Elicitability and identifiability of tail risk measures

TL;DR

This paper investigates the conditions under which tail risk measures like VaR and Expected Shortfall are identifiable and elicitable, providing new scoring methods and implications for risk modeling and validation.

Contribution

It establishes joint identifiability and elicitability of tail risk measures with their quantiles, introducing novel weighted scoring functions for improved risk assessment.

Findings

Joint identifiability and elicitability of tail risk measures with quantiles.

Introduction of a new class of weighted scoring functions.

Facilitates model fitting, comparison, and validation in risk management.

Abstract

Tail risk measures are fully determined by the distribution of the underlying loss beyond its quantile at a certain level, with Value-at-Risk, Expected Shortfall and Range Value-at-Risk being prime examples. They are induced by law-based risk measures, called their generators, evaluated on the tail distribution. This paper establishes joint identifiability and elicitability results of tail risk measures together with the corresponding quantile, provided that their generators are identifiable and elicitable, respectively. As an example, we establish the joint identifiability and elicitability of the tail expectile together with the quantile. The corresponding consistent scores constitute a novel class of weighted scores, nesting the known class of scores of Fissler and Ziegel for the Expected Shortfall together with the quantile. For statistical purposes, our results pave the way to easier model fitting for tail risk measures via regression and the generalized method of moments, but also model comparison and model validation in terms of established backtesting procedures.