Large sample autocovariance matrices of linear processes with heavy tails

Johannes Heiny and Thomas Mikosch

Stochastic Processes and their Applications, 2021, vol. 141, issue C, 344-375

Abstract: We provide asymptotic theory for certain functions of the sample autocovariance matrices of a high-dimensional time series with infinite fourth moment. The time series exhibits linear dependence across the coordinates and through time. Assuming that the dimension increases with the sample size, we provide theory for the eigenvectors of the sample autocovariance matrices and find explicit approximations of a simple structure, whose finite sample quality is illustrated for simulated data. We also obtain the limits of the normalized eigenvalues of functions of the sample autocovariance matrices in terms of cluster Poisson point processes. In turn, we derive the distributional limits of the largest eigenvalues and functionals acting on them. In our proofs, we use large deviation techniques for heavy-tailed processes, point process techniques motivated by extreme value theory, and related continuous mapping arguments.

Keywords: Regular variation; Sample autocovariance matrix; Linearly dependent entries; Largest eigenvalues; Point process convergence; Large deviations (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1016/j.spa.2021.07.010

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