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Barucca P. Localization in covariance matrices of coupled heterogenous Ornstein-Uhlenbeck processes. Phys Rev E Stat Nonlin Soft Matter Phys. 2014;90(6):062129doi: 10.1103/PhysRevE.90.062129.
Barucca, P. (2014). Localization in covariance matrices of coupled heterogenous Ornstein-Uhlenbeck processes. Physical review. E, Statistical, nonlinear, and soft matter physics, 90(6), 062129. https://doi.org/10.1103/PhysRevE.90.062129
Barucca, Paolo. "Localization in covariance matrices of coupled heterogenous Ornstein-Uhlenbeck processes." Physical review. E, Statistical, nonlinear, and soft matter physics vol. 90,6 (2014): 062129. doi: https://doi.org/10.1103/PhysRevE.90.062129
Barucca P. Localization in covariance matrices of coupled heterogenous Ornstein-Uhlenbeck processes. Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Dec;90(6):062129. doi: 10.1103/PhysRevE.90.062129. Epub 2014 Dec 19. PMID: 25615066.
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Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Dec;90(6):062129. doi: 10.1103/PhysRevE.90.062129. Epub 2014 Dec 19.

Localization in covariance matrices of coupled heterogenous Ornstein-Uhlenbeck processes.

Physical review. E, Statistical, nonlinear, and soft matter physics

Paolo Barucca

Affiliations

  1. Dipartimento di Fisica, Università La Sapienza, P. le A. Moro 2, I-00185 Rome, Italy.

PMID: 25615066 DOI: 10.1103/PhysRevE.90.062129

Abstract

We define a random-matrix ensemble given by the infinite-time covariance matrices of Ornstein-Uhlenbeck processes at different temperatures coupled by a Gaussian symmetric matrix. The spectral properties of this ensemble are shown to be in qualitative agreement with some stylized facts of financial markets. Through the presented model formulas are given for the analysis of heterogeneous time series. Furthermore evidence for a localization transition in eigenvectors related to small and large eigenvalues in cross-correlations analysis of this model is found, and a simple explanation of localization phenomena in financial time series is provided. Finally we identify both in our model and in real financial data an inverted-bell effect in correlation between localized components and their local temperature: high- and low-temperature components are the most localized ones.

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