Forecasting Risk with Markov-Switching GARCH Models: A Large-Scale Performance Study
International Journal of Forecasting, Vol 34, Issue 4, pp. 733-747, 2018
36 Pages Posted: 16 Feb 2017 Last revised: 28 Mar 2019
Date Written: March 2, 2018
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.
Keywords: GARCH, MSGARCH, forecasting performance, large-scale study, Value-at-Risk, Expected Shortfall, risk management
JEL Classification: C11, C22, C51, C53, C55, C58, G17
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