IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v89y2024ipbp446-457.html

Volatility spillovers between oil and coal prices and its implications for energy portfolio management in China

Author

Listed:
  • Guo, Yanfeng
  • Zhao, Huanyu

Abstract

In the structure of energy consumption of China, coal and oil rank the top and the second place. It is obviously important to better understand the dynamics of the price volatility between crude oil and coal in China, and then to build a reasonable energy portfolio. As the listing of China's crude oil futures offers a new sample to study the volatility contagion of oil and coal prices, by using daily data from 2nd May 2018 to January 23, 2020, four different multivariate GARCH models are compared and contrasted in this paper. Then, based on the results of the four MGARCHs, we investigate the oil-coal portfolios' optimal weights and hedge ratios. The results indicate that, first, a long-run co-integration relationship exists in China's crude oil and coal. Second, in short run, futures price of crude oil leads that of coal, the volatility spillover effect of crude oil on coal is stronger than that of coal on crude oil. Third, the DCC-GARCH model fits best for data. Last, weights of optimal portfolio and ratios of hedge show that the crude oil is a tool to make the risk of portfolio minimal in energy markets. These results are expected to have implications for energy producers, users and trading enterprises to inform their decision-making.

Suggested Citation

  • Guo, Yanfeng & Zhao, Huanyu, 2024. "Volatility spillovers between oil and coal prices and its implications for energy portfolio management in China," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 446-457.
    • Handle: RePEc:eee:reveco:v:89:y:2024:i:pb:p:446-457
    DOI: 10.1016/j.iref.2023.10.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S105905602300374X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2023.10.004?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

    for a different version of it.

