BPF_STAF Statistics for finance

Faculty of Economics and Administration
Spring 2025
Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
prof. Ing. Štefan Lyócsa, PhD. (lecturer)
doc. Ing. Tomáš Výrost, PhD. (lecturer)
prof. Ing. Štefan Lyócsa, PhD. (seminar tutor)
doc. Ing. Tomáš Výrost, PhD. (seminar tutor)
Guaranteed by
prof. Ing. Štefan Lyócsa, PhD.
Department of Finance – Faculty of Economics and Administration
Contact Person: Iva Havlíčková
Supplier department: Department of Finance – Faculty of Economics and Administration
Prerequisites
The basic terms in calculus of probability and mathematical statistics.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
At the end of the course students should be able to:
- understand and explain the basics terms of time series theory;
- apply taught procedures to real data ;
- operate the statistical software.
Learning outcomes
After graduation of the course student should be able to:
- determine statistical methods appropriate for particular application context
- solve tasks based on real data by means of sw. R
- interpret properly outputs of analyses
Syllabus
  • 1. Characteristics of time-series data: auto-correlation, partial auto-correlation, testing for serial-correlation and auto-correlation.
  • 2. Stationary and non-stationary time-series, covariance-stationarity, weekly dependent time-series, unit-root tests (ADF, ADF-GLS, KPSS), differencing.
  • 3. Forecasting non-stationary data part I: Moving average, Exponential moving average, Principles of forecasting and time-series model cross-validation.
  • 4. Forecasting evaluation, mean square error, mean absolute error, asymmetric loss functions, economic loss functions (profit/loss, costs,...).
  • 5. Forecasting non-stationary data part II: Classical additive and multiplicative decomposition of a time series.
  • 6. Forecasting non-stationary data part III: Holt's double exponential and Brown's double exponential moving average, Holt and Winter's triple exponential moving average.
  • 7. Static models, Finite Distributed Lag Models, OLS assumptions and inference under time-series (auto-correlation and heteroscedasticity for estimation and inference), robust inference.
  • 8. Trend and seasonality in regression models.
  • 9. AR(1) model, AR(p) models, ARDL models, recursive and direct forecasting.
  • 10. Co-integration (Granger - Engle test)
  • 11. Granger causality (uni-variate)
  • 12. Event study time-series (market models, abnormal returns, statistical tests).
Literature
    required literature
  • WOOLDRIDGE, Jeffrey M. Introductory econometrics : a modern approach. Seventh edition. Boston: Cengage Learning, 2020, xxi, 826. ISBN 9781337558860. info
  • HYNDMAN, RJ and G ATHANASOPOULOS. Forecasting: principles and practice. 2nd edition. Melbourne, Australia: OTexts, 2018. URL info
Teaching methods
Theoretical lectures; computer seminar sessions.
Assessment methods
The final grade is given by the score of the final test.
Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.
Language of instruction
English
Further Comments
The course is taught annually.
The course is taught: every week.
  • Enrolment Statistics (recent)
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