This course will explore nonlinear time series models designed to account for the empirical properties of financial time series. The primary focus will be on modeling nonlinear conditional heteroscedasticity as well as model comparison approaches. In addition, computer exercises will be offered using R after a few lectures.

The purpose of this course is to provide students with a comprehensive understanding of nonlinear time series models, with a focus on financial time series data. The students will begin by reviewing the basic concepts of time series, such as linear models, unit roots, seasonality, and empirical specification strategies. They will then explore the typical features of financial time series and delve into regime-switching models for returns and volatility, including GARCH models. The main activities are lectures, discussions, and practical exercises utilizing R to apply theoretical concepts to real-world financial data.

Franses, P. H., & Dijk, D. van. (2000). Non-Linear Time Series Models in Empirical Finance. Cambridge University Press. https://doi.org/10.1017/CBO9780511754067