Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models - Archive ouverte HAL
Communication Dans Un Congrès Année : 2022

Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models

Résumé

Expectiles define a least-squares analogue of quantiles. They have been the focus of a substantial quantity of research in the context of actuarial and financial risk assessment over the last decade. The behaviour and estimation of unconditional extreme expectiles using independent and identically distributed heavy-tailed observations has been investigated recently. We build a general theory for the estimation of extreme conditional expectiles in heteroscedastic regression models with heavy-tailed noise; our approach is supported by general results of independent interest on,residual-based extreme value estimators in heavy-tailed regression models, and is intended to cope with covariates having a large but fixed dimension. We demonstrate how our results can be applied to a wide class of important examples, among which linear models, single-index models as well as ARMA and GARCH time series models. The estimators are showcased on a numerical simulation study and on real sets of actuarial and financial data.
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Dates et versions

hal-03683646 , version 1 (31-05-2022)

Identifiants

  • HAL Id : hal-03683646 , version 1

Citer

Antoine Usseglio-Carleve, Stéphane Girard, Gilles Stupfler. Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models. Insurance Data Science Conference 2022, Jun 2022, Milan, Italy. pp.1-65. ⟨hal-03683646⟩
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