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 language | English |
|---|---|
| Pages (from-to) | 733-747 |
| Number of pages | 15 |
| Journal | International Journal of Forecasting |
| Volume | 34 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Oct 2018 |
Funding
An earlier version of this paper was circulated under the title “Forecasting performance of Markov-switching GARCH models: A large-scale empirical study”. We are grateful to the Editor (Esther Ruiz), the Associate Editor, and two anonymous referees for their useful comments, which improved the paper significantly. We thank Samuel Borms, Peter Carl, Dirk Eddelbuettel, Richard Gerlach, Lennart Hoogerheide, Eliane Maalouf, Brian Peterson, Enrico Schumann, and conference participants at the R/Finance 2017 (Chicago), the 37th International Symposium on Forecasting (Cairns),UseR 2017 (Brussels), Quant Insights 2017 (London),MAFE 2018 (Madrid), and eRum 2018 (Budapest), as well as seminar participants at HEC Liège, Paris–Dauphine, and IAE–AMSE Aix–Marseille. We acknowledge Industrielle-Alliance, the International Institute of Forecasters , Google Summer of Code 2016 and 2017, FQRSC (Grant # 2015-NP-179931 ) and Fonds de Donations at the University of Neuchâtel for their financial support. We thank Félix-Antoine Fortin and Calcul Québec (clusters Briaree, Colosse, Mammouth and Parallèle II), as well as Laurent Fastnacht and the Institute of Hydrology at the University of Neuchâtel (cluster Galileo), for computational support. All computations have been performed using the R package MSGARCH (Ardia, Bluteau, Boudt, Catania, Peterson et al., forthcoming; Ardia, Bluteau, Boudt, Catania & Trottier, 2017) , which is available from the CRAN repository at https://cran.r-project.org/package=MSGARCH .
Keywords
- Expected shortfall
- Forecasting performance
- GARCH
- Large-scale study
- MSGARCH
- Risk management
- Value-at-risk
