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The Time-Varying Connectedness Between China’s Crude Oil Futures and International Oil Markets: A Return and Volatility Spillover Analysis

Author

Listed:
  • Jiasha Fu

    (Southwestern University of Finance and Economics)

  • Hui Qiao

    (Southwestern University of Finance and Economics)

Abstract

This paper examines the relationship between world crude oil markets following the introduction of Shanghai crude oil futures from the perspective of network connectedness based on the vector autoregressive model. The connectedness measurement method proposed by Diebold and Yilmaz (Econ J 119(534):158–171, 2009, Int J Forecast 28(1):57–66, 2012. https://doi.org/10.1016/j.ijforecast.2011.02.006 , J Econom 182(1):119–134, 2014. https://doi.org/10.1016/j.jeconom.2014.04.012 ) is adopted to study a time-varying interdependence relationship. The empirical results show that the world crude oil markets exhibit a high degree of integration from both returns and volatility; however, the direction and magnitude contributed by each market varies significantly. Specifically, the West Texas Intermediate futures and Brent spot and futures markets were found to have the highest contributions to the world oil market over the entire sample period and take leading roles, whereas Dubai futures market was found to be the most important receiver, and has received the most spillover from other markets and passed it throughout the system. Shanghai crude oil futures is not yet highly connected with other markets. Moreover, heterogeneous changes in the direction, intensity, and persistence of the spillover were observed across markets after the outbreak of the COVID-19 pandemic in 2020. This study reveals the integration level of Shanghai crude oil futures and the dynamics of linkages between regional crude oil markets, which is of great significance for market participants, policymakers, and future researchers.

