基于时变Copula相关性分析及风险度量
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摘要
构建ARMA-EGARCH-t-Copula模型,研究上证B股指数和深证B股指数相关关系。选取上证B股和深证B股指数的日收盘指数序列,采用ARMA-EGARCH-t模型进行拟合边缘分布,并判断其具有杠杆效应。利用静态Copula和时变Copula模型构建两序列间的相关性,比较发现,利用动态SJC-Copula探究上海与深圳两市间的相关关系更合理。根据两股指间相关关系,采用蒙特卡洛模拟其VaR值来刻画两者间风险。实证结果表明,上海和深圳间的下尾相关性要强于上尾相关性,且在同一置信水平下,不同权重组合的VaR值不同,在相同权重组合下,置信区间提高, VaR值增大。
The ARMA-EGARCH-t-Copula is constructed to study the correlation of Shanghai-B Index and Shenzhen-B Index. Firstly, the model of ARMA-EGARCH-t is used to construct marginal distribution of Copula function for the daily closing sequence of the Shanghai-B Index and Shenzhen-B Index,and the leverage effect is judged. The correlation between the two sequences is constructed by using static Copula and time-varying Copula models and it is found that the dynamic SJC-Copula is more reasonable to study the correlation between Shanghai and Shenzhen. Finally, according to the correlation between the two indexes, the VaR value of monte carlo simulation is used to describe the risk between the two indexes. The empirical results show that the lower tail correlation between Shanghai and Shenzhen is stronger than the upper tail correlation; at the same confidencce level, the VaR values of different weight combinations are different, and at the same weight combinations, the confidence interval rises and the VaR values increases.
The ARMA-EGARCH-t-Copula is constructed to study the correlation of Shanghai-B Index and Shenzhen-B Index. Firstly, the model of ARMA-EGARCH-t is used to construct marginal distribution of Copula function for the daily closing sequence of the Shanghai-B Index and Shenzhen-B Index,and the leverage effect is judged. The correlation between the two sequences is constructed by using static Copula and time-varying Copula models and it is found that the dynamic SJC-Copula is more reasonable to study the correlation between Shanghai and Shenzhen. Finally, according to the correlation between the two indexes, the VaR value of monte carlo simulation is used to describe the risk between the two indexes. The empirical results show that the lower tail correlation between Shanghai and Shenzhen is stronger than the upper tail correlation; at the same confidencce level, the VaR values of different weight combinations are different, and at the same weight combinations, the confidence interval rises and the VaR values increases.
引文
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[15] 采俊玲.基于Copula理论的股票投资组合VaR风险度量研究[D].北京:北京化工大学,2016.CAI J L.Research on VaR risk measurement of stock portfolio based on Copula theory[D].Beijing:Beijing University of Chemical Technology,2016.(in Chinese)
[16] 周爱民,韩菲.股票市场和外汇市场间风险溢出效应研究——基于GARCH-时变Copula-CoVaR模型的分析[J].国际金融研究,2017(11):54-64.ZHOU A M,HAN F.Study on risk spillover effect between stock market and foreign exchange market:Based on GARCH-time-varying Copula-CoVAR model[J].Studies of International Finance,2017(11):54-64.(in Chinese)
[17] 李月琪,李丛文.沪、港股市联动与风险溢出效应研究——基于沪港通实施前后对比分析[J].上海金融,2017(10):70-80.LI Y Q,LI C W.Research on stock market linkage and risk spillover effect between Shanghai and Hong Kong:Based on the comparative analysis before and after the implementation of shanghai-hong kong stock connect[J].Shanghai Finance,2017(10):70-80.(in Chinese)
[18] 杨湘豫,李强.基于贝叶斯方法与时变Copula模型的基金风险的度量[J].财经理论与实践,2018,39(1):63-68.YANG X Y,LI Q.Measurement of fund risk based on bayesian method and time-varying Copula model[J].The Theory and Practice of Finance and Economics,2018,39(1):63-68.(in Chinese)
[2] 卢俊香,武宇,杜艳丽.基于Copula的沪深股市相依结构与相关模式研究[J].四川理工学院学报(自然科学版),2016,29(2):70-74.LU J X,WU Y,DU Y L.Research on The Dependence Structure and Correlation Pattern of Shanghai and Shenzhen Stock Market Based on Copula[J].Journal of Sichuan University of Science & Engineering(Natural Science Edition),2016,29(2):70-74.(in Chinese)
