FIUNKNFE Financial Econometrics

School of Business Administration in Karvina
Summer 2023
Extent and Intensity
16/0/0. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Iveta Palečková, Ph.D. (lecturer)
Guaranteed by
doc. Ing. Iveta Palečková, Ph.D.
Department of Finance and Accounting – School of Business Administration in Karvina
Contact Person: Ing. Irena Szarowská, Ph.D., MPA
Timetable
Sat 4. 3. 11:25–13:00 B207, Fri 24. 3. 12:15–13:50 B101, Sat 25. 3. 11:25–13:00 B207
Prerequisites (in Czech)
FAKULTA(OPF) && TYP_STUDIA(N) && FORMA(K)
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 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 0/30
fields of study / plans the course is directly associated with
Syllabus
  • 1. Theory and models Objectives of the financial econometrics. Specification and verification of the econometric model. Econometric softwares. 2. Financial time series data analysis and linear regression. Descriptive statistics, stationary and non-stationary time series data. Linear regression analysis, multiple linear regression. Methods for estimating model parameters, ordinary least squares method. Evaluation and diagnostic tests of the model. Regression analysis in practical examples, capital asset pricing model. Discrete choice models, Logit, Probit and Tobit models. 3. Models of one-dimensional and multidimensional time series Autocorrelation and partial autocorrelation functions. Autoregression, autoregressive processes (AR), ARMA model. Non-stationary time series and ARIMA model. Seasonal time series models. Multidimensional linear process. Vector autoregression models. 4. Causality in financial time series data Correlation analysis, advantages and disadvantages. Granger causality test. Endogeneity and exogenousity. 5. Cointegration and error correction models Cointegration order testing - Johansen method. Error Correction model and vector error correction (VEC) model. 6. Panel data analysis Advantages and disadvantages of the panel data analysis. Static linear model. Fixed and random individual effects. Dynamic linear models. Panel data analysis in practical examples. 7. Nonlinearity of financial time series and volatility models Time series nonlinearity testing. Volatility models. ARCH, GARCH models. Asymmetric models of type EGARCH and TARCH.
Literature
    required literature
  • PALEČKOVÁ, I., 2021. Finanční ekonometre. Karviná: SU OPF.
  • ALJANDALI, A. a M. TATAHI, 2018. Economic and Financial Modelling with EViews: A Guide for Students and Professionals. London: Springer. ISBN 978-3-319-92984-2.
  • GUIDOLIN, M. a M. PEDIO, 2018. Essentials of Time Series for Financial Applications. London: Elsevier. ISBN 978-0-12-813409-2.
    recommended literature
  • LINTON, O. 2019. Financial Econometrics. Cambridge: Cambridge University Press. ISBN 978-1-107-17715-4.
  • DOUGHERTY, CH. Introduction to Econometrics. 5th ed. Oxford: Oxford University Press, 2016. ISBN 978-0199676828. info
  • BROOKS, CH. Introductory econometrics for finance. 3rd ed. Cambridge: Cambridge University Press, 2014. ISBN 978-1107661455. info
  • CIPRA, T. Finanční ekonometrie. Praha: Ekopress, 2008. ISBN 978-80-86929-43-9. info
Teaching methods (in Czech)
Demonstrace dovedností
Assessment methods
Exam
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course can also be completed outside the examination period.
Information on the extent and intensity of the course: Přednáška 16 HOD/SEM.
The course is also listed under the following terms Summer 2021, Summer 2022, summer 2024.
  • Enrolment Statistics (Summer 2023, recent)
  • Permalink: https://is.slu.cz/course/opf/summer2023/FIUNKNFE