSs (CMBSs), secured by commercial and multifamily properties, and
residential MBSs (RMBSs).
But IMF (2009) highlights that a lack of clarity in the tax treatment of new instruments
can lead to further complexities through the use of strategies aimed at assuring tax
minimization: one way to trying to ensure that SPVs themselves—which are just
326
intermediating receipts —are not subject to additional layer of tax, for example, is by
locating them in low-tax jurisdictions.
3 Tax Benefits for Corporate Debt Financing
Tax is one of many determinants of corporate financial policies. Although the theory
expects the existence of complete markets, perfect information, and no taxation, the
reality is otherwise. Informational imperfections introduce considerations that can lead to
a determinate choice and parceling of returns between equity and debt has real
consequence. As Lloyd (2009) mentions, one longstanding issue is that there is an
overall  bias  in  many  countries’  tax  systems  which  work  to  encourage corporate leverage.
Changes in investment patterns and cross-border financial flows brought about by the
twin forces of globalization and financial innovation may have significantly increased the
impact of this bias in recent years. A systemic bias in favor of corporates financing
themselves with debt (as opposed to equity) results from treating interest as a business
cost in arriving at corporate profits, and so deductible for tax purposes against annual
corporate profits, while treating returns to equity finance as a distribution of corporate
profits, and not deductible in computing those. As a result profits may be taxed both at
corporate and personal level when they are distributed as dividends.
According to Alworth and Arachi (2012), the greatest tax distortions in favor of debt
financing will be in situations where there is no compensating increase in taxation at the
level of the investor, compared with the taxation of dividends or capital gains on shares.
Such a compensating increase could in theory arise if tax systems systematically
compensated for the bias to corporate debt through reduced taxation of dividends and
capital gains on shares. In practice, the distortion is greatest when the investor is tax
exempt, or when tax is evaded, or when hybrid structures are used to achieve either a
double  deduction  (“double  dip”)  for  the  interest  expense  or  relief  for  the  interest  expense  
with no corresponding taxation. Double-dip financing is a tax-planning strategy in certain
cases involving the use of conventional debt, depending on the effective tax rate on
interest income in the home country, compared with the effective tax rate on profit in the
host country.
From a financial stability point of view, the key problem with high levels of leverage is
that this makes companies more vulnerable to economic shocks and increases the
probability of bankruptcy. Specifically, highly leveraged companies are particularly
susceptible to volatility in profits (since they will be required to make interest payments
irrespective of profitability) and–unless they have hedged–to volatility in currency or
interest rates. If leverage levels become unsustainable and lead to a credit crunch, firms
and households are left without access to the credit they need, leading to a collapse in
demand. As the study of IMF (2009) concludes, this tax distortion has gained more
attention recently as the crisis has highlighted the fact that many companies have too
high leverage ratios. This could lead to liquidity constraints, especially in times when
banks tend to restrict their credit supply.
Hemmelgarn and Nicodeme (2010) show that current corporate tax system in Europe
favor debt financing over equity financing. While, in general, interest payments on
corporate debt are deductible from the corporate tax base, return on equity is not. This
leads to a higher leverage for firms since financing investments through debt is taxfavored.
This tax distortion has gained more attention recently as the crisis has
highlighted the fact that many companies have too high leverage ratios. This could lead
to liquidity constraints, especially in times when banks tend to restrict their credit supply.
A well-designed tax base that reduces the distortion of the leverage could make
companies less vulnerable to a short-term reduction in credits available on the capital
market. There is indeed empirical evidence that the leverage of companies is indeed
influenced by taxes. Several studies have analyzed this issue and find that debt policy is
consistent with tax considerations (e.g. Desai et al., 2004 or Huizinga et al., 2008).
Huizinga et al. (2008) have found for instance that for stand-alone companies, an
increase in the effective tax rate by one percentage-point increases the ratio of debt to
327
assets by 0.18%. The impact is larger for multinationals as it reaches 0.24% for two
equal-size companies (with one foreign subsidiary) within the same group.
In principle, two opposing measures exist that might eliminate this distortion by treating
both sources of finance in the same way: an allowance for corporate equity (ACE) or a
comprehensive business income tax (CBIT). The ACE would grant a similar deduction for
return on equity than for interest paid. This would abolish the tax advantage of debt. At
the same time, ACE reduces the tax burden on marginal investment. ACE would also lead
to a narrower tax base. In order to collect the same amount of tax revenue either the
statutory corporate tax rate or other taxes have to be increased to finance such a reform.
The CBIT, on the contrary, broadens the tax base by disallowing a deduction for interest
payments on debt. If the tax rate remains unchanged, this leads to an increase in tax
revenue. The additional revenue can be either used for a reduction in the statutory
corporate tax rate or of other taxes if the reform is supposed to be revenue neutral. The
tax neutrality of the financing decision is the same as in the ACE case since equity and
debt financing are equally treated. ACE and CBIT have been discussed extensively in the
economic literature as evidenced by Devereux and de Mooij (2009). Both systems are
appealing due to their efficiency properties with regard to the financing decision of
companies; however, there is no clear recommendation on which system is most
favorable and there are key trade-offs when designing a reform towards any of these
pure systems. While in the context of open economies ACE is more prone to profit
shifting (in particular when its narrow tax base is accompanied by higher corporate tax
rates), CBIT might lead to increased distortions of the marginal investment.
Recently, Lierse (2012) has pointed on evidence of the tax bias to debt encouraging
higher levels of gearing by companies, and banks have tended not only to gear up to the
levels of debt allowed under regulatory capital rules but also to issue hybrid, equity-like,
forms of debt, rather than ordinary share capital, where that satisfied both the regulators
and the conditions for a tax deduction.
We can find an unclear premise concerning the tax incentive to debt financing. Generally,
it presumes that, in order to obtain the credit and thus tax advantages of interest
deductibility, corporations must change the risk profile of their obligations to the
providers of capital. This may not be true, though, if a corporation can obtain the tax
advantages without altering the character of its obligations. In principle this could be
done by issuing hybrid instruments such as convertible debt obligations. Moreover,
Shaviro (2009) notes, corporations often favor hybrid financing that qualifies as debt for
tax but not for accounting purposes, thereby generating deductions against taxable
income but not against financial statement earnings.
4 The Taxation of Financial Institutions
Although James Tobin first conceived the Financial Transaction Tax over 40 years ago,
wider discussions about a possible taxation of the financial institutions has appeared after
the financial crisis in 2008. According to Alworth and Arachi (2011), financial institutions
face qualitatively the same tax considerations in balancing equity and debt finance as
non-financial corporations. Banks have traditionally been able to sustain very high debt
ratios by virtue of having relatively safe assets, and implicit or explicit deposit
guarantees reinforce this. Besides, the high profitability of financial institutions in recent
years will have made debt more attractive for them than for many non-financials, since
the low probability of tax exhaustion it implies means a high effective corporate income
tax rate. IMF (2009) has presented the tax bias to debt runs counter to regulatory
objectives. Banks face both an explicit tax advantage of debt and, through regulatory
requirements, an implicit penalty—with evident risk of policy incoherence. Tax incentives
towards high leverage may have undercut the effectiveness of regulatory requirements.
The tension between regulatory objectives is reflected in the emergence of already
mentioned hybrid financial instruments, which are treated like debt obligations for tax
purposes (i.e., interest payments are deductible), but they are treated as capital rather
than liabilities under banking regulations.
328
Hasman et al. (2011) have analyzed the effectiveness of different government policies to
prevent the emergence of banking crises. They have studied the impact on welfare of
using tax-payers money to recapitalize banks, government injection of money into the
banking system through credit lines, the creation of a buffer and taxes on financial
transactions (the Tobin tax). Whilst the Tobin tax is an emergency policy (applied when a
banking crisis is imminent), the creation of a buffer is a preventive one.
Acharya et al. (2011) have stressed the negative externalities of large, complex financial
institutions and recommended that policy makers quantify their systemic risk and tax
their contributions to this risk. Systemic risk can be broadly thought of as the failure of a
significant part of the financial sector –one large institution or many smaller ones –
leading to a reduction in credit availability that has the potential to adversely affect the
real economy. As stated by Slemrod (2009) the tax should be implemented through
capital requirements or deposit insurance fees, rather than by trying to apply a tax
directly to a base associated with the negative externality.
European Commission also has focused its attention on a possible taxation of the
financial sector and has concluded that EU should introduce a system of levies or taxes
on financial institution. On September 28, 2011, the European Commission formally
proposed a plan to implement an EU-wide financial transactions tax (EC, 2011). In
October 2012, after discussions failed to establish unanimous support for an EU-wide
financial transactions tax (FTT), the European Commission proposed that the use of
enhanced cooperation should be permitted to implement the tax in the states which
wished to participate. The proposal, supported by 11 EU member states representing
more than 90% of Eurozone GDP. The European Parliament resoundingly approved the
plan in December 2012. On February 14, 2013 the European Commission adopted a
proposal for an 11-nation financial transactions tax and it will come into force after being
approved by the participating member states and the European Parliament. The target
starting date is January 1, 2014 (EC, 2013).
The tax would be levied on all transactions on financial instruments between financial
institutions when at least one party to the transaction is located in the EU. It would cover
85% of the transactions between financial institutions (banks, investment firms,
insurance companies, pension funds, hedge funds and others), but not affect citizens and
businesses. House mortgages, bank loans to small and medium enterprises, contributions
to insurance contracts, as well as spot currency exchange transactions and the raising of
capital by enterprises or public bodies through the issuance of bonds and shares on the
primary market would not be taxed, with the exception of trading bonds on secondary
markets.
The European Commission itself expects the FTT to have the following impact on financial
markets and the real economy:
• up to a 90 per cent reduction in derivatives transactions;
• negative or positive effect on economic growth;
• an effective curb on automated high-frequency trading and highly leveraged
derivatives;
• an increase in capital costs, which could be mitigated by excluding primary
markets for bonds and shares from the tax
• the real economy could be protected by ensuring the tax is levied only on
secondary financial products, thus not affecting transactions such as salary
payments,  corporate  and  household  loans  (Hagelüken,  2012)  .
However, there is not unambiguous consent that FTT on its own would prevent financial
crises. The authors like Griffith-Jones and Persaud (2012) argue that FTT would
somewhat reduce systemic risk, and therefore the likelihood of future crises, as prudent
macroeconomic policies and effective financial regulation as well as supervision also have
a major role to play in crisis prevention. However, by significantly reducing the level of
noise trading in general and reducing (or eliminating) high frequency trading in
329
particular, the FTT would make some contribution to the reduction of severe
misalignments and hence the probability of violent adjustments. Moreover, in financial
crises  “gross”  exposures matter more than the net ones, and financial transaction taxes
will reduce the gap between the two.
5 Conclusions
The aim of the paper was to review the main channels through which the tax policy can
contribute to the crisis. We can conclude that the most important elements of the tax
system affecting financial crisis are: the tax benefits for corporate debt financing, the
taxation of financial institutions, tax competition, tax reliefs for housing and capital gains,
the incoherence of capital income taxation like tax arbitrage, tax clienteles and derivative
securities, the use of tax havens for creating tax efficient securitization instruments and
the tax prioritization of the performance-based remuneration.
There is evidence that the tax system played a major role in triggering the tax crisis. On
the other side, a number of special taxes have been introduced and proposed to recover
the  cost  of  the  ―bailout.  There  is  a  bias  that  in  many  countries  tax  systems  encourage  
corporate leverage. Changes in investment patterns and cross-border financial flows
brought about by the twin forces of globalization and financial innovation may have
significantly increased the impact of this bias in recent years. A systemic bias in favor of
corporates financing themselves with debt results from treating interest as a business
cost in arriving at corporate profits, and so deductible for tax purposes against annual
corporate profits, while treating returns to equity finance as a distribution of corporate
profits, and not deductible in computing those. As a result profits may be taxed both at
corporate and personal level when they are distributed as dividends.
The debate has highlighted that taxation may be used as corrective instrument to
complement prudential regulation of the banking sector. Financial transaction tax has
been adopted as a tool to stabilize financial markets and improve their functioning
because a large number of transactions are either speculative or of no social use.
It should be stressed that many of the defining elements of the pre-crisis financial sector
were global in scope. The main root causes of the crisis, e.g. cross-border debt balances,
exploitation of differences in regulation and in market prices and the market for the
highest-yielding investments, were also global. In spite of that, there is a tension
between global integration of markets and nationally based regulation. But in a globally
competitive market, market players will seek the most advantageous regulatory
environment for financial transactions and exploit differences in national regulations, so
the regulatory activities must also be global.
Acknowledgments
This  paper  ensued  thanks  to  the  support  of  the  grant  GAČR  403/11/2073  "Procyclicality  
of financial markets, asset price bubbles and macroprudential regulation".
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Stress testing in relation to the financial crisis
Zuzana  Töröková1
,  Boris  Šturc2
1
University of Economics
Faculty of National Economy, Department of Banking and International Finance
Dolnozemská  cesta  1,  852  35  Bratislava, Slovakia
E-mail: zuzana.torokova@euba.sk
2
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  507/41a,  602 00 Brno, Czech Republic
E-mail: sturc@mail.muni.cz, boris.sturc@gmail.com
Abstract: According to experts, the causes of the global financial crisis can be found on
the poor management of banks and other institutions of financial intermediation, also on
failure of credit rating agencies in the assigning credit ratings as well as
misunderstanding or their poor interpretations. The cause lies in the absence of crisis
scenarios and inadequate risk assessment by using stress testing. The purpose of paper
deals with stress testing at national and international level, stress test scenarios, their
results and impact on individual economies.
Keywords: stress testing, stress test scenarios, crisis scenarious
JEL codes: G21, G01
1 Introduction
At the time of the global financial and economic crisis, which has affected the whole
world, the credit rating agencies are in some circles considered to be one of the main
culprits of the crisis The main reason is that they gave high ratings to certain structured
finance products, as well as financial institutions. These ratings had to be revised after
the outbreak of the crisis. Following the downgrading some securities became nearly
worthless and illiquid. This had a negative impact on the financial institutions that hold
these securities in the portfolio. Therefore, the rating became unreliable and it was
necessary to find an alternative to it.
Many specialists from national and international supervisory authorities argue that one of
the causes of the current crisis lies in the absence of crisis scenarios and inadequate risk
assessment using stress testing. In response to the problems caused by the financial
crisis, lack of credibility of risk assessment through rating and inadequate risk
assessment using stress testing, the first stress testing was made in the 2009 at
European level. This test should show how banks would respond to scenarios created and
whether in the case of the occurrence of situations that European banks were stable or
not.
The aim of the paper is to analyze the stress tests that were carried out at national and
international level, to find out whether those stress tests administered realistic picture of
the condition in the banking sector and to draw on that basis conclusions wheather stress
testing is an effective and credible.
2 Theoretical basis of stress testing
Stress tests are an important tool for the management of the bank, because they provide
useful information about the reliability of the internal system of financial institutions. This
is a simulation technique which aims to determine how certain crisis scenarios affect the
value of the portfolios of individual banks. Stress tests are used by the banking
supervisory authorities as well as central banks to assess the reliability of the financial
systems of banks in case of extreme, but still acceptable shocks. (Quagliariello, 2009)
332
Stress testing is the process of identifying events that are very unlikely but still possible.
These events may have a significant adverse impact on the financial health of banks and
it is necessary to know to quantify their impact. Stress testing is usually based on the
creation of stress scenarios and evaluating their impact on costs and revenues and the
resulting bank profit. Outcome of a process is the estimation the impact of simulated
market situations affecting the economy and position of the bank.
