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

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  • Ardia, David
  • Bluteau, Keven
  • Boudt, Kris
  • Catania, Leopoldo

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.

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  • Ardia, David & Bluteau, Keven & Boudt, Kris & Catania, Leopoldo, 2018. "Forecasting risk with Markov-switching GARCH models:A large-scale performance study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 733-747.
    • Handle: RePEc:eee:intfor:v:34:y:2018:i:4:p:733-747
    DOI: 10.1016/j.ijforecast.2018.05.004
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