Restricted accessResearch articleFirst published online 2024-5
Analyzing the risk spillovers of international crude oil on China's corn and biofuel ethanol markets: A transition toward green economy and environmental sustainability
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.
ChiuF-PHsuC-SHoA,et al.Modeling the price relationships between crude oil, energy crops and biofuels. Energy2016; 109: 845–857.
4.
LiJZhangJChenQ. Path choice of developing bio-energy while keeping food security—a general equilibrium model. Energy Procedia2017; 105: 3485–3492.
5.
NatanelovVMcKenzieAMVan HuylenbroeckG. Crude oil-corn-ethanol—nexus: a contextual approach. Energy Policy2013; 63: 504–513.
6.
LiuJSongQQiY,et al.Measurement of systemic risk in global financial markets and its application in forecasting trading decisions. Sustainability2020; 12: 4000.
7.
Gil-AlanaLAMongeM. Crude oil prices and COVID-19: persistence of the shock. Energy Res Lett2020; 1: 13200.
8.
ChengGZhuM. COVID-19 pandemic is affecting food security: trends, impacts and recommendations. China Rural Econ2020; 5: 13–20.
9.
CabreraBLSchulzF. Volatility linkages between energy and agricultural commodity prices. Energy Econ2016; 54: 190–203.
10.
CiaianPKancsd'A. Interdependencies in the energy-bioenergy-food price systems: a cointegration analysis. Resou Energy Econ2011; 33: 326–348.
11.
GardebroekCHernandezMA. Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets. Energy Econ2013; 40: 119–129.
12.
Duc HongVTan NgocVAnh TheV,et al.Modeling the relationship between crude oil and agricultural commodity prices. Energies2019; 12: 1344.
13.
PalDMitraSK. Correlation dynamics of crude oil with agricultural commodities: a comparison between energy and food crops. Econ Model2019; 82: 453–466.
14.
RomanMGóreckaADomagałaJ. The linkages between crude oil and food prices. Energies2020; 13: 6545.
15.
KoizumiT. Biofuels and food security. Renew Sustain Energy Rev2015; 52: 829–841.
16.
SubramaniamYMasronTAAzmanNHN. The impact of biofuels on food security. Int Econ2019; 160: 72–83.
17.
ChangT-HSuH-M. The substitutive effect of biofuels on fossil fuels in the lower and higher crude oil price periods. Energy2010; 35: 2807–2813.
18.
MaghyerehAISweidanOD. Do structural shocks in the crude oil market affect biofuel prices?Int Econ2020; 164: 183–193.
19.
AlsalehMZubairAOAbdul-RahimAS. The impact of global competitiveness on the growth of bioenergy industry in EU-28 region. Sustain Dev2020; 28: 1304–1316.
20.
DimitriadisDKatrakilidisC. An empirical analysis of the dynamic interactions among ethanol, crude oil and corn prices in the US market. Ann Oper Res2020; 294: 47–57.
21.
RoblesMCookeB. Recent food prices movements: A time series analysis. IFPRI Discussion Paper No. 942,Washington, DC: International Food Policy Research Institute (IFPRI), 2009.
SerraTZilbermanD. Biofuel-related price transmission literature: a review. Energy Econ2013; 37: 141–151.
24.
AlsalehMAbdul-RahimAMohd-ShahwahidH. An empirical and forecasting analysis of the bioenergy market in the EU28 region: evidence from a panel data simultaneous equation model. Renew Sustain Energy Rev2017; 80: 1123–1137.
25.
CiaianP. Interdependencies in the energy-bioenergy-food price systems: a cointegration analysis. Resour Energy Econ2011; 33: 326–348.
26.
VoDHVuTNVoAT,et al.Modeling the relationship between crude oil and agricultural commodity prices. Energies2019; 12: 1344.
27.
WuHLiS. Volatility spillovers in China’s crude oil, corn and fuel ethanol markets. Energy Policy2013; 62: 878–886.
28.
SerletisAXuL. The ethanol mandate and crude oil and biofuel agricultural commodity price dynamics. J Commodity Mark2019; 15: 100068.
29.
WuHGeYHuoX,et al.Impact of international energy prices on China’s corn prices. J China Agric Univ2016; 21: 164–172.
30.
DahlREOglendAYahyaM. Dynamics of volatility spillover in commodity markets: linking crude oil to agriculture. J Commodity Mark2020; 20: 100111.
