{"title":"Correlation Analysis of Stock Market and Fund Market Based on M-Copula-EGARCH-M-GED Model","authors":"Ruihua Wang, Hongjun Wang","doi":"10.21078/JSSI-2020-240-13","DOIUrl":null,"url":null,"abstract":"Abstract In this paper, M-Copula is used to analyze the correlation between Shanghai Composite Index and Shanghai Fund Index. By analyzing the characteristics of the logarithmic yields sequence of two samples, the marginal distribution model is established by using EGARCH-M-GED model. According to the correlation between two logarithmic yields sequence, the M-Copula model is selected to model its correlation structure, and its parameters are estimated by EM algorithm. Because M-Copula combines characteristics of different Copulas, it has more flexible distribution forms and more prominent ability to describe the fat tails and correlation characteristics of data, and more importantly, the effect is better than single Copula.","PeriodicalId":258223,"journal":{"name":"Journal of Systems Science and Information","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Systems Science and Information","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21078/JSSI-2020-240-13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract In this paper, M-Copula is used to analyze the correlation between Shanghai Composite Index and Shanghai Fund Index. By analyzing the characteristics of the logarithmic yields sequence of two samples, the marginal distribution model is established by using EGARCH-M-GED model. According to the correlation between two logarithmic yields sequence, the M-Copula model is selected to model its correlation structure, and its parameters are estimated by EM algorithm. Because M-Copula combines characteristics of different Copulas, it has more flexible distribution forms and more prominent ability to describe the fat tails and correlation characteristics of data, and more importantly, the effect is better than single Copula.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