    References listed on IDEAS

    as
    1. repec:aen:journl:2006v27-02-a04 is not listed on IDEAS
    2. Mjelde, James W. & Bessler, David A., 2009. "Market integration among electricity markets and their major fuel source markets," Energy Economics, Elsevier, vol. 31(3), pages 482-491, May.
    3. Apostolos Serletis & Ricardo Rangel-Ruiz, 2007. "Testing for Common Features in North American Energy Markets," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 14, pages 172-187, World Scientific Publishing Co. Pte. Ltd..
    4. Sarwar, Suleman & Khalfaoui, Rabeh & Waheed, Rida & Dastgerdi, Hamidreza Ghorbani, 2019. "Volatility spillovers and hedging: Evidence from Asian oil-importing countries," Resources Policy, Elsevier, vol. 61(C), pages 479-488.
    5. Kroner, Kenneth F & Ng, Victor K, 1998. "Modeling Asymmetric Comovements of Asset Returns," The Review of Financial Studies, Society for Financial Studies, vol. 11(4), pages 817-844.
    6. Zhang, Long & Sovacool, Benjamin K. & Ren, Jingzheng & Ely, Adrian, 2017. "The Dragon awakens: Innovation, competition, and transition in the energy strategy of the People’s Republic of China, 1949–2017," Energy Policy, Elsevier, vol. 108(C), pages 634-644.
    7. Joëts, Marc & Mignon, Valérie, 2012. "On the link between forward energy prices: A nonlinear panel cointegration approach," Energy Economics, Elsevier, vol. 34(4), pages 1170-1175.
    8. Robert Engle & Clive Granger, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    9. Chen, Yufeng & Zheng, Biao & Qu, Fang, 2020. "Modeling the nexus of crude oil, new energy and rare earth in China: An asymmetric VAR-BEKK (DCC)-GARCH approach," Resources Policy, Elsevier, vol. 65(C).
    10. Hassan Mohammadi, 2011. "Long-run relations and short-run dynamics among coal, natural gas and oil prices," Applied Economics, Taylor & Francis Journals, vol. 43(2), pages 129-137.
    11. Mensi, Walid & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Dynamic frequency relationships and volatility spillovers in natural gas, crude oil, gas oil, gasoline, and heating oil markets: Implications for portfolio management," Resources Policy, Elsevier, vol. 73(C).
    12. Sadorsky, Perry, 2012. "Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies," Energy Economics, Elsevier, vol. 34(1), pages 248-255.
    13. Mohammadi, Hassan, 2009. "Electricity prices and fuel costs: Long-run relations and short-run dynamics," Energy Economics, Elsevier, vol. 31(3), pages 503-509, May.
    14. Li, Jianglong & Xie, Chunping & Long, Houyin, 2019. "The roles of inter-fuel substitution and inter-market contagion in driving energy prices: evidences from China’s coal market," LSE Research Online Documents on Economics 102540, London School of Economics and Political Science, LSE Library.
    15. 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.
    16. Raymond Li & David C. Broadstock, 2021. "Coal Pricing in China: Is It a Bit Too Crude?," Energies, MDPI, vol. 14(13), pages 1-13, June.
    17. Elder, John & Serletis, Apostolos, 2009. "Oil price uncertainty in Canada," Energy Economics, Elsevier, vol. 31(6), pages 852-856, November.
    18. Narjes Zamani, 2016. "The Relationship between Crude Oil and Coal Markets: A New Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 6(4), pages 801-805.
    19. Kroner, Kenneth F. & Sultan, Jahangir, 1993. "Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(4), pages 535-551, December.
    20. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    21. Li, Jianglong & Xie, Chunping & Long, Houyin, 2019. "The roles of inter-fuel substitution and inter-market contagion in driving energy prices: Evidences from China’s coal market," Energy Economics, Elsevier, vol. 84(C).
    22. Rahman, Sajjadur & Serletis, Apostolos, 2012. "Oil price uncertainty and the Canadian economy: Evidence from a VARMA, GARCH-in-Mean, asymmetric BEKK model," Energy Economics, Elsevier, vol. 34(2), pages 603-610.
    23. Ewing, Bradley T. & Malik, Farooq & Ozfidan, Ozkan, 2002. "Volatility transmission in the oil and natural gas markets," Energy Economics, Elsevier, vol. 24(6), pages 525-538, November.
    24. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    25. 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.
    26. Serletis, Apostolos & Xu, Libo, 2016. "Volatility and a century of energy markets dynamics," Energy Economics, Elsevier, vol. 55(C), pages 1-9.
    27. Jian-Ling Jiao & Ying Fan & Yi-Ming Wei, 2009. "The structural break and elasticity of coal demand in China: empirical findings from 1980-2006," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 31(3/4), pages 331-344.
    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. Wang, Kai-Hua & Jiang, Xin-Yu & Tang, Yun, 2025. "Artificial intelligence, cloud computing, blockchain, and the energy market in the era of energy transition," Energy Economics, Elsevier, vol. 151(C).
    2. Zhou, Jinlan & Li, Zhensheng & Liu, Zhuang, 2025. "Oil market uncertainty and China's macroeconomy: Causality-in-quantiles test and quantile spillover effects analysis," Energy Economics, Elsevier, vol. 148(C).
    3. Ye, Rendao & Xiao, Jian & Zhang, Yilan, 2024. "Risk spillover effect of the new energy market and its hedging effectiveness: New evidence from industry chain," Economic Analysis and Policy, Elsevier, vol. 83(C), pages 1061-1079.
    4. Wang, Tiantian & Wu, Fei & Dickinson, David & Zhao, Wanli, 2024. "Energy price bubbles and extreme price movements: Evidence from China's coal market," Energy Economics, Elsevier, vol. 129(C).
    5. Wang, Hui & Zhang, Yiyi & Zhang, Yi & Wang, Jilong & Xie, Yuzhi & Luo, Shen, 2025. "Mixed-frequency data-driven forecasting of thermal coal price: A novel hybrid model," Energy, Elsevier, vol. 334(C).
    6. Man, Yuanyuan & Zhang, Sunpei & He, Yongda, 2024. "Dynamic risk spillover and hedging efficacy of China’s carbon-energy-finance markets: Economic policy uncertainty and investor sentiment non-linear causal effects," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 1397-1416.

