Data Fusion and Calibration Methods for Spatial Risk Analysis

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Regression-type analysis for multivariate extreme values

Publication . de Carvalho, Miguel; Kumukova, Alina; dos Reis, Gonçalo; CMA - Centro de Matemática e Aplicações; Springer

This paper devises a regression-type model for the situation where both the response and covariates are extreme. The proposed approach is designed for the setting where the response and covariates are modeled as multivariate extreme values, and thus contrarily to standard regression methods it takes into account the key fact that the limiting distribution of suitably standardized componentwise maxima is an extreme value copula. An important target in the proposed framework is the regression manifold, which consists of a family of regression lines obeying the latter asymptotic result. To learn about the proposed model from data, we employ a Bernstein polynomial prior on the space of angular densities which leads to an induced prior on the space of regression manifolds. Numerical studies suggest a good performance of the proposed methods, and a finance real-data illustration reveals interesting aspects on the conditional risk of extreme losses in two leading international stock markets.

2022-12Artigo científico

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Entidade financiadora

Fundação para a Ciência e a Tecnologia

Programa de financiamento

3599-PPCDT

Número da atribuição

PTDC/MAT-STA/28649/2017