Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes
Boris Podobnik,
Davor Horvatic,
Alfonso Lam Ng,
H. Eugene Stanley and
Plamen Ch. Ivanov
Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 15, 3954-3959
Abstract:
We investigate how simultaneously recorded long-range power-law correlated multivariate 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.
Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (54)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437108000915
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:15:p:3954-3959
DOI: 10.1016/j.physa.2008.01.062
Access Statistics for this article
Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis
More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().