154-0553/01 – Financial Econometrics (FE)
| Gurantor department | Department of Finance | Credits | 3 |
| Subject guarantor | doc. Ing. Aleš Kresta, Ph.D. | Subject version guarantor | doc. Ing. Aleš Kresta, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Choice-compulsory type B |
| Year | 2 | Semester | winter |
| | Study language | English |
| Year of introduction | 2019/2020 | Year of cancellation | 2021/2022 |
| Intended for the faculties | EKF | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The aim of the course is to provide a general knowledge of econometrics with application to the practical finance and financial modelling. It is placed the great emphasis on the general principles and methods in order students to be able to solve the real problems. It is possible in the course to work on real problem of diploma thesis.
Students will be able after the course:
- to apply estimation methods correctly and suitably,
- to create empirical models not only for financial time series,
- to make predictions of future trends,
- to work with mathematical software.
Teaching methods
Lectures
Tutorials
Summary
The course is focused on application of econometrical methods to the finance. It aims at creating empirical models applicable both in corporate finance as well as in financial modelling. Within the seminars, selected problems of proposing empirical models are solved and the emphasis is placed on their practical application. The course is a good complement to the course Econometrics. Problems are solved mainly in Microsoft Excel.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Credit - defence of seminar work
E-learning
Other requirements
There are no other requirements on student.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Basic problems in financial econometrics.
2. Simulation Monte-Carlo – random numbers generators.
3. Estimation methods – capabilities and restrictions.
4. Regression analysis – estimation of empirical arbitrage model.
5. Regression analysis – generalized linear models and applications.
6. Introduction to stochastic optimization – applications and solution techniques.
7. Application of principal component analysis.
8. Controlling of extremal losses – estimation of risk with low probabilities.
9. Introduction to Visual Basic for Application.
10. Application of given mixture probability distributions.
11. Application of stochastic processes.
12. Modelling volatility with asymmetric effect.
13. Modelling dependences, covariance matrices.
14. Introduction to data envelopment analysis (DEA).
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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