中国碳金融市场价格跳跃扩散效应及风险研究
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摘要
深入研究中国碳金融市场价格的跳跃扩散效应、度量动态极端跳跃风险,能够为建立全国统一碳金融市场提供实证支撑。以中国6个代表性碳金融市场的成交价为研究对象,基于SVCJ模型估计中国碳金融市场价格的跳跃概率、跳跃幅度,通过跳跃扩散强度和条件跳跃扩散概率探讨跳跃扩散效应的特征,再结合极值理论建立SVCJ-POT-VaR模型,度量中国碳金融市场的动态极端跳跃风险。研究结果表明:中国碳金融市场的跳跃特征明显,各市场之间存在跳跃扩散效应,还未形成稳定的碳金融市场;广州和深圳市场拥有较高的跳跃扩散强度,在全国碳金融市场中最为活跃,但整个市场的跳跃扩散强度处于较低水平;天津市场的跳跃信息向其他市场传递的概率最高,市场稳定性最差,当出现不可控的政策调整或风险事件时,天津市场最有可能引起全国碳金融市场的波动;在考虑收益率波动的极端跳跃行为时,SVCJ-POT-VaR模型对中国碳金融市场具有良好的风险测算能力。
An in-depth study of the jump-diffusion effect of China carbon financial market price and the measurement of dynamic extreme jump risk can provide an empirical support for establishing a unified national carbon financial market. With the transaction price of six representative carbon financial markets in China as the research object and by adopting the SVCJ Model Based on MCMC, this paper estimates the jump probability and jump amplitude of China carbon financial market price, studies the characteristics of jump diffusion effect by jump diffusion intensity and conditional jump diffusion probability, and then combined with extreme value theory, establishes the SVCJ-POT-VaR model to measure the dynamic extreme jump risk of China carbon financial market. The results show that the jump characteristics of China carbon financial market are obvious, there is a jump-diffusion effect among the markets, and no stable carbon financial market has formed; Guangzhou and Shenzhen markets have higher jump diffusion intensity, which is the most active in the national carbon financial market, but the jump diffusion intensity of the whole market is at a low level; Tianjin market has the highest probability of transmitting its jump information to other markets with the worst market stability, and when there are uncontrollable policy adjustments or risk events, Tianjin market is most likely to cause fluctuations in the national carbon financial market; and that when considering the extreme jump behavior of yield volatility, the SVCJ-POT-VaR model has a good risk estimation ability for China carbon financial market.
An in-depth study of the jump-diffusion effect of China carbon financial market price and the measurement of dynamic extreme jump risk can provide an empirical support for establishing a unified national carbon financial market. With the transaction price of six representative carbon financial markets in China as the research object and by adopting the SVCJ Model Based on MCMC, this paper estimates the jump probability and jump amplitude of China carbon financial market price, studies the characteristics of jump diffusion effect by jump diffusion intensity and conditional jump diffusion probability, and then combined with extreme value theory, establishes the SVCJ-POT-VaR model to measure the dynamic extreme jump risk of China carbon financial market. The results show that the jump characteristics of China carbon financial market are obvious, there is a jump-diffusion effect among the markets, and no stable carbon financial market has formed; Guangzhou and Shenzhen markets have higher jump diffusion intensity, which is the most active in the national carbon financial market, but the jump diffusion intensity of the whole market is at a low level; Tianjin market has the highest probability of transmitting its jump information to other markets with the worst market stability, and when there are uncontrollable policy adjustments or risk events, Tianjin market is most likely to cause fluctuations in the national carbon financial market; and that when considering the extreme jump behavior of yield volatility, the SVCJ-POT-VaR model has a good risk estimation ability for China carbon financial market.
引文
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[2]BALCILAR M, DEMIRER R, HAMMOUDEH S, et al. Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk[J]. Energy Economics,2016,54:159-172.
[3]BYUN S J, CHO H. Forecasting carbon futures volatility using GARCH models with energy volatilities[J]. Energy Economics, 2013,40:207-221.
[4]CHEVALLIER J. Variance risk-premia in CO2 markets[J].Economic Modelling, 2013,31:598-605.
[5]SANIN M E,VIOLANTE F,MANSANET-BATALLER M.Understanding volatility dynamics in the EU-ETS market[J].Energy Policy,2015,82:321-331.
[6]KIM J,PARK Y J,RYU D.Stochastic volatility of the futures prices of emission allowances: A Bayesian approach[J].Physica A: Statistical Mechanics and its Applications,2017,465:714-724.
[7]ZHANG Y J,SUN Y F.The dynamic volatility spillover between European carbon trading market and fossil energy market[J].Journal of Cleaner Production,2016,112:2654-2663.
[8]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.
[9]WANG Y,GUO Z.The dynamic spillover between carbon and energy markets:New evidence[J].Energy,2018,149:24-33.
[10]孙春.中国碳市场与EU碳市场价格波动溢出效应研究[J].工业技术经济,2018(3):97-105.
[11]张晨,杨玉,张涛.基于Copula模型的商业银行碳金融市场风险整合度量[J].中国管理科学,2015,23(4):61-69.
[12]杜莉,孙兆东,汪蓉.中国区域碳金融交易价格及市场风险分析[J].武汉大学学报(哲学社会科学版),2015,68(2):86-93.
[13]王婷婷, 张亚利, 王淼晗. 中国碳金融市场风险度量研究[J].金融论坛,2016(9):57-68.
[14]邱谦,郭守前.我国区域碳金融交易市场的风险研究[J].资源开发与市场,2017,33(2):188-193.
[15]王倩,高翠云.中国试点碳市场间的溢出效应研究——基于六元VAR-GARCH-BEKK模型与社会网络分析法[J].武汉大学学报(哲学社会科学版),2016,69(6):57-67.
[16]DUFFIE D, PAN J, SINGLETON K. Transform analysis and asset pricing for affine jump-diffusions[J].Econometrica,2000,68(6):1343-1376.
[17]ERAKER B,JOHANNES M,POLSON N.The impact of jumps in volatility and returns[J].The Journal of Finance,2003,58(3):1269-1300.
[18]IGNATIEVA K,RODRIGUES P,SEEGER N.Stochastic volatility and jumps: Exponentially affine yes or no?An empirical analysis of S&P500 dynamics[J].Social Science Electronic Publishing,2009.
[19]曾昭法,左杰.沪港证券市场收益的跳跃与波动溢出关系研究——基于MCMC算法的SVCJ模型[J].中国管理科学,2013,21(增刊1):334-340.
[20]王垒,赵忠超,刘新民,等.政府财政配置能力与外资参与程度对碳经济绩效的驱动效应[J].中国人口·资源与环境,2018,28(4):55-67.
[21]MCNEIL A J,FREY R.Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach[J].Journal of Empirical Finance,2000,7(3):271-300.
[22]王垒,刘新民,吴士健,等.创业企业IPO所有权类型集中度、董事会主导功能与多元化战略选择[J].南开管理评论,2018,21(3):103-115.
[23]KUPIEC P.Techniques for verifying the accuracy of risk measurement models[J].Social Science Electronic Publishing,1995,3(2):73-84.