This course is designed as an introduction to empirical finance. The focus is on the analysis of financial data as well as on applications of econometric methods to portfolio management, risk management and forecasting.
The detailed study of all fields of financial econometrics is clearly not feasible within this course. Therefore we have chosen a selection of topics which we believe delivers a sound basis for
- Risk measurement and forecasting,
- Portfolio analysis,
- High-frequency finance,
The main aims of this course can be summarized as follows:
1. to spark interest in the field by stressing the most important empirical and practical implications of financial econometrics which can lead to a more specific research;
2. to endow the participants with an econometric toolbox for the analysis of financial data;
3. to equip the participants with a profound knowledge of data handling and programming skills in Python.
- Econometrics I (or equivalent courses)
- Basic knowledge of Time Series Analysis
Campbell, J. Y., A. W. Lo and A. C. MacKinlay (1997): The Econometrics of Financial Markets, Princeton University Press.
Francq, C. and Zakoian J.M. (2011): GARCH models: structure, statistical inference and financial applications, Wiley. com
Gourieroux C. and J. Jasiak (2001): Financial Econometrics, Princeton University Press.
Hayashi, F. (2002): Econometrics, Princeton University Press.
McNeil, A.J., R. Frey and P. Embrechts: Quantitative Risk Management: Concepts, Techniques and Tools, Princeton University Press.
Tsay, R. S. (2005). Analysis of financial time series (Vol. 543). John Wiley & Sons.
Andersen T., Davis R., Kreiß J. and Mikosch T. (2009): Handbook of Financial Time Series, Springer
Form of Assessment
Grading is based on the Final Exam (70%) and one Take Home Exam (30%).
The lecture will be held in three blocks in the months October, November and January.
For the computer tutorial you need your Mdm/Pop ID and password.
The Mdm/Pop ID can be retrieved here.
Your password is the same as your @uni-konstanz.de Email password.
The tutorials start on 23.10.19.
Please register for the lecture using ILIAS. The ILIAS password will be announced in the first lecture. Once you are registered, you get all news via mail and you can download the course material.