文献类型：期刊文献

英文题名：Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces

作者：Qian, Xi-Yuan[1,2];Liu, Ya-Min[1];Jiang, Zhi-Qiang[2,3];Podobnik, Boris[4,5,6,7,8];Zhou, Wei-Xing[1,2,3];Stanley, H. Eugene

机构：[1]E China Univ Sci & Technol, Sch Sci, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Res Ctr Econophys, Shanghai 200237, Peoples R China;[3]E China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[4]Boston Univ, Ctr Polymer Studies, Boston, MA 02215 USA;[5]Boston Univ, Dept Phys, Boston, MA 02215 USA;[6]Univ Rijeka, Fac Civil Engn, Rijeka 51000, Croatia;[7]Zagreb Sch Econ & Management, Zagreb 10000, Croatia;[8]Univ Ljubljana, Fac Econ, Ljubljana 1000, Slovenia

年份：2015

卷号：91

期号：6

外文期刊名：PHYSICAL REVIEW E

收录：;EI(收录号：20152801031434);WOS:【SCI-EXPANDED(收录号:WOS:000356929400006)】；

基金：This work was partially supported by the National Natural Science Foundation of China under Grant No. 11375064, Fundamental Research Funds for the Central Universities, and Shanghai Financial and Securities Professional Committee.

语种：英文

外文关键词：Brownian movement - Fractals - Time series analysis - Asphaltenes - Crude oil - Factor analysis - White noise - Correlation methods

摘要：When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multiscale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross correlation between crude oil and gold futures by taking into consideration the impact of the U.S. dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the multifractal DCCA method fails.