Dissecting multifractal detrended cross-correlation analysis

In this work we address the question of the Multifractal detrended cross-correlation analysis method that has been subject to some controversies since its inception almost two decades ago. To this end we propose several new options to deal with negative cross-covariance among two time series, that may serve to construct a more robust view of the multifractal spectrum among them. We compare these novel options with the proposals already existing in the literature, and we provide fast code in C, with wrapper code for R and Python, for both new and the already existing proposals. We test different algorithms on synthetic series with an exact analytical solution, uncorrelated white noise series, and on daily price series of ethanol and sugar in Brazil from 2010 to 2023.