[3] 马梅,卢俊香,武宇,等.Copula模型选择及在金融市场的应用[J].西安工程大学学报,2016,30(3):375-379.MA M,LU J X,WU Y,et al.The selection on Copula model and its application in financial markets[J].Journal of Xi′an Polytechnic University,2016,30(3):375-379.(in Chinese)
[4] 薛凯丽,卢俊香.基于MA-EGARCH-t-Copula模型的金融市场相关关系的研究[J].河南科学,2018,36(6):951-956.XUE K L,LU J X.Research on the correlation of financial market on MA-EGARCH-t-Copula model[J].Henan Science,2018,36(6):951-956.(in Chinese)
[5] 葛亮.基于Copula-GARCH模型的新兴产业与上证指数相依性研究[J].统计与决策,2016(24):170-173.GE L.Study on the dependency between Emerging industries and Shanghai index based on the Copula-GARCH model[J].Statistics & Decision,2016(24):170-173.(in Chinese)
[6] 谢凤杰,吴东立,赵思喆.基于Copula方法的大豆收入保险费率测定:理论与实证[J].农业技术经济,2017(2):111-121.XIE F J,WU D L,ZHAO S Z.Determination of soybean income insurance rate based on Copula method:Theory and evidence[J].Journal of Agrotechnical Economics,2017(2):111-121.(in Chinese)
[7] 张卫国,朱从飞,江雨田,等.基于Copula函数的甬江流域雨潮遭遇组合分析[J].水电能源科学,2018,36(1):24-27.ZHANG W G,ZHU C F,JIANG Y T,et al.Encounter analysis of flood and tide of Yongjiang rieve basin based on Copula function[J].Water Resources and Power,2018,36(1):24-27.(in Chinese)
[8] PATTON A J.Modelling time-varying exchange rate dependence using the conditional copula[J].SSRN Journal,2001:5-51.(in Chinese)
[9] 韦艳华,张世英.Copula理论及其在金融分析上的应用[M].北京:清华大学出版社,2008.WEI Y H,ZHANG S Y.Copula theory and its application in financial analysis[M].Beijing:Tsinghua University Press,2008.(in Chinese)
[10] 袁洪.基于半参数Copula-ARMA-GARCH模型的能源业与银行业动态相依性研究[J].兰州财经大学学报,2017,33(2):25-32.YUAN H.Research on dynamic dependence of energy industry and banking industry based on semi-parametric copula-arma-garch model[J].Journal of Lanzhou University of Finance and Economics,2017,33(2):25-32.(in Chinese)
[11] 杜艳丽.基于时变Copula方法的股票收益率相关性研究[D].西安:西安工程大学,2017.DU Y L.Research on correlation of stock return rate based on time-varying Copula method[D].Xi′an Polytechnic University,2017.(in Chinese)
[12] 陈艳声,邹辉文,蔡立雄.指数分布下的信用风险传染效应——基于时变Copula的实证分析[J].时代金融,2018(9):213-218.CHEN Y S,ZOU H W,CAI L X.Contagion effect of credit risk under exponential distribution:Empirical analysis based on time-varying Copula[J].Times Finance,2018(9):213-218.(in Chinese)
[13] 郭文伟,王礼昱.“一带一路”沿线国家(地区)股市时变联动性及其相依结构演化研究[J].西安交通大学学报(社会科学版),2018,38(2):39-49.GUO W W,WANG L Y.An empirical research on the time-varying linkage and evolution of its dependence structure of stock markets among the belt and road initiative[J].Journal of Xi′an Jiaotong University(Social Sciences),2018,38(2):39-49.(in Chinese)
[14] 王永巧,蒋学伟.基于时变Copula的金融系统性风险度量[M].北京:中国经济出版社,2016.WANG Y Q,JIANG X W.Financial systemic risk measurement based on time-varying Copula [M].Beijing:China Economic Press,2016.(in Chinese)
[15] 采俊玲.基于Copula理论的股票投资组合VaR风险度量研究[D].北京:北京化工大学,2016.CAI J L.Research on VaR risk measurement of stock portfolio based on Copula theory[D].Beijing:Beijing University of Chemical Technology,2016.(in Chinese)
[16] 周爱民,韩菲.股票市场和外汇市场间风险溢出效应研究——基于GARCH-时变Copula-CoVaR模型的分析[J].国际金融研究,2017(11):54-64.ZHOU A M,HAN F.Study on risk spillover effect between stock market and foreign exchange market:Based on GARCH-time-varying Copula-CoVAR model[J].Studies of International Finance,2017(11):54-64.(in Chinese)
[17] 李月琪,李丛文.沪、港股市联动与风险溢出效应研究——基于沪港通实施前后对比分析[J].上海金融,2017(10):70-80.LI Y Q,LI C W.Research on stock market linkage and risk spillover effect between Shanghai and Hong Kong:Based on the comparative analysis before and after the implementation of shanghai-hong kong stock connect[J].Shanghai Finance,2017(10):70-80.(in Chinese)
[18] 杨湘豫,李强.基于贝叶斯方法与时变Copula模型的基金风险的度量[J].财经理论与实践,2018,39(1):63-68.YANG X Y,LI Q.Measurement of fund risk based on bayesian method and time-varying Copula model[J].The Theory and Practice of Finance and Economics,2018,39(1):63-68.(in Chinese)