Estimating probabilities of multivariate failure sets based on pairwise tail dependence coefficients

TL;DR

This paper introduces a semi-parametric method leveraging pairwise tail dependence coefficients to efficiently estimate probabilities of multivariate extreme failure events, with applications in finance and climate risk assessment.

Contribution

It develops an algorithm for decomposing the tail pairwise dependence matrix and constructing max-linear models for accurate failure probability estimation.

Findings

Effective estimation of multivariate failure probabilities demonstrated on real datasets.

The max-linear model provides a computationally simple approach for risk assessment.

The method offers conditions for exact approximation of tail dependence structures.

Abstract

Estimating the probability of extreme events involving multiple risk factors is a critical challenge in fields such as finance and climate science. This paper proposes a semi-parametric approach to estimate the probability that a multivariate random vector falls into an extreme failure set, based on the information in the tail pairwise dependence matrix (TPDM) only. The TPDM provides a partial summary of tail dependence for all pairs of components of the random vector. We propose an efficient algorithm to obtain approximate completely positive decompositions of the TPDM, enabling the construction of a max-linear model whose TPDM approximates that of the original random vector. We also provide conditions under which the approximation turns out to be exact. Based on the decompositions, we can construct max-linear random vectors to estimate failure probabilities, exploiting its computational simplicity. The algorithm allows to obtain multiple decompositions efficiently. Finally, we apply our framework to estimate probabilities of extreme events for real-world datasets, including industry portfolio returns and maximal wind speeds, demonstrating its practical utility for risk assessment.