Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models

Abstract

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.
No file

Dates and versions

hal-03501801 , version 1 (23-12-2021)

Identifiers

  • HAL Id : hal-03501801 , version 1

Cite

Antoine Usseglio-Carleve, Stéphane Girard, Gilles Stupfler. Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models. CMStatistics 2021 - 14th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2021, London, United Kingdom. ⟨hal-03501801⟩
27 View
0 Download

Share

Gmail Mastodon Facebook X LinkedIn More