A Distributionally Robust Optimization Framework for Extreme Event Estimation

TL;DR

This paper introduces a distributionally robust optimization framework as a nonparametric alternative to traditional extreme value theory methods, aiming to improve tail event estimation especially with limited data.

Contribution

It proposes a novel nonparametric approach that bypasses bias-variance tradeoffs in EVT using worst-case optimization under shape constraints.

Findings

Outperforms traditional EVT in small-sample scenarios

Reduces bias in tail estimation

Provides computational tools for robust optimization

Abstract

Conventional methods for extreme event estimation rely on well-chosen parametric models asymptotically justified from extreme value theory (EVT). These methods, while powerful and theoretically grounded, could however encounter a difficult bias-variance tradeoff that exacerbates especially when data size is too small, deteriorating the reliability of the tail estimation. In this paper, we study a framework based on the recently surging literature of distributionally robust optimization. This approach can be viewed as a nonparametric alternative to conventional EVT, by imposing general shape belief on the tail instead of parametric assumption and using worst-case optimization as a resolution to handle the nonparametric uncertainty. We explain how this approach bypasses the bias-variance tradeoff in EVT. On the other hand, we face a conservativeness-variance tradeoff which we describe how to tackle. We also demonstrate computational tools for the involved optimization problems and compare our performance with conventional EVT across a range of numerical examples.