    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. Kumar, Satish & Pradhan, Ashis Kumar & Tiwari, Aviral Kumar & Kang, Sang Hoon, 2019. "Correlations and volatility spillovers between oil, natural gas, and stock prices in India," Resources Policy, Elsevier, vol. 62(C), pages 282-291.
    2. Raymond Li & David C. Broadstock, 2021. "Coal Pricing in China: Is It a Bit Too Crude?," Energies, MDPI, vol. 14(13), pages 1-13, June.
    3. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    4. Asl, Mahdi Ghaemi & Canarella, Giorgio & Miller, Stephen M., 2021. "Dynamic asymmetric optimal portfolio allocation between energy stocks and energy commodities: Evidence from clean energy and oil and gas companies," Resources Policy, Elsevier, vol. 71(C).
    5. Moutinho, Victor & Vieira, Joel & Carrizo Moreira, António, 2011. "The crucial relationship among energy commodity prices: Evidence from the Spanish electricity market," Energy Policy, Elsevier, vol. 39(10), pages 5898-5908, October.
    6. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2022. "Long-memory and volatility spillovers across petroleum futures," Energy, Elsevier, vol. 243(C).
    7. Wang, Xiaoyu & Wang, Jiaojiao & Wang, Wenhuan & Zhang, Shuquan, 2023. "International and Chinese energy markets: Dynamic spillover effects," Energy, Elsevier, vol. 282(C).
    8. Serletis, Apostolos & Xu, Libo, 2016. "Volatility and a century of energy markets dynamics," Energy Economics, Elsevier, vol. 55(C), pages 1-9.
    9. Martínez, Beatriz & Torró, Hipòlit, 2015. "European natural gas seasonal effects on futures hedging," Energy Economics, Elsevier, vol. 50(C), pages 154-168.
    10. Tadahiro Nakajima, 2019. "Expectations for Statistical Arbitrage in Energy Futures Markets," JRFM, MDPI, vol. 12(1), pages 1-12, January.
    11. Ahmed Ghorbel & Wajdi Hamma & Anis Jarboui, 2017. "Dependence between oil and commodities markets using time-varying Archimedean copulas and effectiveness of hedging strategies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(9), pages 1509-1542, July.
    12. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    13. Furió, Dolores & Chuliá, Helena, 2012. "Price and volatility dynamics between electricity and fuel costs: Some evidence for Spain," Energy Economics, Elsevier, vol. 34(6), pages 2058-2065.
    14. Yousaf, Imran & Beljid, Makram & Chaibi, Anis & Ajlouni, Ahmed AL, 2022. "Do volatility spillover and hedging among GCC stock markets and global factors vary from normal to turbulent periods? Evidence from the global financial crisis and Covid-19 pandemic crisis," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    15. Zhong, Yi & Liu, Jiapeng, 2021. "Correlations and volatility spillovers between China and Southeast Asian stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 57-69.
    16. Sadorsky, Perry, 2012. "Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies," Energy Economics, Elsevier, vol. 34(1), pages 248-255.
    17. Sibande, Xolani & Demirer, Riza & Balcilar, Mehmet & Gupta, Rangan, 2023. "On the pricing effects of bitcoin mining in the fossil fuel market: The case of coal," Resources Policy, Elsevier, vol. 85(PB).
    18. Wang, Tiantian & Wu, Fei & Dickinson, David & Zhao, Wanli, 2024. "Energy price bubbles and extreme price movements: Evidence from China's coal market," Energy Economics, Elsevier, vol. 129(C).
    19. Li, Jingyu & Liu, Ranran & Yao, Yanzhen & Xie, Qiwei, 2022. "Time-frequency volatility spillovers across the international crude oil market and Chinese major energy futures markets: Evidence from COVID-19," Resources Policy, Elsevier, vol. 77(C).
    20. Serra, Teresa & Zilberman, David, 2013. "Biofuel-related price transmission literature: A review," Energy Economics, Elsevier, vol. 37(C), pages 141-151.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    Statistics

    Access and download statistics

    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:reveco:v:89:y:2024:i:pb:p:446-457. 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/inca/620165 .

    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.