This course introduces students to a comprehensive range of techniques used in financial econometrics and their practical applications. A background in statistics and econometrics would be beneficial, but this is not a prerequisite. Each student is required to submit a project applying the course knowledge to a financial dataset. Accordingly, one of the course's objectives is to equip students with the skills necessary to conduct independent research projects and to provide them with the necessary background to apply their knowledge to additional topics of interest without significant difficulty.

The course will primarily focus on time series methods. The course begins with a review of fundamental methods in statistics and econometrics, introducing regression analysis, the method of least squares, and their important extensions. A wide range of time series methods are then discussed, including estimation and forecasting of ARMA and ARIMA models, conditional heteroskedasticity models (ARCH/GARCH), vector autoregression models, and cointegration. Each topic is presented with an application from finance, focusing on the unique characteristics of financial data and methods specifically developed for working with such data.