Role of correlations in the maximum distribution of multiscale stationary Markovian processes

TL;DR

This paper investigates how strong correlations in multiscale stationary Markovian processes influence the distribution of extreme values, revealing that correlations reduce heterogeneity and slow convergence in autocorrelation estimates.

Contribution

It demonstrates that correlations alter the tail behavior of maximum distributions and significantly affect the convergence rate of autocorrelation function estimations.

Findings

Correlated variables have maximum distributions with tails decaying as 1/x^{α+2}.

Autocorrelation function estimates converge slowly, following a power-law with respect to series length.

Strong correlations reduce the heterogeneity of maximum values.

Abstract

We are interested in investigating the statistical properties of extreme values for strongly correlated variables. The starting motivation is to understand how the strong-correlation properties of power-law distributed processes affect the possibility of exploring the whole domain of a stochastic process (the real axis in most cases) when performing time-average numerical simulations and how this relates to the numerical evaluation of the autocorrelation function. We show that correlations decrease the heterogeneity of the maximum values. Specifically, through numerical simulations we observe that for strongly correlated variables whose probability distribution function decays like a power-law $1/x^\alpha$, the maximum distribution has a tail compatible with a $1/x^{\alpha+2}$ decay, while for i.i.d. variables we expect a $1/x^\alpha$ decay. As a consequence, we also show that the numerically estimated autocorrelation function converges to the theoretical prediction according to a factor that depends on the length of the simulated time-series $n$ according to a power-law: $1/n^{\alpha^\delta}$ with $\delta<1$, This accounts for a very slow convergence rate.