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Tail Dependency and Risk Spillover between Oil Market and Chinese Sectoral Stock Markets—An Assessment of the 2013 Refined Oil Pricing Reform

Author

Listed:
  • Jiliang Sheng

    (School of Statistics, Jiangxi University of Finance and Economics, Nanchang 330013, China)

  • Juchao Li

    (School of Statistics, Jiangxi University of Finance and Economics, Nanchang 330013, China)

  • Jun Yang

    (F. C. Manning School of Business Administration, Acadia University, Wolfville, NS B4P 2R6, Canada)

Abstract

The Chinese refined oil pricing reform in 2013 has brought its refined oil price to be more aligned with the international oil price, helping to mitigate prior distorted pricing mechanisms. Its impact on the correlation, tail risks, and spillover effects between the international crude oil market and Chinese sectoral stock markets warrants empirical assessments. Time-varying copula models and conditional VaR (CoVaR) are employed to examine the correlation between the international oil market and Chinese sectoral stock indexes before and after the 2013 pricing reform, as well as the tail risk and spillover effects of the extreme and moderate oil markets. The results show that: (1) the correlation between the oil market and all 11 Chinese stock sectors is positive both before and after the reform, but the correlation is weaker after the reform than before; (2) The downside tail risk of the extreme and moderate oil markets to most Chinese stock market sectors, and the upside tail risk of the moderate oil market to most stock sectors are lower after the reform; (3) Tail risk spillover effects of extreme oil market on all sectors exist before and after the reform; (4) The upside tail risk spillover effects of moderate oil market exist in most sectors before the reform, but they almost all disappear after the reform. The downside risk spillover effects of the moderate oil market do not exist before or after the reform. The findings provide valuable references for portfolio management and future policy update.

Suggested Citation

  • Jiliang Sheng & Juchao Li & Jun Yang, 2022. "Tail Dependency and Risk Spillover between Oil Market and Chinese Sectoral Stock Markets—An Assessment of the 2013 Refined Oil Pricing Reform," Energies, MDPI, vol. 15(16), pages 1-19, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:6070-:d:894142
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    References listed on IDEAS

