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.
Prerequisites
- Econometrics I (or equivalent courses)
- Basic knowledge of Time Series Analysis
Introductory Literature
| Andersen T., Davis R., Kreiss J. and Mikosch T. (2009): Handbook of |
| Financial Time Series, Springer |
| Campbell, Lo & MacKinlay (1997): The Econometrics of Financial |
| Markets, Princeton University Press, Princeton. |
| Fan, J. and Yao, Q. (2017): The Elements of Financial Econometrics, |
| Cambridge University Press |
| Gourieroux & Jasiak (2001): Financial Econometrics, Princeton |
| University Press, Princeton |
| McNeil, Frey & Embrechts (2005): Quantitative Risk Management: |
| Concepts, Techniques and Tools, Princeton University Press, Princeton. |
| Brockwell, P.J. and Davis, R.A. (2016): Introduction to time series and |
| forecasting, Springer. |
| Hamilton (1994): Time Series Analysis, Princeton University Press, |
| Princeton. |
| Lütkepohl (2006): New Introduction to Multiple Time Series, Springer, |
| Heidelberg. |
| Nagel (2021), Machine learning in asset pricing, Princeton University Press |
Form of Assessment
Grading is based on the Final Exam (70%) and one Take Home Exam (30%).
Lecture
| Date | Time | Room |
|---|---|---|
| Tuesday, | 13:30 – 15:00 | F420 |
Tutorials
Schedule
| Date | Time | Room |
|---|---|---|
| Wednesday | 17.00 - 18.30 | D432 |
Tutorials start on the 23rd of October 2023. 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 course using ZEUS. The registration on ZEUS is open from October 19 . Please register for both, the course and the tutorial. Once you are registered, you will automatically be registered for ILIAS. Via ILIAS you will get all news and can access the course material.