Forecasting Risk with Markov-Switching GARCH Models : A Large-Scale Performance Study

We perform a large-scale empirical study to compare the forecasting performance of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that, for daily, weekly, and ten-day equity log-returns, MSGARCH models yield more accurate Value-at-Risk, Expected Shortfall, and left-tail distribution forecasts than their single-regime counterpart. Also, our results indicate that accounting for parameter uncertainty improves left-tail predictions, independently of the inclusion of the Markov-switching mechanism