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The Business Cycle’s Impact on Volatility Forecasting: Recapturing Intrinsic Jump Components

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  • Son-Nan Chen

    (Shanghai Advanced Institute of Finance, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Pao-Peng Hsu

    (Department of Insurance and Finance Management, Chaoyang University of Technology, Taichung 41349, Taiwan)

Abstract

This study investigates the leverage effect and realized volatility (RV) of stocks in the presence of asymmetric jumps across economic expansions and contractions. We extend the heterogeneous autoregressive-realized volatility (HAR-RV) model by incorporating a two-period Markov regime-switching model to capture Taiwan’s economic expansion and contraction. Using Taiwan’s COVID-19 insurance-oversold events as a case–control setting, we compare the asymmetric jump risk effects on RV and realized semivariance (RSV). The results reveal that business cycle (BC) effects offset jump risk impacts, rendering intrinsic jump components statistically insignificant when BC information is omitted. During contraction periods, asymmetric jumps generate stronger negative RSV shocks, amplifying the leverage effect. Moreover, the predictive accuracy of RV critically depends on the prevailing business cycle state. By incorporating BC effects into the model, we recapture significant jump components and enhance volatility forecasting performance.

Suggested Citation

  • Son-Nan Chen & Pao-Peng Hsu, 2025. "The Business Cycle’s Impact on Volatility Forecasting: Recapturing Intrinsic Jump Components," Risks, MDPI, vol. 13(11), pages 1-17, November.
    • Handle: RePEc:gam:jrisks:v:13:y:2025:i:11:p:221-:d:1787670
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