Extreme expectile estimation for short-tailed data, with an application to market risk assessment

TL;DR

This paper develops new methods for estimating extreme expectiles in short-tailed distributions, which are useful for risk management, especially when the tail behavior is bounded and not heavy.

Contribution

It introduces the first approach to tail expectile estimation in short-tailed settings, deriving asymptotic properties and proposing two semiparametric estimators.

Findings

Proposed estimators perform well in simulations.

Application to real market data demonstrates practical usefulness.

Theoretical results establish asymptotic properties of estimators.

Abstract

The use of expectiles in risk management has recently gathered remarkable momentum due to their excellent axiomatic and probabilistic properties. In particular, the class of elicitable law-invariant coherent risk measures only consists of expectiles. While the theory of expectile estimation at central levels is substantial, tail estimation at extreme levels has so far only been considered when the tail of the underlying distribution is heavy. This article is the first work to handle the short-tailed setting where the loss (e.g. negative log-returns) distribution of interest is bounded to the right and the corresponding extreme value index is negative. We derive an asymptotic expansion of tail expectiles in this challenging context under a general second-order extreme value condition, which allows to come up with two semiparametric estimators of extreme expectiles, and with their asymptotic properties in a general model of strictly stationary but weakly dependent observations. A simulation study and a real data analysis from a forecasting perspective are performed to verify and compare the proposed competing estimation procedures.