Detrended cross-correlations and their random matrix limit: an example from the cryptocurrency market

TL;DR

This paper introduces a new method for analyzing correlations in complex, nonstationary systems like cryptocurrencies by using detrended cross-correlation matrices, revealing meaningful collective modes and distinguishing genuine signals from noise.

Contribution

It develops a scale- and fluctuation-dependent correlation matrix framework that accounts for nonstationarity and heavy tails, providing a refined spectral baseline for complex systems analysis.

Findings

Spectral properties deviate from random matrices even without cross-correlations.

Identification of a dominant market factor and sectoral components.

Filtering out the market mode aligns eigenvalue bulk with random matrix predictions.

Abstract

Correlations in complex systems are often obscured by nonstationarity, long-range memory, and heavy-tailed fluctuations, which limit the usefulness of traditional covariance-based analyses. To address these challenges, we construct scale and fluctuation-dependent correlation matrices using the multifractal detrended cross-correlation coefficient $\rho_r$ that selectively emphasizes fluctuations of different amplitudes. We examine the spectral properties of these detrended correlation matrices and compare them to the spectral properties of the matrices calculated in the same way from synthetic Gaussian and $q$Gaussian signals. Our results show that detrending, heavy tails, and the fluctuation-order parameter $r$ jointly produce spectra, which substantially depart from the random case even under absence of cross-correlations in time series. Applying this framework to one-minute returns of 140 major cryptocurrencies from 2021-2024 reveals robust collective modes, including a dominant market factor and several sectoral components whose strength depends on the analyzed scale and fluctuation order. After filtering out the market mode, the empirical eigenvalue bulk aligns closely with the limit of random detrended cross-correlations, enabling clear identification of structurally significant outliers. Overall, the study provides a refined spectral baseline for detrended cross-correlations and offers a promising tool for distinguishing genuine interdependencies from noise in complex, nonstationary, heavy-tailed systems.