“Stress-testing at the level of individual institutions has been widely applied by
internationally  active  banks  since  the  early  1990s.”  (Sorge,  2004)  “System  stress  tests  
can complement stress tests conducted by individual institutions, and act as a crosscheck
for other types of analysis. The information provided by system stress tests can
also help to identify weaknesses in data collection, reporting systems, and risk
anagement.”  (Jones  – Hilbers – Slack, 2004)
In the stress test is one of the most important steps creating stress scenarios. We can
say that in general there are several ways of making them. In the most common we
could include historical and hypothetical stress scenarios. Hypothetical scenarios based
on possible fluctuations in the measured parameters. Historical scenarios modeled in
turn, that there is fluctuation in the measured parameters, to the extent that they have
ever occurred in the past. In both methods of scenario we can distinguish two types of
usage. The first method consists in applying changes only to one parameter, and
determines the impact of changes in the banking portfolio. The second method consists
in quantifying the impact of change in several parameters simultaneously, as in the
economy generally no change does occur separately, but by the rule it is accompanied by
a change of several other parameters.
Stress scenarios are designed to be able to evaluate the relative importance, as both
credit and market risk in the financial companies. Since this is a relatively comprehensive
estimate of the development in the financial sector, depending on the selected stress
scenarios, it is necessary to adopt a relatively large number of assumptions. Therefore it
is important to note that the results of stress tests are used rather to the relative
comparison of the types of risks in financial companies, or for the relative comparison of
different companies within the financial sector, rather than for the absolute quantification
of potential gains / losses in various scenarios. (Klacso, 2011)
Stress testing works at national and as well as international level. At the EU level the
European Banking Authority (EBA) is responsible for stress testing. The EBA was
established at 2011 and has taken over all existing and ongoing tasks and responsibilities
from the Committee of European Banking Supervisors (CEBS). At the national level is
responsible for stress testing the national supervisory authorities of each country.
3 Stress tests on international and national level
The first stress test on European level was in 2009 and was coordinated by The
Committee of European Banking Supervisors (CEBS) in cooperation with the European
Commission and European Central Bank (ECB). This EU-wide forward-looking stress test
of the banking system was built on common guidelines and scenarios. The sample
considered of 22 major European cross-border banking groups, which represents 60% of
the total EU banking sector on a consolidated bases.
The outcome of this test was on aggregated basis. Under the baseline scenario, reflecting
current macro-economic   predictions,   the   banks’   aggregate Tier 1 capital ratios will be
well above 9%. According to Basel, a requirement was at least at 4%. Under the second
– more adverse scenario the aggregate Tier 1 ratio for the banks in the sample would
remain above 8% and no bank would see its Tier 1 ratio falling under 6%. This was
because the financial position and expected results of banks are sufficient to maintain an
adequate level of capital also under such negative circumstances.
The next stress test coordinated by CEBS was at 2010. The scope of this test has been
extended to include the major EU banking groups and also key domestic credit
institutions in Europe. The sample has been built by including major banks in country to
333
cover at least 50% of the national banking sector. Stress scenario was applied at
91 banks, which represent 65% of the EU banking sector.
Banking groups have been tested on a consolidated level, so subsidiaries and branches of
an EU cross-border banking group have been included in the exercise as a part of the
test of the group as a whole. Banks in Slovak banking sector, which are subsidiaries or
branches of cross-border banking groups based in the EU, are included in the stress test
only as part of these banking groups as a whole. Slovak subsidiaries of foreign banking
groups that were included in the stress tests represent 78% of the assets of the Slovak
banking sector (CEBS’s   press   release   on   the   results   of   the   EU-wide stress testing
exercise, 2009)
The EU-wide   stress   test   exercise   is   a   “bottom-up”   exercise,   where   commonly agreed
scenarios  and  key  assumptions  have  been  applied  to  institutions’  internal  risk  parameters  
and portfolios, with the aim at better assessing the specificities of institutions in the
sample for the exercise. The results should provide information about the vulnerability of
the European banking sector on overall economic decline and movement of external
variables such as interest rates, GDP, consumer price index and unemployment. Like the
previous test, also this was based on commonly agreed macro-economic scenarios (basic
and adverse) for 2010 and 2011, designed in close cooperation with the ECB and the
European Commission. The result was that most of the tested financial groups should not
have a problem with equity. Stress testing has not undergone only seven of the 91 banks
that have undergone testing.
A wave of criticism withstand for this "round" stress tests after a few months, given by
the problems of Irish banks. Neither of troubled banks did appear in "a seven" which did
not pass stress tests. Although, Ireland had to take help from the EFSF to save the
country and banks from bankruptcy. Therefore, stress test had tougher criteria in 2011
to be credible. Banks of this testing were selected based on the same parameters as has
been previously tested. Indirectly, Slovak banks were also selected and tested on a
consolidated basis.
The scenario evaluated against the bank's devaluation, which would occur under the
influence of a significant change compared to the base forecast of main macroeconomic
indicators such as GDP, unemployment and housing prices. The test also took into
account interest rate changes and spreads on government bonds because they have an
impact on funding costs for banks in "stress". The test was conducted on the basis of
verification in three rounds and EBA relied on the quality of national supervisors and the
internal processes of banks in assessing areas such trends were earnings, asset quality,
modelling results and the extent of the impact on assets and liabilities. The result was
that only eight banks during the stress test did not fulfill the limit of 5% over two years,
representing a deficit of 2.5 billion. And EUR 16 banks reached Tier 1 between 5% and
6%.
Among the seven banking groups that have subsidiaries in Slovakia, which was part of
the stress test showed six banking groups in the stress tests adequate capital adequacy
stress tests did not pass one banking group Oesterreichische Volksbank AG (Core Tier 1
ratio dropped at the end of the stress period to 4.5%, slightly below 5%). Stress-test
results showed relatively strong banking sector resilience to adverse macroeconomic
scenario, but for mitigation of adverse scenario, the banks have to prepare for a wide
range of measures such as the use of countercyclical provisions, divestments, sales,
capital   growth.   (CEBS’s   press   release   on   the   results   of   the   EU-wide stress testing
exercise, 2011)
However it have to be said, that on the basis of EBA test has recommended to national
banking supervision to carry out the banks with a decrease in Core Tier 1 ratio under 5%
detailed analysis of capital and at the same time take measures so that the shortcomings
found in the stress test were immediately removed. It was mainly the fact that national
supervisors ensure that the banks submit measures to restore the capital position of at
least 5% of the reference values.
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The stress test has been conducted In the Slovak Republic at national level of financial
sector annually since 2005, and its results have been published in the Analysis of the
Slovak Financial Sector.
In 2007, when the subprime crisis erupted in the U.S., the National Bank of Slovakia
shall submit the banking sector in Slovakia to stress test risk of decline in real estate
prices. The reason for this test was high housing price growth in the last five years. The
subject of stress testing was a direct connection of the banking sector on the real estate
market through the use of property as collateral for a loan. In this section the indicator
LTV - loan to value (value of the loan-to-value) examined. Stress scenarios were
constructed for 30% and 50% drops in real   estate   prices.   Household’s   loans   (secure  
housing) and business sector loans (secured by commercial real estate) were tested
separately. The output of this model was the quantification of the lending costs which
represented a loss from a decline in real estate prices. These costs have an impact on the
Bank's equity and therefore it can be stated impact of the fall in real estate prices on the
capital adequacy of banks. Based on this test it was concluded that the impact of
changes in real estate prices on the banking sector is weak. For this result we owe
conservative   policy‘s   of   banks   as   well   as   the   continual   dynamic   growth   in   real   estate  
prices. (Lintner – Rychtárik,  2007)
Recent testing of Slovak banking sector took place in 2012. In this test, banks were
tested under three scenarios, which imply the 2.5 year period from the end of the first
half of 2012 to the end of 2014. The baseline scenario is based on the official mediumterm
forecast of the National Bank of Slovakia (NBS). Negative developments are
expected in the real economy, which will also be reflected in the financial markets. It is
further assumed depreciation of the euro against the dollar, the increase in credit
margins when interest rates in the money markets, even if government bond yields. All
this will result in a fall in the domestic economy and the consequent increase in
unemployment.
The negative development of the scenario "economic downturn" should be in the scenario
"crisis states" strengthened by intensifying sovereign crisis in the euro area, and it should
come up and pronounced deterioration of market indicators as in the previous scenario.
As a result of these developments was, decline of domestic economy, rising
unemployment and increased inflation. The development would have been worse than in
the previous scenario.
Impacts of scenarios on the banking sector in the Slovak Republic are as following:
– Resistance of banking sector compared to the previous stress test slightly worse.
In previous stress testing of the end of 2011, the results were aggravated due to
losses from Greek bonds. In the current stressed the impact of in-depth stringent
stress scenarios, as well as taking account of bank levy in its present form during
the entire period of testing.
– High capital cushion continues to help the banking sector to withstand adverse
developments. Given the high capital adequacy of banks operating in the Slovak
Republic, neither of which shows the capital adequacy of less than 12.5%. Under
the baseline stress scenario would neither bank finished below the 8% capital
adequacy it in scenario "economic downturn" by just under 2 banks ended up with
a scenario "crisis states" 3 banks.
– The ability to generate net interest income is the second most important factor in
helping to cushion the negative impact of stress test scenarios.
– The most important risk is credit risk. The biggest impact it have losses on
corporate lending, further losses on loans to households.
– An important risk is becoming a market risk and systemic risk.
– A potential risk is the outflow of profits.
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4 Conclusions
The global financial and economic crisis has caused mistrust to credit rating agencies, as
well as to the entire financial sector. To restore confidence in banks, EU leaders decided
to seek the possibility of a comprehensive assessment of risks in the banking sector,
which led to the subjugation of bank stress test by the European Union as well as
national levels. The methodology of the stress tests is evaluated by the scientific
community is very positive, but the results of stress tests are the most heavily influenced
by just setting appropriate testing parameters such as economic development but also
other variables such as unemployment, interest rates etc..
Stress testing was also under a wave of criticism when, after testing in 2010 were in
trouble Irish banks and none of them has been evaluated in the test as risky. The answer
to the question of how the banks could pass the test and then get into trouble we can
find the the setting of stress scenarios.
Regarding testing at the national level, it can be concluded that the stress test
parameters are set tighter as in testing conducted by EBA. It should be emphasized that
the setting of stress testing parameters at the transnational level is more difficult, since
the banks in different economies also influenced by various factors
Acknowledgments
This   contribution   is   the   result   of   the   project   implementation   “Creating   excellence   in  
economic research department for addressing the challenges of civilization in the 21th
century”   (ITMS   26240120032)   supported   by   the   Research   &   Development   Operational  
Program   funded   by   the   ERDF,   VEGA   (1/0329/11)   ”Capital   market   and   its   role   in  
supporting  the  real  economy  of  eurozone”  and  grant  project No. I-13-102-00 (project of
young researchers  EU)  „The  financial  and  debt  crisis  and  its  impact  on  financial  markets"
References
Sorge, M. (2004). Stress-testing Financial Systems: An Overview of Current
Methodologies. BIS Working Papers, No 165.
CEBS’s  press  release  on  the  results  of  the  EU-wide stress testing exercise (2009, 2010)
Jones, M. T., Hilbers, P., Slack, G. (2004). Stress Testing Financial Systems: What to Do
When the Governor Calls, IMF Working Paper, WP/04/127.
Klacso,  J.  (2011).  Výsledky  makrostresového  testovania  slovenského  finančného  sektora,  
Biatec, 19, 4, pp. 7–12.
Lintner, V., Rychtárik,   Š.   (2007).   Stress   testing   of   real   estate   price   decline   risk   in the
Slovak banking sector, Biatec, 15, 5.
Quagliariello, M. (2009). Stress-testing the Banking System: Methodologies and
Applications, Cambridge: Cambridge University Press, p. 354.
336
Influence of a merger on the EBIT of merged companies
in the Czech Republic
Petr Valouch1
,  Alois  Konečný1,
Maria  Králová2
1
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová  41a,  603 00 Brno, Czech Republic
E-mail: valouch@econ.muni.cz
alois@econ.muni.cz
2
Masaryk University
Faculty of Economics and Administration, Department of Applied Mathematics and Statistics
Lipová  41a,  603 00 Brno, Czech Republic
E-mail: kralova@econ.muni.cz
Abstract: The aim of this paper is to assess whether mergers of companies in the Czech
Republic influence the value of the EBIT of merged companies in the period of three
years after the merger. The effect of the merger on the value of EBIT of merged
company within 3 years after the merger compared with the sum of EBIT of merging
companies at the date of the merger is analyzed on the sample of 309 merged
companies in the Czech Republic in the years 2001–2010.
Keywords: Mergers, EBIT, sample, companies
JEL codes: G34, K22, M41
1 Introduction
Investigations of merger efficiency at a global scale is not new: a number of researchers
have explored this (Lang, 2003; Cassiman and Kolombo, 2006; Werner and Thompson,
1992). However, this is not the case of the Czech Republic. The development of the
number  of  mergers  in  the  last  years  and  their  structure  have  been  explored  (Hýblová  and  
Sedláček   and   Křížová,   2012;;   Sedláček   and   Valouch   and   Hýblová,   2012;;   Sedláček   and  
Valouch and Konečný,   2011;;   Skálová   and   Tumpach,   2010;;   Špatná,   2010)   but   the  
economic impacts of mergers have not been given sufficient attention yet. Therefore, the
project of the Czech Science Foundation no. P403/11/0447 - The Analysis of Taxation
and Accounting Practices during Mergers implemented at the Department of Finance,
Faculty of Economics and Administration, Masaryk University concentrates on merger
efficiency in the Czech Republic. In the past years, several parameters of economic
efficiency of mergers were evaluated as well as the possible reasons for merger
implementations in the Czech Republic in 2001–2010   (Sedláček   and   Valouch   and  
Konečný,   2013;;   Sedláček   and   Kuhrová,   2012;;   Valouch   and   Králová,   2012,   Konečný  
2009); now this part of the project is further developed as the impact of mergers on the
value of EBIT of merged Czech companies in 2001–2010 is assessed.
2 Methodology and Data
The aim of this paper is to assess whether mergers implemented among businesses
headquartered in the territory of the Czech Republic in 2001–2010 had an influence on
the value of EBIT of the merged company, three years after the merger (or after the
decisive day), in relation to the sum of EBIT values of the merging companies as of the
decisive day. The period of the three years after the merger was chosen for the reason
that the effect of some accounting methods is diminished within the three years;
moreover, it is expected that the three-year period is sufficient to exclude short-term
effects of mergers caused by the necessary new settings of business processes in the
merged company that occur immediately after the merger and can thus have an essential
impact on the assessment of economic parameters of the merger during its preparation
and immediately after its implementation.
337
The data from the Trade Bulletin and the Trade Register of the Czech Republic showed
that 2,396 mergers in total were implemented in the territory of the Czech Republic in
2001–2010. Out of these, 2,255 mergers are of the type in which no new company is
formed and only 141 mergers of the type in which a new company is formed. The
analysis demanded us to gain the financial statements of the merging companies as of
the decisive day (or the day preceding) and the financial statements of the merged
company in the following 3 years after the decisive day. Although these accounting units
are obligated to publish their financial statements in compliance with Art. 21a of Act no.
563/1991 Coll., on accounting, by inserting them in the collection of documents of the
Trade Register, only 314 companies, i.e. only about 14% of the merged companies, met
this condition in the demanded years. Five more companies were excluded for the reason
that some data in their financial statements were missing, which made their complex
analysis of EBIT impossible. As a results, the statistical evaluation of the influence of
mergers on the value of total assets used data of 309 companies that met their legal
obligation and published all necessary data in the financial statements in all the
demanded periods necessary for the EBIT indicator calculation. This fact needs to be
taken into consideration when interpreting the conclusions. When interpreting the pvalues
of undermentioned tests it is essential to bear in mind the fact that the sample of
309 companies cannot be considered completely random as it purely consists of merging
companies that published their financial statements in all 4 demanded periods in
compliance with the Act on accounting.
The actual statistical evaluation of the influence of mergers on EBIT of merged
companies 3 years after the merger proceeded in three stages. At the first stage, we
inspected whether a merger influences the EBIT of a merged company 3 years after the
merger regardless of its size. At the second stage, we explored the question whether the
merger influence on the EBIT of a merged company 3 years after a merger is affected by
