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Exploring the Driving Forces of the Correlations Between China's Crude Oil Futures and Global and Regional Benchmarks

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  • Min Liu
  • Chien‐Chiang Lee

Abstract

The launch of the Shanghai International Energy Exchange crude oil futures (INECOFs) is a milestone in China's path to a dominant position in the global energy market. As INECOFs attract more and more investors, understanding the long‐term correlations between INECOFs and global and regional benchmarks, as well as the driving forces of these correlations, is of paramount interest to investors wishing to conduct risk management and portfolio diversification. This article makes the first attempt to explore the determinants of such correlations using the mixed‐frequency approach. Our results show that INECOFs are highly correlated with the regional benchmarks and less correlated with the global benchmarks. China's crude oil imports, RMB internationalization, the RMB index, economic and trade policy uncertainty, and geopolitical risks significantly impact the dynamics of the correlations in question. China's gross industrial product and price levels cannot drive the movements of all the studied correlations.

Suggested Citation

  • Min Liu & Chien‐Chiang Lee, 2025. "Exploring the Driving Forces of the Correlations Between China's Crude Oil Futures and Global and Regional Benchmarks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(4), pages 379-392, April.
  • Handle: RePEc:wly:jfutmk:v:45:y:2025:i:4:p:379-392
    DOI: 10.1002/fut.22569
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    1. Sun, Chuanwang & Min, Jialin & Sun, Jiacheng & Gong, Xu, 2023. "The role of China's crude oil futures in world oil futures market and China's financial market," Energy Economics, Elsevier, vol. 120(C).
    2. Narayan, Seema & Doytch, Nadia, 2017. "An investigation of renewable and non-renewable energy consumption and economic growth nexus using industrial and residential energy consumption," Energy Economics, Elsevier, vol. 68(C), pages 160-176.
    3. Feng Ma & M. I. M. Wahab & Jing Liu & Li Liu, 2018. "Is economic policy uncertainty important to forecast the realized volatility of crude oil futures?," Applied Economics, Taylor & Francis Journals, vol. 50(18), pages 2087-2101, April.
    4. Huang, Xiaohong & Huang, Shupei, 2020. "Identifying the comovement of price between China's and international crude oil futures: A time-frequency perspective," International Review of Financial Analysis, Elsevier, vol. 72(C).
    5. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
    6. Moghaddam, Mohsen Bakhshi, 2023. "The relationship between oil price changes and economic growth in Canadian provinces: Evidence from a quantile-on-quantile approach," Energy Economics, Elsevier, vol. 125(C).
    7. Neluka Devpura & Paresh Kumar Narayan, 2021. "Hourly Oil Price Volatility - The Role of COVID-19," Energy RESEARCH LETTERS, Asia-Pacific Applied Economics Association, vol. 1(1), pages 1-4.
    8. Girardin, Eric & Joyeux, Roselyne, 2013. "Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach," Economic Modelling, Elsevier, vol. 34(C), pages 59-68.
    9. Khoo, Joye & Cheung, Adrian (Wai Kong), 2021. "Does geopolitical uncertainty affect corporate financing? Evidence from MIDAS regression," Global Finance Journal, Elsevier, vol. 47(C).
    10. Christopher Aitken & Erkal Ersoy, 2023. "War in Ukraine: The options for Europe's energy supply," The World Economy, Wiley Blackwell, vol. 46(4), pages 887-896, April.
    11. Yung Chul Park, 2010. "RMB Internationalization and Its Implications for Financial and Monetary Cooperation in East Asia," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 18(2), pages 1-21, March.
    12. Engle, Robert & Colacito, Riccardo, 2006. "Testing and Valuing Dynamic Correlations for Asset Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 238-253, April.
    13. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    14. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    15. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    16. Xu Gong & Boqiang Lin, 2022. "Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 610-640, January.
    17. Yinghua Ren & Lin Chen & Ye Liu, 2018. "The Onshore–Offshore Exchange Rate Differential, Interest Rate Spreads, and Internationalization: Evidence from the Hong Kong Offshore Renminbi Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(13), pages 3100-3116, October.
    18. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    19. Yang‐Chao Wang & Jui‐Jung Tsai & Shushu Li & Yiying Huang, 2023. "The impacts of RMB internationalization on onshore and offshore RMB markets," International Review of Finance, International Review of Finance Ltd., vol. 23(3), pages 502-523, September.
    20. 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).
    21. Brandt, Michael W. & Gao, Lin, 2019. "Macro fundamentals or geopolitical events? A textual analysis of news events for crude oil," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 64-94.
    22. Ziliang Yu & Jian Yang & Robert I. Webb, 2023. "Price discovery in China's crude oil futures markets: An emerging Asian benchmark?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(3), pages 297-324, March.
    23. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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