This course introduces the student to a wide range of techniques in financial econometrics, and their practical applications. Prior knowledge of statistics and econometrics is very useful, but it isn't necessary. Each student is required to hand in a class project that applies class material to real financial data. Accordingly, one of the aims of the course is to give students the skills necessary to pursue independent research projects, and the backgrounds to be able to extend their knowledge to additional topics of interest without much difficulty.

The course will mostly be based on Time Series econometric methods. The course starts by reviewing basic tools of statistics and econometrics, and makes brief introductions to regression analysis, least squares methods, and some extensions of these topics. Then, numerous time series methods are discussed, including the estimation and forecasting of ARMA and ARIMA models, models of conditional heteroscedasticity (ARCH/GARCH), vector autoregressions, and cointegration. Each topic is discussed along with its applications in finance, keeping in mind the peculiarities of financial data and methods that are designed to work with such data.