文献类型：期刊文献

中文题名：Joint multifractal analysis based on wavelet leaders

英文题名：Joint multifractal analysis based on wavelet leaders

作者：Zhi-Qiang Jiang[1,2,3];Yan-Hong Yang[1,2,3];Gang-Jin Wang[3,4];Wei-Xing Zhou[1,2,5]

机构：[1]School of Business, East China University of Science and Technology, Shanghai 200237, China;[2]Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China;[3]Department of Physics and Center for Polymer Studies, Boston University, Boston, MA 02215, USA;[4]Business School and Center of Finance and Investment Management, Hunan University, Changsha 410082, China;[5]5Department of Mathematics, East China University of Science and Technology, Shanghai 200237, China

年份：2017

卷号：12

期号：6

起止页码：127

中文期刊名：Frontiers of physics

外文期刊名：物理学前沿（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2017_2018】；PubMed;

基金：We acknowledge financial support from the National Natural Science Foundation of China （11375064 and 71532009）, the Program for Changjiang Scholars and Innovative Research Team in University （IRT1028）, and the Fundamental Re- search Funds for the Central Universities.

语种：英文

中文关键词：joint multifractal analysis;wavelet leader;binomial measure;bivariate fractional Brownianmotion;econophysics;online world

外文关键词：joint multifractal analysis, wavelet leader, binomial measure, bivariate fractional Brownianmotion, econophysics, online world

摘要：Mutually interacting components form complex systems and these components usually have long- range cross-correlated outputs. Using wavelet leaders, we propose a method for characterizing the joint multifractal nature of these long-range cross correlations; we call this method joint multifractal analysis based on wavelet leaders （MF-X-WL）. We test the validity of tile MF-X-WL method by performing extensive numerical experiments on dual binomial measures with multifractal cross correlations and bivariate fractional Brownian motions （bFBMs） with monofractal cross correlations. Both experiments indicate that MF-X-WL is capable of detecting cross correlations in synthetic data with acceptable estimating errors. We also apply the MF-X-WL method to pairs of series from financial markets （returns and volatilities） and online worlds （online numbers of different genders and different societies） and determine intriguing joint multifractal behavior.
Mutually interacting components form complex systems and these components usually have long- range cross-correlated outputs. Using wavelet leaders, we propose a method for characterizing the joint multifractal nature of these long-range cross correlations; we call this method joint multifractal analysis based on wavelet leaders （MF-X-WL）. We test the validity of tile MF-X-WL method by performing extensive numerical experiments on dual binomial measures with multifractal cross correlations and bivariate fractional Brownian motions （bFBMs） with monofractal cross correlations. Both experiments indicate that MF-X-WL is capable of detecting cross correlations in synthetic data with acceptable estimating errors. We also apply the MF-X-WL method to pairs of series from financial markets （returns and volatilities） and online worlds （online numbers of different genders and different societies） and determine intriguing joint multifractal behavior.