Estimation of extreme risk regions under multivariate regular variation

J. Cai, J.H.J. Einmahl, L.F.M. de Haan

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
280 Downloads (Pure)

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 {z ∈ Rd : f (z) ≤ β}, where f is the joint density and β 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.
Original languageEnglish
Pages (from-to)1803-1826
JournalThe Annals of Statistics
Volume39
Issue number3
Publication statusPublished - 2011

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