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

    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.

    Original languageEnglish
    Pages (from-to)733-747
    Number of pages15
    JournalInternational Journal of Forecasting
    Volume34
    Issue number4
    DOIs
    Publication statusPublished - Oct 2018

    Keywords

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

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