Finite sample properties of power-law cross-correlations estimators
arXiv:1409.6857 · doi:10.1016/j.physa.2014.10.068
Abstract
We study finite sample properties of estimators of power-law cross-correlations -- detrended cross-correlation analysis (DCCA), height cross-correlation analysis (HXA) and detrending moving-average cross-correlation analysis (DMCA) -- with a special focus on short-term memory bias as well as power-law coherency. Presented broad Monte Carlo simulation study focuses on different time series lengths, specific methods' parameter setting, and memory strength. We find that each method is best suited for different time series dynamics so that there is no clear winner between the three. The method selection should be then made based on observed dynamic properties of the analyzed series.
19 pages, 18 tables
References in corpus (5)
- Detrended Cross-Correlation Analysis: A New Method for Analyzing Two Non-stationary Time Series
- Multifractal detrended cross-correlation analysis for two nonstationary signals
- Cross-correlations between volume change and price change
- Algorithm to estimate the Hurst exponent of high-dimensional fractals
- On the interplay between short and long term memory in the power-law cross-correlations setting