Research On The Risk Of Carbon Market Based On Copula Theory

As the globalization trend of carbon trading accelerates,the carbon market fluctuates more and more intense,and the carbon market risk is not limited to a single market itself,it will quickly spread to other carbon markets.This risk contagion has a relatively obvious vector characteristic,with both size and direction.In order to grasp the information and risk transmission mechanism between the carbon markets,and help with the formulation and improvement of the carbon market risk management mechanism,more and more research is committed to the carbon market risk research field,mainly focusing on the risk measurement and risk spillover effect of the carbon market.In the study of the risk spillover effect of the carbon market,the main challenge is the estimation of the joint distribution.For the usual nonlinear dependence between multiple variables,the Copula method broken through the limitation of linear dependence and can solve the nonlinear,asymmetric dependence structure problem.In this paper,we study the dependence structure between multiple carbon markets and the risk spillover effects based on the Copula function.First,based on the research of the existing literature,this paper introduced the relevant knowledge of carbon market risk and Copula theory and time series analysis.Relevant risk measures,the definition and parameters estimation of the R-Vine Copula function,and the calculation of CoVaR values based on the Copula function are also introduced.Secondly,take the carbon market in Shanghai,Beijing,Hubei,Guangdong and Shenzhen as the research objects to analyze the dependence structure between the carbon markets.The ARMA-GARCH(1,1)model was first established for the closing price yield sequence of the nearly seven years,and then the optimal marginal distribution was selected by the criteria of AIC.The results showed that the optimal marginal distribution of each sequence is different and does not obey a certain identical distribution.Then the R-Vine structure was established by the maximum spanning tree method,and the resulting R-Vine Copula model and the parametric results indicated the dependence between the carbon markets.The results showed that there is a chain of D-Vine structure between the five carbon markets,indicating a weak overall dependence between the carbon markets.There is an asymmetric unconditional tail dependence between carbon markets in Shanghai and Shenzhen,and carbon markets in Hubei and Guangdong,while there is a symmetrical unconditional tail dependence between Shanghai and Beijing carbon market and Beijing and Guangdong carbon market.Overall,conditional dependence is weaker than non-conditional dependence.This is related to the relative independent geographical location of the carbon markets,but also related to the regional development mechanism of the carbon markets.In addition,China started to establish a national unified carbon market in 2017.The current development is still very immature,and the information transmission between the carbon markets is less or weak.Finally,the risk spillover effects between carbon markets are analyzed by studying the EU and the five Chinese carbon markets.Firstly established the R-Vine Copula model for the daily closing yield sequence for nearly seven years,then CoVaR values were then calculated based on the Copula function in the Vine structure,based on which the risk spillover effect between the carbon markets was analyzed.The results showed that from the perspective of R-Vine Copula structure,the five carbon markets in Shanghai,Beijing and Hubei are centered on the EU carbon market,indicating that these three carbon markets are greatly affected by the EU carbon market.From the perspective of CoVaR values,the EU has a one-way risk spillover effect on the carbon markets in Shanghai,Beijing and Hubei,among which the EU carbon trading market has the strongest risk spillover effect on the carbon market in Hubei.Hubei carbon market,as the most active carbon market and the most liquid market,is also the most affected by the EU carbon market.