Fractional Gaussian noise criterion for correlations characterization: a random-matrix-theory inspired perspective

Abstract

We introduce a particular construction of an autocorrelation matrix of a time series and its analysis based on the random-matrix theory ideas that is capable of unveiling the type of correlations information which is inaccessible to the straight analysis of the autocorrelation function. Exploiting the well-studied hierarchy of the fractional Gaussian noise (fGn), an \emph{in situ} criterion for the sake of a quantitative comparison with the autocorrelation data is offered. We illustrate the applicability of our method by two paradigmatic examples from the orthodox context of the stock markets and the turbulence. Quite strikingly, a remarkable agreement with the fGn is achieved notwithstanding the non-Gaussianity in returns of the stock market. In the latter context, on the contrary, a significant deviation from an fGn is observed despite a Gaussian distribution of the velocity profile of the turbulence.