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

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.

arXiv commentin Extremes; Statistical Theory and Applications in Science, Engineering and Economics; ISSN 1386-1999; (2016)

References in corpus (6)

• Random matrices: Universality of local eigenvalue statistics up to the edge
• Universality of the local spacing distribution in certain ensembles of hermitian Wigner matrices
• Large deviations for random walks under subexponentiality: the big-jump domain
• Factor modeling for high-dimensional time series: Inference for the number of factors
• Spectral measure of heavy tailed band and covariance random matrices
• Poisson Statistics for the Largest Eigenvalues in Random Matrix Ensemble

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• The eigenstructure of the sample covariance matrices of high-dimensional stochastic volatility models with heavy tails
• The asymptotic distribution of the condition number for random circulant matrices