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  • Jiasha Fu & Hui Qiao, 2022. "The Time-Varying Connectedness Between China’s Crude Oil Futures and International Oil Markets: A Return and Volatility Spillover Analysis," Letters in Spatial and Resource Sciences, Springer, vol. 15(3), pages 341-376, December.
  • Handle: RePEc:spr:lsprsc:v:15:y:2022:i:3:d:10.1007_s12076-021-00288-z
    DOI: 10.1007/s12076-021-00288-z
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    as
    1. Chang, C-L. & McAleer, M.J. & Tansuchat, R., 2010. "Analyzing and Forecasting Volatility Spillovers and Asymmetries in Major Crude Oil Spot, Forward and Futures Markets," Econometric Institute Research Papers EI 2010-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Scholes, Myron & Williams, Joseph, 1977. "Estimating betas from nonsynchronous data," Journal of Financial Economics, Elsevier, vol. 5(3), pages 309-327, December.
    3. Bouri, Elie & Cepni, Oguzhan & Gabauer, David & Gupta, Rangan, 2021. "Return connectedness across asset classes around the COVID-19 outbreak," International Review of Financial Analysis, Elsevier, vol. 73(C).
    4. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    5. Schotman, Peter C. & Zalewska, Anna, 2006. "Non-synchronous trading and testing for market integration in Central European emerging markets," Journal of Empirical Finance, Elsevier, vol. 13(4-5), pages 462-494, October.
    6. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2010. "Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets," Energy Economics, Elsevier, vol. 32(6), pages 1445-1455, November.
    7. Ding, Zhihua & Liu, Zhenhua & Zhang, Yuejun & Long, Ruyin, 2017. "The contagion effect of international crude oil price fluctuations on Chinese stock market investor sentiment," Applied Energy, Elsevier, vol. 187(C), pages 27-36.
    8. Jin, Xiaoye & Xiaowen Lin, Sharon & Tamvakis, Michael, 2012. "Volatility transmission and volatility impulse response functions in crude oil markets," Energy Economics, Elsevier, vol. 34(6), pages 2125-2134.
    9. Andrew N. Kleit, 2001. "Are Regional Oil Markets Growing Closer Together?: An Arbitrage Cost Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-15.
    10. Li, Raymond & Leung, Guy C.K., 2011. "The integration of China into the world crude oil market since 1998," Energy Policy, Elsevier, vol. 39(9), pages 5159-5166, September.
    11. Apergis, Nicholas & Lau, Marco Chi Keung & Yarovaya, Larisa, 2016. "Media sentiment and CDS spread spillovers: Evidence from the GIIPS countries," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 50-59.
    12. Yang, Kun & Wei, Yu & Li, Shouwei & Liu, Liang & Wang, Lei, 2021. "Global financial uncertainties and China’s crude oil futures market: Evidence from interday and intraday price dynamics," Energy Economics, Elsevier, vol. 96(C).
    13. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2011. "Crude oil hedging strategies using dynamic multivariate GARCH," Energy Economics, Elsevier, vol. 33(5), pages 912-923, September.
    14. Liu, Tangyong & Gong, Xu, 2020. "Analyzing time-varying volatility spillovers between the crude oil markets using a new method," Energy Economics, Elsevier, vol. 87(C).
    15. Baruník, Jozef & Kočenda, Evžen & Vácha, Lukáš, 2016. "Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers," Journal of Financial Markets, Elsevier, vol. 27(C), pages 55-78.
    16. Fang, Sheng & Egan, Paul, 2018. "Measuring contagion effects between crude oil and Chinese stock market sectors," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 31-38.
    17. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    18. Reboredo, Juan C., 2011. "How do crude oil prices co-move?: A copula approach," Energy Economics, Elsevier, vol. 33(5), pages 948-955, September.
    19. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    20. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    21. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    22. Friendly M., 2002. "Corrgrams: Exploratory Displays for Correlation Matrices," The American Statistician, American Statistical Association, vol. 56, pages 316-324, November.
    23. Eun, Cheol S. & Shim, Sangdal, 1989. "International Transmission of Stock Market Movements," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(2), pages 241-256, June.
    24. Yue-Hua Dai & Wen-Jie Xie & Zhi-Qiang Jiang & George J. Jiang & Wei-Xing Zhou, 2016. "Correlation structure and principal components in the global crude oil market," Empirical Economics, Springer, vol. 51(4), pages 1501-1519, December.
    25. Wen, Xiaoqian & Wei, Yu & Huang, Dengshi, 2012. "Measuring contagion between energy market and stock market during financial crisis: A copula approach," Energy Economics, Elsevier, vol. 34(5), pages 1435-1446.
    26. Jia, Xiaoliang & An, Haizhong & Sun, Xiaoqi & Huang, Xuan & Wang, Lijun, 2017. "Evolution of world crude oil market integration and diversification: A wavelet-based complex network perspective," Applied Energy, Elsevier, vol. 185(P2), pages 1788-1798.
    27. Zhou, Xiangyi & Zhang, Weijin & Zhang, Jie, 2012. "Volatility spillovers between the Chinese and world equity markets," Pacific-Basin Finance Journal, Elsevier, vol. 20(2), pages 247-270.
    28. Wei, Yu & Qin, Songkun & Li, Xiafei & Zhu, Sha & Wei, Guiwu, 2019. "Oil price fluctuation, stock market and macroeconomic fundamentals: Evidence from China before and after the financial crisis," Finance Research Letters, Elsevier, vol. 30(C), pages 23-29.
    29. 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.
    30. Korobilis, D & Yilmaz, K, 2018. "Measuring Dynamic Connectedness with Large Bayesian VAR Models," Essex Finance Centre Working Papers 20937, University of Essex, Essex Business School.
    31. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    32. Antonakakis, Nikolaos & Gabauer, David, 2017. "Refined Measures of Dynamic Connectedness based on TVP-VAR," MPRA Paper 78282, University Library of Munich, Germany.
    33. Kuck, Konstantin & Schweikert, Karsten, 2017. "A Markov regime-switching model of crude oil market integration," Journal of Commodity Markets, Elsevier, vol. 6(C), pages 16-31.
    34. Lin, Boqiang & Su, Tong, 2021. "Does COVID-19 open a Pandora's box of changing the connectedness in energy commodities?," Research in International Business and Finance, Elsevier, vol. 56(C).
    35. Jian Yang & Yinggang Zhou, 2020. "Return and volatility transmission between China's and international crude oil futures markets: A first look," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(6), pages 860-884, June.
    36. 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.
    37. M. A. Adelman, 1984. "International Oil Agreements," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 1-10.
    38. Zhang, Dayong & Ji, Qiang & Kutan, Ali M., 2019. "Dynamic transmission mechanisms in global crude oil prices: Estimation and implications," Energy, Elsevier, vol. 175(C), pages 1181-1193.
    39. Zhang, Bing & Wang, Peijie, 2014. "Return and volatility spillovers between china and world oil markets," Economic Modelling, Elsevier, vol. 42(C), pages 413-420.
    40. Broadstock, David C. & Li, Raymond & Wang, Linjin, 2020. "Integration reforms in the European natural gas market: A rolling-window spillover analysis," Energy Economics, Elsevier, vol. 92(C).
    41. Weiner, R.J., 1991. "Is the World Oil Market "One Great Pool?"," Papers 9120, Laval - Recherche en Energie.
    42. Robert J. Weiner, 1991. "Is the World Oil Market "One Great Pool"?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 95-108.
    43. S. Gurcan Gulen, 1999. "Regionalization in the World Crude Oil Market: Further Evidence," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 125-139.
    44. Zhang, Dayong, 2017. "Oil shocks and stock markets revisited: Measuring connectedness from a global perspective," Energy Economics, Elsevier, vol. 62(C), pages 323-333.
    45. Cai, Yijie & Chou, Ray Yeutien & Li, Dan, 2009. "Explaining international stock correlations with CPI fluctuations and market volatility," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2026-2035, November.
    46. Martens, Martin & Poon, Ser-Huang, 2001. "Returns synchronization and daily correlation dynamics between international stock markets," Journal of Banking & Finance, Elsevier, vol. 25(10), pages 1805-1827, October.
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    More about this item

    Keywords

    Market integration; Information spillover; Shanghai crude oil futures; Connectedness; Contagion; COVID-19;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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