Financial Econometrics (FIM)
This Module is offered by TUM Department of Mathematics.
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective
MA9976 is a semester module
in English language
at Master’s level
which is offered in summer semester.
|Total workload||Contact hours||Credits (ECTS)|
This course is an intensive introduction to various econometric concepts like sampling, estimation, hypotheses testing, and (generalized) linear regression used in applied financial research. The emphasis will be on developing and applying regression-based techniques in both cross-sectional and time-series contexts. Their usefulness will also be examined in the light of current financial studies.
After successful completion of the module, students are able to analyze cross-sectional and time-series data with regression-based techniques. Furthermore, students will learn how to set up and to estimate econometric models that can be used to test theories or to make forecasts. They understand the properties and limitations of these models and are able to assess how they fit different applications. Students will be able to use a programming software like Matlab or R to implement and evaluate the models.
Courses and Schedule
Learning and Teaching Methods
Lectures with beamer presentation and whiteboard, tutorials where students work under instructor assistance on assignments for implementation using programming software like Matlab or R
Presentation slides, whiteboard, assignment sheets, programming software like Matlab or R
Econometric Analysis, Greene, W.H. (2008), 6th ed., New York: Prentice Hall.
Additional Reading: Market Risk Analysis: Quantitative Methods in Finance (Market Risk Analysis). Carol Alexander. Wiley; Har/Cdr edition 2008.
Description of exams and course work
The module examination is based on a written exam (90 minutes) with both theoretical and practical components. Students have to show their theoretical understanding of a generalized linear regression model by answering questions on model set-up and assumptions, the generalized least squares estimation methodology, finite and asymptotic properties as well as hypothesis testing. In the practical section, students have to demonstrate their understanding of the methodology on an economically motivated application. By analyzing and interpreting results from a variety of candidate models, students are led to reach a decision about the most plausible model for the application at hand. The exam includes a one-page formula sheet provided in advance to the students via lecture notes as well as an Appendix with output in R-code related to the practical component. The theoretical part represents a 55% of the total grade while the practical component takes the remaining 45%.