Abstract：Based on the daily trading data of 5 carbon trading centers in Shenzhen, Hubei, Guangdong, Shanghai, and Beijing from 2015 to 2020, four trading scenarios of Ave, Med, Max, and Min are set up, and the TGARCH-VaR model is used to analyze carbon in different scenarios. The market risk of emissions trading has been studied. The results show that there are differences in carbon emissions trading market risks in different scenarios, and there are certain laws in the market stability and policy response of each scenario:The fluctuation range of the carbon spot yield in the Ave scenario is the smallest, while the Max scenario and the Min scenario have fluctuations in the carbon spot yield. The larger fluctuation of the spot yield means that the risk of the carbon market in the Ave scenario is minimal. The carbon market in the four scenarios has a risk leverage effect, and the impact of bad news on market volatility is greater than that of good news. The Med and Min scenarios are highly dependent on policies, and are susceptible to greater volatility risks due to external factors; the carbon market in the Max scenario is the least sensitive to news, and it is difficult to reflect the market's proper regulatory role; The Ave scenario market is more stable and can better reflect the regulatory effect of the policy. The VaR value variance of the carbon market under the Ave scenario is the smallest, and the VaR value variance of the carbon finance market under the Max scenario is significantly higher than the other three markets, indicating that the carbon market price volatility risk is small in the Ave scenario; the carbon market risk is the smallest in the Ave scenario and the market is stable It is highly sexual and can better reflect the regulatory role of the policy. When China is unifying the carbon emission market, it should take into account the differences in various regions and enthusiasm of enterprises to participate in carbon trading, helps to implement the dual carbon goals.
刘红琴, 胡淑慧. 不同情境下中国碳排放权交易市场的风险度量[J]. 中国环境科学, 2022, 42(2): 962-970.
LIU Hong-qin, HU Shu-hui. Risk measurement of China's carbon emissions trading market in different circumstances. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(2): 962-970.
周迪,刘奕淳.中国碳交易试点政策对城市碳排放绩效的影响及机制[J]. 中国环境科学, 2020,40(1):453-464. Zhou D, Liu Y C. Impact of China's carbon emission trading policy on the performance of urban carbon emission and its mechanism[J]. China Environmental Science, 2020,40(1):453-464.
[2]
王勇,赵晗.中国碳交易市场启动对地区碳排放效率的影响[J]. 中国人口×资源与环境, 2019,29(1):50-58. Wand Y, Zhao H. The impact of China's carbon trading market on regional carbon emission efficiency[J]. China Population, Resources and Environment, 2019,29(1):50-58.
[3]
Donald F Larson, Paul Parks. Risks, lessons learned, and secondary markets for greenhouse gas reductions[Z]. Washington D.C.:The World Bank, 1999.
[4]
樊威,陈维韬.碳金融市场风险形成机理与防范机制研究[J]. 福建论坛(人文社会科学版), 2019,(5):54-64. Fan W, Chen W T. Research on the formation mechanism and prevention mechanism of carbon financial market risk[J]. Fujian Tribune, 2019,(5):54-64.
[5]
禹湘,陈楠,李曼琪.中国低碳试点城市的碳排放特征与碳减排路径研究[J]. 中国人口×资源与环境, 2020,30(7):1-9. Yu X, Chen N, Li M Q. Research on carbon emission characteristics and carbon emission reduction paths of low-carbon pilot cities in China[J]. China Population·Resources and Environment, 2020,30(7):1-9.
[6]
Wu Q, Wang M, Tian L. The market-linkage of the volatility spillover between traditional energy price and carbon price on the realization of carbon value of emission reduction behavior[J]. Clean Prod., 2020, 245,118682.
[7]
汪文隽,周婉云,李瑾,等.中国碳市场波动溢出效应研究[J]. 中国人口×资源与环境, 2016,26(12):63-69. Wang W J, Zhou W Y, Li J, et al. Research on volatility spillover effects of China's carbon market[J]. China Population, Resources and Environment, 2016,26(12):63-69.
[8]
刘立新,李鹏涛.碳交易对中国低碳指数的溢出效应[J]. 中国人口×资源与环境, 2018,28(S1):17-19. Liu L X, Li P T. The spillover effect of carbon trading on China's low carbon index[J]. China Population, Resources and Environment, 2018, 28(S1):17-19.
[9]
Tao A Y, Yu Z Y. Comparative analysis of risk measurement methods in financial market[J]. Stat. Decis., 2009,4(8):7-9.
