Fractal approach towards power-law coherency to measure cross-correlations between time series

Ladislav Krištoufek ()

Papers from arXiv.org

Abstract: We focus on power-law coherency as an alternative approach towards studying power-law cross-correlations between simultaneously recorded time series. To be able to study empirical data, we introduce three estimators of the power-law coherency parameter $H_{\rho}$ based on popular techniques usually utilized for studying power-law cross-correlations -- detrended cross-correlation analysis (DCCA), detrending moving-average cross-correlation analysis (DMCA) and height cross-correlation analysis (HXA). In the finite sample properties study, we focus on the bias, variance and mean squared error of the estimators. We find that the DMCA-based method is the safest choice among the three. The HXA method is reasonable for long time series with at least $10^4$ observations, which can be easily attainable in some disciplines but problematic in others. The DCCA-based method does not provide favorable properties which even deteriorate with an increasing time series length. The paper opens a new venue towards studying cross-correlations between time series.

Date: 2016-08, Revised 2017-02
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Published in Communications in Nonlinear Science and Numerical Simulation, Volume 50, September 2017, Pages 193-200

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1608.06781

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