the size of the merged company. Last but not least, we examined whether a merger
influenced the EBIT of a merged company 3 years after the merger based on its size. For
these purposes, we divided the companies into three categories: small companies – with
the value of total assets up to 100 mil. Czk, medium companies – with the value of total
assets over 100 million Czk but up to 500 million Czk, and large companies – with the
value of total assets over 500 million Czk. All statistical tests were conducted at the
statistical  significance  level  α  =  5%.
3 Results
For the analysis of the influence of a merger on the EBIT of a merged company three
years after the merger, the following pair of hypotheses were formulated:
H0: The merger has no influence on the value of EBIT of the merged company three
years after the merger.
H1: The value of the EBIT of a merged company three years after the merger is
higher than the sum of EBIT of merging companies at the moment of the merger.
The following table shows the results of descriptive statistics for the entire sample of
companies regardless of their size:
Table 1 Descriptive statistics for variable EBIT
Descriptive Statistics (Merge.sta)
Exclude cases: 43;888
Variable Valid N Mean Median Minimum Maximum Variance Std.Dev.
EBIT
EBIT (3 yrs after merger)
EBIT difference
311 85343,07 9344,00 -537939 34517021,265486E+11 355736,8
309 80563,58 12165,00 -1490398 30133261,172979E+11 342487,8
309 -5613,77 478,00 -4762302 34389312,218454E+11 471004,7
338
Table 1 shows that the mean value of EBIT of a merged company three years after the
merger dropped while the median went up. This is caused by some outlying values which
are however still worth retaining in the analysis. These outliers does not affect results of
further performed test which is based on median rather than on mean. The aim of the
test is to find out whether the merger leads to the increase of the EBIT or its effect on
the EBIT is insignificant.
The EBIT difference variable (the difference between the EBIT of the merged company
three years after the merger and the sum of values of EBIT of merging companies as of
the merger date) does not have normal distribution; therefore, it was necessary to use
non-parametric tests to test the hypotheses. The histogram for this variable (see Figure
1) can be considered symmetric so the Wilcoxon test was used as also other assumptions
for its application were met - continuous distribution in all size categories and an
acceptable assumption of symmetry around the median.
Figure 1 Histogram of the EBIT difference variable
Histogram of EBIT difference
Merge.sta 59v*2922c
Exclude cases: 43;888
-4,7623E6
-4,2497E6
-3,7371E6
-3,2246E6
-2,712E6
-2,1994E6
-1,6868E6
-1,1743E6
-6,6169E5
-1,4911E5
3,6347E5
8,7605E5
1,3886E6
1,9012E6
2,4138E6
2,9264E6
3,4389E6
EBIT difference
0
50
100
150
200
250
300
Noofobs
The results of the Wilcoxon matched pairs test are presented in the following table:
Table 2 Results of the Wilcoxon matched pairs test
All Groups
Wilcoxon Matched Pairs Test (Merge.sta)
Marked tests are significant at p <,05000
Exclude cases: 43;888
Pair of Variables
Valid
N
T Z p-value
EBIT & EBIT (3 years after the decisive day) 309 22698,500,7946260,426832
The p-value of the two-tailed alternative of the Wilcoxon test is about 0.4268. In the
one-tailed alternative, testing whether the value of EBIT of a merged company three
years after the merger increased, is 0.4268/2 = 0.2134. These results show that the
Wilcoxon  matched  pairs  test  at  a  significance  level  α  =  5  %  did  not  prove  that  a  merger  
leads to a statistically significant increase in the EBIT of a merged company three years
after the merger.
At the second stage, we explored the question whether the influence of a merger on the
value of EBIT of a merged company 3 years after the merger is affected by the size of
the company. Also in this case, the assumption of normal distribution of data was
rejected for each of the three groups based on size and non-parametric tests had to be
used. Moreover, the sloping of the histograms differs so the median test was used. The
following pair of tested hypotheses were formulated:
339
H0: The company size expressed by the amount of total assets does not affect the
influence of a merger on the EBIT of a merged company.
H1: The company size expressed by the amount of total assets affects the influence
of a merger on the EBIT of a merged company.
The following table shows the results of descriptive statistics for particular size categories
of the companies:
Table 3 Descriptive statistics by size categories of companies
size=large
Descriptive Statistics (Merge.sta)
Exclude cases: 43;888
Variable Valid N Mean Median Minimum Maximum Variance Std.Dev.
EBIT difference 116 -19270,6 9658,500 -4762302 34389315,922185E+11769557,3
size=medium
Descriptive Statistics (Merge.sta)
Exclude cases: 43;888
Variable Valid N Mean Median Minimum Maximum Variance Std.Dev.
EBIT difference 119 882,285717,00000 -72493,0 189822,01,002841E+0931667,67
size=small
Descriptive Statistics (Merge.sta)
Exclude cases: 43;888
Variable Valid N Mean Median Minimum Maximum Variance Std.Dev.
EBIT difference 74 5347,824248,5000 -50399,0 175906,0 950493265 30830,07
The above mentioned descriptive statistics show that the medians of the difference
between the EBIT of a merged company three years after the merger and the EBIT of a
merging company at the moment of a merger are positive for all size categories,
indicating that the EBIT of a merged company three years after the merger increased. In
the case of medium companies, the EBIT difference median is positive, but only slightly,
and in the case of large companies, there is a positive EBIT difference median but the
mean value of the difference dropped. The reason is some outliers in the sample;
therefore, we can say that the results of the median are more relevant but the increase
is not very considerable even in this case. The median test, whose results are presented
in the following table, tested  whether  the  size  of  company  does  influence  the  merger’s  
effect on the value of the EBIT.
Table 4 Results of the median test
Median Test, Overall Median = 478,000; EBIT difference (Merge.sta)
Independent (grouping) variable: size
Chi-Square = 2,665153 df = 2 p = ,2638
Exclude cases: 43;888Dependent:
EBIT difference small medium large Total
<= Median: observed
expected
obs.-exp.
> Median: observed
expected
obs.-exp.
Total: observed
42,00000 61,0000 52,0000155,0000
37,11974 59,6926 58,1877
4,88026 1,3074 -6,1877
32,00000 58,0000 64,0000154,0000
36,88026 59,3074 57,8123
-4,88026 -1,3074 6,1877
74,00000119,0000116,0000309,0000
The  median  test  conducted  at  the  significance  level  α  =  5  %  shows  that  it  was  not  proved  
that an influence of a merger on the EBIT of a merged company is affected by its size,
which is confirmed by the resulting p-value of the median test of 0.2638.
340
Due to the resulting p-value of the test (which is not less than 0.05 but still small), we
further analysed the influence of a merger on the EBIT separately for small, medium and
large companies. For this part of analysis, the following pairs of hypotheses were tested,
separately for small, medium and large companies:
H0: The merger does not influence the EBIT of a merged company in the particular
size category three years after the merger.
H1: The value of EBIT of a merged company in the particular size category three
years after the merger is higher than the sum of EBIT of merging companies at the
moment of merger.
The analysis itself was again performed using the Wilcoxon matched pairs test and its
results are presented in the following tables.
Table 5 Results of the Wilcoxon matched pairs test for particular size categories of
companies
large
Wilcoxon Matched Pairs Test (Merge.sta)
Marked tests are significant at p <,05000
Exclude cases: 43;888
Pair of Variables
Valid
N
T Z p-value
EBIT & EBIT (3 years after the decisive day) 116 3109,0000,7823910,433985
medium
Wilcoxon Matched Pairs Test (Merge.sta)
Marked tests are significant at p <,05000
Exclude cases: 43;888
Pair of Variables
Valid
N
T Z p-value
EBIT & EBIT (3 years after the decisive day) 119 3341,0000,6072650,543676
small
Wilcoxon Matched Pairs Test (Merge.sta)
Marked tests are significant at p <,05000
Exclude cases: 43;888
Pair of Variables
Valid
N
T Z p-value
EBIT & EBIT (3 years after the decisive day) 74 1330,0000,3097660,756739
The null hypothesis that a merger has no influence on the EBIT of a merged company
could  not  be  rejected  in  any  of  the  size  categories  of  companies  at  the  significance  level  α  
= 5 %.
Should we want to prove that the EBIT of large companies grows after a merger, the pvalue
of the Wilcoxon test is 0.434/2 = 0.217. It means we could not prove a statistically
significant increase in the EBIT of a merged company three years after the merger.
The same analysis conducted for medium companies yields similar results because the pvalue
of the one-tailed alternative of the Wilcoxon test equals 1 – 0.5437/2 = 0.7282.
Neither in this case could a statistically significant increase in the EBIT of a merged
medium company three years after the merger be proved.
And finally, as regards small companies: the p-value of the one-tailed alternative of the
Wilcoxon test is 0.7567/2 = 0.3784, meaning that even in the case of small companies a
statistically significant increase in the EBIT three years after the merger could not be
proved.
341
4 Conclusions
When planning and implementing a merger, one of the main motives is the effort to
increase the efficiency of company processes and the overall profitability of the company.
The question that has remained unanswered though is whether the merged companies in
the Czech Republic are able to achieve this expected aim. The analysis performed using a
sample of 309 Czech companies merging in 2001–2010 shows that if we take the EBIT of
the companies as the indicator of their profitability, this aim is statistically not achieved.
Based on the conducted statistical evaluation of the influence of mergers on the EBIT of
merged companies three years after the merger (or after the decisive day of the
merger), we can conclude that there is no statistically significant increase in the EBIT of
the merged companies when compared with the sum of EBIT of merging companies as of
the date of merger. This conclusion is valid both for the entire sample of analysed
companies and when we divide the sample based on size into small, medium and large
companies. No statistically significant increase in the EBIT of a merged company three
years after the merger was found in any of the cases. Even when considering the above
mentioned limitation of the information capacity of the analysis, it seems that mergers of
Czech companies in the past decade did not lead to one of the essential aims of mergers,
i.e. increase in profitability (naturally, this aim was achieved in individual cases). This
leads to the conclusion that if the increase in profitability is the only motive for a merger
implementation, we cannot recommend companies to merge as we can see that this aim
is usually not met. However, this may not be the main and certainly not the only motive
for mergers so we cannot deny that mergers find their use for other motives. Our
conclusion is a partial result reached through an analysis of a separate economic
indicator of profitability, even though this one is considered one of the most important in
the practice. That is why it is devoted great attention in financial analyses of companies.
Acknowledgments
This article is one of research outputs of the project of the Czech Science Foundation no.
P403/11/0447 - The Analysis of Taxation and Accounting Practices during Mergers.
References
Hýblová,   E.,   Sedláček,   J.,   Křížová,   Z.   (2012). Development of Mergers in the Czech
Republic in 2001-2010. Acta universitatis agriculturae et silviculturae Mendelianae
Brunensis, no. 4, pp. 133-139.
Konečný,   A.   (2009). Daňový   motiv   jako   možný   důvod   integrace   podnikatelů   v   ČR. In:
NOVÉ   TRENDY   – NOVÉ   NÁPADY   2009.   Znojmo:   SOUKROMÁ   VYSOKÁ   ŠKOLA  
EKONOMICKÁ  ZNOJMO s.r.o., pp. 459-464
Lang, D. W. (2003). The future of merger: What do we want mergers to do: Efficiency or
diversity? The Canadian Journal of Higher Education, 33(3), 19-46.
Cassiman, B., Colombo, M.G. (ed.) (2006). Mergers and Acquisitions, Edward Elgar
Publishing Limited.
Sedláček,   J.,   Kuhrová,   K.   (2012). An Analysis of Unsuccessful Mergers in the Czech
Environment. In: Proceedings of the International Conference European Financial
Systems 2012. pp. 194-200.
Sedláček,  J.,  Valouch,  P.,  Hýblová,  E.  (2012). Analysis of the Development of Mergers in
the Czech Republic in the Period 2001–2009 from the Perspective of Ownership
Structure, International Journal of Applied Mathematics and Informatics, vol. 6, no. 3,
pp. 109-116.
Sedláček, J., Valouch, P. Konečný,   A. (2011). Development Trends at Markets with
Mergers and Acquisitions in 2001–2010. In: Recent Researches in Economics Proceedings
of the 2nd International   Conference   on   Finance   and   Accounting   (ICFA’11),
pp. 105-110.
342
Sedláček,  J.,  Valouch,  P.,  Konečný,  A. (2013). Economic Motives and Success of Mergers
in Czech Republic. In Daniela C. Monete and Alexander N. Pisarchik. In: Mathematics and
Computers in Contemporary Science - Proceedings of the 7th WSEAS International
Conference  on  Business  and  Administration  (ICBA’13), pp. 78-83.
Skálová,  J.,  Tumpach  M.  (2010).  Cezhraničné  fúzie  medzi  kapitálovými  spoločnosťami  v  
Českej   republike a v Slovenskej republike, Účtovníctvo,   Audítorstvo,   Daňovníctvo,   vol.
XVIII, no. 9, p. 323-326.
Špatná,  B. (2010). Přeshraniční  a  nadnárodní  fúze  obchodních  společností  z  ČR  a  dalších  
členských  států  EU,  KEY Publishing.
Valouch,  P.,  Králová,  M. (2012). Influence of a merger on the value of assets of merged
companies in the Czech Republic. Proceedings of the International Conference European
Financial Systems 2012. pp. 226–231.
Werner FM De, B., Thompson, H. E. (1992). Is economic efficiency the driving force
behind mergers? Managerial and Decision Economics (1986-1998), 13(1), 31-31.
343
Assessing the progress of the implementation
of the Solvency II regulation
Eva  Vávrová
Mendel University in Brno
Faculty of Business and Economics, Department of Finance
Zemědělská  1,  613 00 Brno, Czech Republic
E-mail: vavrova@mendelu.cz
Abstract: The main aim of the paper is to present the findings of the analysis of some
aspects in relation with the implementation of the Solvency II, the coming regulatory
regime for insurance industry of the European Union within the commercial insurance
companies in the insurance market of the Czech Republic. In accordance with its design,
the presented scientific paper focuses on methodological aspects in relation to the
realization of the fifth quantitative impact study (QIS5) and its importance for the Czech
insurance market. Furthermore, the paper deals with the evaluation of the QIS5 and
possible impacts in the context of the implementation of Solvency II. The scientific paper
describes partial results reached within the research project of the Faculty of Business
and Economics of the Mendel University in Brno MSM 6215648904 / research 02
following the aims and methodology of the given research project.
Keywords: insurance industry, Solvency II, regulation, quantitative impact study, capital
requirement
JEL codes: F36, G22, L51
1 Introduction
In the year 2001, the European Commission started to work on a solvency project in
order to improve current solvency regime as well as to harmonize the insurance sector
within the EU Member States (Doff, 2008). The project called Solvency II is realized in
three phases. Different international organizations and associations, and national
supervisory authorities are involved in developing Solvency II regime in a professional
way.
The first phase included an analysis of the existing situation, discussion of the concepts
and construction of the general conditions (Doff, 2008). It started in May 2001 and was
completed with the document of European Commission on September 19, 2003.
In the second phase, which is a follow-up to the first one, technical details and
implementation rules are developed. European Commission involved four workgroups of
the CEIOPS1
for creation of the proposals. In July 2007 the Solvency II Framework
Directive was published already with respect to three quantitative impact studies that
had been made. Quantitative impact study QIS1 was carried out from July to December
2005, QIS2 from May to August 2006 and QIS3 from April to July 2007.
The third phase focuses mainly on the implementation of Solvency II to the national
legislations. Quantitative impact studies QIS4 (from April to June 2008) and QIS5 (from
August to November 2010) investigate the last discrepancies in modeling, standard
formula and calibration of models and parameters.
The former date when Solvency II was supposed to come into force was October 31,
2012. However in January 2011, the European Commission published a proposal for
Omnibus II Directive which postpones Solvency II implementation deadline until January
1, 2013. For the firms, the Solvency II requirements would be binding from January 1,
2014. Omnibus II amended the Solvency II Framework Directive, to bring it in line with
the   Lisbon   Treaty   (Herboczková,   Beránková,   2012).   Negotiations   on   the   Omnibus   II  
1
CEIOPS - Committee of European Insurance and Occupational Pensions Supervisors (until
December, 2010).
344
Directive  are  still  stretching.  Therefore,  in  September  2012  a  Directive  called  “Quick  Fix”  
was adopted, which moves the transposition of the Solvency II to June 30, 2013 and the
date of its effectiveness to January 1, 2014. In the meantime, negotiations on the
Omnibus II Directive have to be completed so that regulatory and supervisory framework
becomes effective from January 1, 2014
2 Aim of the Paper, Methodology and Data
The main aim of the paper is to present the findings of the analysis of implementation of
the Solvency II within commercial insurance companies in the insurance market of the
Czech Republic. The presented paper focuses on the realization of the fifth quantitative
impact study QIS5 and its importance for the Czech insurance market. Furthermore, the
paper deals with the evaluation of the QIS5 and possible impacts in the context of the
implementation of Solvency II.
In order to achieve the aim of the paper, methods of description, analysis and
comparison are used. Graphs and tables are elaborated utilizing relevant sources and
own calculations.
A quantitative impact study (QIS) is a field-testing exercise, run to assess the
practicability, implications and possible impact of specified approaches (Baranoff, 2012)
to   insurers’   capital   setting   under   Solvency   II.   Usually participating in a quantitative
impact study asks the national supervisors to invite different types and different sizes of
undertakings to participate in completion of a spreadsheet and a questionnaire based on
their financial position at the end of the previous year. They are intended to assist with
such requirement with which insurers must comply under Solvency II (Gatzert, Wesker,
2012).
There have been five quantitative impact studies so far. They are requested by the
European Commission and run by EIOPA1
which is responsible for a delivery of an advice
on the development of Solvency II. The summary reports as well as other specifications
can be found and downloaded from the EIOPA website. While searching for the
information  concerning  QIS’s  the  web-site of EIOPA and the Summary Reports of QIS1 –
QIS5 were very useful. On the other hand, the particular national reports for the Czech
Republic were difficult to find.
3 Results and Discussions
So far the last quantitative impact study QIS5, the fifth in a row, took place from August
to November 2010 after the European Commission had published a Call for Advice and
technical specifications on July 6, 2010. It was necessary to verify the proposed
calibration which had been often discussed due to the financial crisis. It provided an
