TY - JOUR
T1 - Forecasting risk with Markov-switching GARCH models
T2 - A large-scale performance study
AU - Ardia, David
AU - Bluteau, Keven
AU - Boudt, Kris
AU - Catania, Leopoldo
PY - 2018/10/1
Y1 - 2018/10/1
N2 - 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.
AB - 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.
KW - Expected shortfall
KW - Forecasting performance
KW - GARCH
KW - Large-scale study
KW - MSGARCH
KW - Risk management
KW - Value-at-risk
UR - http://www.scopus.com/inward/record.url?scp=85051373011&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2018.05.004
DO - 10.1016/j.ijforecast.2018.05.004
M3 - Article
AN - SCOPUS:85051373011
VL - 34
SP - 733
EP - 747
JO - International Journal of Forecasting
JF - International Journal of Forecasting
SN - 0169-2070
IS - 4
ER -