Statistics and Econometrics for Finance

Teachers

Included in study programs

Teaching results

Knowledge and competences:
The desired outcome of the subject is to connect economics- and finance-related concepts and theories to real world data. Students will be able to understand and analyze various financial data at the micro- and macro-level using different quantitative methods. Upon successful completion of this course, students should acquire knowledge of regression modelling of financial data, hypothesis testing and forecasting, and interpretation of empirical results.
Skills:
Students will improve their mathematical ability and computer literacy, analytical skills, written and oral communication skills, problem-solving skills, a high level of accuracy and attention to detail the capacity to work alone and within teams.

Indicative content

1. General introduction to the course
2. Review of statistical datasets
3. Review of basics statistics and probability
4. Linear regression with one regressor and hypothesis testing
5. Linear regression with multiple regressors
6. Regression models with discrete variables
7. Introduction to panel data analysis
8. Regression analysis with panel data – fixed and random effects models
9. Introduction to time series analysis
10. ARMA models and forecasting
11. Modelling volatility with ARCH and GARCH models and forecasting
12. Discussion of some empirical econometric results in published articles
13. Recapitulation and tips for writing a research paper / thesis

Support literature

- Cipra, T. (2008). Finanční ekonometrie (Vol. 30). Ekopress.
- Stock J.H., and Watson, M. (2015). Introduction to Econometrics. Updated 3rd Edition.

Requirements to complete the course

20 % continuous work during the seminars
20 % semestral paper
60 % written exam

Student workload

- Lectures 26h
- Seminars 26h
- Preparation for seminars 13h
- Seminar paper 13h
- Preparation for semestral paper 26 h
- Individual studying 52h

Date of approval: 11.03.2024

Date of the latest change: 28.12.2021