The course starts with a concise coverage of elementary concepts and computational tools for Bayesian modeling of financial data. Furthermore, it aims at complementing students' econometric competence in data analysis with a focus on Bayesian approaches. Towards the end of this course, state-of-the art univariate and multivariate volatility models are discussed and applied to real world data. Focus will be placed on topics that are of particular interest to the participants.

After completing this course the student will have the ability to:

• fundamentally understand concepts, techniques and tools in Bayesian data analysis
• know about various computational approaches towards Bayesian econometrics
• acquire a comprehensive understanding of Bayesian regression analysis, including shrinkage estimation 
• apply univariate and multivariate models for capturing heteroskedasticity in financial time series
• understand different approaches to point- and density-prediction and evaluation of forecasting techniques
• connect to state-of-the art literature in Bayesian modeling of economic and financial data

For this lecture participation is obligatory. Students are allowed to miss a maximum of 20% (no matter if excused or not excused).

The course consists of a mix between lectures, reading assignments, case studies and students' presentations. Participants are required to independently apply the methods discussed to actual data problems.

The grade is composed as follows:

60 points: case studies / homework
15 points: students' presentations
20 points: final exam

  5 points: active classroom participation

Overall, 100 points can be achieved. The final grade is computed according to

 1 (at least 90),  2 (at least 80),  3 (at least  70),  4 (at least 60)

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A solid understanding of “classical” (financial) econometrics. Good programming skills in a high-level language such as R.