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Analyzing the risk spillovers of international crude oil on China's corn and biofuel ethanol markets: A transition toward green economy and environmental sustainability

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  • Jin Zhang
  • Zhenqing Lin
  • Jinkai Li

Abstract

This study aims to analyze the risk spillover effects between the global crude oil market and the biofuel ethanol and corn markets in China, employing a DCC-GARCH-Copula-CoVaR model and basing the weekly price data from 2012 to 2021. The empirical results revealed that there were dynamic conditional correlations among international crude oil, China's biofuel ethanol, and corn markets. Following the COVID-19 outbreak, the CoVaR and ΔCoVaR changed, which caused a sharp increase in the mean values and volatility. Additionally, China's biofuel ethanol market is more vulnerable to the risk spillovers from the international crude oil market than China's corn market. However, China's markets do not appear to have obvious risk spillover effects on the global market. The implications of the results are discussed in financial market supervision, including the risk management and portfolio adjustment.

Suggested Citation

  • Jin Zhang & Zhenqing Lin & Jinkai Li, 2024. "Analyzing the risk spillovers of international crude oil on China's corn and biofuel ethanol markets: A transition toward green economy and environmental sustainability," Energy & Environment, , vol. 35(3), pages 1216-1234, May.
    • Handle: RePEc:sae:engenv:v:35:y:2024:i:3:p:1216-1234
    DOI: 10.1177/0958305X221140566
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    1. O'Hara, Sabine & Toussaint, Etienne C., 2021. "Food access in crisis: Food security and COVID-19," Ecological Economics, Elsevier, vol. 180(C).
    2. Aktham I. Maghyereh & Osama D. Sweidan, 2020. "Do structural shocks in the crude oil market affect biofuel prices?," International Economics, CEPII research center, issue 164, pages 183-193.
    3. Subramaniam, Yogeeswari & Masron, Tajul Ariffin & Azman, Nik Hadiyan Nik, 2019. "The impact of biofuels on food security," International Economics, Elsevier, vol. 160(C), pages 72-83.
    4. Taghizadeh-Hesary, Farhad & Rasoulinezhad, Ehsan & Yoshino, Naoyuki, 2018. "Volatility Linkages between Energy and Food Prices: Case of Selected Asian Countries," ADBI Working Papers 829, Asian Development Bank Institute.
    5. Dahl, Roy Endré & Oglend, Atle & Yahya, Muhammad, 2020. "Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture," Journal of Commodity Markets, Elsevier, vol. 20(C).
    6. Nader Trabelsi, 2017. "Tail dependence between oil and stocks of major oil-exporting countries using the CoVaR approach," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 17(4), pages 228-237, December.
    7. Serletis, Apostolos & Xu, Libo, 2019. "The ethanol mandate and crude oil and biofuel agricultural commodity price dynamics," Journal of Commodity Markets, Elsevier, vol. 15(C), pages 1-1.
    8. Dongdong Song & Yuewen Liu & Tianbao Qin & Hongsong Gu & Yang Cao & Hongjun Shi, 2022. "Overview of the Policy Instruments for Renewable Energy Development in China," Energies, MDPI, vol. 15(18), pages 1-14, September.
    9. Abuzayed, Bana & Al-Fayoumi, Nedal, 2021. "Risk spillover from crude oil prices to GCC stock market returns: New evidence during the COVID-19 outbreak," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    10. Nemati, Mehdi, 2017. "Relationship among Energy, Bioenergy and Agricultural Commodity Prices: Re-Considering Structural Changes," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 5(3), July.
    11. Hung, Ngo Thai, 2021. "Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Resources Policy, Elsevier, vol. 73(C).
    12. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    13. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    14. Dimitrios Dimitriadis & Constantinos Katrakilidis, 2020. "An empirical analysis of the dynamic interactions among ethanol, crude oil and corn prices in the US market," Annals of Operations Research, Springer, vol. 294(1), pages 47-57, November.
    15. Reboredo, Juan C. & Ugolini, Andrea, 2016. "Quantile dependence of oil price movements and stock returns," Energy Economics, Elsevier, vol. 54(C), pages 33-49.
    16. Pal, Debdatta & Mitra, Subrata K., 2019. "Correlation dynamics of crude oil with agricultural commodities: A comparison between energy and food crops," Economic Modelling, Elsevier, vol. 82(C), pages 453-466.
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