Applied Financial Econometrics

10.03.2023: In the summer term 2023 the course will be held in person. Note that lectures as exercise classes take place on Wednesdays.

10.06.2016:

Literature for Bayesian methodology:

• Bayesian Econometrics:
  • Zellner - Introduction to Bayesian Inference 
  • Marin, Robert - Bayesian Core: A Practical Approach o Computational Bayesian Statistics (EBook at UR)
• Bayesian Statistics:
  • Carlin, Stern, Dunson, Vehtan, Rubin - Bayesian Data Analysis
  • Albert - Bayesian Computation with R (EBook at UR)

Aim of the Course:

Participants of this course study the theory and practice for modeling univariate (financial) time series. Students perform empirical projects including programming tasks in R.

The course is taught in English (on request German). 

Course Outline 

1. The basics of time series modeling: autoregressive and moving average processes
2. Forecasting (financial) time series
3. More on time series modeling: unit root tests and diagnostic tools
4. Modeling volatility dynamics: ARCH, GARCH, and TGARCH models as well as appropriate maximum likelihood estimators and their properties
5. Long-run forecasting
6. Explaining returns and estimating factor models 

Literature

Information about the literature can be found on the slides.

Audience / Qualification 

A prerequisite for the participation in the course Applied Financial Economtrics is the participation in the course Econometrics I or an equivalent course plus some basics in R.

The course Applied Financial Economtrics is a compulsory part of the study specializations Empirical Economics and Financial Markets for economics, an optional compulsory part of the study specialization Corporate Finance for business administration, and optional for all other students. 

Grading System

The course consists of one midterm exam (Lernzielkontrolle), the exercise presentation, and a final exam. Details are given in GRIPS or the Modulkatalog

Schedule