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Functional estimation of extreme conditional expectiles

Abstract : Quantiles and expectiles can be interpreted as solutions of convex minimization problems. Unlike quantiles, expectiles are determined by tail expectations rather than tail probabilities, and define a coherent risk measure. For these reasons, among others, they have recently been the subject of renewed attention in actuarial and financial risk management. Here, we focus on the challenging problem of estimating extreme expectiles, whose order converges to one as the sample size increases, given a functional covariate. We construct a functional kernel estimator of extreme conditional expectiles by writing expectiles as quantiles of a different distribution. The asymptotic properties of the estimators are studied in the context of conditional heavy-tailed distributions. We also provide and analyse different ways of estimating the functional tail index, as a way to extrapolate our estimates to the very far conditional tails. A numerical illustration of the finite-sample performance of our estimators is provided on simulated and real datasets.
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https://hal.inria.fr/hal-03117547
Contributor : Stephane Girard <>
Submitted on : Monday, March 1, 2021 - 9:03:14 AM
Last modification on : Wednesday, March 10, 2021 - 3:04:03 PM

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  • HAL Id : hal-03117547, version 2

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Stéphane Girard, Gilles Stupfler, Antoine Usseglio-Carleve. Functional estimation of extreme conditional expectiles. 2021. ⟨hal-03117547v2⟩

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