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Journal Articles Statistics and Computing Year : 2022

On automatic bias reduction for extreme expectile estimation

Abstract

Expectiles induce a law-invariant risk measure that has recently gained popularity in actuarial and financial risk management applications. Unlike quantiles or the quantile-based Expected Shortfall, the expectile risk measure is coherent and elicitable. The estimation of extreme expectiles in the heavy-tailed framework, which is reasonable for extreme financial or actuarial risk management, is not without difficulties; currently available estimators of extreme expectiles are typically biased and hence may show poor finite-sample performance even in fairly large samples. We focus here on the construction of bias-reduced extreme expectile estimators for heavy-tailed distributions. The rationale for our construction hinges on a careful investigation of the asymptotic proportionality relationship between extreme expectiles and their quantile counterparts, as well as of the extrapolation formula motivated by the heavy-tailed context. We accurately quantify and estimate the bias incurred by the use of these relationships when constructing extreme expectile estimators. This motivates the introduction of a class of bias-reduced estimators whose asymptotic properties are rigorously shown, and whose finite-sample properties are assessed on a simulation study and three samples of real data from economics, insurance and finance.
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Dates and versions

hal-03086048 , version 1 (22-12-2020)
hal-03086048 , version 2 (05-01-2021)

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Stéphane Girard, Gilles Stupfler, Antoine Usseglio-Carleve. On automatic bias reduction for extreme expectile estimation. Statistics and Computing, 2022, 32, pp.64. ⟨10.1007/s11222-022-10118-x⟩. ⟨hal-03086048v2⟩
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