Birkbeck, University of London
Department of Economics, Mathematics and Statistics
M.Sc. in Financial Engineering
Financial Econometrics
Autumn Term 2013/14
Instructor: Professor Haris Psaradakis (Office: Room 725; E-mail: z.psaradakis@bbk.ac.uk)
Office Hours: Wednesday 4:00 — 5:00 p.m. or by e-mail appointment (preferable).
Aims
This course is intended to provide students with a foundation in econometric methods relevant for
carrying out empirical work in finance. Some of the material may be familiar to students with
a good background in econometrics and time series analysis, but there will be scope for deeper
analysis. The course is in two parts: the main focus in the Autumn term will be estimation and
inferential procedures for regression models; the Spring-term course will focus on the modelling of
financial time-series data.
Learning Outcomes
On completing the first part of the course, students should: (a) have a good understanding of
the properties of a variety of estimation and testing procedures, and be able to establish these
properties in relatively simple settings; (b) be able to understand and interpret critically empirical
research of the sort that appears in the main economics and finance journals.
Teaching Arrangements
The first part of the course is taught over 10 weeks. There is one double lecture a week (Wednesday,
6:00 — 9:00 p.m.) and one class (beginning in Week 2). There will be nine problem sets, which will
be discussed in class. It is important that you attempt these exercises before coming to class.
Assessment
The grade for the course will be determined through a three-hour written examination in June.
Prerequisites
Students should have a good understanding of elementary statistical distribution theory and inference, elementary multivariate calculus, limits, and elementary matrix algebra.
Reading
A set of lecture notes, which cover the main topics, are available on the course’s website (you will
need your ITS username and password to login):
http://www.ems.bbk.ac.uk/for_students/msc_finEng/finmetrics_emec045p/.
These notes are not a substitute for a textbook.
1
A textbook that covers the material that will be discussed in the Autumn term at a level which is
appropriate for the course is:
• Greene, W. H., Econometric Analysis, 7th Edition, Harlow: Pearson, 2012.
There is a website at http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm,
where errata and solutions to exercises are posted.
Another good textbook worth considering is:
• Davidson, R., and MacKinnon, J. G., Econometric Theory and Methods, Oxford: Oxford
University Press, 2004.
Errata, data sets, and solutions to selected exercises can be found on the book’s website at
http://qed.econ.queensu.ca/ETM/.
Students with a good statistics/econometrics background will appreciate the following textbook,
which gives an excellent treatment of many of the methods that will be discussed in the course:
• Hayashi, F., Econometrics, Princeton: Princeton University Press, 2000.
There is a website at http://fhayashi.fc2web.com/hayashi_econometrics.htm, where errata
and solutions to analytical exercises are posted.
Other recommended textbooks are:
• Baltagi, B. H., Econometrics, 5th Edition, New York: Springer, 2011.
• Johnston, J., and DiNardo, J., Econometric Methods, 4th Edition, London: McGraw—
Hill, 1997.
• Verbeek, M., A Guide to Modern Econometrics, 4th Edition, Chichester: Wiley, 2012.
These lie at a lower level than Greene, Davidson & MacKinnon, and Hayashi, and may be useful
companions to one of the other texts, especially for students who have not completed an undergraduate course in econometrics and mathematical statistics. Verbeek’s textbook puts the emphasis on
the practical application of econometric techniques.
A deeper analysis of many of the topics that will be discussed can be found in:
• Hamilton, J., Time Series Analysis, Princeton: Princeton University Press, 1994.
• Ruud, P. A., An Introduction to Classical Econometric Theory, Oxford: Oxford University
Press, 2000.
Texts that focus on financial applications of econometric modelling are:
• Campbell, J. Y., Lo, W., and MacKinlay, A. C., The Econometrics of Financial Markets, Princeton: Princeton University Press, 1997.
• Gourieroux, C., and Jasiak, J., Financial Econometrics, Princeton: Princeton University
Press, 2001.
Textbooks which may be useful as background reading on statistical distribution theory and inference are:
• Casella, G., and Berger, R. L., Statistical Inference, 2nd Edition, Pacific Grove: Duxbury,
2002.
• Rice, J. A., Mathematical Statistics and Data Analysis, 3rd Edition, Belmont: Duxbury,
2007.
2
Outline
Week 1: review of matrix algebra and distribution theory
Quadratic forms
Partitioned matrices
Matrix differentiation
Functions of random variables
Multivariate normal distribution
Distributions of quadratic forms
Greene: Appendix A, B, C
Johnston and DiNardo: Appendix A, B
Weeks 2—3: classical least-squares theory
Multivariate linear regression model
Least-squares estimator
Sampling properties of least-squares estimator
Hypothesis testing
Restricted least squares
Davidson and MacKinnon: Chs. 1, 2, 3, 4, 5
Greene: Chs. 1, 2, 3, 4, 5.1—5.5
Hayashi: Ch. 1
Johnston and DiNardo: Ch. 3
Week 4: approximate inference
Stochastic convergence
Laws of large numbers
Central limit theorems
Large-sample properties of least-squares estimator
Davidson and MacKinnon: Chs. 2, 3, 4
Greene: Ch. 4.4—4.5, Appendix D
Hayashi: Ch. 2
Week 5: maximum likelihood theory
Maximum likelihood estimator
Finite-sample and large-sample properties
Linear regression model
Davidson and MacKinnon: Ch. 10
Greene: Ch. 14
Hayashi: Chs. 2, 7, 8
Johnston and DiNardo: Ch. 5
Week 6: reading week
3
Week 7: likelihood-based testing
Classical asymptotic tests
Classical tests and linear regression models
Tests for non-nested hypotheses
Davidson and MacKinnon: Chs. 4, 5, 15
Greene: Chs. 5.6—5.10, 14.6
Hayashi: Chs. 1, 2
Johnston and DiNardo: Ch. 5
Week 8: generalised least-squares theory
Generalised least-squares estimator
Heteroskedasticity
Serial correlation
Multiple equation models
Davidson and MacKinnon: Ch. 7
Greene: Chs. 9, 10.1—10.3, 14.9.2—14.9.3, 20
Hamilton: Ch. 8
Hayashi: Chs. 1, 2
Johnston and DiNardo: Ch. 6
Weeks 9—10: instrumental variables and generalised method of moments
Instrumental variables estimation
Efficient GMM estimation
Covariance matrix estimation
GMM-based specification tests
Davidson and MacKinnon: Chs. 8, 9
Greene: Chs. 8, 13
Hamilton: Ch. 14
Hayashi: Chs. 3, 4
Johnston and DiNardo: Chs. 5, 10
Week 11: revision lecture
25 September 2013
4