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

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
VOR-ORT

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

Fr 07.03. 15:00-16:30 Seminarraum 8, Kolingasse 14-16, OG01

Details

max. 24 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Freitag 14.03. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Freitag 21.03. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Freitag 04.04. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Freitag 11.04. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Freitag 02.05. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Freitag 09.05. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Freitag 23.05. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Freitag 30.05. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Freitag 06.06. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Freitag 13.06. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Freitag 20.06. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
  • Freitag 27.06. 15:00 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.
* Volatility and risk. GARCH models.
* High frequency (intraday) data. Realized Variance estimator, GARCH and RV
* Capital Asset Pricing Model, factor pricing models, dynamic and static factor sequences and high-dimensional time series
* Forecasting financial time series (e.g. stock returns)
* Methods for model selection

Art der Leistungskontrolle und erlaubte Hilfsmittel

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.

Mindestanforderungen und Beurteilungsmaßstab

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 paper - 70%
2) Project presentations - 20%
3) Seminar participation - 10%

Prüfungsstoff

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)

Literatur

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/

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Do 23.01.2025 11:45