Conditioning on an extreme component: Model consistency with regular variation on cones

TL;DR

This paper investigates the consistency of models conditioned on extreme components in multivariate distributions, clarifying their relationship with regular variation on cones and addressing unresolved issues in multivariate extreme value theory.

Contribution

It provides a clarification of the consistency of different conditional models and explores their connection with regular variation on cones, advancing theoretical understanding.

Findings

Established conditions for model consistency when conditioning on extreme components

Linked conditional extreme value models with regular variation on cones

Resolved previous ambiguities in multivariate extreme value theory

Abstract

Multivariate extreme value theory assumes a multivariate domain of attraction condition for the distribution of a random vector. This necessitates that each component satisfies a marginal domain of attraction condition. An approximation of the joint distribution of a random vector obtained by conditioning on one of the components being extreme was developed by Heffernan and Tawn [12] and further studied by Heffernan and Resnick [11]. These papers left unresolved the consistency of different models obtained by conditioning on different components being extreme and we here provide clarification of this issue. We also clarify the relationship between these conditional distributions, multivariate extreme value theory and standard regular variation on cones of the form $[0,\infty]\times(0,\infty]$.