Finite sample properties of power-law cross-correlations estimators

• Finite sample properties of power-law cross-correlations estimators are studied.
• DCCA, DMCA and HXA methods are compared.
• Each of the methods is better suited for specific characteristics.
• There is no clear winner.

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. We present a broad Monte Carlo simulation study that 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.