Spectral density of a Wishart model for nonsymmetric Correlation Matrices

TL;DR

This paper derives a self-consistent equation for the spectral density of a nonsymmetric Wishart-like matrix model where two correlated matrices are multiplied, extending classical symmetric correlation matrix results.

Contribution

It introduces a spectral density analysis for nonsymmetric correlation matrices constructed from correlated matrices, providing a new theoretical framework.

Findings

Derived a Pastur self-consistent equation for spectral density.

Validated analytical results with numerical simulations.

Extended Wishart model to correlated nonsymmetric matrices.

Abstract

The Wishart model for real symmetric correlation matrices is defined as $\mathsf{W}=\mathsf{AA}^{t}$, where matrix $\mathsf{A}$ is usually a rectangular Gaussian random matrix and $\mathsf{A}^{t}$ is the transpose of $\mathsf{A}$. Analogously, for nonsymmetric correlation matrices, a model may be defined for two statistically equivalent but different matrices $\mathsf{A}$ and $\mathsf{B}$ as $\mathsf{AB}^{t}$. The corresponding Wishart model, thus, is defined as $\mathbf{C}=\mathsf{AB}^{t}\mathsf{BA}^{t}$. We study the spectral density of $\mathbf{C}$ for the case when $\mathsf{A}$ and $\mathsf{B}$ are not statistically independent. The ensemble average of such nonsymmetric matrices, therefore, does not simply vanishes to a null matrix. In this paper we derive a Pastur self-consistent equation which describes spectral density of large $\mathbf{C}$. We complement our analytic results with numerics.