Universität Wien

040014 KU Econometrics in Finance (MA) (2021S)

8.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
REMOTE
Moodle

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. 40 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Digital

  • Wednesday 03.03. 15:00 - 16:30 Digital
  • Thursday 04.03. 15:00 - 16:30 Digital
  • Wednesday 10.03. 15:00 - 16:30 Digital
  • Thursday 11.03. 15:00 - 16:30 Digital
  • Wednesday 17.03. 15:00 - 16:30 Digital
  • Thursday 18.03. 15:00 - 16:30 Digital
  • Wednesday 24.03. 15:00 - 16:30 Digital
  • Thursday 25.03. 15:00 - 16:30 Digital
  • Wednesday 14.04. 15:00 - 16:30 Digital
  • Thursday 15.04. 15:00 - 16:30 Digital
  • Wednesday 21.04. 15:00 - 16:30 Digital
  • Thursday 22.04. 15:00 - 16:30 Digital
  • Wednesday 28.04. 15:00 - 16:30 Digital
  • Thursday 29.04. 15:00 - 16:30 Digital
  • Wednesday 05.05. 15:00 - 16:30 Digital
  • Thursday 06.05. 15:00 - 16:30 Digital
  • Wednesday 12.05. 15:00 - 16:30 Digital
  • Wednesday 19.05. 15:00 - 16:30 Digital
  • Thursday 20.05. 15:00 - 16:30 Digital
  • Wednesday 26.05. 15:00 - 16:30 Digital
  • Thursday 27.05. 15:00 - 16:30 Digital
  • Wednesday 02.06. 15:00 - 16:30 Digital
  • Wednesday 09.06. 15:00 - 16:30 Digital
  • Thursday 10.06. 15:00 - 16:30 Digital
  • Wednesday 16.06. 15:00 - 16:30 Digital
  • Thursday 17.06. 15:00 - 16:30 Digital
  • Wednesday 23.06. 15:00 - 16:30 Digital
  • Thursday 24.06. 15:00 - 16:30 Digital
  • Wednesday 30.06. 15:00 - 16:30 Digital

Information

Aims, contents and method of the course

Recent years have witnessed a growing need for econometric methods in financial research and practice. As a result, financial econometrics has become one of the most active areas of research in econometrics. This is documented by the award of the Nobel Prize 2003 to Robert F. Engle for his contribution to the modelling of time-varying asset return volatility and the award of the 2013 Nobel Prize to Lars Peter Hansen for his pioneering work on the empirical analysis of asset prices.

This course aims to provide students an introduction into the field and an overview of the most important topics and techniques. Having predominantly an applied focus, it attempts to balance between derivations of basic theoretical relations, fundamental methodology, the analysis of specific financial econometric models, applications thereof as well as the discussion of important empirical findings.

The course deals with fundamental time series techniques to model and to predict financial data and with the modeling of time-varying volatility. In this context, the concept of realized volatility as well as the analysis of financial high-frequency data is covered.
More advanced topics include the analysis of panel data and the difference in differences estimation.

An important objective is to provide a comprehensive knowledge to do empirical work in financial research and practice. Therefore, a part of the course consists of practical exercises where students are instructed to apply econometric concepts to real financial data. In this context, students will be introduced to basic programming and application steps using the statistical software package R.

Assessment and permitted materials

The assessment is made up of three part.

The first part consists of weekly exercises which are either of theoretical nature or require programming in R. The solutions of these exercises are presented by students in the first half of the Wednesday class.

The second part is a presentation of a scientific publication. The topics are assigned in April. The 30min Presentation is given in June.

The third part is an oral exam at the end of the term.

Minimum requirements and assessment criteria

Admitted students have to attend the first lecture on Wednesday 6th of March to confirm their participation!

The students can earn up to 30, 40 and 30 in the three parts described above. A total of 50 points is minimally required to pass the course. More than 63, 75 resp. 87 points yield the grades 3, 2 resp 1.

Examination topics

1) Financial Data: Basic Concepts and Properties
2) Univariate Time Series Analysis
3) Multivariate Time Series Analysis
4) Volatility Concepts
5) Machine Learning in Finance
6) Bonus: Advanced Techniques

Reading list

"Introductory Econometrics of Finance" by Brooks
"Applied Quantitative Finance" by Härdle, Hautsch and Overbeck
"The Econometrics of Financial Markets" by Campbell, Lo and MacKinlay
"Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems." by Géron

Association in the course directory

Last modified: Fr 12.05.2023 00:12