excellent opportunity to the insurers to assess the likely financial impact of the new
regime and to assess their practical readiness, by checking systems, processes and its
senior management. The European Commission set out a target participation rate of 60%
for solo undertakings. This objective has been outperformed and only in the Czech
Republic the 23 participating companies represented 99.5% of life undertakings and
94.4% of non-life undertakings (CNB, 2011) as measured by premiums written. Numbers
of participating insurance companies in the Czech Republic are shown in Figure 1.
According to the EIOPA Report on the fifth Quantitative Impact Study (QIS5) for
Solvency II, which was published on March 14, 2011 the participation rate indicates that
Solvency II is a priority to all insurers, regardless of size and that insurance and
reinsurance undertakings and groups are striving to be ready for the implementation
date. According to EIOPA (2011), 2520 insurers of 30 countries participated in the QIS5,
out of them 1512 small commercial insurance companies, 791 medium insurance
companies and 217 large insurers. In the Czech Republic, no insurance company belongs
1
EIOPA – European Insurance and Occupational Pensions Authority (since January 1, 2011)
345
to the category of large insurance companies by EIOPA definition, i.e. Czech participants
were small and medium insurers only.
Figure 1 Participation of Czech insurance companies in QIS5
Source:  based  on  Justová  and Kotaška  (2011)
The results of QIS5 are driven by the difference of valuation of the assets and liabilities
(for more see Zweifel and Eisen, 2012) while there is a fundamental change in calculation
of technical provisions. According to the obtained data a decrease in technical provisions
can be expected, about 24% lower amount than the current prudent level of provisions in
the Czech insurance companies. The decrease will be reflected in a relative increase in
the amount of eligible own funds, up to 52% in the Czech Republic, and an increase in
capital requirements. The difference in technical provisions under current accounting and
Solvency II regime is represented in the graph in Fig. 2.
Figure 2 Technical provisions of Czech insurance companies (in billions CZK)
Source: based on CNB Report (2011)
5
17
13
4
7
12
0
2
4
6
8
10
12
14
16
18
life insurance companies non-life insurance companies companies providing both (life
and non-life insurance)
Companies at the Czech insurance market Companies participating in QIS5
0
20
40
60
80
100
120
140
160
180
200
Life undertakings Non-life undertakings
Current accounting Solvency II
346
Approximately 92% of available own funds has been classified as being the highest
quality (Fabozzi, Neave, Zhou, 2012), i.e. Tier 11
. This feature had been the same in the
previous quantitative impact studies. Even though the own funds saw an increase,
average solvency ratio decreased due to solvency capital requirement (SCR) increase.
For the Czech Republic, the SCR is about 2.5 times higher than the current solvency
requirement. The overall outcome of these changes leads to a mild increase of available
own funds for the Czech insurers (CNB, 2011) as seen on the chart in Fig. 3.
Figure 3 Capital requirement in QIS5 (in millions CZK)
Source: based on CNB Report (2011)
The following graph completed on Fig. 4 indicates the relative proportion of the
distribution of different risks in the calculation of SCR. In the Czech Republic, the highest
share is represented by non-life underwriting risk where the most significant segment
stands for the floods. However, this phenomenon was later commented in the qualitative
part of the quantitative impact study QIS5. QIS5 highlighted the need to adjust the
design and calibration of the catastrophe risk sub-module. Central European countries
expressed concerns about the complexity and limitations of the structure for catastrophe
risk which produces a capital level that does not appropriately reflect the underlying risk
of each type of man-made or natural catastrophe (Gatzert, Wesker, 2012). Market risk
and life underwriting risk are also significant (see Table 1).
Table 1 Calculation of SCR in QIS5 (in per cent)
Insurance companies providing life
and non-life insurance
Calculation of SCR
(in %)
Non-life underwriting risk 31,29
Life underwriting risk 25,43
Market risk 25,25
Counterparty default risk 16,55
Health underwriting risk 1,21
Intangible asset risk 0,28
Source:  Kvardová  (2011)
1
Tier 1 - The best and at the same time in Czech banks the most important part of regulatory
capital. The dominant components of Tier 1 are capital, retained earnings and statutory reserve
funds.
0
20
40
60
80
100
120
Available capital Capital requirement Capital surplus
Solvency I Solvency II
347
Figure 4 Relative proportion of risks in the calculation of SCR in QIS5 (in per cent)
Source: based on CNB Report (2011)
About 10% of all participating insurance companies submitted the internal model
calculations. Across solo respondents the SCR results were very close to those derived by
applying the standard formula. The most significant difference between standard and
internal model was observed among groups. There the internal model results showed a
capital requirement of about 0.8 times the size of the capital requirement based on the
standard formula calculation.
In this paper more emphasis is put on the findings in insurance companies in the Czech
Republic. However, these are not very different from the European average (EIOPA,
2011). Some difference may be noticed in the amount of guidance of quantitative impact
studies provided by the national supervisor. EIOPA explains this by the variation in ability
of undertakings in different countries to complete the spreadsheets. Some supervisors
helped the companies overcome problems by providing a pre-specified stress test, others
required the data and made the calculation themselves, or there was very little to no
guidance (Gatzert, Wesker, 2012).
4 Conclusions
One of the most discussed questions on the European insurance market is the date of
launching the new regulatory and supervisory regime set out in the Solvency II Directive.
The legislative process at the European level has not been finished yet. The pressure on
the implementation of Solvency II as of January 1, 2014 is very strong. However, the
negotiations have not been closed yet and the implementation date may change. The
circumstances of ongoing negotiations are changing rapidly, which causes a great
uncertainty for both legislators and especially the market.
There are still open issues with a potentially strong impact on the capital positions of
European insurers. According to Herboczková  and  Beránková  (2012),  the  most  relevant  
topics for the Czech insurance market are calibration of the catastrophe module within
standard model proposals, where calibration of some natural disasters such as flood was
considered an overestimate from the position of the Czech Insurance Association. In
addition, definition of man-made catastrophe where insurance companies do not have all
required data available. Another important issue under discussion, which can have a
major impact on available capital, are contract boundaries. It is the definition of
Operational risk
Market risk
Intangible asset
risk
Counterparty
default risk
Life underwriting
risk
Health
underwriting risk
Non-life
underwriting risk
348
cashflows from contract which can be taken into account in technical provisions
calculation and future profits can be part of available capital.
More authors evaluate regulatory measures adopted or drafted in response to the
financial and economic crises (Baranoff, 2012; Fabozzi, Neave, Zhou, 2012). According to
Zweifel  and  Eisen  (2012)  and  Daňhel  and  Ducháčková  (2012),  perhaps  the  fundamental  
idea is that the regulatory measures and consumer protection should have certain limits.
It is always necessary to take into account the impact of these measures on insurers and
on the efectiveness of their operations.
Acknowledgments
The scientific paper describes partial results reached within the research project of the
Faculty of Business and Economics of the Mendel University in Brno MSM 6215648904 /
research 02 following the aims and methodology of the given research project.
References
Baranoff, E. G. (2012). The Financial and Economic Attributes of Insurers. The Geneva
Papers, vol. 37, pp. 401-404.
CNB   (2011).   Zpráva   o   finanční   stabilitě   2010/2011.   [online]. 2011. URL:
http://www.cnb.cz/miranda2/export/sites/www.cnb.cz/cs/financni_stabilita/zpravy_fs/fs_
2010-2011/fs_2010-2011.pdf
Doff, R. (2008). A Critical Analysis of the Solvency II Proposals. The Geneva Papers, vol.
33, pp. 193-206.
Ducháčková,   E.,   Daňhel,   J.   (2012). Pojistné   trhy. Změny   v postavení   pojišťovnictví  
v globální  éře. Praha: Professional Publishing.
EIOPA (2011). Report on the fifth Quantitative Impact Study (QIS5) for Solvency II.
[online].2011.URL: http://eiopa.europa.eu/fileadmin/tx_dam/files/publications/reports/
QIS5_Report_Final.pdf
Fabozzi, F. J., Neave, E. H., Zhou, G. (2012). Financial Economics. Hoboken: John
Wiley&Sons.
Gatzert, N., Wesker, H. (2012). A Comparative Assessment of Basel II/III and Solvency
II. The Geneva Papers, vol. 37, pp. 539-570.
Herboczková,   J.,   Beránková,   H.   (2012).   Vývoj   Solvency   II   a   prováděcích   opatření  
v Evropské  unii.  Pojistné  rozpravy, vol. 29, pp. 5-15.
Justová,   I.,   Kotaška,   M.   (2011).   QIS5:   Výsledky   českých   pojišťoven   se   nevymykají  
z evropského  průměru.  Pojistný  obzor, vol. LXXXVIII, issue 1, pp. 16-18.
Kvardová,  L.  (2011).  QIS5:  Vyhodnocení  studie  podle  České  asociace  pojišťoven.  Pojistný  
obzor, vol. LXXXVIII, issue 1, pp. 18-20.
Zweifel, P., Eisen, R. (2012). Insurance Economics. Berlin: Springer-Verlag.
349
Some specifics of the valuation of bank stocks
Jitka  Veselá
1
University of Economics, Prague
Faculty of Finance and Insurance, Department of Banking and Insurance
Winston Churchill Sq. 4, 13067 Prague 3, Czech Republic
E-mail: veselaj@vse.cz
Abstract: There are three main reasons that cause difficulties in the valuation of bank
stocks. The first reason is the nature of the business activities of banks, in consequence
of which it is hard to define debt and reinvestment, and therefore it is difficult to make
an estimation of future cash flows and it is inappropriate to use valuation models with the
weighted average cost of capital. The second reason is the fact that banks are strictly
regulated and changes in the regulatory requirements and rules can significantly affect
the stock value. The stringent regulatory requirements restrict competition and
contribute to the formation of an oligopolistic industry structure. The third reason may be
the fact that banks cyclically respond to developments in the economy.
Keywords: stock, valuation, dividend discount model, excess return model, required rate
of return
JEL codes: G12, G32
1 Introduction
Fundamental   stock   analysis,   as   Musílek   states   (2011,   p   333),   focusses   on   finding  
overvalued and undervalued stocks. To identify these stocks, it is essential to calculate
their   intrinsic   value,   which   is   a   sort   of   “right   price”   at   which,   taking   into   account   the  
fundamental macroeconomic, sector and corporate factors, the stocks should be traded.
To establish which stocks are undervalued and which are overvalued, it is necessary to
compare the calculated intrinsic value with the current market price of the stocks. It is
clear that the calculation of intrinsic value plays a key role in fundamental analysis, as
Veselá  points  out  (2011,  p  335).
The standard valuation models and methods of fundamental stock analysis used to
calculate the intrinsic value of stocks are always based on certain assumptions, which are
generally correct when stable, mature or steadily growing or declining firms are being
valuated. If, however, analysts have to valuate a new, starting-up firm, a loss-making
firm or a firm with a specific activity, some of the assumptions of the valuation models
cease to apply. Some valuation models can then not be used to value stocks, or it may
be necessary to amend fundamentally the input data for those models.
In the process of valuing stocks, the analyst may be faced with particular problems
caused by the specific character of the company being valuated. Most of these problems
can be solved. Nevertheless, in order to maintain the utility of intrinsic value, it is always
necessary, when calculating the intrinsic value of stocks, to take those problems into
account. Damodaran (2002, 2010), Tregler (2005) and Veselá (2011) deal with the
specific analytical problems of stock valuation in greater detail.
The aim of this contribution is to point out the specific features of banking institutions,
and to indicate how these features can be dealt with and considered in the process of
valuating banking stocks.
2 The specifics and problems of valuating banking stocks
As has already been indicated, banks, in comparison with ordinary industrial firms,
display distinct differences and typical features. The object of their business activity is
highly specific. Banks generally attract the interest of analysts and investors. Their stocks
are usually publically tradable on the capital markets.When analyzing and valuating bank
stocks, it is necessary to take into account the specific financial structure of banking
350
institutions, the high degree of regulation and supervision in the banking sector, the
market structure typical for the sector, the difficulties in determining reinvestments, and
the  banking  sector’s  sensitivity  to  the  economic  cycle.
The specific financial structure of banks
Banks work with a large amount of loan capital. The share of loan capital in banking
institutions exceeds 85%, and is therefore much greater than the share used by
industrial companies. This capital, as used by banks, should   be   regarded   as   a   “raw  
material”  or  “material”  which  is  an  indispensable  input  for  the  activities  of  banks,  as  it  
enables them to provide financial products and services and thus create profit. The basic
rules and principles for managing the financial structures of industrial firms cannot be
used for banking institutions. When calculating the intrinsic value of stocks from an
analytical point of view, one can regard only equity as capital whose use within in a
company is linked to certain risks and costs. When setting the value of a bank as a firm,
analysts  therefore  set  the  value  only  from  the  shareholder’s  point  of  view.  They  apply  the  
“value  of  equity”  approach.  On  the  other  hand,  when  valuating  a  bank,  analysts  do  not  
set the value of the bank as a firm for shareholders and creditors together, meaning that
they  do  not  apply  the  “value  of  firm”  approach.
Strict regulation and supervision in the banking sector
The banking sector is subject to very strict regulation and supervision. The reasons for
this and the mechanisms used to enforce it are mentioned by Musílek  (2002,  chapter 3).
The regulatory and supervisory bodies set relatively strict boundaries for banking
activity, thereby setting boundaries for the profitability and profit that banks can achieve.
On the other hand, the strict regulation of the entry of new subjects to the banking
sector allows banks to achieve stable and above average profits. The regulation of new
subjects in the sector can protect the existing banks from increased risk. However, any
changes to the regulatory rules and requirements lead to increased risk and uncertainty.
At the same time, it is very difficult to predict the impact of changes in regulation. The
stricter regulations and conditions which were adopted as a reaction to the crisis of 2008
mean restrictions and interventions into the commercial and investment activity of
banking institutions and increased costs arising from the implementation of the new
rules. These changes may have a negative impact on the economic results of banks and
even negatively affect their clients.
The oligopolistic structure of the banking sector
Subjects entering the banking sector must obtain permission – a licence to operate –
from the regulatory and supervisory body. This fact presents a certain barrier or obstacle
to entry into the sector, which, however, can be overcome. The existence of this barrier,
together with the large amount of capital required and the necessity for specialised
qualifications, is one of the factors that generally give the banking sector the character of
an oligopoly, as only a few large firms operate within the sector. The remaining subjects
in the sector merely play a supplementary or marginal role. In an oligopolistic sector, the
large, mature banks enjoy stable profits. That stability can, however, be disturbed by the
cyclical fluctuations of the economy. The considerable economic strength of the large
banking institutions often allows the banks to achieve above average profits, even when
they have large costs. The lower level of competition in the sector can then have a
negative impact on the clients, for example in the form of high or senseless charges.
Problems in determining reinvestments
If the cash flow model is used when valuating stocks, it is essential to determine the size
of reinvestments and the rate of growth of reinvestments in the company being valuated.
Both figures represent significant factors determining the value of a firm. When
determining the size of reinvestments and their rate of growth, it is essential to start
from the depreciation of fixed tangible assets, from investment expenditure on the
renovation and expansion of fixed tangible assets, changes in working capital and
operating earnings before tax and the payment of interest. A detailed calculation process
351
is presented by Damodaran (2002), Pinto et al. (2010) and Veselá (2011). In comparison
with industrial firms, banks have a smaller volume of fixed assets, and therefore also
have smaller depreciations and investment expenditure. As a result of this, the level of
reinvestments can be a negative figure. A typical feature of banks is a high level of
intangible assets and large investments in human capital. However, standard valuation
models do not directly take those items into account. Working capital, which contains
items of short-term assets and short-term liabilities, is very volatile in banking
institutions, which complicates or even makes impossible the prediction of the level of
reinvestments and the rate of cash flow growth. When valuating banks, it is possible,
with regard to the specific features mentioned above, to classify as reinvestments
investments in regulatory capital, which can replace investment expenditure and the
problematic change in working capital. At the same time, it is also possible to classify as
reinvestments expenditure on training and the education of human capital. Nevertheless,
it is difficult to ascertain this figure from accounting statements. These amendments to
the method are subjective and certainly represent a modification of the standard cash
flow valuation method. On the other hand, however, they are an attempt to eliminate the
negative impact of the specific features of banking institutions on the value of a firm.
The sensitivity of the banking sector to the economic cycle
The banking sector displays considerable sensitivity to the economic cycle. During a
boom, when the economy is growing, banks achieve their greatest profits, while in a
recession, bank profits fall, and banks may often even make a temporary loss. While
banks’  greatest  profits  are  made  during  a  boom,  this  is  only  true  for  the  middle  or  late  
phase of the boom. Banks react with a certain delay to developments in the economic
cycle. In that respect they differ from the construction sector or the automotive industry,
which is why it is very important for an analyst to be aware that although some of the
above mentioned factors stabilise bank profits, the cyclical nature of the banking sector
conversely destabilises the development of bank profits. When predicting the future
