CAJ | 학술논문

碳排放交易市场的建立,是一个基于经济学理论来解决气候变暖问题的具有价值的途径,其目的是发展低碳经济。在欧盟排放交易体系一级市场上,以欧盟排放配额（ European Union Allowances, EUA）作为主要交易标的物的碳排放权交易市场已经成为一个重要的新兴贸易市场。随着碳排放权交易市场的不断发展,该市场的资本化程度逐渐深化,其金融属性也日益显著,并逐步融入到国际资本市场体系之中。与其它资本市场相类似,碳排放权交易市场之间也存在着复杂的非线性相关关系,而Copula函数可以用来捕捉这种相依结构特征。因此,文章选取欧盟排放配额（ EUA）期货的日价格时间序列数据,首先假设新息序列服从学生t分布,运用ARMA-GARCH模型对经调整的对数收益率序列进行过滤,采用极大似然方法估计模型的参数,并得到残差序列,同时将其标准化而得到标准化残差；然后,将Kendall’ s tau秩相关系数作为权重,采用最大生成树算法（ maximum spanning tree algorithm）的序贯Copula选择方法构建合适的规则藤Copula模型,并运用基于序贯的极大似然方法估计规则藤Copula模型,以描述碳排放权交易市场之间复杂的相依结构特征。研究结果发现：在无条件下,t-copula函数可以较好地捕捉碳排放权市场之间的相依关系,说明市场存在明显的对称尾部；在 Dec10EUA、Dec12EUA、Dec13EUA 市场相依结构固定下, Dec11EUA 与 Dec14EUA 市场之间的相依结构可以采用Gaussian copula函数来描述,而在Dec10EUA、Dec13EUA市场相依结构确定不变情形下,Dec12EUA与Dec14EUA市场之间的相依结构则适合采用Frank copula函数来捕捉,说明这些市场之间并没有出现尾部特征。进一步地,文章分别选择White信息矩阵等式拟合优度检验和基于概率积分转换（probability integral transform,PIT）与经验Copula过程（empirical copula process,ECP）混合方法的拟合优度检验,并基于Bootstrap方法,以Cramer von Mises（ CvM）检验统计量作为度量测度,来对模型进行拟合优度的检验。研究发现,构建的规则藤Copula模型能够较好地捕捉碳排放权市场之间的相依结构。这一研究结果,为准确探讨碳排放权交易市场之间、碳排放权交易市场与其它资本市场之间套期保值策略提供了一定的参考意义,也有利于提高碳排放权市场产品定价的准确度。碳排放交易市場的建立,是一箇基于經濟學理論來解決氣候變暖問題的具有價值的途徑,其目的是髮展低碳經濟。在歐盟排放交易體繫一級市場上,以歐盟排放配額（ European Union Allowances, EUA）作為主要交易標的物的碳排放權交易市場已經成為一箇重要的新興貿易市場。隨著碳排放權交易市場的不斷髮展,該市場的資本化程度逐漸深化,其金融屬性也日益顯著,併逐步融入到國際資本市場體繫之中。與其它資本市場相類似,碳排放權交易市場之間也存在著複雜的非線性相關關繫,而Copula函數可以用來捕捉這種相依結構特徵。因此,文章選取歐盟排放配額（ EUA）期貨的日價格時間序列數據,首先假設新息序列服從學生t分佈,運用ARMA-GARCH模型對經調整的對數收益率序列進行過濾,採用極大似然方法估計模型的參數,併得到殘差序列,同時將其標準化而得到標準化殘差；然後,將Kendall’ s tau秩相關繫數作為權重,採用最大生成樹算法（ maximum spanning tree algorithm）的序貫Copula選擇方法構建閤適的規則籐Copula模型,併運用基于序貫的極大似然方法估計規則籐Copula模型,以描述碳排放權交易市場之間複雜的相依結構特徵。研究結果髮現：在無條件下,t-copula函數可以較好地捕捉碳排放權市場之間的相依關繫,說明市場存在明顯的對稱尾部；在 Dec10EUA、Dec12EUA、Dec13EUA 市場相依結構固定下, Dec11EUA 與 Dec14EUA 市場之間的相依結構可以採用Gaussian copula函數來描述,而在Dec10EUA、Dec13EUA市場相依結構確定不變情形下,Dec12EUA與Dec14EUA市場之間的相依結構則適閤採用Frank copula函數來捕捉,說明這些市場之間併沒有齣現尾部特徵。進一步地,文章分彆選擇White信息矩陣等式擬閤優度檢驗和基于概率積分轉換（probability integral transform,PIT）與經驗Copula過程（empirical copula process,ECP）混閤方法的擬閤優度檢驗,併基于Bootstrap方法,以Cramer von Mises（ CvM）檢驗統計量作為度量測度,來對模型進行擬閤優度的檢驗。研究髮現,構建的規則籐Copula模型能夠較好地捕捉碳排放權市場之間的相依結構。這一研究結果,為準確探討碳排放權交易市場之間、碳排放權交易市場與其它資本市場之間套期保值策略提供瞭一定的參攷意義,也有利于提高碳排放權市場產品定價的準確度。
탄배방교역시장적건립,시일개기우경제학이론래해결기후변난문제적구유개치적도경,기목적시발전저탄경제。재구맹배방교역체계일급시장상,이구맹배방배액（ European Union Allowances, EUA）작위주요교역표적물적탄배방권교역시장이경성위일개중요적신흥무역시장。수착탄배방권교역시장적불단발전,해시장적자본화정도축점심화,기금융속성야일익현저,병축보융입도국제자본시장체계지중。여기타자본시장상유사,탄배방권교역시장지간야존재착복잡적비선성상관관계,이Copula함수가이용래포착저충상의결구특정。인차,문장선취구맹배방배액（ EUA）기화적일개격시간서렬수거,수선가설신식서렬복종학생t분포,운용ARMA-GARCH모형대경조정적대수수익솔서렬진행과려,채용겁대사연방법고계모형적삼수,병득도잔차서렬,동시장기표준화이득도표준화잔차；연후,장Kendall’ s tau질상관계수작위권중,채용최대생성수산법（ maximum spanning tree algorithm）적서관Copula선택방법구건합괄적규칙등Copula모형,병운용기우서관적겁대사연방법고계규칙등Copula모형,이묘술탄배방권교역시장지간복잡적상의결구특정。연구결과발현：재무조건하,t-copula함수가이교호지포착탄배방권시장지간적상의관계,설명시장존재명현적대칭미부；재 Dec10EUA、Dec12EUA、Dec13EUA 시장상의결구고정하, Dec11EUA 여 Dec14EUA 시장지간적상의결구가이채용Gaussian copula함수래묘술,이재Dec10EUA、Dec13EUA시장상의결구학정불변정형하,Dec12EUA여Dec14EUA시장지간적상의결구칙괄합채용Frank copula함수래포착,설명저사시장지간병몰유출현미부특정。진일보지,문장분별선택White신식구진등식의합우도검험화기우개솔적분전환（probability integral transform,PIT）여경험Copula과정（empirical copula process,ECP）혼합방법적의합우도검험,병기우Bootstrap방법,이Cramer von Mises（ CvM）검험통계량작위도량측도,래대모형진행의합우도적검험。연구발현,구건적규칙등Copula모형능구교호지포착탄배방권시장지간적상의결구。저일연구결과,위준학탐토탄배방권교역시장지간、탄배방권교역시장여기타자본시장지간투기보치책략제공료일정적삼고의의,야유리우제고탄배방권시장산품정개적준학도。
