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

Fingerprint

Dive into the research topics of 'Forecasting risk with Markov-switching GARCH models: A large-scale performance study'. Together they form a unique fingerprint.

Cite this