| By: |
Zhi-Qiang Jiang (ECUST, BU);
Yan-Hong Yang (ECUST, BU);
Gang-Jin Wang (HNU, BU);
Wei-Xing Zhou (ECUST) |
| Abstract: |
Mutually interacting components form complex systems and the outputs of these
components are usually long-range cross-correlated. Using wavelet leaders, we
propose a method of characterizing the joint multifractal nature of these
long-range cross correlations, a method we call joint multifractal analysis
based on wavelet leaders (MF-X-WL). We test the validity of the MF-X-WL method
by performing extensive numerical experiments on the dual binomial measures
with multifractal cross correlations and the bivariate fractional Brownian
motions (bFBMs) with monofractal cross correlations. Both experiments indicate
that MF-X-WL is capable to detect the cross correlations in synthetic data
with acceptable estimating errors. We also apply the MF-X-WL method to the
pairs of series from financial markets (returns and volatilities) and online
worlds (online numbers of different genders and different societies) and find
an intriguing joint multifractal behavior. |
| Date: |
2016–11 |
| URL: |
http://d.repec.org/n?u=RePEc:arx:papers:1611.00897&r=ets |