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

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  • Jian, Zhihong
  • Li, Xupei
  • Zhu, Zhican

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.

Suggested Citation

  • Jian, Zhihong & Li, Xupei & Zhu, Zhican, 2020. "Sequential forecasting of downside extreme risk during overnight and daytime: Evidence from the Chinese Stock Market☆," Pacific-Basin Finance Journal, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:pacfin:v:64:y:2020:i:c:s0927538x20306661
    DOI: 10.1016/j.pacfin.2020.101454
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    More about this item

    Keywords

    Overnight risk; Daytime risk; Value-at-risk; Expected shortfall; Sequential modeling;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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