Decompositions of Dependence for High-Dimensional Extremes

Daniel Cooley; Emeric Thibaud

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arXiv · 1612.07190

Decompositions of Dependence for High-Dimensional Extremes

Daniel Cooley·Emeric Thibaud

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Abstract

Employing the framework of regular variation, we propose two decompositions which help to summarize and describel high-dimensional tail dependence. Via transformation, we define a vector space on the positive orthant, yielding the notion of basis. With a suitably-chosen transformation, we show that transformed-linear operations applied to regularly varying random vectors preserve regular variation. Rather than model regular-variation's angular measure, we summarize tail dependence via a matrix of pairwise tail dependence metrics. This matrix is positive semidefinite, and eigendecomposition allows one to interpret tail dependence via the resulting eigenbasis. Additionally this matrix is completely positive, and a resulting decomposition allows one to easily construct regularly varying random vectors which share the same pairwise tail dependencies. We illustrate our methods with Swiss rainfall data and financial return data.

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Daniel Cooley, Emeric Thibaud. 2016-12-20. Decompositions of Dependence for High-Dimensional Extremes. https://arxiv.org/abs/1612.07190

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