Extreme value analysis for the sample autocovariance matrices of heavy-tailed multivariate time series

TL;DR

This paper develops asymptotic theory for the largest eigenvalues of sample covariance matrices of heavy-tailed multivariate time series, including limit laws and point process convergence, with applications to financial data.

Contribution

It provides new limit results for eigenvalues of heavy-tailed time series covariance matrices, extending existing theory to dependent data and autocovariance matrices.

Findings

Limit laws for eigenvalues and autocovariance matrices

Convergence of point processes to Poisson or cluster Poisson processes

Application to S&P 500 stock index data

Abstract

We provide some asymptotic theory for the largest eigenvalues of a sample covariance matrix of a p-dimensional time series where the dimension p = p_n converges to infinity when the sample size n increases. We give a short overview of the literature on the topic both in the light- and heavy-tailed cases when the data have finite (infinite) fourth moment, respectively. Our main focus is on the heavytailed case. In this case, one has a theory for the point process of the normalized eigenvalues of the sample covariance matrix in the iid case but also when rows and columns of the data are linearly dependent. We provide limit results for the weak convergence of these point processes to Poisson or cluster Poisson processes. Based on this convergence we can also derive the limit laws of various function als of the ordered eigenvalues such as the joint convergence of a finite number of the largest order statistics, the joint limit law of the largest eigenvalue and the trace, limit laws for successive ratios of ordered eigenvalues, etc. We also develop some limit theory for the singular values of the sample autocovariance matrices and their sums of squares. The theory is illustrated for simulated data and for the components of the S&P 500 stock index.