Financial Econometrics

Module: Financial Econometrics (6 Credits)
Name in diploma supplement	Financial Econometrics
Responsible	Prof. Dr. Yannick Hoga
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	BWL EaF Master > Wahlpflichtbereich > 1.-3. Sem, Elective ECMX Master > Wahlpflichtbereich > ME7 Econometric Methods > 1.-3. Sem, Elective MuU Master > Wahlpflichtbereich I > Wahlpflichtbereich I A.: Methodologie und allgemeine Theorien zur Untersuchung von Märkten und Unternehmen > 1.-3. Sem, Elective VWL Master > Wahlpflichtbereich I > 1.-3. Sem, Elective WiMathe Master > VWL-M I > 1.-4. Sem, Elective WiMathe Master > VWL-M II > 1.-4. Sem, Elective
Elements	Lecture Financial Econometrics (3 Credits) Exercise Financial Econometrics (3 Credits)
Module: Financial Econometrics (WIWI‑M0961)

Lecture: Financial Econometrics (3 Credits)
Name in diploma supplement	Financial Econometrics
Organisational Unit	Lehrstuhl für Finanzmarktökonometrie
Lecturers	Prof. Dr. Yannick Hoga
Hours per week	2	Language	English
Cycle	irregular	Participants at most	###LABEL_NOLIMIT###
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.
Lecture: Financial Econometrics (WIWI‑C1254)

Exercise: Financial Econometrics (3 Credits)
Name in diploma supplement	Financial Econometrics
Organisational Unit	Lehrstuhl für Finanzmarktökonometrie
Lecturers	Prof. Dr. Yannick Hoga, wissenschaftliche Mitarbeiter(innen)
Hours per week	2	Language	English
Cycle	irregular	Participants at most	###LABEL_NOLIMIT###
Preliminary knowledge See lecture
Contents See lecture
Literature See lecture
Teaching concept Working on theoretical as well as practical exercises; the latter in R
Exercise: Financial Econometrics (WIWI‑C1255)