[10]
张雪梅,李春华,赵燕.VaR方法在海外矿业投资风险管理中的应用框架研究[J]. 中国人口×资源与环境, 2014,24(S2):344-348. Zhang X M, Li C H, Zhao Y. Research on the application framework of VaR method in overseas mining investment risk management[J]. China Population, Resources and Environment, 2014,24(S2):344-348.
[11]
Artzner P, Delbaen F, Eber J M, et al. Coherent measures of risk[J]. Mathematical Finance, 1999,9(3):203-228.
[12]
Gao H, Wei R, Li K Q. Carbon price fluctuation and carbon market risk measurement under structural mutation an empirical study based on EUA carbon futures settlement data[J]. Financ. Econ., 2018,491(7):61-68.
[13]
李翠霞,陈媛媛.基于经验分布下GARCH模型对VaR的金融测度[J]. 中山大学学报(自然科学版), 2021,60(4):177-182. Li C X, Chen Y Y. The financial measurement of VaR under the GARCH model based on empirical distribution[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2021,60(4):177-182.
[14]
胡锐.基于GARCH-VaR方法的碳金融交易市场风险研究[J]. 现代营销(学苑版), 2021,(5):30-31. Hu R. Research on the market risk of carbon finance trading based on GARCH-VaR method[J]. Marketing Management Review, 2021, (5):30-31.
[15]
蒋惠琴,张潇,邵鑫潇,鲍健强.中国试点碳市场收益率波动性研究及启示——基于ARMA-GRACH模型的实证分析[J]. 广州大学学报(社会科学版), 2017,16(10):72-78. Jiang H Q, Zhang X, Shao X X, Bao J Q. Research on the rate of return volatility of China's pilot carbon market and its enlightenment:an empirical analysis based on ARMA-GRACH model[J]. Journal of Guangzhou University (Social Science Edition), 2017,16(10):72-78.
[16]
申晨,林沛娜.中国碳排放权交易试点市场的现状特征及风险分析[J]. 产经评论, 2017,8(4):123-134. Shen C, Lin P N. Study on the characteristics and market risks of the carbon emissions trading pilot market in China[J]. Industrial Economic Review, 2017,8(4):123-134.
[17]
王影,张远晴,董锋.中国碳市场风险测度[J]. 环境经济研究, 2020,5(4):30-53. Wang Y, Zhang Y Q and Dong F. The estimation of carbon market risk in China[J]. Journal of Environmental Economics, 2020,5(4):30-53.
[18]
李雄英,黄时文,黎中彦.稳健ARMA-TGARCH控制图的构建及应用[J]. 统计与决策, 2020,36(12):38-43. Li X Y, Huang S W, Li Z Y. Construction and application of robust ARMA-TGARCH control chart[J]. Statistics & Decision, 2020, 36(12):38-43.
[19]
邓朝晖,刘洋,薛惠锋.基于VAR模型的水资源利用与经济增长动态关系研究[J]. 中国人口×资源与环境, 2012,22(6):128-135. Deng Z H, Liu Y, Xue H F. Research on the dynamic relationship between water resources utilization and economic growth based on VAR model[J]. China Population, Resources and Environment, 2012, 22(6):128-135.
[20]
柴尚蕾,周鹏.基于非参数Copula-CVaR模型的碳金融市场集成风险测度[J]. 中国管理科学, 2019,27(8):1-13. Chai S L, Zhou P. Measuring the integrated risk of carbon financial market by a non-parametric Copul-CVaR modal[J]. Chinese Journal of Management Science, 2019,27(8):1-13.
[21]
刘达,王尔康,牛东晓.小波分析和考虑外生变量的广义自回归条件异方差模型在电价预测中的应用[J]. 电网技术, 2009,33(18):99-104. Liu D, Wang E K, Niu D X. Application of wavelet analysis and generalized autoregressive conditional heteroscedasticity model considering exogenous variables in electricity price forecasting[J]. Power System Technology, 2009,33(18):99-104.
[22]
Wang H W, Cai L R, Zeng W. Research on the evolutionary game of environmental pollution in system dynamics model[J]. Journal of Experimental & Theoretical Artificial Intelligence, 2011,23(1):39-50.
[23]
左秀霞.带高次趋势项的ADF单位根检验[J]. 数量经济技术经济研究, 2019,36(1):152-169. Zuo X X. ADF unit root test with high order trend term[J]. The Journal of Quantitative & Technical Economics Research, 2019, 36(1):152-169.
