The course provides students with an understanding of important empirical methods and their application in finance. It covers topics in asset pricing, corporate finance, and market microstructure. Students will learn to perform empirical analysis using the software package Stata. The course enables students to plan and carry out empirical research in finance on their own and prepares for an empirical seminar or master thesis in the finance area. Part of the course consists of the practical application of the methods learned in the lecture to various case studies.
The students will have a sound understanding of empirical methods and their underlying assumptions. The students will be able to choose appropriate methods for given empirical problems and apply them in an efficient way. The case studies enable the students to develop basic programming skills in Stata.
Module CC 502 or CC 503 and Module FIN 5XX or equivalent courses. Completing FIN 604 Stata in Finance or acquisition of equivalent knowledge is highly recommended.
|Forms of teaching and learning||Contact hours||Independent study time|
|Lecture||2 SWS||10 SWS|
|Exercise class||1 SWS||16 SWS|
|Form of assessment||Written exam (45%; 60 min.), Case Studies (45%), Class Participation (10%)|
Prof. Dr. Erik Theissen
Prof. Dr. Erik Theissen
|Duration of module||1 semester|
|Range of application||M.Sc. MMM, M.Sc. WiPäd, M.Sc. VWL, M.Sc. Wirt. Inf., M.Sc. Wirt. Math.|
|Preliminary course work||–|
|Program-specific Competency Goals||CG 1, CG 4|
|Literature||Stock, J. and M. Watson (2007): Introduction to Econometrics, 2nd edition, Pearson.|
Campbell, J.Y., A.W. Lo and A.C. MacKinley (1997), The Econometrics of Financial Markets, Princeton University Press, New Jersey.
Wermers, R. (2011): Performance Measurement of Mutual Funds, Hedge Funds, and Institutional Accounts. Annual Review of Financial Economics 3, 537–574.
The course further makes use of multiple scientific research papers.
The course requires the use of the statistical software Stata.
|Course outline||1. Data Problems: Survey data, selection bias, survivorship bias, data mining, multicollinearity, endogeneity
2. Portfolio Theory: Empirical Tests and Individual Investor Behavior: Measuring risk and returns, diversification, individual investor portfolios, market efficiency
3. Empirical Asset Pricing: Beta estimation, empirical pricing tests (Fama MacBeth 1973, Black Jensen Scholes 1972), multifactor models, assessing the pricing ability of multifactor models
4. Performance Evaluation: Classical performance and performance attribution measures, holding-based measures
5. Event Studies: Steps of event studies, statistical tests, cross-sectional analysis, long-horizon event studies|
6. Panel Data: The fixed-effects model, the random-effects model, goodness of fit, dynamic panels