Syllabus

Title
5067 Y2E Advanced Topics in Financial Econometrics
Instructors
Darjus Hosszejni, Ph.D.
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/01/22 to 02/18/22
Registration at the institute
Notes to the course
Dates
Day Date Time Room
Wednesday 03/02/22 09:00 AM - 12:30 PM D1.1.074
Monday 03/07/22 03:00 PM - 04:00 PM D2.0.038
Wednesday 03/09/22 09:00 AM - 12:30 PM D2.0.392
Monday 03/14/22 03:00 PM - 04:00 PM D2.0.038
Wednesday 03/16/22 09:00 AM - 12:30 PM D2.0.374
Monday 03/21/22 03:00 PM - 04:00 PM D2.0.038
Wednesday 03/23/22 09:00 AM - 12:30 PM D2.0.038
Monday 03/28/22 03:00 PM - 04:00 PM D2.0.392
Wednesday 03/30/22 09:00 AM - 12:30 PM D4.0.019
Monday 04/04/22 03:00 PM - 04:00 PM D2.0.038
Wednesday 04/06/22 09:00 AM - 12:30 PM D2.0.392
Wednesday 04/27/22 09:00 AM - 12:30 PM TC.4.14
Friday 04/29/22 03:00 PM - 04:00 PM D2.0.392
Monday 05/02/22 09:00 AM - 12:30 PM D5.0.002
Contents
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 comptetence 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.

Assessment

The grade is composed as follows:

  • 5 points: active classroom participation
  • 15 points: students' presentations
  • 60 points: case studies / 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
Readings

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

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)

Email: darjus.hosszejni@wu.ac.at

Office hours: the Monday entries and the Friday entry in the calendar. No need to register; I will be there.

Last edited: 2022-05-17



Back