4975 Y2E Advanced Topics in Financial Econometrics
Darjus Hosszejni, Ph.D.
Contact details
Weekly hours
Language of instruction
02/01/23 to 02/17/23
Registration via LPIS
Notes to the course
Day Date Time Room
Wednesday 03/01/23 09:00 AM - 12:30 PM D1.1.074
Wednesday 03/08/23 09:00 AM - 12:30 PM D2.0.030
Wednesday 03/15/23 09:00 AM - 12:30 PM D2.0.038
Wednesday 03/22/23 09:00 AM - 12:30 PM D2.0.038
Wednesday 03/29/23 09:00 AM - 12:30 PM D2.0.342 Teacher Training Raum
Wednesday 04/12/23 09:00 AM - 12:30 PM D2.0.038
Wednesday 04/19/23 09:00 AM - 12:30 PM D3.0.233
Wednesday 04/26/23 10:00 AM - 11:30 AM D2.0.392

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.

Learning outcomes

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

  • understand fundamental concepts, techniques and tools in Bayesian data analysis
  • know about various computational approaches towards Bayesian econometrics
  • use hierarchical models to answer economic questions
  • apply univariate and multivariate models for capturing heteroskedasticity in (financial) time series
  • independently and competently perform Bayesian analysis of economic time series
  • compare different approaches to point- and density-prediction
  • evaluate various forecasting techniques
  • connect to state-of-the art literature in Bayesian modeling of economic and financial data
Attendance requirements

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

Teaching/learning method(s)

The course consists of a mix between lectures and tutorials, reading assignments, case studies and students' presentations. Participants are required to independently apply the models to actual data problems, both in-class as well as between classes.


The grade is composed as follows:

  • 5 points: active classroom participation
  • 15 points: students' presentations
  • 60 points: homework
  • 20 points: final exam

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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Recommended previous knowledge and skills
A solid understanding of “classical” (financial) econometrics. Good programming skills in a high-level language such as R.
Availability of lecturer(s)


Office hours: two days after each lecture, i.e. on Fridays from 2pm to 3pm in my office. No need to register.
My office:

Last edited: 2023-03-07