Sequential forecasting of downside extreme risk during overnight and daytime: Evidence from the Chinese Stock Market☆

Zhihong Jian, Xupei Li and Zhican Zhu

Pacific-Basin Finance Journal, 2020, vol. 64, issue C

Abstract: This paper proposes a sequential methodology to forecast the stock markets downside extreme risk during overnight and daytime periods. We jointly characterize Value at Risk (VaR) and Expected Shortfall (ES) dynamics during overnight and daytime using a sequential hybrid GAS model conditional on lagged information. We implement the Chernozhukov-Hong MCMC methods for parameter estimation and model evaluation. The results show that the sequential models in forecasting VaR and ES outperform the non-sequential ones, and accumulated information during non-trading overnight hours can help improve the forecasting of daytime downside extreme risk. Moreover, overnight and daytime extreme risks evidently increase during the economic downturn.

Keywords: Overnight risk; Daytime risk; Value-at-risk; Expected shortfall; Sequential modeling (search for similar items in EconPapers)
JEL-codes: C22 C53 G10 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:64:y:2020:i:c:s0927538x20306661

DOI: 10.1016/j.pacfin.2020.101454

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