Formation of the carbon emission trading market is a valuable approach to deal with the global warming problems based on economic theory, which aims at the development of a low-carbon economy. In the primary market of the European Union emission trading system, the carbon emission trading market becomes an important emerging market where the European Union Allowance （ EUA） is taken as the main object of the market transaction. With the development of the carbon emission trading market, its capitalization gradually deepens, the financial properties significantly increase and the market becomes integrated into the system of the international capital markets. Due to its similarity to other capital markets, a complex non-linear correlation exists in the carbon emission trading market, and the copula functions can be used to capture the characteristics of the dependence structure. Therefore, the paper chose the data on daily price series of EUA futures, assuming the series of innovations follows the Student’ s t-distribution, filtered the adjusted log-returns of EUA futures using the ARMA-GARCH model, and estimated the parameters in the model, obtained the series of the residuals and standardized them. Then, it took the coefficients of the Kendall’ s tau as the weights of the trees in the vine structure, constructed a feasible regular vine copula model by using the sequential selection approach based on the maximum spanning tree algorithm, and estimated the parameters by applying the sequential maximum likelihood method to describe the characteristics of the dependence structure of the carbon emission trading market. It is shown that the t-copula function can capture the dependence of the markets in the unconditional context, indicating that the carbon emission trading market has significant symmetric tails. Besides, the Gaussian copula function and Frank copula function describe the dependence of Dec11EUA and Dec14EUA conditional on Dec10EUA, Dec12EUA and Dec13EUA, and the dependence of Dec12EUA and Dec14EUA conditional on Dec10EUA and Dec13EUA indicating that there are no tails in the market. Furthermore, the paper tested the performance of the modeled regular vine copula framework by using the Goodness-of-fit tests of the White’ s information matrix equation and the combination of probability integral transform （ PIT） approach and empirical copula process （ ECP ） , which is based on the bootstrap method and Cramer von Mises （ CvM） test statistics. The results show that the modeled regular vine copula framework performs well to describe the nonlinear dependence structure of the carbon emission market. This result provides some references to the discussion of the hedging strategy within the carbon emission trading market and with other capital markets. It is also beneficial to improve the accuracy of the risk management and pricing strategies in the carbon emission market.