Financial Statistics

Financial econometrics is the intersection of statistical techniques and finance with the aim to ascertain how financial prices are determined and to test models that try to replicate how financial markets work. The course will cover the tools of financial econometrics and empirical finance with a moderate degree of sophistication, starting by introducing the extensions to the basic generalized autoregressive conditional heteroskedasticity model (GARCH) in terms of statistical properties, estimated parameters and volatilities. Then, we will discuss models of high-frequency financial data and present non parametric estimators of the integrated volatility. Finally, we present alternative models of the interest rate term structure and use the Kalman filter for the estimation of the unobserved components. In the two last weeks of the course there will be a heavy emphasis on applications.

1. Extensions of volatility models 1.1. GARCH models with leverage effect 1.2. Statistical properties of the new models 1.3. Estimating parameters and volatilities 1.4. Stochastic volatility models and its statistical properties 1.5. Estimation methods and some examples 2. Multivariate volatility models 2.1. Multivariate GARCH models 2.2. Multivariate stochastic volatility models 2.3. Estimation methods and some examples 3. Real examples proposed by the practitioner 3.1. The use of financial econometrics in the risk management process: The case of Repsol 3.2. Asymmetric densities and other topics associated to time series involved in the daily work of Repsol

Students will work with Excel and Matlab. Every class, the Professor will present the main theoretical concepts using PDF presentations. After 2hours of theory, the students solve exercises using matlab and excel for consolidating the concepts learnt before. The exercises are solved individually with the help of the Professor.