SE-419 Financial Econometrics

The course starts with a primer on formulating statistical models in R. By means of examples, the R packages relevant for the course are introduced. 

A discussion of the key concepts of statistical inference provides the conceptual basis of the course. Estimation methods (least squares, maximum likelihood, and generalized method of moments), principles underlying hypothesis testing (likelihood ratio, Wald, Lagrange-multiplier) and principles of Monte-Carlo simulation will be motivated and put to work in the context of the multiple linear regression model. In this context, the main stages of a model building process, including model diagnoses, and specification testing will be demonstrated. A module on forecasting completes the treatment of this first approach to time series regression.  

Next, by adopting a dynamic process perspective, univariate linear time series models will be introduced. After exploring key concepts as trends, cycles, stationarity, and autocorrelation in financial data, the focus will shift to estimation, testing and forecasting in the context of autoregressive moving average (ARMA) models. In the sequel, methods for studying the relationships between several financial variables across time, as vector autoregressive models (VARs) and techniques for modelling the long-run (error correction models and cointegration techniques), will be discussed and applied.  A module on modelling volatility in financial data (ARCH, GARCH) will provide a first look at non-linear time series analysis. Finally, a first glimpse at statistical techniques and models for financial panel data finalizes the course.