DOI: 10.5817/FAI2013-3-2 No. 3/2013
25
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
E-mail: wyrobekj@uek.krakow.pl
2
Cracow University of Economics
Department of Macroeconomics
ul. Rakowicka 27, 31-510 Kraków
E-mail: stanczyz@uek.krakow.pl
Abstract: The purpose of this paper was to present the decoupling
hypothesis which says that the performance of emerging economies
becomes relatively independent of the changes in advanced economies,
and to empirically verify this hypothesis. The Christiano-Fitzgerald bandpass
filter and spectral analyses have been applied to examine the
hypothesis. On the basis of obtained results, comparing 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. The paper
analyzes the situation from the Poland’s point of view as the country
which is on the verge of joining the ERM2 system and adopting the euro
(synchronization divagations are important for this decision) and because
Poland is a good example of an emerging economy.
Keywords: decoupling, business cycle synchronization, spectral analysis,
emerging economies
JEL codes: F44, C32, O1
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 be used to
Financial Assets and Investing
26
describe the performance of certain Latin American countries, for
example, Brazil. Indeed, some Latin American countries started to grow
faster than the U.S. economy and their growth path now appears 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
the growing differences between business cycles and the growing
differences in reaction to global shocks. Dervis (2012) also calls it a
delinking of cyclical movements, and it is 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 been 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. We
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 a wider selection
of emerging markets and long time series. In the opinion of the authors
of this paper, Kose et al. (2008), Kose and Prasad (2010), World
Economic Outlook (2007) and Wälti (2009) 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
No. 3/2013
27
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 the 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 the 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)
Financial Assets and Investing
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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 of the conclusions stated in
the paper, the authors’ calculations confirmed 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 their long-term trend and compared them for a different
time shift. The emerging market economies came from 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. Advanced economies
were grouped in four ways: all advanced economies, the 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 in the 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) suggest that the change in the economic
conditions occurred, and we face the phase of re-coupling after the phase
of decoupling (in other words, after the phase of low synchronization of
business cycles we could see re-synchronization of business cycles
between advanced and emerging economies, and between economies
from different continents).
The aim of this 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
No. 3/2013
29
rejected. Poland is used as a reference country for the purpose of the
calculations carried out.
1 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 quarterly 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. The time period chosen for the analysis
depended mostly on: the availability of data, high requirements of the
research method for long time series and the purpose of the paper which
was to compare synchronization before and during the last global crisis
(the authors used 2 datasets: for years 1995–2006 and 1995–2009).
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 nonstationary
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:
Financial Assets and Investing
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 
      .,....,2,1ˆmin
2
1,...,ˆ
,,
TtfordSeBeB y
i
t
i
ttTjB tj







(2)
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  i
eB 
, which is defined as
follows:
     
 





],,(,),[0
,,,1


for
for
eB i
(3)
where:  /2 is the frequency expressed in radians with a period
equal to  . Figures U /2 and L /2 (  0 ) determine
the lower and upper frequency of the filter, which causes the filter to cut
off fluctuations with a period longer than U and less than L . The
calculations assumed 32U and 6L .
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. The 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 



2
1
for   , , where



2
 is the frequency
corresponding to the period ”. Due to the fact that the spectrogram
No. 3/2013
31
calculated using the above method is very "fuzzy", certain methods are
used to reduce this variability (smoothing methods), and one of the most
popular 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 



 
(4)
   ,1,...1,0
1
ˆ
1
 

 Tkforyyyy
T
kt
T
kt
t
y
k (5)
and Parzen window weights are:
   
 










.0
,2//12
,2//6/61
3
32
Hkfor
HkHforHk
HkforHkHk
wk (6)
The maximum allowable lag time for Parzen window, called the truncation
lag, is chosen according to the rule  TH 2int .
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)”. We can 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
Financial Assets and Investing
32
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 

 (8)
Similarly to the single spectrum periodogram (spectrogram), the crossspectrum
periodogram is also smoothed, e.g. by a Parzen window.
For cross-spectrum analysis, we usually calculate the following three
measures: squared coherence, gain value and phase shift between the
series. The squared coherence measures the strength of association
between two series, the gain (value) estimates the magnitude of changes
of one time series in relation to the other for a certain frequency, the
phase shift estimates to which extent each frequency component of one
series leads the other.
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, cross-spectrum)
of these variables is defined as the Fourier transform  
t
yx
k of the
cross-covariance series of these variables and is given by this formula:
       

 
,
2
1
 



foriqceS
k
yxyx
kiyx
kyx , (9)
where:
   




k
yx
kyx kc  cos2 1
(10)
No. 3/2013
33
is called co-spectrum and is a real part of cross-spectrum, while
   




