On the asymptotic behaviour of the eigenvalue distribution of block correlation matrices of high-dimensional time series

Abstract

We consider linear spectral statistics built from the block-normalized correlation matrix of a set of $M$ mutually independent scalar time series. This matrix is composed of $M\times M$ blocks that contain the sample cross correlation between pairs of time series. In particular, each block has size $L\times L$ and contains the sample cross-correlation measured at $L$ consecutive time lags between each pair of time series. Let $N$ denote the total number of consecutively observed windows that are used to estimate these correlation matrices. We analyze the asymptotic regime where $M,L,N\to +\infty$ while $ML/N\to c_{\star }$, $0<c_{\star }<\infty$. We study the behavior of linear statistics of the eigenvalues of this block correlation matrix under these asymptotic conditions and show that the empirical eigenvalue distribution converges to a Marcenko-Pastur distribution. Our results are potentially useful in order to address the problem of testing whether a large number of time series are uncorrelated or not.

References in corpus (1)

• Large random matrix approach for testing independence of a large number of Gaussian time series