development of banking shares, it is therefore essential to take into account the outlook
for the economic cycle and to carry out the so-called adjustment of profits to the
expected course of the economic cycle. The alternative in this situation is to carry out
so-called profit normalisation, which means excluding extraordinary events which will not
reoccur in the new future.
3 Methods and models suitable for valuating bank stocks
From the large number of methods and models for valuating stocks, the only suitable
options for bank stocks are dividend discount models, earnings models, historical models,
some combined models, such as the excess return model, or, to a certain extent, the free
cash flow to equity (FCFE) model.
For the valuation of banks, it is totally unsuitable to use the FCFF model (free cash flow
to firm), the APV model (adjusted present value), or a model using the EVA (economic
value added) indicator. These models work with the figure for weighted average cost of
capital (WACC), which is problematic in the case of banks and difficult to determine,
because it considers the capital structure of a company and the price not only of the
company’s   equity,   but   also   of   loan   capital.   When   valuing   bank   stocks,   it   is   more
appropriate to use only those models which emphasise only the risks and return from
own capital, because banks have a significant share of other loan capital and it is viewed
in a different way from that of a standard industrial firm.
Banking institutions often have a high return potential, which is why the use of balance
sheet models to value bank stocks may not be completely appropriate or accurate.
Balance sheet models derive the value of a firm mainly from its assets, which may not
always take into account  the  full  extent  of  a  firm’s  return  potential.  Nevertheless,  the  use  
of balance sheet models to value bank stocks is possible under certain conditions.
The dividend discount model is often recommended for valuating bank stocks, because
banks generally pay sizeable dividends and display considerable stability in the
352
development of their dividend payout ratio. Out of the various existing versions of the
dividend discount model, which differ in the number of dividend growth rates they use
and the presumed model for their development, the most suitable for valuating bank
stocks  tends  to  be  Gordon’s  one-stage dividend discount model. This model assumes an
unlimited period for holding stocks, a single, stable rate of growth (or decline) of
dividends, and a stable required rate of return or cost of equity. These assumptions
correspond, to a large extent, to the specific features of banking institutions regarding
the size of an institution, the level of competition in the sector, the higher level of
regulation and the stability of dividend policy. Only if a bank is reacting to the
development of the economic cycle (the alternation of recession and boom), is it
necessary to use multi-stage models, of which the most practical has proved to be the H-
model.
Banks, especially in the model of the global banking sector, are big institutions, and it is
therefore not possible to envisage for them a long-term above average growth rate, but
rather an average or slightly below average growth rate, which is, however, stable. If
there are a few big players in the sector, which is typical for its oligopolistic structure, we
cannot at the same time expect an above average growth rate for dividends and
earnings, but rather stable, average development. Stable and unchanging regulation
therefore supports a stable growth rate for dividends and earnings. The existence and
tightening of any regulatory barriers increase the growth rate. However, changes in the
regulatory mechanisms and measures tend to destabilise the rate of growth.
The dividend policy followed by banks is usually characterised by its relatively high
dividend payout ratio. Banks generally pay out a significant part of their net earnings in
the form of dividends. As Damodaran (2012) states, the average dividend payout ratio of
banks ranges globally between 50 and 60%, while among industrial firms it averages at
around a mere 20%, although there are, of course, deviations from the norm. The
greater part of net earnings paid out by banks in the form of dividends is made possible
by their lower investment expenditure and, at the same time, the above average
earnings achieved by those institutions. Another reason for the high payout ratio of
banking institutions is the historical experience of investors and a certain tradition, which
lead to an expectation among investors of a higher dividend payout ratio in the future
too. An interesting feature and typical characteristic of bank stocks which has been
observed and presented by Damodaran (2012) is the fact that the historic sequence of
dividends is, in the case of banking institutions, more correlated with the future, which
enables a more accurate prediction of the rate of growth based on the use of historical
data. While the correlating coefficient for banks, which quantifies the mutual connection
in the historical sequence of dividends, has, according to Damodaran (2012), an average
value of +0.35, the same correlating coefficient for other firms has an average value of
only +0.17. It is apparent that the mutual connection between the development of
dividends is stronger among banking institutions than is the case among other firms. A
highly appropriate model for determining the growth rate of dividends for banking stocks
is the sustainable growth model, in which the growth rate of dividends is calculated as
the sum of the return on equity (ROE) and the rate of retained earnings at a corporate
level. ROE here expresses the yield value of capital deposited in the bank by
shareholders. The influence of the problematic item of loan capital is completely
eliminated in this model.
An appropriate valuation model for banking stocks is also provided by most earnings
models which work with the quantity of net earnings. Earnings models are based on
similar principles and assumptions to the dividend discount models as they are derived
from those models. Earnings models may be used directly to calculate the intrinsic value
of stocks, or they may be used to derive the favourite indicators of the capital market
which are known as the P/E ratio, the P/BV ratio or the P/S ratio. With regard to the fact
that banking institutions are generally traded publically on the capital market, the
indicators of P/E (price/earnings ratio), P/BV (price/book value ratio), P/S (price/sales
ratio) and, in some cases, D/Y (dividend/yield) are very popular and highly sought-after
by analysts and investors. When banking stocks are being valued, there is no problem in
353
using the indicators of P/E ratio, P/BV ratio or D/Y ratio. The P/S ratio, however, should
not be used, because banks do not have revenue from sales in the strict sense of the
word.
Damodaran (2012) recommends the excess return model as a very appropriate valuating
model for bank stocks. This model can be classified as a combined model. It is a model
which derives intrinsic value from the accounting value of stocks, that is, from the
balance sheet model in combination with the yield model based on the difference
between return on equity and the required rate of return. The great advantage of this
model is its relative simplicity as well as the fact that it is not negatively affected by the
specific characteristics of banking institutions.
It is also possible to use the FCFE model to valuate banking stock, following certain
necessary modifications. Nevertheless, in this case the analyst must solve the problems
connected with marked changes in working capital. Low investment expenditure and
depreciations and the problematic development of working capital can be replaced by
investments in regulatory capital, which, in the case of a global or commercial bank, are
set as the difference between the sum of capital adequacy and the volume of credit and
loans provided in the previous year. To those loans and credit could be added other
banking products provided by the bank. Expenditure on training and educating human
capital may be included as investment expenditure. Because of considerable fluctuations
in the quantity of working capital, it is not often possible to calculate the growth rate of
cash flow by the standard method using the quantity of the growth rate of
reinvestments, as it is not possible to capture statistically the tempo or character of the
development of working capital or its reaction to selected financial or economic
quantities. If analysts do not want to use investments in regulatory capital, they can
subjectively estimate the growth rate of cash flow on an ad hoc basis taking into account
the expected development of earnings. Alternatively, is it possible in some cases to use
the historical development of the value of cash flow or of return on capital to estimate
the growth rate of cash flow.
All the above recommended models and methods for valuating bank stocks have one
common input value, which is the required rate of return. As most banks are publically
traded, the CAPM model may be seen as an appropriate method for determining the
required rate of return. The beta factor, as the only independent variable in the CAPM
model, is, in this case, generally set as a regressive beta factor on the basis of historical
data on the return rates of given stock and the return rates of the share index. The
regressive beta factors of banking institutions tend to be much more stable than those in
other sectors, which is the result both of stricter regulation and of the greater
homogeneity of the capital structure of banking institutions and the similar characteristics
of the objects of their business activity. The function of the risk-free rate of return is
fulfilled by the return rate of state bonds, whether short-term or medium-term. The
market return rate can be derived from the development of the domestic share index,
which fulfils the function of a benchmark for the domestic market. Alternatively, the
extensive American databases can be used as a starting point and, using the rating of a
given  country  and  the  volatility  of  its  capital  market,  a  specific  premium  for  the  country’s  
domestic risk may be included in the calculation. This process is described in greater
detail,  for   example   by   Veselá   (2011   pp   430-433). According to Damodaran (2002), as
well as the CAPM model, processes for determining the required rate of return include the
APT model or the dividend discount model.
4 The valuation of Komerční banka stocks
In the previous chapter the following models were suggested as suitable for valuating
banking stocks: the dividend discount model, earnings models as P/E and P/BV ratio, the
excess return model and the FCFE model. The basis of these models is explained in
greater detail by Damodaran  (2002),  Veselá  (2011)  and  Musílek  (2002).  The models will
now be applied to Komerční  banka  stocks. The FCFE model will be modified, as the low
354
level of investment expenditure and depreciations will be replaced by investments in
regulatory capital.
The valuation of the stocks will be carried out according to the situation as of 30.04.
2013. On the basis of a comparison of the calculated intrinsic values and the current
rates as of 30.04.2013 it will be established whether the stocks in question are
undervalued or overvalued, and a recommendation of investments will be derived from
this. Publically available data from the websites of the company Patria  Direct,  ČNB  (the
Czech National Bank) and the Prague Stock Exchange (BCPP) will be used.
Table 1 Input  date  for  the  valuation  of  Komerční  banka  stock
Input data Numerical value of the data
Common dividend 230  Kč
Number of shares issued 38 009 852 ks
Current market price (30.04.2013) 3 744  Kč                    
Beta factor 1.02
Risk-free rate of return 0.5 %
Market rate of return (PX index) 10.53 %
Required rate of return (using CAPM model) 10.73 %
Return on equity (ROE) 14.27 %
Return on capital using gross profit (ROC) 2.55 %
Total net earnings 13 953  mil.  Kč
Net earnings per share 367.09  Kč
Dividend payout ratio (p) 62.65 %
Retention ratio (retained earnings per share- b) 37.35 %
Growth rate of dividend or earnings (ROE x b) 5.33 %
Total gross profit 20 076  mil.  Kč
Investments in regulatory capital - IRC 8 169.68  mil.  Kč
FCFE0 total KB 11 906.32  mil.  Kč
FCFE0 per share 313.24  Kč
Growth rate of cash flow derived from ROC 2.55 %
Book value per share 2 573.23  Kč
Current P/E ratio (P0/E0 ratio; 30.04.2013) 10.20
Current P/BV ratio (P0/BV0 ratio; 30.04.2013) 1.45
Source: website  Patria  Direct,  ČNB,  BCPP
Table 2 Valuations of Komerční  banka  stocks
Model used Intrinsic value of stocks
Dividend  discount  model  (Gordon’s  model) 4 486.28  Kč
P/E ratio model (V0/E0 ratio) 12.22
P/BV ratio model (V0/BV0 ratio) 1.74
Excess return model with growth 4 350.04  Kč
FCFE model 3 927.0  Kč
The   comparison   of   the   calculated   intrinsic   values   of   Komerční   banka   stocks   and   the  
current market rate clearly shows  that  the  bank’s  stocks  are  undervalued.  We  can  expect  
their value to rise, and, on the basis of that fundamental analysis we can recommend
their purchase.
5 Conclusions
Banking institutions are typified by their cyclical nature, their large share of loan capital,
their particular concept and view of the use of loan capital within the firm, their specific
structure and their development of depreciation, investment expenditure and working
capital, as well as strict regulation and the oligopolistic structure of their sector. These
355
specific characteristics determine, to a large extent, the choice of a valuation model for
bank stocks and may also require the adjustment of input data for this model. When
valuating bank stocks it is necessary to use dividend discount models, earnings models,
the excess return model or the FCFE model. Use of the FCFE model, however, requires
the subjective adjustment of input data to determine the level of reinvestments, as a
bank’s  depreciations  and  investment  expenditure  are  too low and the quantity of working
capital fluctuates considerably. The FCFF model, the APV model and models using the
EVA indicator or the P/S ratio indicator are completely unsuitable for valuating bank
stocks.
Acknowledgments
This article was written as one of the presentations for the IP 100040 project.
References
Damodaran, A. (2002). Investment Valuation: Tools and Techniques for Determining the
Value of Any Asset. New York: John Wiley.
Damodaran, A. (2010). The Dark Side of Valuation : Valuing Young, Distressed, and
Complex Businesses. Upper Saddle River: FT Press.
Damodaran, A. (2013). Valuing Financial Service Firms. In: Damodaran on-line. Research
and Papers. [online] Available at: <http://pages.stern.nyu.edu/~adamodar/>.
Musílek, P. (2002). Trhy cenných  papírů. Praha: Ekopress.
Musílek, P. (2011). Trhy  cenných  papírů. Praha: Ekopress.
Pinto, J. E., Henry, E., Robinson, T. R., Stowe, J. D. (2010). Equity Asset Valuation. New
Jersey: John Wiley.
Tregler, K. (2005). Oceňování  akciových  trhů:  metody měření  správnosti  ocenění. Praha:
C.H. Beck.
Veselá, J. (2011). Investování   na   kapitálových   trzích. Praha:   Wolters   Kluwer   Česká  
republika.
ČNB (2013). Aukce   SD   a   jejich   výsledky   - historie. Available at: http://www.cnb.cz
/cs/financni_trhy/trh_statnich_dluhopisu/sd/
356
Stock selection based on financial ratios on the Warsaw Stock
Exchange. Analysis between 2001 and 2010
Tomasz  Węgrzyn
University of Economics in Katowice
Department of Applied Mathematics
Ul. 1 Maja 50, 40-287 Katowice, Poland
E-mail: tomasz.wegrzyn@ue.katowice.pl
Abstract: The process of a portfolio optimisation is preceded by a stock selection. The
article is concentrated on using the synthetic measure of development in the stock
selection. The synthetic measure of development proposed by Hellwig (Hellwig, 1968) is
one of the methods of linear arrangement. It enables the classification of companies in
relation to the set variables, that are financial ratios. The purpose of the article is to
study if one of five quantile portfolios is systematically giving higher rate of return than
the benchmark portfolio. Companies are chosen to quantile portfolios due to their
position in the ranking that is constructed on the base of the synthetic measure of
development. The synthetic measure of development is computed using the financial
ratios. The author uses data of companies listed on the Warsaw Stock Exchange between
2001 and 2010. The rankings and portfolios are built separately for each year. As a
result, it can be stated that the second portfolio of the ranking systematically gives
higher rate of return than a benchmark portfolio.
Keywords: financial ratios, stock selection, synthetic measure of development
JEL codes: C38, G11, G32
1 Introduction
A stock selection is an important part of a portfolio construction. There are many
methods that allow to choose stocks and one of them is the method based on the
synthetic measure of development. The synthetic measure of development was proposed
by  Hellwig  (Hellwig,  1968).  Tarczyński  (Tarczyński,  1994)  is  the  first  one  who  proposed  
using the synthetic measure of development in order to construct securities portfolio on
account of their financial strength. He introduced the notion of the TMAI that is
Taxonomic   Measure   Attractiveness   of   Investment   (Tarczyński,   2002).   The   possibility   of  
using  the  TMAI  was  examined  in  details  by  Tarczyński  (Tarczyński,  2002)  and  Łuniewska  
(Łuniewska,  2003). They use the chosen financial ratios as the set variables. Their results
are in accordance with other researches that pointed out the accounting-based market
anomalies (Subramanyam & Wild, 2009, p. 45).
Węgrzyn  proposed  to  use  financial  ratios  that  describe each of four areas of a company
activity   as   a   set   variables   for   the   synthetic   measure   of   development   (Węgrzyn   2007a;;  
Węgrzyn,  2007b).  These  four  areas  of  companies  activity  are  measured  by  profitability  
ratios, turnover ratios (asset utilisation and efficiency ratios), liquidity ratios, leverage
ratios (capital structure and solvency ratios) (see Subramanyam & Wild, 2009; Bodie,
Kane & Marcus, 2008).
The purpose of the article is to point out the best quantile portfolio constructed on the
base of the synthetic measure of development. The synthetic measure of development is
computed on the base of chosen financial ratios. Companies are linear arranged by the
value of the synthetic measure of development. Then, companies are selected to one of
five quantile portfolios by the position in the ranking. The quantile portfolios are not
optimised. Their performances are compared with the performance of well-diversified
proportional portfolio that contain all analysed companies. The study is performed in the
period between 2001 and 2010.
The purpose of the article is not to construct optimal portfolios that lie on the efficient
frontier. Such portfolios can be constructed on the base of Markowitz portfolio theory
357
(Markowitz,   1952).   Moreover,   Sroczyńska-Baron point out the possibility of using the
game  theory  in  order  to  construct  the   optimal  portfolio  (Sroczyńska-Baron, 2009). The
game theory can be used in case to optimise the process of purchasing and selling stocks
(Sroczyńska-Baron, 2008). The optimised process of purchasing and selling stocks can
improve the profit produced by the portfolio.
2 Synthetic measure of development
The synthetic measure of development is one of the methods of linear arrangement. It
enables the classification of companies in relation to the set variables (Hellwig, 1968). In
case  of  companies’  analysing,  the  value  of  the  synthetic  measure  of  development  points  
out  the  financial  strength  of  companies  (Tarczyński,  2002).
The synthetic measure of development for a given company is computed using the
procedure described  in  the  monograph  (Tarczyński,  2002):
0
1
d
d
TMAI i
i  (1)
where: di – Euclidean distance between the company and the model object given by the
formula:
 

