Syllabus
Registration via LPIS
| Day | Date | Time | Room |
|---|---|---|---|
| Tuesday | 11/26/24 | 02:00 PM - 05:30 PM | D4.0.127 |
| Tuesday | 12/03/24 | 02:00 PM - 05:30 PM | D4.0.127 |
| Tuesday | 12/10/24 | 02:00 PM - 05:30 PM | D4.0.127 |
| Tuesday | 12/17/24 | 02:00 PM - 05:30 PM | D4.0.127 |
| Tuesday | 01/07/25 | 02:00 PM - 05:30 PM | D4.0.127 |
| Tuesday | 01/14/25 | 02:00 PM - 05:30 PM | D4.0.127 |
| Tuesday | 01/21/25 | 02:00 PM - 03:30 PM | TC.5.05 |
The topics covered in this course are:
- Review of elementary properties of stationary time series and ARIMA models.
- Volatility modeling, in particular GARCH and stochastic volatility models.
- Multivariate time series, in particular vector autoregressive (VAR) models, VEC, and cointegrated VAR models, the latter allowing for cointegration, leading to non-standard estimation procedures and inference.
- State space models and the Kalman filter.
- Dynamic factor models.
- Machine learning methods in financial time series (if time permits).
After completion of the course the student will be able
- to understand and apply more specific methods for modeling data of financial markets.
- to apply a selection of frequently used procedures for financial data, covering ARIMA models, volatility models, VAR (vector autoregressive models), VEC (vector error correction models), Kalman filter and state space models, as well as dynamic factor models,
- to interpret the output of empirical estimates.
Participants will be trained in
- manipulating formulas,
- reading and executing R scripts
- interpreting the results of small empirical projects
when doing the assignments and presenting them in class.
The final exam covers theoretical aspects, deeper understanding as well as empirical applications.
The course is organized as follows: Lecture with slides. The methods are illustrated using real data sets. There is a discussion of the assignments in class.
Grading will be based on homework assignments, presentations of the class assignments, and the final exam. The assignments must be solved individually. The students indicate before the beginning of the class which assignments they have solved and are willing to discuss in class.
The contributions to the grade are
- 12% for each homework assignment and classroom presentation; in total 5 assignments, i.e., max 60% of the grade,
- 5% active participation in discussions during the course,
- 35% final exam; the final exam is voluntary, and no minimum requirement of credit points is therefore needed to pass the class
Grading is as follows:
1 (at least 90% of total credit points), 2 (at least 80%), 3 (at least 70%), 4 (at least 60%)
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