{"title":"财务相关矩阵的噪声去除与信息识别","authors":"Jianqiang Sun","doi":"10.1109/ISCSCT.2008.345","DOIUrl":null,"url":null,"abstract":"We apply the random-matrix approach to undress the noise of the cross correlation matrix constructed from Shanghai Stock Exchange (SSE) for the period 2001-2008. The empirical evidence shows that, about 7.4% of the eigenvalues fall out the RMT bounds, and the eigenvalues within the bounds agree with the universal properties of random matrix, implying a large degree of noise in the correlation matrix. We also find that SSE has a particularly high value of the largest eigenvalues of 209.26, which is significantly different from other exchanges.","PeriodicalId":228533,"journal":{"name":"2008 International Symposium on Computer Science and Computational Technology","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Noise Undressing and Information Identifying of the Financial Correlation Matrix\",\"authors\":\"Jianqiang Sun\",\"doi\":\"10.1109/ISCSCT.2008.345\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We apply the random-matrix approach to undress the noise of the cross correlation matrix constructed from Shanghai Stock Exchange (SSE) for the period 2001-2008. The empirical evidence shows that, about 7.4% of the eigenvalues fall out the RMT bounds, and the eigenvalues within the bounds agree with the universal properties of random matrix, implying a large degree of noise in the correlation matrix. We also find that SSE has a particularly high value of the largest eigenvalues of 209.26, which is significantly different from other exchanges.\",\"PeriodicalId\":228533,\"journal\":{\"name\":\"2008 International Symposium on Computer Science and Computational Technology\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Symposium on Computer Science and Computational Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCSCT.2008.345\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Symposium on Computer Science and Computational Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCSCT.2008.345","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Noise Undressing and Information Identifying of the Financial Correlation Matrix
We apply the random-matrix approach to undress the noise of the cross correlation matrix constructed from Shanghai Stock Exchange (SSE) for the period 2001-2008. The empirical evidence shows that, about 7.4% of the eigenvalues fall out the RMT bounds, and the eigenvalues within the bounds agree with the universal properties of random matrix, implying a large degree of noise in the correlation matrix. We also find that SSE has a particularly high value of the largest eigenvalues of 209.26, which is significantly different from other exchanges.
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