Estimation of extreme risk regions under multivariate regular variation

Abstract

When considering d possibly dependent random variables, one is often interested in extreme risk regions, with very small probability p. We consider risk regions of the form $\mathbf{z}\in {\mathbb{R}}^d:f(\mathbf{z})\le \beta$, where f is the joint density and $\beta$ a small number. Estimation of such an extreme risk region is difficult since it contains hardly any or no data. Using extreme value theory, we construct a natural estimator of an extreme risk region and prove a refined form of consistency, given a random sample of multivariate regularly varying random vectors. In a detailed simulation and comparison study, the good performance of the procedure is demonstrated. We also apply our estimator to financial data.

Published in at http://dx.doi.org/10.1214/11-AOS891 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (1)

• Optimal rates for plug-in estimators of density level sets

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