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## Econometrics for Quantitative Risk Management

2019-2020

### Course Objective

Upon successful completion, students should
• have a good understanding of econometric estimation and inference
methodology (regression, maximum likelihood, asymptotics), with
awareness to typical statistical complications in financial
econometrics.
• be able to develop procedures for answering finance/econometric
questions of interest, relating to the academic literature;
• be able to implement econometric methods in computer code using
simulated or real data, study the properties of estimation and inference
procedures, and critically interpret the results obtained.
In this way students should be well prepared for the team research
project in Block 3, and for the academic thesis in Block 5/6.

### Course Content

This is a course for the Duisenberg Honours Programme in
Quantitative Risk Management. It is accessible for outside students, if
they have sufficient background in probability, statistics, linear
algebra, econometrics and programming.

The course starts out with the theory behind common estimation methods
for linear, non-linear, or even non-parametric models. This knowledge is
applied to the study of factor, principal component and panel data
models. The second part of the course continues with a focus on time
series models with time varying parameters, both in a univariate as in a
multivariate setting.
Students are required to implement some of the methods in case
assignments using computer coding. We use Python as our standard
programming language, but students are free to choose some other
language if they prefer.

### Teaching Methods

2h lectures, 2h tutorials, over two periods

### Method of Assessment

Intermediate exam, final written exam, case work.

### Entry Requirements

Students should have a sound knowledge of Probability Theory and
Mathematical Statistics, Linear Algebra and Calculus, as well as an
introductory knowledge in Econometrics. They should also be familiar
with basic bachelor level finance concepts. Students should also master
a matrix-oriented programming language. During the course, Python is
used (see e.g. https://www.kevinsheppard.com/Python_for_Econometrics). A
bootcamp ‘Principles of Programming in Econometrics’
(FIN_PPECTRM_COMMUNITY_2019_1) is organized for Python in the last week
of August, before the start of the course. Please register by signing up
in Canvas for the bootcamp.

Indication of entry level:
● Edwards, C. H. and D. E. Penney (2007). Calculus, with Early
Transcendentals. New International ed of 7th Revised. Pearson.
● Casella, G. and R. L. Berger (2008). Statistical Inference.
International edition of 2nd revised. Cengage Learning, Inc.
● Stock, J. H. and M. W. Watson (2011). Introduction to
Econometrics. 3rd. UK: Pearson Education.
● Bodie, Z., A. Kane, and A. Marcus (2013). Investments. 10th.
McGraw-Hill Education.

### Literature

Tsay, R. S. (2010): Analysis of Financial Time Series, 3rd edition. John
Wiley & Sons. http://dx.doi.org/10.1002/9780470644560
Hansen, B. E. (2019): Econometrics.
http://www.ssc.wisc.edu/~bhansen/econometrics

Recommended:
Magnus, J. R. (2017). Introduction to the Theory of Econometrics.
Amsterdam: VU University Press.

### General Information

Course Code E_FIN_EQRM 6 EC P1+2 400 English School of Business and Economics dr. C.S. Bos dr. C.S. Bos dr. C.S. Bos prof. dr. A. Lucas

### Practical Information

You need to register for this course yourself

Teaching Methods Lecture, Study Group
Target audiences

This course is also available as: