Syllabus

Title
5457 Y2E Advanced Topics in Financial Econometrics
Instructors
Mag.Mag.Mag.Dr. Gregor Kastner
Contact details
gregor.kastner@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/07/19 to 02/24/19
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Elective - Advanced Topics in Financial Econometrics
Dates
Day Date Time Room
Wednesday 03/06/19 03:00 PM - 07:00 PM D4.0.127
Wednesday 03/13/19 03:00 PM - 07:00 PM D4.0.127
Wednesday 03/20/19 03:00 PM - 07:00 PM D4.0.127
Wednesday 03/27/19 03:00 PM - 07:00 PM D4.0.127
Wednesday 04/03/19 03:00 PM - 07:00 PM D4.0.127
Wednesday 04/10/19 03:00 PM - 07:00 PM D4.0.127
Wednesday 05/08/19 03:00 PM - 07:00 PM D4.0.127
Wednesday 05/15/19 03:00 PM - 06:30 PM TC.4.01

On this page:

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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.
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Learning outcomes

After completing this class 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
  • 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
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Attendance requirements

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

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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.
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Assessment

The grade is composed as follows:

05 points: active classroom participation
15 points: students' presentations
40 points: case studies / homework
40 points: final exam

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

min(5, (110-x)/10), rounded towards the better grade,

where x is the number of points achieved.

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Readings
1 Author: Hoff, Peter D.
Title:

A First Course in Bayesian Statistical Methods

2 Author: Koop, Gary
Title: Bayesian Econometrics

3 Author: Blake, Andrew & Mumtaz Haroon
Title: Applied Bayesian econometrics for central bankers

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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.
Last edited: 2019-01-10



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