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

ArXiv ID: 2512.06473 “View on arXiv”

Authors: Stanisław Drożdż, Paweł Jarosz, Jarosław Kwapień, Maria Skupień, Marcin Wątorek

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 $ρ_r$ 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.

Keywords: Multifractal Detrended Cross-Correlation, Eigenvalue Spectrum, Nonstationarity, Heavy-Tailed Fluctuations, Complex Systems, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical frameworks including multifractal detrended cross-correlation analysis, random matrix theory, and q-Gaussian distributions, with detailed derivations of the spectral properties of correlation matrices. Empirically, it demonstrates a backtest-ready methodology applied to 140 cryptocurrency assets over four years, analyzing scale-dependent eigenvalue spectra and comparing against synthetic null models to identify market structure.

  flowchart TD
    A["Research Goal:<br>Quantify structure in nonstationary<br>crypto systems via detrended correlations"] --> B["Methodology:<br>Multifractal Detrended Cross-Correlation<br>with fluctuation order parameter r"]
    B --> C["Data Input:<br>1-min returns of 140 major cryptos<br>(Jan 2021 - Jul 2024)"]
    C --> D["Computation:<br>Construct detrended correlation<br>matrices for various scales & r values"]
    D --> E["Eigenvalue Spectrum Analysis:<br>vs. Gaussian/q-Gaussian benchmarks"]
    E --> F["Key Finding 1:<br>Dominant market mode & sectoral<br>components are scale/fluctuation-dependent"]
    E --> G["Key Finding 2:<br>After market-mode removal,<br>bulk aligns with random limit<br>enabling detection of outliers"]
    F --> H["Outcome:<br>Refined spectral baseline for<br>nonstationary, heavy-tailed systems"]
    G --> H