m
j
jiji zz
m
d
1
2
0
1
(2)
zij – value of the jth
variable for ith
company (after standardisation)
z0j – value of the jth
variable for the model object:
 ij
m
j
j zz
1
0 max

 (3)
d0 – the norm that guarantees the value of TMAI between 0 and 1:
 jdd 00 max (4)
As it can be noticed from the equation (2), it is assumed that each financial ratio has the
same weight in the synthetic measure of development.
Variables (financial ratios) are divided into stimulants and destimulants. Stimulants are
those financial ratios for which an increase is assessed positively. Whereas destimulants
are those financial ratios for which an increase is assessed negatively. Then variables are
standardised.
3 Financial ratios
Into the construction of the synthetic measure of development, the following financial
ratios are used:
A. Profitability ratios:
– ROE – return on equity (Jerzemowska, 2006),
– ROA – return on assets (Jerzemowska, 2006),
– ROS – return on sales (Jerzemowska, 2006),
– Mzbs – gross profit on sales (Jerzemowska, 2006),
– Mzop – operating profit on sales,
– Mzb – gross profit margin (Helfert, 2003),
B. Liquidity ratios:
– Wpb – current ratio (Jerzemowska, 2006 and Helfert, 2003),
– Wps – quick ratio (Jerzemowska, 2006 and Helfert, 2003),
– Wpp – acid test (Jerzemowska, 2006),
358
– RGS – operating  cash  flows  on  sales  (Waśniewski  &  Skoczylas,  1996),
– RGZ – net profit on  operating  cash  flows  (Waśniewski  &  Skoczylas,  1996)
C. Asset turnover ratios (activity ratios or efficiency ratios):
– RA – asset turnover in days (Helfert, 2003),
– RNal – receivables conversion period (in days) (Jerzemowska, 2006),
– RZap – inventory conversion period (in days) (Jerzemowska, 2006),
– Cop – operating cycle (in days) ,
– RZob – payables conversion period (in days) (Jerzemowska, 2006),
– CKG – Cash Conversion Cycle (Jerzemowska, 2006),
– RMO – current assets turnover in days (Jerzemowska, 2006),
D. Financial leverage ratios (debt ratios):
– Szo – debt ratio (Waśniewski  &  Skoczylas,  2002),
– WPM – equity to fixed assets,
– WOZ – sum of depreciation and financial costs to net profit (Jerzemowska, 2006),
– WPZ – current liabilities to sum of annual interest expense and depreciation.
4 Data and Assumptions
In the study the nonfinancial companies are included that are listed on the WSE between
04.2001 and 04.2011. They are included, in the end of March for a given year, in one of
the following indexes: WIG20, mWIG401
or sWIG802
. From among such companies the
following are excluded:
– banks, insurances companies and lease companies,
– companies included in the following sectors: finance or finance–other,
– companies for which there is no full financial statements for a previous year3
,
– companies that in balance (that is used in order to compute financial ratios) have
negative value of the shareholders equity,
– companies that in the income statement (that is used in order to compute
financial ratios) have value of revenues from sales equal to zero.
As the result in the consecutive years there are between 95 and 118 companies qualified
for the study. The number of companies qualified for the study for a given year is shown
in the Table 1.
For each company qualified for the study in a given year the ratios described in the
section 3 are computed. In order to compute the financial ratios the financial statement
for a previous year is used. The values of financial ratios are used to the construction of
the synthetic measure of development. The synthetic measure of development is used in
order to conduct a linear arrangement of the companies in analysed year. The position of
the company allows to assign it into one of five quantile portfolios in the following way:
– into the first portfolio 20% of companies with the highest position are put,
– into the second portfolio next 20% of companies with the highest position are put
that are not included in the first portfolio,
– into the third portfolio next 20% of companies with the highest position are put
that are not included in the first or second portfolio,
– into the fourth portfolio next 20% of companies with the highest position are put
that are not included in the first or second or third portfolio,
– into the fifth portfolio remaining companies are put.
As the result five equipotent portfolios are constructed. Each portfolio is bought during
the last session in the first week of April a given year and sold during the last session in
the first week of April a next year. In each company 10.000 PLN is invested, quantity of
1
If there was not an index mWIG40 then an index midWIG is used.
2
If there was not an index sWIG800 then an index WIRR is used.
3
In the study are used financial statements from the following data bases prepared by the Notoria Serwis:
1(39)/2003, 3(45)/2004, version 18.30 may 2010.
359
stocks is rounded down to integer. The quantity of stocks is constant during the
investment period.
Table 1 The number of companies qualified for the study in the consecutive years
Year
Number of
companies
2001 96
2002 95
2003 97
2004 102
2005 108
2006 105
2007 106
2008 113
2009 117
2010 118
In the study the financial companies like banks, insurance companies and lease
companies are not included. As the result, index WIG or any other index cannot be a
benchmark. The benchmark portfolio is constructed like other portfolios. In the
benchmark portfolio all companies qualified for a study in the given year are included. In
each company 10.000 PLN is invested, quantity of stocks is rounded down to integer. The
quantity of stocks is constant during the investment period.
Assessment of each portfolio is done by:
– average geometric rate of return for a ten years period of investment,
– standard deviation of logarithmic rate of return,
– investment rate of return,
– a Sharpe ratio (Sharpe, 1966).
5 Results
There are investment rates of return for each constructed portfolio between 2001 and
2010 in the Table 2. The comparison of the returns achieved by each portfolio with the
return for a benchmark portfolio points out that the Portfolio 2 the most frequently gives
the rate of return that is higher than the rate of return for the benchmark portfolio. That
situation is 7 times during 10 years. The opposite situation is in case of the Portfolio 5.
This Portfolio 3 times gives higher rate of return than the benchmark portfolio.
Table 2 Investment rate of return for constructed portfolios
Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Benchmark
portfolio
-12% -6% 187% 16% 143% 83% -28% -55% 61% 7%
Portfolio 1 -48% -37% 278% 25% 123% 106% -46% -57% 42% 28%
Portfolio 2 -26% -2% 193% 8% 343% 95% -11% -57% 83% 8%
Portfolio 3 -6% -7% 148% 26% 91% 65% -26% -54% 68% -7%
Portfolio 4 11% 7% 207% 8% 67% 81% -29% -54% 52% 3%
Portfolio 5 5% 11% 113% 12% 101% 28% -29% -51% 59% 4%
Note: Bold font is used for values that are higher than they are for the benchmark portfolio.
In the Table 3 there are cumulated rates of return and geometric average rates of return.
The analysis of data from the Table 3 points out that the Portfolio 3 gives the highest
geometric average rate of return, that is 10 percentage points higher than the geometric
average rate of return for the benchmark portfolio. As the result, the cumulated rate of
return for the Portfolio 3 is more than 2 times higher than it is for the benchmark
portfolio. The second portfolio in terms of the value of the geometric average rate of
return is the Portfolio 4. The geometric average rate of return for that portfolio is lower
that it is for the benchmark portfolio by 1,5 percentage points.
360
Table 3 Cumulated rate of return and geometric average rate of return
Cumulated
rate of return
Geometric average
rate of return
Benchmark portfolio 583,0% 21,2%
Portfolio 1 203,1% 11,7%
Portfolio 2 1 407,8% 31,2%
Portfolio 3 352,6% 16,3%
Portfolio 4 504,7% 19,7%
Portfolio 5 306,6% 15,1%
Note: Bold font is used for values that are higher than they are for the benchmark portfolio.
Table 4 The portfolios’  risk  and  the  average  risk
Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Average
risk
Benchmark
portfolio
14% 10% 22% 16% 23% 24% 27% 30% 17% 13% 19,6%
Portfolio 1 20% 19% 31% 25% 32% 33% 36% 34% 27% 19% 27,5%
Portfolio 2 17% 17% 28% 21% 54% 28% 28% 30% 18% 16% 25,8%
Portfolio 3 15% 12% 23% 18% 16% 22% 31% 31% 18% 15% 20,1%
Portfolio 4 15% 12% 26% 18% 17% 28% 23% 29% 16% 12% 19,6%
Portfolio 5 15% 15% 22% 14% 22% 47% 26% 33% 18% 11% 22,4%
Note: Bold font is used for values that are higher than they are for the benchmark portfolio.
There  are  portfolios’  risk  (measured  by  the  standard  deviation)  in  the  Table  4.  It  can  be  
noticed that the Portfolio 4 has the lowest risk. The risk of Portfolio 4 is equal to the risk
of the benchmark portfolio. This Portfolio has 5 out of 10 times lower risk than the
benchmark portfolio. The Portfolio 2, that has the highest geometric average of return,
has the second highest value of the risk. The portfolio 2 has no one time lower value of
risk than the benchmark portfolio.
Table 5 The Sharpe ratio for constructed portfolios
Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Benchmark
portfolio
-2,06 -1,49 4,54 0,55 3,53 2,33 -1,40 -2,85 2,48 0,17
Portfolio 1 -4,06 -3,01 4,06 0,66 2,28 2,08 -1,81 -2,69 1,14 1,07
Portfolio 2 -2,62 -0,67 3,58 0,09 2,59 2,20 -0,58 -3,07 3,10 0,21
Portfolio 3 -1,45 -1,41 3,69 0,96 3,69 2,08 -1,14 -2,68 2,61 -0,79
Portfolio 4 -0,34 -0,23 4,05 0,12 2,72 1,96 -1,68 -2,89 2,31 -0,14
Portfolio 5 -0,71 3,41 3,19 0,35 2,91 0,44 -1,48 -2,32 2,33 -0,06
Note: Bold font is used for values that are higher than they are for the benchmark portfolio.
There are the Sharpe ratios for constructed portfolios in the Table 5. It can be noticed
that:
– the benchmark portfolio 6 out of 10 times has the positive value of the Sharpe
ratio,
– the Portfolio 3 in case of 7 years has higher value of the Sharpe ratio than it is for
the benchmark portfolio. While the positive values of the Sharpe ratio are studied
then the Portfolio 3 in case of 3 years has higher positive value of the Sharpe ratio
than it is for the benchmark portfolio. The opposite situation is also in case of 3
years,
– the Portfolio 4 (with the lowest value of the average risk) in case of 2 years has
higher value of the Sharpe ratio, but each time it is in case of negative value of
the Sharpe ratio,
– the Portfolio 2 (with the highest average geometric rate of return) in case of 4
years has higher value of the Sharpe ratio than it is for the benchmark portfolio.
361
While the positive values of the Sharpe ratio are studied then the Portfolio 2 in
case of 2 years has higher positive value of the Sharpe ratio than it is for the
benchmark portfolio. The opposite situation is in case of 4 years.
6 Conclusions
The article is an attempt to assess the possibility of using the financial ratios to choose
stocks to the portfolio. Each company is described by financial ratios. In order to arrange
them the synthetic measure of development is used. The companies are selected to the
one of five quantile portfolios by their position in the ranking. As a result, it can be stated
that the Portfolio 2 is the best one in terms of the average geometric rate of return.
Moreover, the Portfolio 2 is better than the benchmark portfolio in terms of the average
geometric rate of return. However, no one of constructed portfolios is better of the
benchmark portfolio in terms of risk or the Sharpe ratio.
References
Bodie, Z., Kane, A., Marcus, A. J. (2009). Investments. 8th Ed. Boston: McGraw-Hill.
Helfert, E. A. (2003). Techniki analizy finansowej. Warszawa: PWE.
Hellwig,   Z.   (1968).   Zastosowanie   metody   taksonomicznej   do   typologicznego   podziału  
krajów  ze  względu  na  poziom  ich  rozwoju  i  strukturę  wykwalifikowanych  kadr.   Przegląd  
Statystyczny, iss. 4, pp. 307 – 324.
Jerzemowska, M., ed. (2006). Analiza ekonomiczna w przedsiębiorstwie. Warszawa:
PWE.
Markowitz, H. (1952). Portfolio Selection, The Journal of Finance, 7(1), pp. 77–91.
Łuniewska,  M.  (2003).  Wykorzystanie  metod  ilościowych  do  tworzenia  portfela  papierów  
wartościowych,  Szczecin:  Wyd.  Uniwersytetu  Szczecińskiego.
Sharpe, W. F. (1966). Mutual fund performance, Journal of Business, 39, 1, p. 119.
Sroczyńska-Baron,   A.   (2008).   Zastosowanie   teorii   gier   w   modelu   rozłożenia   w   czasie  
procesu   kupna   akcji   na   GPW,   in   Tarczyński,   W.,   ed.,   Studia   i   prace   Wydziału   Nauk  
Ekonomicznych  i  Zarządzania  Uniwersytetu  Szczecińskiego, nr 10, pp. 650 – 662.
Sroczyńska-Baron, A. (2009). The application of the theory of games for purpose of
making a choice of portfolio; FindEcon Monograph Series: Advances in financial market
analysis, nr 7, pp. 173–183.
Subramanyam, K. R., Wild, J. J. (2009). Financial Statement Analysis. Boston: McGraw-
Hill.
Tarczyński,  W.  (2002).  Fundamentalny  portfel  papierów  wartościowych. Warszawa: PWE.
Tarczyński,   W.   (1994).   Taksonomiczna   miara   atrakcyjności   inwestycji w papiery
wartościowe,  Przegląd  Statystyczny, iss. 3, pp. 275-300
Waśniewski,   T.,   Skoczylas,   W.   (1996).   Analiza   przepływów   środków   pieniężnych   –
pomocą  w  zarządzaniu  finansami  przedsiębiorstwa,  Rachunkowość iss. 6, p. 271
Waśniewski,   T.,   Skoczylas,   W.   (2002). Teoria i praktyka analizy finansowej w
przedsiębiorstwie. Warszawa: Wyd. FRR.
Węgrzyn,  T.  (2007a).  TMAI  - wpływ  grupowania  wskaźników  finansowych  na  uzyskiwane  
stopy   zwrotu   z   budowanego   portfela   papierów   wartościowych,   in   Adamkiewicz-Drwiłło,  
H., ed., Uwarunkowania   rozwoju   przedsiębiorczości.   Determinanty   i   narzędzia  
zdobywania przewagi konkurencyjnej, Warszawa: PWN. p. 394
Węgrzyn,  T.  (2007b).  TMAI  - porównanie  dwóch  metod  wyznaczania  tempa  przyrostu  dla  
ujemnych   wartości   wskaźników   finansowych,   in Tarczyński,   W.,   ed.,   Finanse, Rynki
Finansowe,   Ubezpieczenia,   Zeszyty   Naukowe   Uniwersytetu   Szczecińskiego, iss. 462,
Szczecin, p. 495
362
Decoupling hypothesis and the financial crisis
Joanna Wyrobek1
,  Zbigniew  Stańczyk2
1
Cracow University of Economics
Department of Corporate Finance
ul. Rakowicka 27, 31-510  Kraków, Poland
E-mail: wyrobekj@uek.krakow.pl
2
Cracow University of Economics
Department of Macroeconomics
ul. Rakowicka 27, 31-510  Kraków, Poland
E-mail: stanczyz@uek.krakow.pl
Abstract: The purpose of the paper was to present the decoupling hypothesis which says
that the performance of the emerging economies becomes relatively independent from
the changes in advanced economies, and to empirically verify this hypothesis. The
Christiano-Fitzgerald band-pass filter and spectral analyses have been applied to
examine the hypothesis. On the basis of obtained results, if one compares the deviations
of GDPs from their long-term trend, it seems that the synchronization of cycles between
emerging and advanced economies was already high before the crisis. The last global
crisis, especially if time shifts between the countries are taken into account, even
increased the synchronization of the economies. Therefore, this paper presents evidence
against the decoupling hypothesis, and at the same time it raises doubts whether the
high GDP growth rates in emerging economies can be sustainable in the presence of the
slow-down in the advanced economies.
Keywords: decoupling, business cycle synchronization, spectral analysis, emerging
economies
JEL codes: F44, C32, O1
1 Introduction
The decoupling hypothesis has its origins in the spectacular successes of the economies
of China and India, whose high growth rates do not seem to be influenced by the parlous
state or the shocks sustained by them. Some years ago it appeared as if the decoupling
hypothesis could be applied, not only to certain Asian countries but it can also used to
describe the performance of certain Latin American countries too, for example, Brazil.
Indeed, some Latin American countries started to grow faster than the U.S. economy and
their growth path now appear to have become independent of the economic situation in
the U.S. However, the decoupling can mean different things, and it can be measured in
different things. As it is stressed by Dervis (2012), firstly it can mean the divergence of
the GDP long-term path of emerging economies and advanced economies. However,
according to many macroeconomic models, for example the Solow model, catching-up
economies should have higher rates of growth. Hence, higher long-term growth rates in
emerging economies are not indicators of growing differences between advanced and
emerging economies. Secondly, the decoupling can mean growing differences between
business cycles and growing differences in reaction to global shocks. Dervis (2012) calls
it also a delinking of cyclical movements, and it the most common meaning of the
decoupling.
The decoupling hypothesis has become a very popular topic since the beginning of the
last global financial crisis, and many articles in business newspapers and magazines have
written about it (Decoupling 2.0, 2009, is a good example of such a publication).
However, very seldom can one find comprehensive statistical and econometric studies on
it. One can find some studies on business cycles synchronization between emerging
European countries and the EU (Adamowicz at al. (2009), Konopczak (2009), and
Skrzypczyński,   (2010)) but there are few publications on the decoupling hypothesis
which include wider selection of emerging markets and long time series. Kose et al.
363
(2008), Kose and Prasad (2010), World Economic Outlook (2007)  and  Wälti  (2009),  in  
the opinion of the authors of the paper, are the best examples of such publications.
Research conducted before the last global financial crisis did not provide an answer as to
whether the decoupling hypothesis was valid or not: in fact, research papers were almost
equally divided between confirming and rejecting this hypothesis. The most often quoted
paper supporting the hypothesis is that of Kose et al. (2008), who examined the degree
of synchronization in 106 economies during the years 1960-2005. In this study, a sample
of countries was divided into three groups: advanced economies, emerging market
economies and other developing economies, and three time series were taken into
account: GDP, investment, and consumption. The variances of the time series were
decomposed into variances of three factors and an idiosyncratic component. The
following factors were taken into account: the global factor, which was related to
fluctuations in all countries; the group factor which characterised the fluctuations of
every group of countries; and finally the country specific factor. Kose et al. (2008)
reported that their most important finding was that synchronization of cycles increased
independently for advanced and emerging economies in the years 1985-2005. On the
other hand, according to the authors, the impact of the global factor decreased when
periods 1960-1984 and 1985-2005 were compared, and this finding is supposed to show
that a decoupling of advanced and emerging economies had taken place. The theses of
Kose et al. (2008) were reiterated by Kose and Prasad (2010) for longer time series.
Their results were supported  by  the  IMF’s  World Economic Outlook (2007, p. 139-143),
but the authors of this report grouped countries, not according to level of development,
but according to certain regional criteria. Table 1 presents the results of variance
decomposition into global, regional, country-specific, and idiosyncratic factors. The report
then claims that in the years 1985-2005 regional, and not global, factors were more
important for GDP fluctuations (table 1).
Table 1 Contributions to output; unweighted averages for every region; percentages
Global factor Regional factor Country factor Idiosyncratic
1960–2005
North America 16.9 51.7 14.8 16.6
Western Europe 22.7 21.6 34.6 21.1
Emerging Asia and Japan 7.0 21.9 47.4 23.7
Latin America 9.1 16.6 48.6 25.7
1960–85
North America 31.4 36.4 15.7 16.5
Western Europe 26.6 20.5 31.6 21.3
Emerging Asia and Japan 10.6 9.5 50.5 29.4
Latin America 16.2 19.4 41.2 23.2
1986–2005
North America 5.0 62.8 8.2 24.0
Western Europe 5.6 38.3 27.6 28.5
Emerging Asia and Japan 6.5 34.7 31.1 27.7
Latin America 7.8 8.7 51.7 31.8
Source: World Economic Outlook (2007, p. 14)
Similar conclusions, yet concerning only the real part of the economy (not the financial
sector)   can   be   found   in   the   2012   World   Bank’s   working paper by Yeyati and Williams.
Independently on the conclusions stated in the paper the authors calculations confirmed
a positive, statistically significant and stable in time beta coefficients between real GDP
changes (cyclical parts) of the emerging economies and the G7 group (Yeyati, Williams,
2012).
A   study   by   Wälti   (2009)   is   one   of   the   most   important   papers   which   rejected   the  
decoupling hypothesis. Conducting calculations for thirty four emerging markets and 29
advanced economies, he examined GDP deviations from its long-term trend and
compared them for a different time shift. The emerging market economies came from
364
four different regions of the world: eight East and South Asian economies, nine Latin
American countries, thirteen Eastern and South European economies, and four other
economies from Africa and Middle East, and. Advanced economies were grouped in four
ways: all advanced economies, a European group, the G7 group, and the United States
alone. The Hodrick-Prescott filter and spectral analyses were used for the period 1980-
2007.      The   results   presented   by   Wälti   (2009)   refuted   the   decoupling   hypothesis   – the
strength of ties for countries from different continents turned to be similar to that
between advanced and emerging economies.
Doubts about decoupling became even more pronounced aftermath of the subprime crisis
when practically all countries (from all regions, both rich and poor) were affected by the
crisis.   Certain   economists   (for   example,   Skrzypczyński,   2010,   Rose,   2009,      Krugman,  
2010, and Dervis, 2012) say that the decoupling has never existed, and others (for
example, Korinek et al., 2010) suggested that the change in the economic conditions
occurred, and we faced the phase re-coupling after the phase of the decoupling (in other
words, after the phase of low synchronization of business cycles we could see resynchronization
of business cycles between advanced and emerging economies, and
between economies from different continents).
The aim of the paper is to verify the hypothesis whether the changes which occurred
during and after the global crisis should be treated as a kind of re-coupling or whether
the whole decoupling hypothesis should be rejected. Poland is used as a reference
country for the purpose of the calculations carried out.
2 Methodology and Data
The time series analysis used in this paper is based on the approach described in
Hamilton (1994), who gives a comprehensive description of these methods (an
interesting discussion about practical application of the methods can be found in
Skrzypczyński  (2010)).  Firstly,  time  series  (in our case: GDP growth rates calculated on
the basis of data from the World Bank database) had time trends and (if applicable) drifts
removed and then the resulting time series were subjected to the Christiano-Fitzgerald
filter, and finally to spectral analysis. The methods are briefly presented below.
An outline of the Christiano-Fitzgerald band-pass filter
As it has already been mentioned, the Christiano-Fitzgerald band-pass filter is used to
extract the cyclical part of the time series. The filter was chosen because of its
applicability to almost all time series and its advantages over other methods (it takes into
account stochastic structure of the decomposed variable, removes all non-seasonal
fluctuations, etc.). The Christiano-Fitzgerald filter requires testing of the stationarity of
time series (time series can be: stationary I(0), trend stationary or non-stationary I(1)).
The filter requires the removal of time-trend (if it is present) and for processes stationary
at I(1) one must remove a drift (if  it  is  present)  (Skrzypczyński,  2008,  p.  13-14).
The idea of calculating the cyclic component
c
tyˆ in the band pass filter is based on the
following formula (Nilsson, Gyomai, 2011, p. 7–8):
   
