A new approach to quantify power-law cross-correlation and its application to commodity markets

► DMCA is proposed to detect cross-correlation between two non-stationary time series. ► We compare DMCA and DCCA by generating ARFIMA process and estimating the correlation. ► DMCA improves the estimation of cross-correlation with less calculating amounts. ► DMCA reduces impacts of trends on detection of power-law cross-correlation. ► We apply DMCA to investigate real-world data to detect cross-market correlation.