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Extreme Comovement and Risk Spillovers in Crude Oil Prices: A Tale of Two Events

Author

Listed:
  • Haoyu Shi
  • Yuansheng Wang
  • Xu Zheng

Abstract

In this paper, we investigate the tail dependence and risk spillovers between International Energy Exchange (INE) crude oil futures and global crude oil benchmarks (WTI and Brent), as well as its underlying spot markets, by integrating the ARMA–GARCH‐skewed‐ t model with the Copula‐CoVaR framework. Using high‐frequency data with synchronized trading windows, we find consistently strong tail dependence across all sessions, supporting the role of INE as an emerging Asian benchmark. Risk spillovers are asymmetric, with downside risk dominating. INE functions as an information sender during daytime trading, characterized by notable volatility transmission, whereas nighttime spillover is more stable and symmetric. Moreover, INE is more sensitive to extreme events such as COVID‐19 pandemic and the Russia–Ukraine conflict during its domestic trading hours. Our findings offer practical implications for market regulation, emphasizing the need to improve nighttime liquidity and enhance systemic risk monitoring under time‐varying uncertainty.

Suggested Citation

  • Haoyu Shi & Yuansheng Wang & Xu Zheng, 2026. "Extreme Comovement and Risk Spillovers in Crude Oil Prices: A Tale of Two Events," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 46(2), pages 283-319, February.
    • Handle: RePEc:wly:jfutmk:v:46:y:2026:i:2:p:283-319
    DOI: 10.1002/fut.70059
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    References listed on IDEAS

    as
    1. Huang, Yun & Luk, Paul, 2020. "Measuring economic policy uncertainty in China," China Economic Review, Elsevier, vol. 59(C).
    2. Nikolaos Milonas & Thomas Henker, 2001. "Price spread and convenience yield behaviour in the international oil market," Applied Financial Economics, Taylor & Francis Journals, vol. 11(1), pages 23-36.
    3. Andrew N. Kleit, 2001. "Are Regional Oil Markets Growing Closer Together?: An Arbitrage Cost Approach," The Energy Journal, , vol. 22(2), pages 1-15, April.
    4. Robert J. Weiner, 1991. "Is the World Oil Market “One Great Pool†?," The Energy Journal, , vol. 12(3), pages 95-108, July.
    5. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    6. Yue‐Jun Zhang & Shu‐Jiao Ma, 2021. "Exploring the dynamic price discovery, risk transfer and spillover among INE, WTI and Brent crude oil futures markets: Evidence from the high‐frequency data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2414-2435, April.
    7. repec:aen:journl:2001v22-02-a01 is not listed on IDEAS
    8. Qi Zhang & Peng Di & Arash Farnoosh, 2021. "Study on the impacts of Shanghai crude oil futures on global oil market and oil industry based on VECM and DAG models," Post-Print hal-03151102, HAL.
    9. Hendershott, Terrence & Riordan, Ryan, 2013. "Algorithmic Trading and the Market for Liquidity," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1001-1024, August.
    10. Si, Deng-Kui & Li, Xiao-Lin & Xu, XuChuan & Fang, Yi, 2021. "The risk spillover effect of the COVID-19 pandemic on energy sector: Evidence from China," Energy Economics, Elsevier, vol. 102(C).
    11. Fattouh, Bassam, 2010. "The dynamics of crude oil price differentials," Energy Economics, Elsevier, vol. 32(2), pages 334-342, March.
    12. Sun, Chuanwang & Zhan, Yanhong & Peng, Yiqi & Cai, Weiyi, 2022. "Crude oil price and exchange rate: Evidence from the period before and after the launch of China's crude oil futures," Energy Economics, Elsevier, vol. 105(C).
    13. Konishi, Hizuru, 2002. "Optimal slice of a VWAP trade," Journal of Financial Markets, Elsevier, vol. 5(2), pages 197-221, April.
    14. Lyu, Yongjian & Yi, Heling & Wei, Yu & Yang, Mo, 2021. "Revisiting the role of economic uncertainty in oil price fluctuations: Evidence from a new time-varying oil market model," Economic Modelling, Elsevier, vol. 103(C).
    15. Wei, Yu & Zhang, Yaojie & Wang, Yudong, 2022. "Information connectedness of international crude oil futures: Evidence from SC, WTI, and Brent," International Review of Financial Analysis, Elsevier, vol. 81(C).
    16. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    17. Reboredo, Juan C., 2011. "How do crude oil prices co-move?: A copula approach," Energy Economics, Elsevier, vol. 33(5), pages 948-955, September.
    18. Zhang, Qi & Di, Peng & Farnoosh, Arash, 2021. "Study on the impacts of Shanghai crude oil futures on global oil market and oil industry based on VECM and DAG models," Energy, Elsevier, vol. 223(C).
    19. Ho, Anson T.Y. & Huynh, Kim P. & Jacho-Chávez, David T., 2019. "Using nonparametric copulas to measure crude oil price co-movements," Energy Economics, Elsevier, vol. 82(C), pages 211-223.
    20. Narayan, Paresh Kumar, 2022. "Evidence of oil market price clustering during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 80(C).
    21. Zhang, Yunhan & Ji, Qiang & Zhang, Dayong & Guo, Kun, 2024. "How does Shanghai crude oil futures affect top global oil companies: The role of multi-uncertainties," Energy Economics, Elsevier, vol. 131(C).
    22. Weiner, R.J., 1991. "Is the World Oil Market "One Great Pool?"," Papers 9120, Laval - Recherche en Energie.
    23. Yang, Chen & Lv, Fei & Fang, Libing & Shang, Xingxing, 2020. "The pricing efficiency of crude oil futures in the Shanghai International Exchange," Finance Research Letters, Elsevier, vol. 36(C).
    24. repec:aen:journl:1991v12-03-a07 is not listed on IDEAS
    25. Zhao, Ling, 2023. "Global economic policy uncertainty and oil futures volatility prediction," Finance Research Letters, Elsevier, vol. 54(C).
    26. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    27. Lutz Kilian, 2008. "Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 216-240, May.
    28. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    29. repec:bla:jfinan:v:44:y:1989:i:1:p:1-17 is not listed on IDEAS
    30. Mondria, Jordi & Wang, Xin & Wu, Thomas, 2021. "Familiarity and Surprises in International Financial Markets: Bad news travels like wildfire; good news travels slow," Journal of International Money and Finance, Elsevier, vol. 115(C).
    31. Diego R. Känzig, 2021. "The Macroeconomic Effects of Oil Supply News: Evidence from OPEC Announcements," American Economic Review, American Economic Association, vol. 111(4), pages 1092-1125, April.
    32. Ser-Huang Poon, 2004. "Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications," The Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 581-610.
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