IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v93y2021ics0140988319300428.html
   My bibliography  Save this article

Empirical analysis of the dynamic dependence between WTI oil and Chinese energy stocks

Author

Listed:
  • Li, Jie
  • Li, Ping

Abstract

In this paper, we employ the dynamic copula approach to explore the dependence structure between WTI oil and Chinese energy stock index (300ENI). We first use the AIC (Akaike Information Criteria) together with the binary segmentation procedure to detect the change points of copula families and copula parameters, which extend the existing time-varying copula method. Then we can get the optimal copula between WTI oil and 300ENI for each time period, and analyze the dependence between them combining with the national and international economic environment. We find that the recession of both the international oil market and Chinese energy stock market causes the significant concordance and symmetric tail dependence. The asymmetric dependence turns out to be in Chinese economic recession and recovery time. Furthermore, we apply our results to analyze the effectiveness of hedging WTI oil using 300ENI. Results show that the hedging is more effective in China's sluggish time than in economic boom. Finally, our economic cycle analysis shows that different economic situation pairs of WTI oil and Chinese energy stocks exhibit different tail dependency, which could be used to predict the market future and provide market participants and policy makers with some suggestions on investment, hedging and decision making.

Suggested Citation

  • Li, Jie & Li, Ping, 2021. "Empirical analysis of the dynamic dependence between WTI oil and Chinese energy stocks," Energy Economics, Elsevier, vol. 93(C).
    • Handle: RePEc:eee:eneeco:v:93:y:2021:i:c:s0140988319300428
    DOI: 10.1016/j.eneco.2019.01.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988319300428
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2019.01.027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Su-Fang & Zhu, Hui-Ming & Yu, Keming, 2012. "Oil prices and stock market in China: A sector analysis using panel cointegration with multiple breaks," Energy Economics, Elsevier, vol. 34(6), pages 1951-1958.
    2. Westner, Günther & Madlener, Reinhard, 2012. "Investment in new power generation under uncertainty: Benefits of CHP vs. condensing plants in a copula-based analysis," Energy Economics, Elsevier, vol. 34(1), pages 31-44.
    3. repec:ipg:wpaper:2013-023 is not listed on IDEAS
    4. Nguyen, Cuong C. & Bhatti, M. Ishaq, 2012. "Copula model dependency between oil prices and stock markets: Evidence from China and Vietnam," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(4), pages 758-773.
    5. 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.
    6. D. Guegan & J. Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 421-430.
    7. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    8. Zhuang, Xiaoyang & Wei, Yu & Ma, Feng, 2015. "Multifractality, efficiency analysis of Chinese stock market and its cross-correlation with WTI crude oil price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 101-113.
    9. Andrea Bastianin, 2009. "Modelling Asymmetric Dependence Using Copula Functions: An application to Value-at-Risk in the Energy Sector," Working Papers 2009.24, Fondazione Eni Enrico Mattei.
    10. Charfeddine, Lanouar & Benlagha, Noureddine, 2016. "A time-varying copula approach for modelling dependency: New evidence from commodity and stock markets," Journal of Multinational Financial Management, Elsevier, vol. 37, pages 168-189.
    11. Dominique Guegan & Jing Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," PSE-Ecole d'économie de Paris (Postprint) halshs-00368334, HAL.
    12. Bing-Yue Liu & Qiang Ji & Ying Fan, 2017. "A new time-varying optimal copula model identifying the dependence across markets," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 437-453, March.
    13. Narayan, Paresh Kumar & Sharma, Susan Sunila, 2011. "New evidence on oil price and firm returns," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3253-3262.
    14. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    15. Awartani, Basel & Maghyereh, Aktham Issa, 2013. "Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries," Energy Economics, Elsevier, vol. 36(C), pages 28-42.
    16. repec:ipg:wpaper:23 is not listed on IDEAS
    17. 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.
    18. Heni Boubaker & Nadia Sghaier, 2013. "Instability and time," Working Papers 2013-23, Department of Research, Ipag Business School.
    19. Kayalar, Derya Ezgi & Küçüközmen, C. Coşkun & Selcuk-Kestel, A. Sevtap, 2017. "The impact of crude oil prices on financial market indicators: copula approach," Energy Economics, Elsevier, vol. 61(C), pages 162-173.
    20. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    21. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 228-248, April.
    22. Singhal, Shelly & Ghosh, Sajal, 2016. "Returns and volatility linkages between international crude oil price, metal and other stock indices in India: Evidence from VAR-DCC-GARCH models," Resources Policy, Elsevier, vol. 50(C), pages 276-288.
    23. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173, March.
    24. Broadstock, David C. & Filis, George, 2014. "Oil price shocks and stock market returns: New evidence from the United States and China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 417-433.
    25. Zhu, Hui-Ming & Li, Rong & Li, Sufang, 2014. "Modelling dynamic dependence between crude oil prices and Asia-Pacific stock market returns," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 208-223.
    26. Dominique Guegan & Jing Zhang, 2010. "Change analysis of a dynamic copula for measuring dependence in multivariate financial data," Post-Print halshs-00368334, HAL.
