Financial Econometrics - University of Florida
Financial Econometrics
FIN 6930, section 9872
Fall 2010
Instructor: Farid AitSahlia
farid.aitsahlia@warrington.ufl.edu
Meeting Time: MW 5-6
Meeting Location: CBD 324
This course is designed as a first graduate course in financial econometrics. It has two main objectives: (i) directly link fundamental financial models and statistical techniques with market data, and (ii) ensure that students understand the assumptions and limitations of their models. Applications will be illustrated on classical equilibrium and arbitrage pricing models as well as on more recent issues such as statistical arbitrage, high-frequency trading, and volatility calibration. The tentative list of topics to be covered includes:
• Linear and nonlinear least-squares regression
• Quantile regression
• Heteroskedasticity and multivariate regression
• Empirical likelihood
• Bootstrapping and nonparametric techniques
• Generalized method of moments
• Auto-regressive moving averages
• Vector autoregressive models
• Unit-root test and cointegration
• Conditional heteroskedastic models: ARCH and GARCH
• Simulation methods
• Implied volatility and stochastic volatility in option pricing
• Technical analysis, trading strategies, and data snooping checks
The lectures will follow relatively closely the material contained in the first reference below. The second book is still in progress but provides some complementary material to the former and will be used as well. The third is a classic text and will be used for additional perspectives. The first is an e-book accessible through the UF library website. The third reference will be on reserve at Library West for a 24-hr check-out period.
References:
➢ Statistical Models and Methods for Financial Markets, by T. L. Lai and H. Xing, Springer, 2008. Link:
➢ Econometrics, by B. E. Hansen, is available online at:
➢ The Econometrics of Financial Markets, by J. Y. Campbell, A. W. Lo, & A. C. MacKinlay, Princeton University Press, 1996.
Pre-requisites:
Basic financial economics concepts and models, including fundamental asset and derivative pricing. Familiarity with matrix algebra, multivariate calculus, and statistical inference.
Evaluation of Student Progress:
There will be bi-weekly homework assignments during, roughly, the first half of the semester, accounting for 20% of the final grade. A midterm exam covering the homework material will account for 30% of the final grade. 50% of the final grade will be based on a project making use of actual data, with presentation given at the end of the semester, in which the student must demonstrate the ability to statistically and rigorously analyze a substantial financial data set. It is expected that the topic and data selection be determined for each student about a third of the way through the course.
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