Matlab pour la Finance appliquée
Les objectifs du cours
Related to the "Econometrics of Financial Markets" courses, the aim of this class is to provide sufficient bases in Matlab-programming to be able to deal with concrete financial applications from data acquisition (from various data sources) to publishing the results.
Plan du cours
1/ Introduction to Matlab (2x3h)
- Language and environment
- Data acquisition
2/ The first step in financial data analysis (3h)
- Dealing with missing, incomplete or corrupted data
- Constructing time series, displaying financial data
- Basic statistics, Parameter estimation, Robust estimation
3/ Traditional Econometric methods implementation (3x3h)
- Markowitz efficient frontier
- Statistic tests in the CAPM framework
- Factor Selection in a multi-factor framework
4/ Pricing models and inverse methods (3h)
- Black & Scholes model
- Implied Volatility
5/ Dealing with intraday transaction data (3h)
Bibliographie
- Matlab Getting started guide: http://www.mathworks.com/help/pdf_doc/matlab/getstart.pdf
- John C. Hull, Options, Futures, and Other Derivatives, Prentice Hall
- Little, R. J. A and . B. Rubin, 2002, Statistical Analysis with Missing Data, 2nd ed., John Wiley & Sons, Inc..
- Fama E. F. and K. R. French, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics, vol. 33, pp. 3-56.
- Maronna R. A., 1976, Robust M-estimators of multivariate location and scatter, Annals of Statistics, vol. 4, no. 1, pp. 51-67.
- M. Markov, V. Mottl, and I. Muchnik, 2004,Principles of nonstationary regression estimation: A new approach to dynamic multi-factor models in finance, Technical Report 2004-47, DIMACS, October.
- Hamilton J. D., 1994, Time Series Analysis, Princeton University Press.
- Van Trees H. L., 2002, Optimum Array Processing, Part IV of Detection, Estimation and Modulation Theory, John Wiley & Sons.
- Rissanen J.,1978, Modeling by shortest data description, Automatica, vol. 14, pp. 465-471.
Examen
To be announced