Journal
JOURNAL OF FORECASTING
Volume 39, Issue 5, Pages 797-810Publisher
WILEY
DOI: 10.1002/for.2668
Keywords
extreme shocks; GARCH-MIDAS; stock volatility; volatility forecasting
Categories
Funding
- Double First-Class project of Southwest Jiaotong University [JDSYLYB2018033]
- Key Laboratory of Statistical information technology and data mining, State Statistics Bureau [SDL201711]
- Humanities and Social Science Fund of the Ministry of Education [17YJC790105, 17XJCZH002]
- National Natural Science Foundation of PR China [71671145, 71701170]
Ask authors/readers for more resources
This paper introduces a novel generalized autoregressive conditional heteroskedasticity-mixed data sampling-extreme shocks (GARCH-MIDAS-ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short- and long-term effects caused by extreme events, we extend the standard GARCH-MIDAS model to characterize the different responses of the stock market for short- and long-term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in-sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH-MIDAS-SLES model by including the short- and long-term impacts of extreme shocks for all forecasting horizons. The out-of-sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short- and long-term effects to improve the accuracy of the DJIA volatility forecasts.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available