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

0473066 - ÚTIA 2018 RIV NL eng J - Journal Article
Krištoufek, Ladislav
Fractal approach towards power-law coherency to measure cross-correlations between time series.
Communications in Nonlinear Science and Numerical Simulation. Roč. 50, č. 1 (2017), s. 193-200. ISSN 1007-5704. E-ISSN 1878-7274
R&D Projects: GA ČR(CZ) GP14-11402P
Institutional support: RVO:67985556
Keywords : power-law coherency * power-law cross-correlations * correlations
OECD category: Applied Economics, Econometrics
Impact factor: 3.181, year: 2017
http://library.utia.cas.cz/separaty/2017/E/kristoufek-0473066.pdf

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 Hp 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 104 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.
Permanent Link: http://hdl.handle.net/11104/0271360