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Directional spillover effect and realized volatility forecasting: An extreme market regime switching model

Author

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  • Wang, Xueqing
  • Yuan, Ying
  • Qu, Yong

Abstract

This paper proposes an Extreme Market Regime Switching (EMRS) model to enhance the prediction of stock market realized volatility by incorporating the directional spillover effect. Specifically, we construct four types of directional spillover indicators using a dynamic Copula-CoVaR approach, including upside and downside volatility transmitter indicators (VTIu and VTId) and volatility receiver indicators (VRIu and VRId), designed for different extreme market states. Building on these indicators, we further introduce our EMRS model within the HAR framework to adapt time-varying market states. The core mechanism of the EMRS model is to determine whether the current market state is in an extreme expansion or recession by comparing the relative magnitude of VTIu (VRIu) and VTId (VRId), enabling dynamic switching among the four indicators. Empirical results reveal the superior forecasting performance and the practical application value of the EMRS model.

Suggested Citation

  • Wang, Xueqing & Yuan, Ying & Qu, Yong, 2026. "Directional spillover effect and realized volatility forecasting: An extreme market regime switching model," Finance Research Letters, Elsevier, vol. 87(C).
    • Handle: RePEc:eee:finlet:v:87:y:2026:i:c:s1544612325023608
    DOI: 10.1016/j.frl.2025.109111
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    as
    1. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Gupta, Rangan & Bouri, Elie, 2025. "Forecasting spot and futures price volatility of agricultural commodities: The role of climate-related migration uncertainty," Research in International Business and Finance, Elsevier, vol. 80(C).
    2. Chen, Lin & Wen, Fenghua & Li, Wanyang & Yin, Hua & Zhao, Lili, 2022. "Extreme risk spillover of the oil, exchange rate to Chinese stock market: Evidence from implied volatility indexes," Energy Economics, Elsevier, vol. 107(C).
    3. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    4. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    5. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    6. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    7. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    8. Mzoughi, Hela & Urom, Christian & Guesmi, Khaled, 2022. "Downside and upside risk spillovers between green finance and energy markets," Finance Research Letters, Elsevier, vol. 47(PA).
    9. Giovanni Cespa & Thierry Foucault, 2014. "Illiquidity Contagion and Liquidity Crashes," The Review of Financial Studies, Society for Financial Studies, vol. 27(6), pages 1615-1660.
    10. Jin, Xiaoye, 2018. "Downside and upside risk spillovers from China to Asian stock markets: A CoVaR-copula approach," Finance Research Letters, Elsevier, vol. 25(C), pages 202-212.
    11. Hung, Ngo Thai & Vo, Xuan Vinh, 2021. "Directional spillover effects and time-frequency nexus between oil, gold and stock markets: Evidence from pre and during COVID-19 outbreak," International Review of Financial Analysis, Elsevier, vol. 76(C).
    12. Yang, Jie & Feng, Yun & Yang, Hao, 2024. "The spillover and comovement of downside and upside tail risks among crude oil futures markets," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    13. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    14. Yao, Can-Zhong & Li, Min-Jian, 2023. "GARCH-MIDAS-GAS-copula model for CoVaR and risk spillover in stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    15. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
    16. Maheu, John M. & McCurdy, Thomas H., 2011. "Do high-frequency measures of volatility improve forecasts of return distributions?," Journal of Econometrics, Elsevier, vol. 160(1), pages 69-76, January.
    17. Whitney Newey & Kenneth West, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    18. Engle, Robert F. & Ng, Victor K. & Rothschild, Michael, 1990. "Asset pricing with a factor-arch covariance structure : Empirical estimates for treasury bills," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 213-237.
    19. Bumho Son & Yunyoung Lee & Seongwan Park & Jaewook Lee, 2023. "Forecasting global stock market volatility: The impact of volatility spillover index in spatial‐temporal graph‐based model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1539-1559, November.
    20. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of the Augmented Dickey-Fuller Test," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 277-280, July.
    21. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    22. Awartani, Basel & Maghyereh, Aktham, 2025. "The value of cross market volatility in improving the forecast accuracy of risk in the gold, the dollar and the oil futures markets," Finance Research Letters, Elsevier, vol. 83(C).
    23. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    24. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    25. Hassan, Kamrul & Hoque, Ariful & Wali, Muammer & Gasbarro, Dominic, 2020. "Islamic stocks, conventional stocks, and crude oil: Directional volatility spillover analysis in BRICS," Energy Economics, Elsevier, vol. 92(C).
    26. Lee, Hsiu-Chuan & Lee, Yun-Huan & Nguyen, Cuong, 2023. "Tail comovements of implied volatility indices and global index futures returns predictability," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).
    27. Zhang, Chao & Pu, Xingyue & Cucuringu, Mihai & Dong, Xiaowen, 2025. "Forecasting realized volatility with spillover effects: Perspectives from graph neural networks," International Journal of Forecasting, Elsevier, vol. 41(1), pages 377-397.
    28. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    29. Ding, Yi & Kambouroudis, Dimos & McMillan, David G., 2025. "Forecasting realised volatility using regime-switching models," International Review of Economics & Finance, Elsevier, vol. 101(C).
    30. Kole, Erik & Koedijk, Kees & Verbeek, Marno, 2007. "Selecting copulas for risk management," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2405-2423, August.
    31. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
    32. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    33. Hamao, Yasushi & Masulis, Ronald W & Ng, Victor, 1990. "Correlations in Price Changes and Volatility across International Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 281-307.
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