文献类型：期刊文献

英文题名：Evolution of worldwide stock markets, correlation structure, and correlation-based graphs

作者：Song, Dong-Ming[1,2];Tumminello, Michele[3,4];Zhou, Wei-Xing[1,2,5];Mantegna, Rosario N.[4]

机构：[1]E China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Sch Sci, Shanghai 200237, Peoples R China;[3]Carnegie Mellon Univ, Dept Social & Decis Sci, Pittsburgh, PA 15213 USA;[4]Univ Palermo, Dipartimento Fis, I-90128 Palermo, Italy;[5]E China Univ Sci & Technol, Res Ctr Econophys, Shanghai 200237, Peoples R China

年份：2011

卷号：84

期号：2

外文期刊名：PHYSICAL REVIEW E

收录：;EI(收录号：20113314243124);WOS:【SCI-EXPANDED(收录号:WOS:000293560900005)】；

基金：We thank Ken Bastiaensen for providing the stock index data. D.-M.S. and W.-X.Z. acknowledge financial support from the National Natural Science Foundation of China (Grant No. 11075054) and the Fundamental Research Funds for the Central Universities. R.N.M. acknowledges financial support from the PRIN project 2007TKLTSR "Indagine di fatti stilizzati e delle strategie risultanti di agenti e istituzioni osservate in mercati finanziari reali ed artificiali.'

语种：英文

外文关键词：Dynamics - Eigenvalues and eigenfunctions - Commerce

摘要：We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period January 1996 to July 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globalization. The fast dynamics is associated with critical events that originate in a specific country or region of the world and rapidly affect the global system. We provide evidence that the short term time scale of correlation among market indices is less than 3 trading months (about 60 trading days). The average values of the nondiagonal elements of the correlation matrix, correlation-based graphs, and the spectral properties of the largest eigenvalues and eigenvectors of the correlation matrix are carrying information about the fast and slow dynamics of the correlation of market indices. We introduce a measure of mutual information based on link co-occurrence in networks in order to detect the fast dynamics of successive changes of correlation-based graphs in a quantitative way.