BPF_STAF Statistics for finance

Faculty of Economics and Administration
Autumn 2026
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: Kristina Charvátová
Supplier department: Department of Finance – Faculty of Economics and Administration
Timetable
Wed 8:00–9:50 P106, except Wed 16. 9., except Wed 4. 11.
  • Timetable of Seminar Groups:
BPF_STAF/01: Wed 10:00–11:50 VT105, except Wed 16. 9., except Wed 4. 11., Š. Lyócsa
BPF_STAF/02: Wed 10:00–11:50 S315, except Wed 16. 9., except Wed 4. 11., T. Výrost
BPF_STAF/03: Wed 12:00–13:50 VT314, except Wed 16. 9., except Wed 4. 11., Š. Lyócsa, T. Výrost
BPF_STAF/04: Wed 16:00–17:50 P103, except Wed 16. 9., except Wed 4. 11., Š. Lyócsa, T. Výrost
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.
The capacity limit for the course is 100 student(s).
Current registration and enrolment status: enrolled: 109/100, only registered: 3/100, only registered with preference (fields directly associated with the programme): 2/100
fields of study / plans the course is directly associated with
Abstract
The higher-level goal of the course is for the student to be able to understand time-series data and perform a forecasting analysis of a time-series data.
Learning outcomes
After successfully completing the course, the student will be able to: - present time-series data, - describe basic time-series properties, - use an appropriate uni-variate model, - select appropriate methods and predict the time-series, - evaluate prediction, - perform fixed and random effect panel data estimation, - perform an event study with financial market data. - work in an appropriate statistical software.
Key topics
1. Characteristics of time-series data - part I: - time-series data - visualization - descriptive statistics -> show event, show extremes, shaded areas - Auto-correlation of a time-series. - Partial auto-correlation. - Long-memory - Arch effects. - Testing for significance in serial-correlation and arch effects. 2. Characteristics of time-series data - part II: - stationarity - co-variance stationarity - weekly dependent time-series. - unit-root. - ADF test, ADF-GLS test, KPSS test. - Differencing and de-trending. 3. Forecasting non-stationary time-series - part I: - moving average. - forecasting framework - rolling & expanding rolling window - loss functions - mse & mae & mpe & ... custom choices. - cross-validation (tuning hyper-parameters). - exponential moving average. - Seasonality. - Cycle. - Trend. - Classical additive decomposition. - Multiplicative decomposition. 4. Forecasting non-stationary time-series - part II: - Holt's double exponential moving average. - Brown's double exponential moving average. - Holt and Winter's triple exponential moving average. - Linear regression model. 5. Forecasting non-stationary time-series - part III: - OLS estimation - summary - Linear regression model static models with deterministic components (trends, seasonality, breaks). - Linear regression model inference under time-series framework (auto-correlation & heteroscedasticity of residuals). - Robust inference. 6. Forecasting stationary time-series - part I: - AR(1) - AR(p) - recursive and direct forecasting - ARDL 7. Forecasting stationary time-series - part II: - MA(1). - MA(q). - Invertibility. - Maximum Likelihood Estimation & alternatives. - ARMA(p,q). - GARCH(p,q) model (forecasts). - Box-Jenkins classical & modern model selection. 8. Machine learning with linear models - part I: - Machine Learning principles. - Backward elimination. - Forward elimination. - Bi-directional elimination. - LASSO. - Ridge. - Elastic Net. 9. Machine learning with linear models - part II: - Feature engineering & data transformations. - Hyper-parameter tuning. - WLS models. - Complete subset linear regressions. - Unconditional combination forecasts. - Conditional combination forecasts. 10. Further topics in statistics in finance - part I: - Bootstrapping. - Bootstrapping in time-series models. - Binary outcome - logistic regression. - Marginal effects. - Complete subset logistic regression. 11. Further topics in statistics in finance - part II: - Panel data structures. - LSDV model. - Panel unit-root tests. - Fixed effect model. - Random effect model. - Estimation and robust inference. - Dynamic panel data alternatives. - Forecasting with panel data. 12. Introduction to event studies in finance: - The structure of an event study - Event study periods - Market models - Abnormal returns - Cumulative abnormal returns - Testing significance of abnormal returns
Study resources and literature
    required literature
  • JAMES, Gareth R.; Daniela WITTEN; Trevor HASTIE and Robert TIBSHIRANI. An introduction to statistical learning : with applications in R. Second edition. New York: Springer, 2021, xv, 607. ISBN 9781071614174. info
  • HYNDMAN, RJ and G ATHANASOPOULOS. Forecasting: principles and practice. 2nd edition. Melbourne, Australia: OTexts, 2018. URL info
    recommended literature
  • WOOLDRIDGE, Jeffrey M. Introductory econometrics : a modern approach. Seventh edition. Boston: Cengage Learning, 2020, xxi, 826. ISBN 9781337558860. info
Approaches, practices, and methods used in teaching
Lectures with key principles, ideas and definitions. Seminars use computers and appropriate statistical software.
Method of verifying learning outcomes and course completion requirements

The final grade is calculated by summing the four scores: 1) the first midterm (up to 20 points), 2) the second midterm that (up to 20 points), 3) the exam - theoretical test (up to 30 points), 4) the exam - practical exercise (up to 30 points).


91-100 -> A, 81-90 -> B, 71-80 -> C, 61-70 -> D, 51-60 -> E, 0-50 -> FX

Alternate completion
The student should contact the teacher who is leading his/her seminars. Usually the student should try to find a suitable alternative course (Erasmus cases). If that is not possible, student will need to: i) take an additional tasks to compensate for not attending seminars, ii) an exam (on-site).
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2025.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/econ/autumn2026/BPF_STAF