时变多元NBSCopula模型与美国股指期货市场可视化相依性分析  

Time-varying Multivariate NBS Copula Model and Visualized DependenceAnalysis of American Stock Index Futures Market

作　　者：肖振宇[1] 王杰 李姗姗 石岿然[1] XIAO Zhenyu;WANG Jie;LI Shanshan;SHI Kuiran(School of Finance,Nanjing Audit University,Nanjing 211815,China;School of Business,Shaoxing University,Shaoxing 312000,China)

机构地区：[1]南京审计大学金融学院,江苏南京211815 [2]绍兴文理学院商学院,浙江绍兴312000

出　　处：《运筹与管理》2023年第5期190-196,共7页Operations Research and Management Science

基　　金：国家社科基金项目(22BGL002);江苏省高校人文社会科学研究项目(2021SJA0369);江苏省金融工程重点实验室招标项目(NSK2021-04)。

摘　　要：基于多元NBS(Normal Birnbaum-Saunders)分布构造了一种新的多元偏斜厚尾Copula,即多元NBS Copula,并进一步采用DCC(Dynamic Conditional Correlation)模型构造了时变NBS Copula模型。以美国道琼斯30指数期货、标准普尔500指数期货和纳斯达克100指数期货为例,可视化分析了收益率序列之间的各种相依特征,比较了DCC-NBS Copula模型与其他一些Copula模型在相依结构拟合上的效果差异。实证结果表明:美国三大股指期货之间的相依结构具有正相依性、厚尾相依性、非对称相依性和时变相依性,其中,NAGARCH模型可以较好地描述收益率序列的动态特征,椭圆Copula优于阿基米德Copula,非对称椭圆Copula优于对称椭圆Copula,厚尾椭圆Copula优于正态Copula,时变椭圆Copula优于静态椭圆Copula。综合来看,DCC-NBSCopula模型是所有模型中对相依结构的拟合效果最优的。There is a complex dependent structure among the return rate series of financial assets.Multivariate NBS Copula is not only suitable for two-and higher-dimensional dependence structure modeling,but also contains multivariate normal copulas and multivariate symmetrical NBS Copula subclasses.The correlation parameter matrix,tail parameter and skew parameter vector can also be used to flexibly describe the positive and negative dependence,tail dependence and asymmetric dependence between pairs.It is also necessary to consider the time-varying dependence in the modeling dynamic structures.However,there are few researches on the dynamics of the multivariate skew ellipse Copula,mainly including DCC-GHST Copula model,GAS-GHST Copula model and DCC-NCCN Copula model.DCC-GHST Copula model,GAS-GHST Copula model and DCC-NCCN Copula model.In this study,a new time-varying NBS Copula model,namely DCC-NBS Copula model,is proposed for a detailed visual analysis of the dependent structure of the return of the three major stock index futures in the United States.Kendall’s rank correlation coefficient,QD(Quantitative Dependence)coefficient and the normal score’s Maria skewness and kurtosis are used to measure the dependence characteristics of the joint distribution.On this basis,a multivariate NBS model is constructed.The parameter set of its distribution includes a correlation parameter matrix,a tail parameter and a skew parameter vector.In order to make the relevant parameter matrix dynamic,the DCC model is introduced and the multivariate time-varying DCC-NBS Copula model is constructed.For the given dependent structure data,the ML(Maximum Likelihood)method can be used to obtain the parameter estimates.Taking Dow 30 index futures of the Chicago Board of Trade(CBOT),S&P 500 index futures of the Chicago Mercantile Exchange(CME)and NASDAQ 100 index futures as research objects,daily closing price data from January 1,2005 to December 31,2020 are selected to calculate daily logarithmic returns.Each closing price series has 4118 data

关 键 词：DCC模型 多元NBS Copula 尾部相依性 非对称相依性 时变相依性 

分 类 号：F832.51[经济管理—金融学]