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Forecasting risk with Markov-switching GARCH models:A large-scale performance study

David Ardia, Keven Bluteau, Kris Boudt and Leopoldo Catania

International Journal of Forecasting, 2018, vol. 34, issue 4, 733-747

Abstract: We perform a large-scale empirical study in order to compare the forecasting performances of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that MSGARCH models yield more accurate Value-at-Risk, expected shortfall, and left-tail distribution forecasts than their single-regime counterparts for daily, weekly, and ten-day equity log-returns. Also, our results indicate that accounting for parameter uncertainty improves the 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 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1016/j.ijforecast.2018.05.004

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Handle: RePEc:eee:intfor:v:34:y:2018:i:4:p:733-747