文献类型：期刊文献

英文题名：Statistical tests for power-law cross-correlated processes

作者：Podobnik, Boris[1,2,3,4];Jiang, Zhi-Qiang[5,6];Zhou, Wei-Xing[5,6];Stanley, H. Eugene[2,3]

机构：[1]Univ Rijeka, Fac Civil Engn, Rijeka 51000, Croatia;[2]Boston Univ, Ctr Polymer Studies, Boston, MA 02215 USA;[3]Boston Univ, Dept Phys, Boston, MA 02215 USA;[4]Univ Ljubljana, Dept Econ, Ljubljana 1000, Slovenia;[5]E China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[6]E China Univ Sci & Technol, Res Ctr Econophys, Shanghai 200237, Peoples R China

年份：2011

卷号：84

期号：6

外文期刊名：PHYSICAL REVIEW E

收录：;EI(收录号：20120114665667);WOS:【SCI-EXPANDED(收录号:WOS:000298675500003)】；

基金：We acknowledge support from the Ministry of Science of Croatia (Grant No. 114-0352827-1370) and the National Science Foundation. ZQJ and WXZ acknowledge financial support from the National Natural Science Foundation of China (Grant No. 11075054) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Commerce - Statistical tests - Time series analysis

摘要：For stationary time series, the cross-covariance and the cross-correlation as functions of time lag n serve to quantify the similarity of two time series. The latter measure is also used to assess whether the cross-correlations are statistically significant. For nonstationary time series, the analogous measures are detrended cross-correlations analysis (DCCA) and the recently proposed detrended cross-correlation coefficient, rho(DCCA)(T,n), where T is the total length of the time series and n the window size. For rho(DCCA)(T,n), we numerically calculated the Cauchy inequality-1 <= rho(DCCA)(T,n) <= 1. Here we derive -1 <= rho(DCCA)(T, n) <= 1 for a standard variance-covariance approach and for a detrending approach. For overlapping windows, we find the range of rho(DCCA) within which the cross-correlations become statistically significant. For overlapping windows we numerically determine-and for nonoverlapping windows we derive-that the standard deviation of rho(DCCA)(T,n) tends with increasing T to 1/T. Using rho(DCCA)(T, n) we show that the Chinese financial market's tendency to follow the U.S. market is extremely weak. We also propose an additional statistical test that can be used to quantify the existence of cross-correlations between two power-law correlated time series.