[24]
叶宗裕.ADF单位根检验法的替代方法[J]. 数量经济技术经济研究, 2017,34(6):148-161. Ye Z Y. Alternative methods of ADF unit root test method[J]. The Journal of Quantitative & Technical Economics Research, 2017,34(6):148-161.
[25]
赵德宇,石颉.基于Davidson-Cole模型与异方差检验的定子绝缘频域介电谱分析[J]. 电工电能新技术, 2021,40(9):71-80. Zhao D Y, Shi J. Spectrum analysis of stator insulation based on Davidson-Cole model and heteroscedasticity test[J]. Advanced Technology of Electrical Engineering and Energy, 2021,40(9):71-80.
[26]
沈豪杰,黄峰.中国股市流动性风险的非对称效应分析[J]. 统计与信息论坛, 2010,25(4):93-98. Shen H J, Huang F. The asymmetric effect of liquidity volatility in mainland China's stock markets[J]. Statistics & Information Forum, 2010,25(4):93-98.
[27]
张志俊,闫丽俊.碳排放权交易价格的VaR风险度量研究[J]. 生态经济, 2020,36(1):19-25,33. Zhang Z J, Yan L J. Research on VaR risk measurement of trading price of carbon emission rights[J]. Ecological Economy, 2020,36(1):19-25,33.
[28]
李薇,董艳艳,卢晗,等."十三五"规划碳减排目标下碳交易机制的博弈分析[J]. 中国环境科学, 2016,36(9):2857-2864. Li W, Dong Y Y, Lu H, et al. A game analysis on carbon trade under carbon mitigation targets during the 13th Five-Year plan period[J]. China Environment Science, 2016,36(9):2857-2864.
[29]
蒋自然,金环环,王成金,等.长江经济带交通碳排放测度及其效率格局(1985~2016年)[J]. 环境科学, 2020,41(6):2972-2980. Jiang Z R, Jin H H, Wang C J, et al. Measurement and efficiency pattern of transportation carbon emissions in the Yangtze River Economic Belt (1985~2016)[J]. Environmental Science, 2020,41(6):2972-2980.
[30]
Qi S Z, Yu X, Tan X J. Quantization on risk of carbon futures in EU:based on GED-GARCH model and VaR model[J]. Technol Financ, 2016,35(7):46-51.
[31]
Tian Y, Chen W, Song W M. The study on measuring the risk of major international carbon emissions trading market based on the model of GARCHEVT-VaR[J]. Sci. Technol. Manag. Res., 2015,324(2):224-231.
[32]
吴永,张静,龚乃林,等.R-vine Copula在中国碳市场中的风险溢出效应研究[J/OL]. 重庆理工大学学报(自然科学):1-10[2021-10-08]. Wu Y, Zhang J, Gong N L, et al. Rsearch on the risk spillover effect of R-Vine Copula in China's carbon markets.[J/OL]. Journal of Chongqing University of Technology (Natural Science):1-10[2021-10-08].
[33]
宋敏,辛强,贺易楠.碳金融交易市场风险的VaR度量与防控——基于中国五所碳排放权交易所的分析[J]. 西安财经大学学报, 2020,33(3):120-128. Song M, Xin Q, He Y N. VaR measurement and prevention of China's carbon financial trading market risk:based on analysis of five carbon emission trading exchanges in China[J]. Journal of Xi'an University of Finance and Economics, 2020,33(3):120-128.
[34]
Zhu B Z, Ye S X, He K J. Measuring the risk of European carbon market:an empirical mode decomposition-based value at risk approach[J]. Ann. Oper. Res., 2018:1-23.
[35]
吴鑫育,李心丹,马超群.基于期权与高频数据信息的VaR度量研究[J]. 中国管理科学, 2021,29(8):13-23. Wu X Y, Li X D, Ma C Q. Measuring VaR based on the information content of option and high-frequency data[J]. Chinese Journal of Management Science, 2021,29(8):13-23.
[36]
赵玉珍,乔亚杰,周黎,等.减排措施如何提升高能耗企业财务绩效——碳绩效的中介作用[J/OL]. 系统工程, 1-16[2021-06-23]. Zhao Yu Z, Qiao Y J, Zhou L, et al. How to improve financial performance of high energy consumption enterprises by emission reduction measures-Mediating role of carbon performance[J/OL]. Systems Engineering:1-16[2021-06-23].
