Multifractality is ubiquitously observed in complex natural and socioeconomic systems.
Multifractal analysis provides powerful tools to understand the complex nonlinear nature of …

In this paper, a new coefficient is proposed with the objective of quantifying the level of cross-
correlation between nonstationary time series. This cross-correlation coefficient is defined in …

Using the eigenvalues and eigenvectors of correlations matrices of some of the main
financial market indices in the world, we show that high volatility of markets is directly linked …

There are a number of situations in which several signals are simultaneously recorded in
complex systems, which exhibit long-term power-law cross correlations. The multifractal …

In order to quantify the long-range cross-correlations between two time series qualitatively,
we introduce a new cross-correlations test Q CC (m), where m is the number of degrees of …

In this short report, we investigate the ability of the DCCA coefficient to measure correlation
level between non-stationary series. Based on a wide Monte Carlo simulation study, we …

We introduce a new method for the detection of long-range cross-correlations and
multifractality—multifractal height cross-correlation analysis (MF-HXA)—based on scaling of …

In this paper we propose, analyze and also quantify cross-correlations between
climatological data. For this purpose we adopt the DCCA cross-correlation coefficient …

We study long-range magnitude cross-correlations in collective modes of real-world data
from finance, physiology, and genomics using time-lag random matrix theory. We find long …

In this paper, we investigate the cross-correlations between Chinese A-share and B-share
markets. Qualitatively, we find that the return series of Chinese A-share and B-share markets …