 
....,2,1ˆˆ,ˆˆ
1
,



t
tTj
j
tjttt
c
t TtforLBLBwhereyLBy (1)
In the formula, L stands for the lag operator of y and B represents a set of parameters
(weights) (Christiano, Fitzgerald, 1999, p. 2). The set of weights tjB ,
ˆ is the solution of
the equation:
 
      .,....,2,1ˆmin
2
1,...,ˆ
,,
TtfordSeBeB y
i
t
i
ttTjB tj







(2)
365
For the CF filter for the I(1) series there is an additional (limiting) condition that:
( )
0ˆ
1
, =∑
−
−−=
t
tTj
tjB for t = 1, 2, ….T, which provides for removal by filter of a stochastic trend.
Operation of the filter, which involves the removal frequencies which are too low or too
high to be treated as part of the business cycle, is based on the function ( )ωi
eB −
, which is
defined as follows:
( ) [ ] [ ]
( )!
"
#
∪−∪−−∈
∪−−∈
≡−
],,(,),[0
,,,1
πωωωωπω
ωωωωωω
for
for
eB i
(3)
where: τπω /2= is the frequency expressed in radians with a period equal to τ . Figures
Uτπω /2= and Lτπω /2= ( πωω <<<0 ) determine the lower and upper frequency of
the filter, which causes the filter to cut off fluctuations with a period longer than Uτ and
less than Lτ . The calculations assumed 32=Uτ and 6=Lτ .
An outline of a single spectrum analysis method
The origin of spectral analysis is based on the idea of representing time series as the sum
of sinusoids at various frequencies (cycles). Spectral analysis of cyclic data requires the
Fourier transform, which is used to transform the time domain representation of the
series into the frequency domain representation of the series. In order to determine the
significance of different frequencies in data one calculates a spectrogram. A spectrogram
displays the power of a signal as a function of both: time and frequency simultaneously.
According to Skrzypczyński (2008, p. 16): “power spectrum of a stochastic process with
discrete time { }+∞
−∞=tty with a zero mean and stationary covariance function is defined as
the Fourier transform of autocovariance series { }+∞
−∞=k
v
ky of this process and is given as:
( ) ∑
+∞
−∞=
−
=
k
kiy
ky eS ϖ
γ
π
ω
2
1
for [ ]ππω ,−∈ , where
τ
π
ω
2
= is the frequency corresponding to
the periodτ ”. Due to the fact that the spectrogram calculated by using the above
method is very "fuzzy", certain methods are used to reduce this variability (smoothing
methods), and one of the most popular of which is the Parzen window. The power
spectrum estimator then takes the form (Skrzypczyński (2008, p. 17) quoting Chatfield
(1996, p. 115)), where empirical autocovariances are
( ) ( ) ,cosˆ2ˆ
2
1
ˆ
2
1ˆ
1
00∑ ∑−= =
−
#
$
%
&
'
(
+==
H
Hk
H
k
y
kk
ykiy
kk kwwewS ωγγ
π
γ
π
ω ω
(4)
( )( ) ,1,...1,0
1
ˆ
1
−=−−= −
+=
∑ Tkforyyyy
T
kt
T
kt
t
y
kγ (5)
and Parzen window weights are:
( ) ( )
( )
!
!
"
!!
#
$
>
≤≤−
≤+−
=
.0
,2//12
,2//6/61
3
32
Hkfor
HkHforHk
HkforHkHk
wk (6)
Maximum allowable lag time for Parzen window, called the truncation lag is chosen
according to the rule ( )TH 2int= .
366
Outline of the cross-spectral analysis
“Cross spectral analysis allows one to determine the relationship between two time series
as a function of frequency. Normally, one supposes that statistically significant peaks at
the same frequency have been shown in two time series as that we wish to see if these
periodicities are related with each other and, if so, what the phase relationship (time
shift) is between them (Hartmann, 2008, p. 165)”. One may do cross spectral analysis
even in the absence of peaks in the power spectrum because even without common
peaks there might be coherent modes at particular frequencies. In this paper, however,
attention will be paid to common peaks in two time series.
There are several methods of calculating the cross-spectrum, one of which is given by
Bloomfield (1976, p. 210-212). The time series X and Y can first be "combined" in the
time domain (before the Fourier transform) by calculating the lagged cross-covariance
function. The resulting function is then subjected to a Fourier transform and a cross
spectrum periodogram is obtained. Cross-covariance can be written as:
∑ −= rttryx yx
n
c
1
,, , where t and t-r = 0, 1, 2, ….., n-1, (7)
and r means a time lag of one series relative to the other.
The Fourier transform is then carried out to obtain the cross-spectrum periodogram:
( ) .
2
1
,,, ∑<
−
=
nr
ir
ryxyx ecI ϖ
π
ω (8)
Similarly to the single spectrum periodogram (spectrogram), the cross-spectrum
periodogram is also smoothed, e.g. by a Parzen window.
For cross-spectrum analysis, one usually calculates the following three measures:
squared coherence, gain value and time shift between the series. Squared coherence
measures strength of association between two series, gain (value) estimates magnitude
of changes of one time series in relation to the other for a certain frequency, phase shift
estimates to which extent each frequency component of one series leads the other.
To quoting Skrzypczyński (2008, p. 17-18) once again: “if we assume that a stochastic
process with discrete time { }+∞
−∞=ttx with zero mean and stationary covariance function is
an independent variable, whereas the process { }+∞
−∞=tty of the analogous properties is the
dependent variable, then the cross power spectrum (cross-spectral density, crossspectrum)
of these variables is defined as the Fourier transform { }+∞
−∞=t
yx
kγ of the crosscovariance
series of these variables and is given by the formula:
( ) ( ) ( ) [ ]ππωωωγ
π
ω ω
,
2
1
−∈−== ∑
+∞
−∞=
−
foriqceS
k
yxyx
kiyx
kyx , (9)
where:
( ) ( )∑
+∞
−∞=
−
=
k
yx
kyx kc ωγπω cos2 1
(10)
is called co-spectrum and is a real part of cross-spectrum, while
( ) ( )∑
+∞
−∞=
−
=
k
yx
kyx kq ωγπω sin2 1
(11)
367
is called the quadrature spectrum, is a negative imaginary part of the cross-spectrum. It
is possible to define three cross-spectral statistics on the basis of cross power spectrum:
gain value (  yxG ), phase shift (  yx ), and squared coherence (  2
yxK ):
 
    
 
 


 ,
2
1
22


 for
S
qc
G
x
yxyx
yx , (12)
 
 
 
 


 ,tan 1








 
 
for
c
q
yx
yx
yx , (13)
 
   
   
 


