Prerequisites :

Basic notions in statistic, probability and finance

Goal :

Develop concepts necessary to applications of econometrics in finance

Content :

The lectures start with complementing basic statistical knowledge and are divided into two parts depending on the presence of temporal dependencies or not in the data. The studied statistical methods are illustrated via applications in economics and finance : Value at Risk, Expected Shortfall, portfolio selection, CAPM, APT, dynamic modeling of financial asset prices via ARMA, ARCH models and co-integration, etc. The analyzed methods will cover parametric and non-parametric tools.

Bibliography :

• Gouriéroux, C.; Scaillet, O. & Szafarz, A. (1997), “Econométrie de la finance : Analyses historiques”, Economica, Paris.
• Gouriéroux, C. (1992), “Modèles ARCH et Applications Financières”, Economica, Paris.
• Gouriéroux, C. & Monfort, A. (1995), “Statistiques et Modèles Econométriques”, Vol. I et II, Economica, Paris.
• Gouriéroux, C. & Monfort, 1 (1995), “Séries temporelles et Modèles Dynamiques”, Economica, Paris.
• Gouriéroux, C. (1989), “Econométrie des Variables Qualitatives”, Economica, Paris.
• Mills, T.(1999), “The Econometric Modeling of Financial Time Series”, Cambridge University Press, Cambridge.
• Campbell, J.; Lo, A. & McKinlay (1997), “The Econometrics of Financial Markets”, Princeton University Press, Princeton.
• Davidson, R, & MacKinnon, J. (1995), “Estimation and Interference in Econometrics”, Oxford University Press, Oxford.
• Hamilton, J. (1994), “Times Series Analysis”, Princeton University Press, Princeton.

Coordinates :

Prof. Olivier Scaillet – GSEM UNI MAIL – Bd du Pont d’Arve, 40 – CH – 1211 Genève 4 Suisse – Tel. : ++41 22 379 88 16 (direct) – ++41 22 379 81 03 (GSEM) – Fax : ++41 22 379 81 04

Teaching Methods :

Ex Cathedra lectures via slides and exercise seminars with corrections

Evaluation Methods :

Written Exam

Language :

English (or French on request for French speaking attendance only)

Campus : S

Semester : 2

Practical information:

The exercise sessions will consist in a brief explanation of the exercise, and then the students solve the problems using MATLAB with the help of the assistant.