040977 SE Seminar in Empirical Finance and Financial Econometrics (MA) (2024S)
Continuous assessment of course work
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Achtung: wird anerkannt für Seminar aus Statistik im Magisterstudium für Studierende der Statistik
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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).
- Registration is open from Mo 12.02.2024 09:00 to We 21.02.2024 12:00
- Registration is open from Mo 26.02.2024 09:00 to Tu 27.02.2024 12:00
- Deregistration possible until Th 14.03.2024 23:59
Details
max. 24 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Friday
01.03.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
Friday
08.03.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
Friday
15.03.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
Friday
22.03.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
Friday
12.04.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
Friday
19.04.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
Friday
26.04.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
Friday
03.05.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
N
Friday
24.05.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
Friday
07.06.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
Friday
14.06.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
Friday
21.06.
09:45 - 11:15
Seminarraum 15, Kolingasse 14-16, OG01
Friday
28.06.
09:45 - 11:15
Seminarraum 15, 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.
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.
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 otherThe 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%
* 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 otherThe 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%
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)
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/
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: Th 29.02.2024 17:25