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

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

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)733-747
Number of pages15
JournalInternational Journal of Forecasting
Issue number4
Publication statusPublished - Oct 2018


  • Expected shortfall
  • Forecasting performance
  • Large-scale study
  • Risk management
  • Value-at-risk

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