Superstatistical generalisations of Wishart-Laguerre ensembles of random matrices

Abstract

Using Beck and Cohen's superstatistics, we introduce in a systematic way a family of generalised Wishart-Laguerre ensembles of random matrices with Dyson index $\beta$ = 1,2, and 4. The entries of the data matrix are Gaussian random variables whose variances $\eta$ fluctuate from one sample to another according to a certain probability density $f(\eta)$ and a single deformation parameter $\gamma$. Three superstatistical classes for $f(\eta)$ are usually considered: $\chi^2$-, inverse $\chi^2$- and log-normal-distributions. While the first class, already considered by two of the authors, leads to a power-law decay of the spectral density, we here introduce and solve exactly a superposition of Wishart-Laguerre ensembles with inverse $\chi^2$-distribution. The corresponding macroscopic spectral density is given by a $\gamma$-deformation of the semi-circle and Mar\v{c}enko-Pastur laws, on a non-compact support with exponential tails. After discussing in detail the validity of Wigner's surmise in the Wishart-Laguerre class, we introduce a generalised $\gamma$-dependent surmise with stretched-exponential tails, which well approximates the individual level spacing distribution in the bulk. The analytical results are in excellent agreement with numerical simulations. To illustrate our findings we compare the $\chi^2$- and inverse $\chi^2$-class to empirical data from financial covariance matrices.