A Regime Switching Model - Applied to the OMXS30 and Nikkei 225 indices

Abstract

This Master of Science thesis investigates the performance of a Simple Regime Switching Model compared to the GARCH(1,1) model and rolling window approach. We also investigate how these models estimate the Value at Risk and the modified Value at Risk. The underlying distributions that we use are normal distribution and Student’s t-distribution. The models are fitted to the Nasdaq OMXS30 and the Nikkei 225 indices for 2013. This thesis shows that the Simple Regime Switching Model with normal distribution performs superior to the other models adjusting for skewness and kurtosis in the residuals. The best model for estimating risk is the Simple Regime Switching Model with normal distribution in combination with the classic Value at Risk. In addition, we show that financial institutions using the Simple Regime Switching Model will possibly lower their cost of risk, compared to using the GARCH(1,1) model.