Assessing Extreme Risk using Stochastic Simulation of Extremes

TL;DR

This paper introduces two non-parametric simulation algorithms based on multivariate extreme theory to better assess extreme risks in data-scarce environments, demonstrated through numerical analyses.

Contribution

It develops novel stochastic simulation algorithms for multivariate extremes, improving risk assessment under data scarcity conditions.

Findings

Algorithms accurately estimate extreme risk metrics

Effective extension of extreme samples for dependent variables

Validated with simulated and real data analyses

Abstract

Risk management is particularly concerned with extreme events, but analysing these events is often hindered by the scarcity of data, especially in a multivariate context. This data scarcity complicates risk management efforts. Various tools can assess the risk posed by extreme events, even under extraordinary circumstances. This paper studies the evaluation of univariate risk for a given risk factor using metrics that account for its asymptotic dependence on other risk factors. Data availability is crucial, particularly for extreme events where it is often limited by the nature of the phenomenon itself, making estimation challenging. To address this issue, two non-parametric simulation algorithms based on multivariate extreme theory are developed. These algorithms aim to extend a sample of extremes jointly and conditionally for asymptotically dependent variables using stochastic simulation and multivariate Generalised Pareto Distributions. The approach is illustrated with numerical analyses of both simulated and real data to assess the accuracy of extreme risk metric estimations.