Universität Wien

040977 SE Seminar in Empirical Finance and Financial Econometrics (MA) (2025S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

Achtung: wird anerkannt für Seminar aus Statistik im Magisterstudium für Studierende der Statistik
Seminar: siehe Homepage

Moodle
Fr 20.06. 15:00-16:30 Seminarraum 8, Kolingasse 14-16, OG01

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 24 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Friday 07.03. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Friday 14.03. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Friday 21.03. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Friday 04.04. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Friday 11.04. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Friday 02.05. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Friday 09.05. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Friday 16.05. 15:00 - 16:30 Seminarraum 17, Kolingasse 14-16, OG02
  • Friday 23.05. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Friday 06.06. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Friday 13.06. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Friday 27.06. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01

Information

Aims, contents and method of the course

The main objective of the seminar is to give a brief overview of methods in modern financial econometrics and modeling of financial time series. This includes a practical knowledge of performing applied empirical analysis as well as an experience of working with financial data. The seminar also aims to provide a ground for students to practice presentation skills and a critical assessment of research papers.
Preliminary list of topics:
* Financial prices and returns. Stylized empirical facts.
* Capital Asset Pricing Model, factor pricing models,
* Static approximate factor models and Generalised Dynamic Factor Models: Theory and application
* Forecasting financial time series (e.g. stock returns)
* Methods for model selection
* Volatility and risk. GARCH models.
* High frequency (intraday) data. Realized Variance estimator, GARCH and RV

Assessment and permitted materials

The course will be taught in class. All necessary information and possible short-term announcements will be provided either in class or through the Moodle site of the course. Assessment is mainly based on a term project (possibly, performed in groups) and seminar participation (that might include several different activities). A project consists of a final paper (to be submitted in August and a presentation of the selected research question and intermediate results during the seminar (in May/June). The research question for a project is supposed to be selected individually and can be based on one of suggested methodological papers.The evaluation involves a simulated anonymous peer-review process among the students.

Minimum requirements and assessment criteria

As a prerequisite, it is expected that students
* have taken core courses in probability and statistics and/or econometrics
* are familiar with basic probabilistic and econometric concepts (e.g., LLN, CLT, stationarity, least squares estimator, maximum likelihood principle, etc.).
* have basic programming skills and experience with statistical analysis software like R or Python or other

The grade will be based on the course project (intermediate presentation and final paper) and seminar participation. Intermediate project presentations will take place in May/June, during seminar meetings. The tentative deadline for the final project paper is August 14.

The final grade is compiled as follows:
1) Project presentations - 20%
2) Referee report - 10%
2) Project paper - 60%
3) Seminar participation - 10%

Examination topics

Preliminary list of topics:
1. Financial prices and returns. Stylized empirical facts.
2. Volatility and risk. GARCH models.
3. High frequency (intraday) data. Realized Variance estimator.
4. Dynamic models for Realized Variance. New generation of GARCH models.
5. Methods for model selection.
6. Factor Models, factor pricing models and high-dimensional time series
7. Forecasting financial time series (e.g. stock returns)

Reading list

There will be no unique course textbook. Instead, research papers will be recommended as a source of relevant material for the projects.

Some useful textbooks are:

Campbell, J. Y., Lo, A. W., MacKinlay, A. C., & Whitelaw, R. F. (1998). The econometrics of financial markets (Princeton University Press).
Fan J. and Yao Q. (2015): The Elements of Financial Econometrics (Science Press).
Hautsch, N. (2012): Econometrics of Financial High-Frequency Data (Springer).
Taylor, S.J. (2005): Asset Price Dynamics, Volatility, and Prediction (Princeton University Press).
Tsay, R.S. (2010): Analysis of Financial Time Series: Financial Econometrics (Wiley, 3rd edition).

Online literature for R:
Heiss, F., “Using R for Introductory Econometrics”, 2016, http://www.urfie.net
Hanck, C., Arnold, M., Gerber, A., and Schmelzer, M., 2019, https://www.econometrics-with-r.org/index.html
Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. R for data science. " O'Reilly Media, Inc.", 2023. https://r4ds.hadley.nz/

Association in the course directory

Last modified: Tu 06.05.2025 11:53