    as
    1. Markus K. Brunnermeier & Yuliy Sannikov, 2014. "A Macroeconomic Model with a Financial Sector," American Economic Review, American Economic Association, vol. 104(2), pages 379-421, February.
    2. Ramos, Sofia B. & Veiga, Helena, 2013. "Oil price asymmetric effects: Answering the puzzle in international stock markets," Energy Economics, Elsevier, vol. 38(C), pages 136-145.
    3. Cui, Jinxin & Goh, Mark & Li, Binlin & Zou, Huiwen, 2021. "Dynamic dependence and risk connectedness among oil and stock markets: New evidence from time-frequency domain perspectives," Energy, Elsevier, vol. 216(C).
    4. Ali, Fahad & Jiang, Yuexiang & Sensoy, Ahmet, 2021. "Downside risk in Dow Jones Islamic equity indices: Precious metals and portfolio diversification before and after the COVID-19 bear market," Research in International Business and Finance, Elsevier, vol. 58(C).
    5. Jiang, Yong & Wang, Gang-Jin & Ma, Chaoqun & Yang, Xiaoguang, 2021. "Do credit conditions matter for the impact of oil price shocks on stock returns? Evidence from a structural threshold VAR model," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 1-15.
    6. Aloui, Riadh & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2013. "A time-varying copula approach to oil and stock market dependence: The case of transition economies," Energy Economics, Elsevier, vol. 39(C), pages 208-221.
    7. Zhang, Yue-Jun & Zhang, Lu, 2015. "Interpreting the crude oil price movements: Evidence from the Markov regime switching model," Applied Energy, Elsevier, vol. 143(C), pages 96-109.
    8. Zhang, Bing & Li, Xiao-Ming, 2016. "Recent hikes in oil-equity market correlations: Transitory or permanent?," Energy Economics, Elsevier, vol. 53(C), pages 305-315.
    9. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    10. Xu, Weiju & Ma, Feng & Chen, Wang & Zhang, Bing, 2019. "Asymmetric volatility spillovers between oil and stock markets: Evidence from China and the United States," Energy Economics, Elsevier, vol. 80(C), pages 310-320.
    11. Lutz Kilian & Cheolbeom Park, 2009. "The Impact Of Oil Price Shocks On The U.S. Stock Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(4), pages 1267-1287, November.
    12. Raza, Naveed & Jawad Hussain Shahzad, Syed & Tiwari, Aviral Kumar & Shahbaz, Muhammad, 2016. "Asymmetric impact of gold, oil prices and their volatilities on stock prices of emerging markets," Resources Policy, Elsevier, vol. 49(C), pages 290-301.
    13. El Hedi Arouri, Mohamed & Jouini, Jamel & Nguyen, Duc Khuong, 2011. "Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1387-1405.
    14. Chia-Lin Chang & Michael McAleer & Jiarong Tian, 2019. "Modeling and Testing Volatility Spillovers in Oil and Financial Markets for the USA, the UK, and China," Energies, MDPI, vol. 12(8), pages 1-24, April.
    15. Wen, Danyan & Wang, Gang-Jin & Ma, Chaoqun & Wang, Yudong, 2019. "Risk spillovers between oil and stock markets: A VAR for VaR analysis," Energy Economics, Elsevier, vol. 80(C), pages 524-535.
    16. René Garcia & Éric Renault & Georges Tsafack, 2007. "Proper Conditioning for Coherent VaR in Portfolio Management," Management Science, INFORMS, vol. 53(3), pages 483-494, March.
    17. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    18. Zhang, Bing, 2013. "Are the crude oil markets becoming more efficient over time? New evidence from a generalized spectral test," Energy Economics, Elsevier, vol. 40(C), pages 875-881.
    19. Kao, Chung-Wei & Wan, Jer-Yuh, 2012. "Price discount, inventories and the distortion of WTI benchmark," Energy Economics, Elsevier, vol. 34(1), pages 117-124.
    20. Liu, Zhenhua & Shi, Xunpeng & Zhai, Pengxiang & Wu, Shan & Ding, Zhihua & Zhou, Yuqin, 2021. "Tail risk connectedness in the oil-stock nexus: Evidence from a novel quantile spillover approach," Resources Policy, Elsevier, vol. 74(C).
    21. Kirkulak-Uludag, Berna & Safarzadeh, Omid, 2018. "The interactions between OPEC oil price and sectoral stock returns: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 631-641.
    22. Wu, Kai & Zhu, Jingran & Xu, Mingli & Yang, Lu, 2020. "Can crude oil drive the co-movement in the international stock market? Evidence from partial wavelet coherence analysis," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    23. Peng, Cheng & Zhu, Huiming & Guo, Yawei & Chen, Xiuyun, 2018. "Risk spillover of international crude oil to China's firms: Evidence from granger causality across quantile," Energy Economics, Elsevier, vol. 72(C), pages 188-199.
    24. Gong, Xiao-Li & Liu, Jian-Min & Xiong, Xiong & Zhang, Wei, 2021. "The dynamic effects of international oil price shocks on economic fluctuation," Resources Policy, Elsevier, vol. 74(C).
    25. Kumar, Satish & Khalfaoui, Rabeh & Tiwari, Aviral Kumar, 2021. "Does geopolitical risk improve the directional predictability from oil to stock returns? Evidence from oil-exporting and oil-importing countries," Resources Policy, Elsevier, vol. 74(C).
    26. Jingran Zhu & Qinghua Song & Dalia Streimikiene, 2020. "Multi-Time Scale Spillover Effect of International Oil Price Fluctuation on China’s Stock Markets," Energies, MDPI, vol. 13(18), pages 1-29, September.
    27. Jin Boon Wong & Qin Zhang, 2020. "Impact of international energy prices on China's industries," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(5), pages 722-748, May.
    28. Wu, Fei & Zhang, Dayong & Zhang, Zhiwei, 2019. "Connectedness and risk spillovers in China’s stock market: A sectoral analysis," Economic Systems, Elsevier, vol. 43(3).
    29. Ajmi, Ahdi Noomen & Hammoudeh, Shawkat & Mokni, Khaled, 2021. "Detection of bubbles in WTI, brent, and Dubai oil prices: A novel double recursive algorithm," Resources Policy, Elsevier, vol. 70(C).
    30. Zhang, Chuanguo & Chen, Xiaoqing, 2011. "The impact of global oil price shocks on China’s stock returns: Evidence from the ARJI(-ht)-EGARCH model," Energy, Elsevier, vol. 36(11), pages 6627-6633.
    31. Xiao, Jihong & Zhou, Min & Wen, Fengming & Wen, Fenghua, 2018. "Asymmetric impacts of oil price uncertainty on Chinese stock returns under different market conditions: Evidence from oil volatility index," Energy Economics, Elsevier, vol. 74(C), pages 777-786.
    32. Reboredo, Juan C. & Ugolini, Andrea, 2016. "Quantile dependence of oil price movements and stock returns," Energy Economics, Elsevier, vol. 54(C), pages 33-49.
    33. Juan Reboredo, 2010. "Nonlinear effects of oil shocks on stock returns: a Markov-switching approach," Applied Economics, Taylor & Francis Journals, vol. 42(29), pages 3735-3744.
    34. Robert Elliott & Hong Miao, 2009. "VaR and expected shortfall: a non-normal regime switching framework," Quantitative Finance, Taylor & Francis Journals, vol. 9(6), pages 747-755.
    35. Du, Limin & He, Yanan, 2015. "Extreme risk spillovers between crude oil and stock markets," Energy Economics, Elsevier, vol. 51(C), pages 455-465.
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