Financial Statistics Computer Tools in Finance (Empirical exercises will be done in Matlab)

Financial econometrics is the intersection of statistical techniques and finance 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 learn to differentiate between volatility, uncertainty, and risk, learning to apply these concepts to different financial assets. Finally, we will learn to estimate the implied volatility of equity indices and discuss the information contained in the implied volatility term structure and smile. Over the whole course, there will be a heavy emphasis on applications.

1. Linear Regression Model Introduction The research question: the variable, the model, estimation and inference. Applied Matlab workshop: the basics. 2. Extensions of volatility models MGARCH models: conditional variance and correlation for a portfolio of assets. Applied Matlab workshop: learning MFE Kevin Sheppard toolbox. Constant or time-varying correlation? GARCH-CCC, GARCH-DCC, BEKK-GARCH Applied Matlab workshop: MGARCH estimation 3. Volatility, uncertainty and risk Realized vs. Expected volatility. Using options to approach expectations. Applied Matlab workshop: estimating the implied volatility (IV). IV Term-structure and the volatility smile. Understanding these concepts. Applied Matlab workshop: estimating IV smiles and term-structure. The VIX: model-free vs. model-based volatility indices. Applied Matlab workshop: replicating the VIX from S&P500 options.

Students will work with Excel and Matlab. Every class, the Professor will present the main theoretical concepts using PDF presentations. After 1.5 hours 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.