 ,
22
2


 for
SS
qc
K
xy
yxyx
yx , (14)
where:  xS means power spectrum of the process  tx , while  yS means power
spectrum of the process  ty ”.
3 Results
The strength of the relationship between cycles (in addition to the length of the business
cycle) of a particular country with other countries may indicate a strong relationship
between their economies. In the case of spectral analysis, the strength of the relationship
between cycles is measured by the squared coherence: the higher the coherence, the
stronger the relationship.
Tables 2 and 3 show the squared coherences calculated by the authors. The bold
numbers indicate very high coherences (80-100%), and the coherences between 60 and
80% can also be treated as quite high. As can be seen in tables, when the squared
coherences for different frequencies (lengths of cycles) are considered, business cycles
all over the world were quite similar even before the global financial crisis, and it is more
evident   for   longer   and   very   short   cycles.   The   results   are   presented   from   Poland’s  
perspective and it can be seen that countries on one continent do have strong
connections  with  each  other.  In  this  case,  Poland’s  business  cycle  is  very  similar  to  other
European countries cycles. Nonetheless, when long business cycles are considered,
Poland had a stronger coherence with the United States than with any European country,
even its main economic partner, Germany, which became especially visible during the
global economic crisis. Also, assuming high coherences with small Asian countries
irrelevant, Poland had a relatively high coherence with another huge economy - of
China.   When   short   business   cycles   are   to   be   considered,   Poland’s   economy   visibly  
belonged to the group of the European countries, especially, members of the EU.
When the Tables 2 and 3 are compared, it seems that the relations of Poland with the
richest European countries have become even stronger for the last years, so one cannot
say that the decoupling has occurred in the case of Poland. The financial crises could
influence the results, but on the other hand during the crisis Poland was doing quite well.
It was the only EU country with positive rate of growth in 2009 when there as a fall of
GDP in rich European countries. Other emerging European economies have also stronger
ties with advanced European economies than with new members of the EU.
According to the results from Tables 2 and 3 the synchronization of cycles between
Poland and emerging economies from Asia and Latin America has increased for the last
years. Not presented in the paper (for the sake of its brevity) other diagrams and
calculations also suggest that real GDP deviations from the long-term trend in the above
mentioned countries became more similar. The diagrams unanimously show that during
the crisis all economies slowed down and the short-term real GDP curves bent down from
the trend. It seems that all emerging countries succumb to external global shocks, and
368
therefore the decoupling of the real economies can hardly be acknowledged. The
defenders of the decoupling hypothesis can argue that it was only one time event, but
such an argument does not seem to be very strong: emerging economies seem to exhibit
high sensitivity to global shocks. They reacted similarly to other global shocks, no matter
where there was the origin of the shock, either in advanced or developing economies.
One can enumerate the Asian crisis of 1987 or the current situation in the EU.
Table 2 Coherence coefficients between business cycle in Poland and other countries
(different cycle length); calculations for years 1995 – 2006, grouped by continents
Europe
Country 24 16 12 9.6 8 6.90 6
Austria 85.5% 56.7% 30.7% 59.7% 49.2% 51.4% 50.8%
Belgium 83.3% 59.6% 43.9% 83.8% 87.0% 74.9% 51.9%
Croatia 83.3% 55.6% 68.2% 79.1% 63.8% 62.6% 52.5%
Czech Rep. 86.3% 44.6% 4.9% 6.4% 46.5% 70.6% 73.4%
Denmark 81.6% 49.1% 24.7% 10.4% 5.9% 10.2% 5.8%
Estonia 26.6% 6.7% 15.5% 32.2% 51.4% 62.4% 44.1%
Finland 85.4% 67.3% 60.7% 34.2% 8.3% 5.6% 23.6%
France 92.5% 81.8% 64.7% 59.0% 44.5% 58.3% 68.3%
Georgia 15.4% 7.2% 8.8% 4.8% 14.5% 7.2% 55.8%
Germany 87.3% 65.1% 50.0% 33.2% 35.1% 66.4% 59.0%
G. Britain 93.2% 80.8% 62.9% 34.7% 45.6% 62.5% 55.4%
Hungary 76.4% 41.1% 28.7% 28.8% 17.9% 42.2% 29.3%
Iceland 26.8% 5.1% 16.2% 54.6% 79.6% 65.9% 48.4%
Ireland 83.1% 53.5% 45.6% 31.9% 26.3% 28.9% 41.8%
Italy 87.5% 76.2% 68.2% 61.0% 39.8% 45.2% 72.4%
Latvia 61.7% 22.4% 16.7% 34.2% 37.0% 56.1% 62.4%
Lithuania 1.6% 6.6% 12.2% 41.1% 48.6% 47.4% 31.7%
Netherlands 94.3% 77.7% 48.8% 27.3% 26.7% 57.1% 59.9%
Norway 10.9% 10.4% 19.1% 70.2% 81.0% 64.2% 48.2%
Portugal 90.7% 59.2% 8.4% 46.6% 57.0% 75.0% 71.3%
Russia 94.7% 81.5% 79.6% 74.7% 72.0% 73.7% 80.0%
Slovakia 51.1% 56.8% 49.1% 65.0% 84.1% 66.5% 17.8%
Slovenia 79.9% 42.8% 40.5% 38.7% 4.4% 5.9% 30.1%
Spain 92.0% 68.1% 53.3% 72.1% 66.3% 56.4% 67.5%
Sweden 87.1% 62.2% 50.4% 53.3% 46.1% 32.9% 40.7%
Switzerland 84.8% 64.9% 64.0% 75.2% 48.0% 4.8% 4.7%
Turkey 67.6% 48.4% 63.3% 49.7% 32.7% 2.0% 20.4%
EU 27 90.3% 73.3% 62.7% 47.6% 45.2% 63.2% 71.0%
Euro 17 90.1% 73.2% 62.9% 52.0% 48.5% 65.8% 78.6%
North and South America
Country 24 16 12 9.6 8 6.90 6
Argentina 64.6% 28.4% 54.0% 28.5% 13.2% 27.7% 54.2%
Bolivia 60.2% 32.8% 18.3% 64.9% 67.4% 39.6% 31.9%
Brazil 81.0% 65.4% 40.8% 24.3% 2.0% 12.4% 24.8%
Canada 88.5% 69.4% 66.8% 45.3% 2.6% 15.3% 20.5%
Chile 2.2% 16.7% 38.6% 73.7% 81.5% 83.6% 76.2%
Colombia 58.2% 35.1% 49.6% 78.2% 83.3% 84.6% 62.0%
Mexico 80.3% 58.9% 59.1% 37.4% 34.8% 50.5% 45.9%
Peru 49.8% 7.6% 48.4% 62.5% 19.5% 2.8% 11.5%
USA 94.3% 81.6% 62.1% 23.2% 7.3% 16.3% 7.6%
Asia
Country 24 16 12 9.6 8 6.90 6
China 62.3% 27.3% 19.1% 3.2% 29.6% 48.0% 58.0%
Hong Kong 68.7% 51.2% 42.0% 51.0% 30.1% 35.4% 39.7%
India 63.9% 32.2% 0.9% 26.6% 34.0% 57.6% 71.1%
Indonesia 68.5% 24.2% 15.7% 63.7% 79.0% 72.9% 64.1%
Iran 53.0% 11.4% 7.7% 15.4% 37.2% 30.0% 15.4%
Israel 87.6% 86.3% 73.1% 55.7% 34.6% 2.7% 26.7%
Japan 61.8% 49.1% 36.3% 5.5% 12.7% 6.7% 16.4%
Malaysia 38.8% 22.0% 52.0% 72.5% 65.4% 70.0% 69.6%
369
Country 24 16 12 9.6 8 6.90 6
Philippines 22.4% 4.7% 40.4% 14.5% 23.8% 3.3% 14.7%
Singapore 84.7% 77.8% 64.6% 52.0% 33.6% 41.5% 35.9%
South Korea 76.4% 28.1% 40.6% 66.0% 42.3% 45.5% 56.9%
Taiwan 98.6% 97.5% 86.7% 34.5% 7.9% 3.3% 22.5%
Thailand 50.9% 17.3% 31.9% 53.1% 45.6% 68.2% 79.0%
Australia and Oceania
Country 24 16 12 9.6 8 6.90 6
Australia 56.8% 42.5% 19.1% 10.0% 2.0% 8.1% 23.9%
N. Zealand 39.8% 45.1% 77.8% 85.0% 70.2% 40.1% 9.7%
Africa
Country 24 16 12 9.6 8 6.90 6
Morocco 45.1% 45.4% 43.2% 46.4% 56.3% 67.0% 36.9%
South Africa 87.4% 45.5% 19.9% 17.2% 10.5% 37.1% 61.3%
Source: based on World Bank data
Table 3 Coherence coefficients between business cycle in Poland and other countries
(different cycle length); calculations for years 1995 – 2009, grouped by continents
Europe
Country 30 20 15 12 10 8.57 7.5 6.67 6
Austria 84.8% 76.1% 72.2% 46.7% 46.4% 60.5% 39.0% 14.4% 20.3%
Belgium 65.1% 51.3% 57.7% 35.4% 41.7% 63.4% 59.9% 49.9% 25.0%
Croatia 68.3% 60.7% 62.1% 64.8% 80.9% 77.2% 51.6% 70.2% 77.3%
Czech Rep. 63.9% 55.2% 58.8% 50.7% 42.4% 14.0% 1.2% 44.7% 84.0%
Denmark 83.2% 75.1% 71.4% 78.2% 77.7% 56.0% 11.9% 4.5% 38.9%
Estonia 63.4% 43.7% 36.9% 64.2% 84.7% 71.5% 14.0% 26.2% 65.4%
Finland 87.4% 82.9% 82.0% 82.7% 83.9% 68.2% 17.3% 6.0% 44.1%
France 85.2% 80.8% 82.3% 85.4% 87.3% 80.1% 45.6% 9.8% 28.2%
Georgia 32.6% 32.2% 58.4% 73.2% 46.9% 45.7% 54.9% 27.6% 88.7%
Germany 79.6% 69.2% 72.3% 85.0% 89.0% 74.4% 27.3% 21.2% 65.9%
G. Britain 86.3% 84.4% 84.9% 81.3% 79.9% 70.7% 30.2% 12.1% 63.7%
Hungary 88.4% 83.8% 73.4% 72.7% 80.6% 65.2% 22.5% 16.7% 67.8%
Iceland 75.1% 47.4% 53.9% 58.0% 24.9% 0.4% 32.1% 70.3% 84.5%
Ireland 80.0% 71.8% 73.6% 85.8% 89.4% 77.2% 33.8% 15.6% 2.0%
Italy 76.9% 67.5% 68.5% 81.1% 88.5% 77.7% 33.4% 8.6% 28.2%
Latvia 77.4% 60.0% 52.6% 62.0% 76.7% 71.8% 33.4% 8.4% 19.0%
Lithuania 49.9% 30.9% 33.7% 58.6% 73.3% 59.2% 17.8% 21.0% 61.5%
Netherlands 91.7% 88.4% 84.6% 80.4% 77.2% 71.0% 38.8% 8.8% 59.1%
Norway 60.7% 40.0% 61.3% 64.2% 78.9% 85.0% 66.6% 12.4% 19.1%
Portugal 81.3% 72.5% 67.8% 45.8% 43.6% 70.6% 71.2% 30.9% 29.1%
Russia 83.8% 83.2% 92.0% 95.2% 91.2% 82.8% 51.3% 18.4% 60.3%
Slovakia 37.0% 30.1% 37.9% 45.9% 35.1% 10.9% 6.2% 17.6% 25.1%
Slovenia 85.2% 76.7% 72.5% 64.9% 66.3% 63.9% 22.2% 7.5% 44.0%
Spain 90.1% 84.9% 82.3% 72.9% 71.6% 73.7% 50.2% 2.8% 28.6%
Sweden 90.0% 90.4% 83.6% 77.1% 86.5% 76.4% 25.2% 6.0% 35.3%
Switzerland 87.4% 72.5% 57.8% 69.9% 87.6% 77.9% 25.7% 8.9% 38.0%
Turkey 71.4% 63.3% 77.7% 89.7% 83.7% 42.5% 3.2% 1.9% 35.0%
EU 27 86.5% 83.0% 82.1% 83.8% 86.8% 76.3% 30.0% 13.8% 63.2%
Euro 17 86.5% 81.5% 80.0% 83.7% 87.9% 78.4% 33.0% 14.0% 63.9%
North and South America
Country 30 20 15 12 10 8.57 7.5 6.67 6
Argentina 64.0% 20.3% 25.2% 22.5% 17.1% 3.9% 3.1% 40.2% 88.0%
Bolivia 14.0% 25.1% 52.0% 28.9% 73.7% 62.3% 32.2% 34.4% 18.8%
Brazil 78.9% 79.6% 89.9% 69.9% 69.6% 54.4% 6.2% 21.2% 18.3%
Chile 37.6% 19.5% 56.6% 65.1% 87.3% 86.7% 59.7% 72.4% 83.5%
Canada 88.6% 84.9% 78.6% 81.0% 87.5% 66.8% 15.4% 4.6% 29.4%
Colombia 10.0% 19.5% 32.7% 48.4% 76.9% 77.7% 67.9% 64.7% 43.9%
Mexico 81.3% 78.1% 80.1% 88.0% 90.1% 58.2% 8.8% 18.0% 61.5%
Peru 44.1% 35.0% 34.3% 27.6% 67.3% 79.6% 31.5% 23.8% 35.8%
USA 88.4% 89.7% 88.2% 88.0% 81.2% 57.9% 11.4% 7.7% 24.1%
370
Asia
Country 30 20 15 12 10 8.57 7.5 6.67 6
China 83.1% 60.7% 31.3% 43.9% 57.8% 63.2% 38.9% 1.7% 61.1%
H. Kong 53.0% 53.5% 70.9% 70.0% 86.1% 78.0% 36.6% 13.3% 45.4%
India 51.7% 18.9% 62.6% 43.6% 9.4% 22.2% 5.4% 61.5% 84.4%
Indonesia 53.0% 36.2% 43.4% 2.4% 31.2% 66.8% 60.1% 76.8% 81.0%
Iran 1.6% 14.4% 54.7% 58.9% 29.0% 20.3% 15.6% 20.7% 44.5%
Israel 64.1% 52.7% 62.1% 76.7% 77.1% 44.3% 4.1% 11.7% 6.5%
Japan 50.2% 54.6% 79.8% 85.6% 72.7% 45.0% 16.0% 1.9% 14.2%
Malaysia 12.3% 41.6% 79.9% 75.8% 83.1% 80.0% 45.1% 29.5% 65.1%
Philippines 54.9% 4.9% 40.2% 31.5% 10.5% 29.9% 24.7% 24.8% 44.7%
Singapore 68.7% 68.1% 81.4% 86.6% 84.8% 71.6% 40.7% 42.2% 52.5%
S. Korea 36.0% 41.2% 62.8% 49.8% 72.6% 78.5% 42.3% 27.4% 61.9%
Taiwan 77.9% 83.2% 90.1% 87.7% 80.6% 48.1% 9.3% 2.4% 36.9%
Thailand 9.4% 38.2% 75.8% 62.6% 60.2% 68.1% 40.4% 29.9% 67.8%
Australia and Oceania
Country 30 20 15 12 10 8.57 7.5 6.67 6
Australia 16.8% 7.5% 36.9% 63.2% 49.1% 25.9% 3.5% 26.8% 27.6%
N.Zealand 66.7% 70.2% 86.1% 77.4% 81.2% 78.5% 53.9% 26.8% 2.7%
Africa
Country 30 20 15 12 10 8.57 7.5 6.67 6
Morocco 36.6% 17.4% 28.2% 49.5% 76.3% 57.2% 15.7% 33.1% 35.5%
S.Africa 68.0% 60.7% 65.5% 60.7% 63.3% 51.1% 6.4% 17.4% 67.4%
Source: based on World Bank data
4 Conclusions
Relations between economic variables can be superficial or accidental, but assuming that
the domestic US consumer demand, encouraging for other investors role of the U.S.
investment funds, rating agencies and the U.S. stock exchanges create information-and
sentiment-based transmission channels, it is hard to ignore the evidence that a longterm
  Poland’s  cycle   seems   to   be   highly   dependent   on   the   changes   in   the   US   economy  
(Poland is preceded by the U.S. economy by 1 – 2 quarters, depending on the analyzed
frequency). What is more, until recently, Poland preceded almost all EU economies
lagging only behind very few world economies, including the U.S. one. Therefore, it is
difficult to reject the hypothesis that whatever happens in the U.S. will find be followed
by the changes Polish economy and, consequently, by all other countries all over the
world, if one analyzes long-term frequencies that reflect strong economic trends lasting
longer than 2 years.
Cutting the long story short, if one assumes that presented results do not originate in
imperfections of chosen econometric methods, one should reject the decoupling
hypothesis for at least long business cycles and support the notion of a strong
synchronization of the path of GDP changes.
The synchronization became especially visible during the last global financial crisis. Some
countries, like China, showed some resistance in the presence of the global shocks (at
the same time influencing other countries), but generally the cyclical part of GDP in both
advanced and emerging countries deflected down in relation to GDP long-term trend
(Kim et al., 2009).
Hence, there seems to exist evidence of quite strong synchronization of GDP changes
between advanced and emerging economies which raises the question whether the high
rates of growth in emerging economies are sustainable without a recovery in advanced
economies.
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Masaryk University
Faculty of Economics and Administration
Department of Finance
and Institute for Financial Market
European Financial Systems 2013
Proceedings
of the 10th
International
Scientific Conference
Edited by Oleg Deev, Veronika  Kajurová, Jan  Krajíček
Editorial Board: Petr  Pirožek, Ladislav  Blažek, Eva  Hýblová, Jaroslav  Maryáš,
Martin Kvizda, Jaroslav Nekuda, Daniel  Němec, Vladimir  Hyánek
Published by Masaryk University, Brno 2013
1st
edition, 2013, number of copies 120
Printed by: Tiskárna  KNOPP  s.r.o.,  Nádražní  219,  549  01  Nové  Město  nad  Metují
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