    27. Ping Li & Ze†Zheng Li, 2015. "Change Analysis for the Dependence Structure and Dynamic Pricing of Basket Default Swaps," European Financial Management, European Financial Management Association, vol. 21(4), pages 646-671, September.
    28. Cong, Rong-Gang & Wei, Yi-Ming & Jiao, Jian-Lin & Fan, Ying, 2008. "Relationships between oil price shocks and stock market: An empirical analysis from China," Energy Policy, Elsevier, vol. 36(9), pages 3544-3553, September.
    29. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Oil price and stock returns of consumers and producers of crude oil," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 34(C), pages 245-262.
    30. Chen, Qian & Lv, Xin, 2015. "The extreme-value dependence between the crude oil price and Chinese stock markets," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 121-132.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2022. "The growth of oil futures in China: Evidence of market maturity through global crises," Energy Economics, Elsevier, vol. 114(C).
    2. Guo, Yawei & Li, Jianping & Li, Yehua & You, Wanhai, 2021. "The roles of political risk and crude oil in stock market based on quantile cointegration approach: A comparative study in China and US," Energy Economics, Elsevier, vol. 97(C).
    3. Adrian Neacsa & Jianu Daniel Muresan & Marian Catalin Voica & Otilia Manta & Mihail Vincentiu Ivan, 2023. "Oil Price—A Sensor for the Performance of Romanian Oil Manufacturing Companies," Energies, MDPI, vol. 16(5), pages 1-18, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. You, Wanhai & Guo, Yawei & Zhu, Huiming & Tang, Yong, 2017. "Oil price shocks, economic policy uncertainty and industry stock returns in China: Asymmetric effects with quantile regression," Energy Economics, Elsevier, vol. 68(C), pages 1-18.
    2. Trabelsi, Nader, 2017. "Asymmetric tail dependence between oil price shocks and sectors of Saudi Arabia System," The Journal of Economic Asymmetries, Elsevier, vol. 16(C), pages 26-41.
    3. Huiming Zhu & Xianfang Su & Yawei Guo & Yinghua Ren, 2016. "The Asymmetric Effects of Oil Price Shocks on the Chinese Stock Market: Evidence from a Quantile Impulse Response Perspective," Sustainability, MDPI, vol. 8(8), pages 1-19, August.
    4. David C. Broadstock, Ying Fan, Qiang Ji, and Dayong Zhang, 2016. "Shocks and Stocks: A Bottom-up Assessment of the Relationship Between Oil Prices, Gasoline Prices and the Returns of Chinese Firms," The Energy Journal, International Association for Energy Economics, vol. 0(China Spe).
    5. Stavros Degiannakis, George Filis, and Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    6. Zhu, Huiming & Guo, Yawei & You, Wanhai & Xu, Yaqin, 2016. "The heterogeneity dependence between crude oil price changes and industry stock market returns in China: Evidence from a quantile regression approach," Energy Economics, Elsevier, vol. 55(C), pages 30-41.
    7. Silvapulle, Param & Smyth, Russell & Zhang, Xibin & Fenech, Jean-Pierre, 2017. "Nonparametric panel data model for crude oil and stock market prices in net oil importing countries," Energy Economics, Elsevier, vol. 67(C), pages 255-267.
    8. 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.
    9. Kirkulak-Uludag, Berna & Safarzadeh, Omid, 2021. "Exploring shock and volatility transmission between oil and Chinese industrial raw materials," Resources Policy, Elsevier, vol. 70(C).
    10. Smyth, Russell & Narayan, Paresh Kumar, 2018. "What do we know about oil prices and stock returns?," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 148-156.
    11. Pal, Debdatta & Mitra, Subrata K., 2019. "Oil price and automobile stock return co-movement: A wavelet coherence analysis," Economic Modelling, Elsevier, vol. 76(C), pages 172-181.
    12. Xiao, Jihong & Hu, Chunyan & Ouyang, Guangda & Wen, Fenghua, 2019. "Impacts of oil implied volatility shocks on stock implied volatility in China: Empirical evidence from a quantile regression approach," Energy Economics, Elsevier, vol. 80(C), pages 297-309.
    13. Yu, Lean & Zha, Rui & Stafylas, Dimitrios & He, Kaijian & Liu, Jia, 2020. "Dependences and volatility spillovers between the oil and stock markets: New evidence from the copula and VAR-BEKK-GARCH models," International Review of Financial Analysis, Elsevier, vol. 68(C).
    14. Zhu, Huiming & Huang, Hui & Peng, Cheng & Yang, Yan, 2016. "Extreme dependence between crude oil and stock markets in Asia-Pacific regions: Evidence from quantile regression," Economics Discussion Papers 2016-46, Kiel Institute for the World Economy (IfW Kiel).
    15. Guo, Yawei & Li, Jianping & Li, Yehua & You, Wanhai, 2021. "The roles of political risk and crude oil in stock market based on quantile cointegration approach: A comparative study in China and US," Energy Economics, Elsevier, vol. 97(C).
    16. Das, Debojyoti & Kannadhasan, M., 2020. "The asymmetric oil price and policy uncertainty shock exposure of emerging market sectoral equity returns: A quantile regression approach," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 563-581.
    17. 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.
    18. Zhang, Dayong, 2017. "Oil shocks and stock markets revisited: Measuring connectedness from a global perspective," Energy Economics, Elsevier, vol. 62(C), pages 323-333.
    19. Arturo Lorenzo Valdés & Leticia Armenta Fraire & Rocío Durán Vázquez, 2016. "A copula-TGARCH approach of conditional dependence between oil price and stock market index: the case of Mexico," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 31(1), pages 47-63.
    20. Jammazi, Rania & Ferrer, Román & Jareño, Francisco & Shahzad, Syed Jawad Hussain, 2017. "Time-varying causality between crude oil and stock markets: What can we learn from a multiscale perspective?," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 453-483.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:93:y:2021:i:c:s0140988319300428. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.