Abstract
In July 2021, China began its national emissions trading scheme, marking a new stage of development for the country’s carbon market. This study analyzes the multidimensional correlation between carbon prices in the Guangdong pilot market and eight influencing factors from three perspectives (the international carbon market, energy prices, and China’s economic situation), using the ARMA-GARCH-vine copula model. The CoVaR between the carbon price and each factor is then calculated using copula-CoVaR. The results show that the crude oil market plays the primary role in the vine structure, and that the carbon market is not strongly correlated with other markets. China’s carbon market is still a regional market driven by government policy, and the international carbon and energy markets (especially the crude oil market) have upward risk spillover effects upon it. This indicates an asymmetric risk spillover between influencing factors and the carbon market. The findings of this study will help market participants prepare risk management strategies and make related investment decisions, and provide a reference for policy makers to formulate national emission trading scheme policies.
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2.3%! China’s economy grows against the trend in 2020, Xinhuanet, 2021-01-18, www.xinhuanet.com/2021-01/18/c_1126995039.htm.
Central Economic Work Conference held in Beijing, Renminnet, 2020-12-19, www.politics.people.com.cn/n1/2020/1219/c1024-31971922.htm.
The national carbon market “opened” for 6 days, full of highlights, Xinhuanet, 2021-07-24 www.gov.cn/xinwen/2021-07/24/content_5627095.htm.
The People’s Bank of China and the International Monetary Fund held a joint high-level seminar on “Green Finance and Climate Policy”, Sina, 2021-04-15, https://finance.sina.com.cn/roll/2021-04-15/doc-ikmxzfmk7055841.shtm.
Suppose there are two assets, X1 and X2, with joint continuous cumulative distribution function F, marginal distributions \({F}_{{X}_{1}}\), \({F}_{{X}_{2}}\), and corresponding copula C.\({\lambda }_{L}=\underset{v\to {0}^{+}}{lim}P\left({F}_{{X}_{1}}\left({X}_{1}\right)\le \nu |{F}_{{X}_{2}}\left({X}_{2}\right)\le \nu \right)=\underset{v\to {0}^{+}}{lim}\frac{P\left({F}_{{X}_{1}}\left({X}_{1}\right)\le \nu ,{F}_{{X}_{2}}\left({X}_{2}\right)\le \nu \right)}{P\left({F}_{{X}_{2}}\left({X}_{2}\right)\le \nu \right)}=\underset{v\to {0}^{+}}{lim}\frac{{c}_{\left(v,v\right)}}{v}\), \({\lambda }_{u}=\underset{v\to {1}^{-}}{lim}P\left({F}_{{X}_{1}}\left({X}_{1}\right)\ge \nu |{F}_{{X}_{2}}\left({X}_{2}\right)\ge \nu \right)=\underset{v\to {1}^{-}}{lim}\frac{P({F}_{{X}_{1}}\left({X}_{1}\right)\ge \nu ,{F}_{{X}_{2}}\left({X}_{2}\right)\ge \nu )}{P({F}_{{X}_{2}}\left({X}_{2}\right)\ge \nu )}=\underset{v\to {1}^{-}}{lim}\frac{1-2v+c\left(v,v\right)}{1-v}\).
Interested readers can contact the author for the results of ARMA-GARCH model.
Basic copula families include Clayton, Gaussian, Gumbel, Frank and Student’s t.
Measures for the administration of Carbon Emission Trading (for Trial Implementation), xinhuanet, 2021–01-01, http://www.xinhuanet.com/energy/2021-01/07/c_1126954718.htm.
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The authors would like to express their gratitude to EditSprings (https://www.editsprings.com/) for the expert linguistic services provided.
Funding
This work was supported by the National Nature Science Foundation of China (Grant Number 71703123; 72173096); the Fundamental Research Funds for the Central Universities (Grant Number 2452019117); the Humanities and Social Sciences project of the Ministry of Education of China (Grant Number 18YJC910011).
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SZ: Conceptualization, Methodology, Software, Writing—original draft. HJ: Conceptualization, Methodology, Supervision, Writing—review & editing, Funding acquisition, Project administration. MT: Supervision, Writing—review& editing, Funding acquisition. BW: Writing—review.
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Zhang, S., Ji, H., Tian, M. et al. High-dimensional nonlinear dependence and risk spillovers analysis between China’s carbon market and its major influence factors. Ann Oper Res 345, 831–860 (2025). https://doi.org/10.1007/s10479-022-04770-9
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DOI: https://doi.org/10.1007/s10479-022-04770-9