文献类型：期刊文献

英文题名：Market Correlation Structure Changes Around the Great Crash: A Random Matrix Theory Analysis of the Chinese Stock Market

作者：Han, Rui-Qi[1];Xie, Wen-Jie[2];Xiong, Xiong[3];Zhang, Wei[3];Zhou, Wei-Xing[4]

机构：[1]East China Univ Sci & Technol, Res Ctr Econophys, Dept Math, Sch Sci, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Finance, Sch Business, Postdoctoral Res Stn,Res Ctr Econophys, Shanghai 200237, Peoples R China;[3]Tianjin Univ, China Ctr Social Comp & Analyt, Coll Management & Econ, Tianjin 300072, Peoples R China;[4]East China Univ Sci & Technol, Res Ctr Econophys, Dept Finance, Sch Business,Dept Math,Sch Sci, Shanghai 200237, Peoples R China

年份：2017

卷号：16

期号：2

外文期刊名：FLUCTUATION AND NOISE LETTERS

收录：;WOS:【SSCI(收录号:WOS:000402141100008)，SCI-EXPANDED(收录号:WOS:000402141100008)】；

基金：This work was supported by National Natural Science Foundation of China (71532009 and 71131007) and Fundamental Research Funds for the Central Universities (222201718006).

语种：英文

外文关键词：Econophysics; random matrix theory; Partial correlation; stock market; financial crisis; eigenvalue

摘要：The correlation structure of a stock market contains important financial contents, which may change remarkably due to the occurrence of financial crisis. We perform a comparative analysis of the Chinese stock market around the occurrence of the 2008 crisis based on the random matrix analysis of high-frequency stock returns of 1228 Chinese stocks. Both raw correlation matrix and partial correlation matrix with respect to the market index in two time periods of one year are investigated. We find that the Chinese stocks have stronger average correlation and partial correlation in 2008 than in 2007 and the average partial correlation is significantly weaker than the average correlation in each period. Accordingly, the largest eigenvalue of the correlation matrix is remarkably greater than that of the partial correlation matrix in each period. Moreover, each largest eigenvalue and its eigenvector reflect an evident market effect, while other deviating eigenvalues do not. We find no evidence that deviating eigenvalues contain industrial sectorial information. Surprisingly, the eigenvectors of the second largest eigenvalues in 2007 and of the third largest eigenvalues in 2008 are able to distinguish the stocks from the two exchanges. We also find that the component magnitudes of the some largest eigenvectors are proportional to the stocks' capitalizations.