The eigenstructure of the sample covariance matrices of high-dimensional stochastic volatility models with heavy tails

Abstract

We consider a $p$-dimensional time series where the dimension $p$ increases with the sample size $n$. The resulting data matrix $X$ follows a stochastic volatility model: each entry consists of a positive random volatility term multiplied by an independent noise term. The volatility multipliers introduce dependence in each row and across the rows. We study the asymptotic behavior of the eigenvalues and eigenvectors of the sample covariance matrix $X{X}^{\prime }$ under a regular variation assumption on the noise. In particular, we prove Poisson convergence for the point process of the centered and normalized eigenvalues and derive limit theory for functionals acting on them, such as the trace. We prove related results for stochastic volatility models with additional linear dependence structure and for stochastic volatility models where the time-varying volatility terms are extinguished with high probability when $n$ increases. We provide explicit approximations of the eigenvectors which are of a strikingly simple structure. The main tools for proving these results are large deviation theorems for heavy-tailed time series, advocating a unified approach to the study of the eigenstructure of heavy-tailed random matrices.

arXiv commentBernoulli 25 (2019), no. 4B, 3590-3622

References in corpus (4)

• Poisson Statistics for the Largest Eigenvalues in Random Matrix Ensemble
• A large deviations approach to limit theory for heavy-tailed time series
• Extreme value analysis for the sample autocovariance matrices of heavy-tailed multivariate time series
• Stochastic volatility models with possible extremal clustering

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