31.
LuYYangLLiuL. Volatility spillovers between crude oil and agricultural commodity markets since the financial crisis. Sustainability2019; 11: 396.
32.
HanifWHernandezJAShahzadSJH,et al.Tail dependence risk and spillovers between oil and food prices. Q Rev Econ Finance2021; 80: 195–209.
33.
LiuSGaoHHouP,et al.Risk spillover effects of international crude oil market on China’s major markets. Aims Energy2019; 7: 819–840.
34.
ChenYZhengBQuF. Modeling the nexus of crude oil, new energy and rare earth in China: an asymmetric VAR-BEKK (DCC)-GARCH approach. Resour Policy2020; 65: 101545.
35.
LuoJZhangQ. Risk contagions between global oil markets and China’s agricultural commodity markets under structural breaks. Appl Econ2021; 53: 628–649.
36.
ZhaoYZhangY. The prices co-movement between West Texas crude oil and China’s biofuel crops. Energy Environ2013; 24: 965–981.
37.
HungNT. Oil prices and agricultural commodity markets: evidence from pre and during COVID-19 outbreak. Resour Policy2021; 73: 102236.
38.
ZhuPTangYWeiY,et al.Multidimensional risk spillovers among crude oil, the US and Chinese stock markets: evidence during the COVID-19 epidemic. Energy2021; 231: 120949.
39.
O'HaraSToussaintEC. Food access in crisis: food security and COVID-19. Ecol Econ2021; 180: 106859.
40.
MusaKSMaijama’aRMohammedN,et al.COVID-19 pandemic, oil price slump and food crisis nexus: an application of ARDL approach. Open Access Libr J2020; 7: 1–19.
41.
EzeakuHCAsonguSANnannaJ. Volatility of international commodity prices in times of COVID-19: effects of oil supply and global demand shocks. Extr Ind Soc2021; 8: 257–270.
42.
NematiM. Relationship among energy, bioenergy and agricultural commodity prices: re-considering structural changes. Int J Food Agric Econ2017; 5: 1–8.
43.
AshfaqSTangYMaqboolR. Volatility spillover impact of world oil prices on leading Asian energy exporting and importing economies’ stock returns. Energy2019; 188: 116002.
44.
RaiKGargB. Dynamic correlations and volatility spillovers between stock price and exchange rate in BRIICS economies: evidence from the COVID-19 outbreak period. Appl Econ Lett2022; 29: 738–745.
45.
ReboredoJC. How do crude oil prices co-move? A copula approach. Energy Econ2011; 33: 948–955.
46.
ReboredoJCUgoliniA. Quantile dependence of oil price movements and stock returns. Energy Econ2016; 54: 33–49.
47.
TobiasABrunnermeierMK. CoVaR. Am Econ Rev2016; 106: 1705.
48.
KayalarDEKüçüközmenCCSelcuk-KestelAS. The impact of crude oil prices on financial market indicators: copula approach. Energy Econ2017; 61: 162–173.
49.
LiuB-YJiQFanY. A new time-varying optimal copula model identifying the dependence across markets. Quant Financ2017; 17: 437–453.
50.
de Melo MendesBVde SouzaRM. Measuring financial risks with copulas. Int Rev Financ Anal2004; 13: 27–45.
51.
SunXLiuCWangJ,et al.Assessing the extreme risk spillovers of international commodities on maritime markets: a GARCH-Copula-CoVaR approach. Int Rev Financ Anal2020; 68: 101453.
52.
YuanXTangJWongW-K,et al.Modeling co-movement among different agricultural commodity markets: a Copula-GARCH approach. Sustainability2020; 12: 393.
53.
HansenBE. Autoregressive conditional density estimation. Int Econ Rev (Philadelphia)1994; 35: 705–730.
54.
SklarM. Fonctions de repartition an dimensions et leurs marges. Publ Inst Stat Univ Paris1959; 8: 229–231.
55.
EngleR. Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat2002; 20: 339–350.
TrabelsiN. Tail dependence between oil and stocks of major oil-exporting countries using the CoVaR approach. Borsa Istanbul Rev2017; 17: 228–237.
58.
AbuzayedBAl-FayoumiN. Risk spillover from crude oil prices to GCC stock market returns: new evidence during the COVID-19 outbreak. N Am J Econ Finance2021; 58: 101476.
