Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes
Statistical Finance
2009-11-13 v1 Statistical Mechanics
Abstract
We investigate how simultaneously recorded long-range power-law correlated multi-variate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component FIARCH process to generate coupled fractal signals with long-range power-law correlations which are at the same time long-range cross-correlated. We study how the degree of cross-correlations between these signals depends on the scaling exponents characterizing the fractal correlations in each signal and on the coupling between the signals. Our findings have relevance when studying parallel outputs of multiple-component of physical, physiological and social systems.
Cite
@article{arxiv.0709.0838,
title = {Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes},
author = {Boris Podobnik and Davor Horvatic and Alfonso Lam Ng and H. Eugene Stanley and Plamen Ch. Ivanov},
journal= {arXiv preprint arXiv:0709.0838},
year = {2009}
}
Comments
8 pages, 5 figures, elsart.cls