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Keine Credits bei Lehrveranstaltungen angegeben

Bei den Modulen unten sind Credits angegeben, bei der (modulunabhängigen) Lehrveranstaltungsliste nicht. Dies liegt darin begründet, dass die Lehrveranstaltungen erst im Kontext eines Modules Credits erhalten. Auch wenn der Fall selten eintritt, ist so die Möglichkeit gegeben, dass die selbe Veranstaltung in unterschiedlichen Studiengängen unterschiedlichen Workload und Credits erhalten kann.

Üblicherweise gilt aber weiterhin natürlich die Faustregel Cr = 1,5 * SWS. 

create MS Word export

If you like to create a change request for the modules, the easiest way is to export this list and then use the "track changes" functionality in MS Word and send the new file to AG Modulhandbuch. As a starting point you can use the word-export above.


https://www.oek.wiwi.uni-due.de/

Lehrstuhl für Finanzmarktökonometrie

assigned LecturersHoga (Prof. Dr. Yannick Hoga)

Responsbile for the modules

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
  • BWL EaFWahlpflichtbereich1st-3rd Sem, Elective
  • ECMXWahlpflichtbereichME7 Econometric Methods1st-3rd Sem, Elective
  • MuUWahlpflichtbereich IWahlpflichtbereich I A.: Methodologie und allgemeine Theorien zur Untersuchung von Märkten und Unternehmen1st-3rd Sem, Elective
  • VWLWahlpflichtbereich I1st-3rd Sem, Elective
  • WiMatheVWL-M I 1st-4th Sem, Elective
  • WiMatheVWL-M II1st-4th Sem, Elective
Elements
  • VO: Financial Econometrics (3 Credits)
  • UEB: Financial Econometrics (3 Credits)
Module: Financial Econometrics (WIWI‑M0961)

Offered Courses

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
Lecture: Financial Econometrics (WIWI‑C1254)
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
Exercise: Financial Econometrics (WIWI‑C1255)