Financial Econometrics

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

Introductory Literature

Gourieroux C. and J. Jasiak (2001): Financial Econometrics, Princeton University Press.

McNeil, A.J., Frey, R. and Embrechts, P. (2005): 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.

Brockwell, P.J. and Davis, R.A. (2016): Introduction to time series and forecasting, Springer.

Fan, J. and Yao, Q. (2017): The Elements of Financial Econometrics, Cambridge University Press.

Form of Assessment

Grading is based on the Final Exam (70%) and one Take Home Exam (30%).


The Exam of Financial Econometrics is taking place on Thursday, February 23th at 11.20 - 12.50 in room C425


Date Time Room
Wednesday 11.45 - 13.15 G227



Date Time Room
Wednesday 17.00 - 18.30 G421

Tutorials start on the 2nd of November 2022. Please install Anaconda prior to the first tutorial.

Please do complete the assigned learning material for the week BEFORE the Wednesday tutorial.

Course Materials

Please register for the lecture using ILIAS. Once you are registered, you get all news via mail and you can download the course material.