Informations about the modules
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Module (6 Credits)
Financial Econometrics
- Name in diploma supplement
- Financial Econometrics
- Responsible
- Admission criteria
- See exam regulations.
- Workload
- 180 hours of student workload, in detail:
- Attendance: 60 hours
- Preparation, follow up: 60 hours
- Exam preparation: 60 hours
- Duration
- The module takes 1 semester(s).
- Qualification Targets
Students
- acquire comprehensive knowledge of financial econometric methods for both cross-sectional data as well as time series data and are proficient in their application
- are able to transfer questions concerning financial market data into suitable models, to estimate the models with the help of current methods, to draw valid conclusions from the data and to question the empirical results
- can competently evaluate and critically examine studies in financial econometrics
- are able to solve practical problems independently with the help of statistical software
- Relevance
The practical relevance is high due to the combination of theory and empirical work.
- Module Exam
Written exam (generally 60-90 minutes) or oral exam (generally 20-40 minutes). The chosen examination method (written or oral exam) is defined by the lecturer during the first weeks of the lecture period.
- Usage in different degree programs
- Elements
Lecture (3 Credits)
Financial Econometrics
- Name in diploma supplement
- Financial Econometrics
- Organisational Unit
- Lecturers
- SPW
- 2
- Language
- English
- Cycle
- irregular
- Participants at most
- no limit
- Explanation for irregular cycle
- The courses in this module take place irregularly and (usually) in summer semesters. Information on whether the course is offered can be obtained from the chair homepage or the LSF.
- Preliminary knowledge
Knowledge of basic econometric and statistical methods as well as knowledge of univariate time series analysis. Knowledge of a statistical programming language such as R is also helpful.
- Abstract
Teaching current financial econometric methods for cross-sectional and time series data.
- Contents
- Stochastic discount factor
- Nonlinear generalized method of moments (GMM)
- Factor pricing models
- Equity premium puzzle
- Predictability of returns
- Multivariate volatility modeling
- Literature
- Cochrane, J.H. (2005). Asset Pricing. Princeton University Press.
- Linton, L. (2019). Financial Econometrics: Models and Methods. Cambridge University Press.
- Newey, W. K. and McFadden, D. (1994). Large sample estimation and hypothesis testing. In Engle, R. F. and McFadden, D., editors, Handbook of Econometrics, volume 4, chapter 36, pages 2111–2245. Elsevier.
- Francq, C. and Zakoian, J.-M. (2019). GARCH Models: Structure, Statistical Inference and Financial Applications. Wiley.
- Teaching concept
Presentation of the material in theory and practice, the latter in R.
- Participants
Exercise (3 Credits)
Financial Econometrics
- Name in diploma supplement
- Financial Econometrics
- Organisational Unit
- Lecturers
- SPW
- 2
- Language
- English
- Cycle
- irregular
- Participants at most
- no limit
- Preliminary knowledge
See lecture
- Contents
See lecture
- Literature
See lecture
- Teaching concept
Working on theoretical as well as practical exercises; the latter in R
- Participants