Simulation Methods for Robust Risk Assessment and the Distorted Mix Approach

TL;DR

This paper introduces a novel simulation approach combining stochastic methods and the distorted mix technique to improve robust risk assessment under tail uncertainty, with applications in finance and cyber risk.

Contribution

It presents an efficient algorithm for worst-case average value at risk computation using the distorted mix dependence model, enhancing risk analysis flexibility.

Findings

Effective in modeling tail dependence with the distorted mix method

Applicable to financial and cyber risk scenarios

Improves robustness of risk assessments under uncertainty

Abstract

Uncertainty requires suitable techniques for risk assessment. Combining stochastic approximation and stochastic average approximation, we propose an efficient algorithm to compute the worst case average value at risk in the face of tail uncertainty. Dependence is modelled by the distorted mix method that flexibly assigns different copulas to different regions of multivariate distributions. We illustrate the application of our approach in the context of financial markets and cyber risk.