Universität Wien FIND

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

8.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung


Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").


max. 40 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert


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


Ziele, Inhalte und Methode der Lehrveranstaltung

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.

Art der Leistungskontrolle und erlaubte Hilfsmittel

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.

Mindestanforderungen und Beurteilungsmaßstab

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.


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


"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

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 03.05.2021 11:07