k
yx
kyx kq  sin2 1
(11)
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 ”.
2 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 the 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
Financial Assets and Investing
34
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 - 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 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; on the
other hand, during the crisis Poland was doing quite well. It was the only
EU country with a positive rate of growth in 2009 when there was 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 in 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 therefore, the decoupling of the real
economies can hardly be acknowledged. The defenders of the decoupling
hypothesis can argue that it was only a 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 the origin of the shock was, either in
advanced or developing economies. One can enumerate the Asian crisis
of 1987 or the current situation in the EU.
No. 3/2013
35
Table 2 Coherence coefficients between the 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%
Financial Assets and Investing
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Country 24 16 12 9.6 8 6.90 6
Malaysia 38.8% 22.0% 52.0% 72.5% 65.4% 70.0% 69.6%
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: authors’ own calculations based on World Bank data
Table 3 Coherence coefficients between the 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%
No. 3/2013
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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%
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: authors’ own calculations based on World Bank data
Conclusions
Relations between economic variables can be superficial or accidental, but
assuming that the domestic US consumer demand, encouraging the role
of the U.S. investment funds for other investors, rating agencies and the
U.S. stock exchanges create information-and sentiment-based
transmission channels, it is hard to ignore the evidence that a long-term
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 behind very few world
economies, including the U.S. one. Therefore, it is difficult to reject the
Financial Assets and Investing
38
hypothesis that whatever happens in the U.S. will find be followed by
changes in 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 the 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 the GDP long-term trend
(Kim et al., 2009).
Hence, there seems to exist evidence of quite a 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.
From the investors’ points of view, with all previously mentioned
limitations of the obtained results and the research method, one can
draw the following conclusions (especially, from the Poland’s
perspective). More and more researchers including the authors of this
paper undermine the theory of decoupled growth rates between emerging
and developed economies, especially during strong disturbances in the
global financial market (but not only during crises, also in the long-term
perspective). There are certainly country and regional factors influencing
the behavior of the business cycles of the emerging economies (which
impacts the value of investments in these countries) but the global factor
and the business cycles of the biggest world economies (such as or
primarily the USA) cannot be marginalized. Emerging economies,
especially in the short-term perspective, can be resistant to economic
downturns influencing developed countries, but the obtained results
suggest that in the long-term perspective emerging economies are going
to follow the same trends. Investors cannot assume that in the long-term
No. 3/2013
39
perspective the general recession in the developed countries is not going
to significantly hinder the growth of the emerging countries.
References
Adamowicz, E.; Dudek, S.; Pachucki, D.; Walczyk, K. (2009).
Synchronizacja cyklu koniunkturalnego polskiej gospodarki z krajami
strefy euro w kontekście struktury tych gospodarek, in Raport na temat
pełnego uczestnictwa Rzeczypospolitej Polskiej w trzecim etapie Unii
Gospodarczej i Walutowej, Projekty badawcze cz. I.Warszawa: NBP.
Bloomfield, P. (1976). Fourier analysis of time series. New York: Wiley.
Chatfield C. (1996). The Analysis of Time Series, An Introduction. Boca
Raton, London, New York, Washington: Chapman and Hall/CRC Texts in
Statistical Science.
Christiano L.; Fitzgerald T. (1999). The Band Pass Filter. Federal Reserve
Bank of Cleveland Working Paper 9906.
Decoupling 2.0: The biggest emerging economies will recover faster than
America, (2009). The Economist, May 21, 2009.
Dervis¸ K. (2012). Convergence, Interdependence, and Divergence.
Finance and Development, September 2012.
Hartmann D., Objective analysis course notes, Cross Spectrum Analysis,
p. 168. [online] [accessed 1 July 2012]. Available from Internet:
<http://www.atmos.washington.edu/~dennis/552_Notes_6c.pdf>.
Hamilton, J. D. (1994). Time series analysis. Princeton: Princeton
University Press. ISBN10-0691042896.
Kim, S.; Lee, J.-L.; Park, C.-Y. (2009). Emerging Asia: Decoupling or
Recoupling. ADB Working Paper Series on Regional Economic Integration,
No. 31.
Konopczak, K. (2009). Analiza zbieżności cyklu koniunkturalnego
gospodarki polskiej ze strefą euro na tle krajów Europy ŚrodkowoWschodniej
oraz państw członkowskich strefy euro, in Raport na temat
pełnego uczestnictwa Rzeczypospolitej Polskiej w trzecim etapie Unii
Gospodarczej i Walutowej, Projekty badawcze cz. I. Warszawa: NBP.
Korinek, A.; Roitman, A.; Vegh, C. (2010). Decoupling and Recoupling.
American Economic Review 100(2), pp. 393-397.
Financial Assets and Investing
40
Kose, M.; Otrok, Ch.; Prasad, E. S. (2008). Global Business Cycles:
Convergence or Decoupling? NBER Working Paper No. 14292.
Kose, M.; Prasad, E. S. (2010). Emerging Markets: Resilience and Growth
amid Global Turmoil. Washington, D.C.: Brookings Institution Press.
Krugman, P. (2010). We Are Not The World. The New York Times.
November 9, 2010. [online] [accessed 1 July 2012]. Available from
Internet: <http://krugman.blogs.nytimes.com/2010/11/09/we-are-not-
the-world/>
Nilsson, R.; Gyomai G. (2011). Cycle extraction, OECD. [online]
[accessed 1 July 2012]. Available from Internet: <http://www.oecd.org>.
Rose, A, (2009). Debunking decoupling. VOX. [online] [accessed 1 July
2012]. Available from the Internet: http://www.voxeu.org/person/
andrew-k-rose>
Skrzypczyński, P. (2010). Metody spektralne w analizie cyklu
koniunkturalnego gospodarki polskiej. Materiały i Studia NBP, No. 252.
Skrzypczyński, P. (2008). Wahania aktywności gospodarczej w Polsce i
strefie euro. Materiały i Studia NBP, no. 227.
Wälti, S. (2009). The myth of decoupling. [online] [accessed 1 July
2012]. Available from the Internet: <http://mpra.ub.umi-muenchen.de>.
World Economic Outlook. Spillovers and Cycles in the Global Economy
2007. Washington, D.C.: IMF.
Yeyati, E. L.; Williams, T. (2012). Emerging economies in the 2000s: Real
decoupling and financial recoupling. The Policy Research Working Paper
5961. Washington, D.C.: The World Bank.