eprintid: 49539 rev_number: 7 eprint_status: archive userid: 1482 importid: 105 dir: disk0/00/04/95/39 datestamp: 2025-01-09 09:00:41 lastmod: 2025-01-09 09:01:10 status_changed: 2025-01-09 09:00:41 type: article succeeds: 47962 metadata_visibility: show creators_name: Daouia, Abdelaati creators_name: Stupfler, Gilles Claude creators_name: Usseglio-Carleve, Antoine creators_idrefppn: 076657000 creators_idrefppn: 159301602 creators_idrefppn: 23039521X creators_affiliation: Toulouse School of Economics creators_affiliation: Université d'Angers creators_affiliation: Avignon Université creators_halaffid: 1002422 title: Bias-reduced and variance-corrected asymptotic Gaussian Inference about extreme expectiles ispublished: pub subjects: subjects_ECO abstract: The expectile is a prime candidate for being a standard risk measure in actuarial and financial contexts, for its ability to recover information about probabilities and typical behavior of extreme values, as well as its excellent axiomatic properties. A series of recent papers has focused on expectile estimation at extreme levels, with a view on gathering essential information about low-probability, high-impact events that are of most interest to risk managers. The obtention of accurate confidence intervals for extreme expectiles is paramount in any decision process in which they are involved, but actual inference on these tail risk measures is still a difficult question due to their least squares nature and sensitivity to tail heaviness. This article focuses on asymptotic Gaussian inference about tail expectiles in the challenging context of heavy-tailed observations. We use an in-depth analysis of the proofs of asymptotic normality results for two classes of extreme expectile estimators to derive bias-reduced and variance-corrected Gaussian confidence intervals. These, unlike previous attempts in the literature, are well-rooted in statistical theory and can accommodate underlying distributions that display a wide range of tail behaviors. A large-scale simulation study and three real data analyses confirm the versatility of the proposed technique. date: 2024-06 date_type: published publisher: Kluwer id_number: 10.1007/s11222-023-10359-4 official_url: http://tse-fr.eu/pub/129470 faculty: tse divisions: tse keywords: Asymptotic normality keywords: Bias correction keywords: Expectiles keywords: Extreme values keywords: Inference keywords: Variance correction language: en has_fulltext: TRUE doi: 10.1007/s11222-023-10359-4 view_date_year: 2024 full_text_status: public publication: Statistics and Computing volume: vol. 34 number: n° 130 place_of_pub: Dordrecht refereed: TRUE issn: 1573-1375 oai_identifier: oai:tse-fr.eu:129470 harvester_local_overwrite: number harvester_local_overwrite: volume harvester_local_overwrite: issn harvester_local_overwrite: pending harvester_local_overwrite: publisher harvester_local_overwrite: place_of_pub harvester_local_overwrite: creators_name harvester_local_overwrite: note harvester_local_overwrite: creators_idrefppn harvester_local_overwrite: creators_halaffid harvester_local_overwrite: hal_id harvester_local_overwrite: hal_version harvester_local_overwrite: hal_url harvester_local_overwrite: hal_passwd oai_lastmod: 2024-07-08T13:15:23Z oai_set: tse site: ut1 hal_id: hal-04875968 hal_passwd: 1qqp698 hal_version: 1 hal_url: https://hal.science/hal-04875968 citation: Daouia, Abdelaati , Stupfler, Gilles Claude and Usseglio-Carleve, Antoine (2024) Bias-reduced and variance-corrected asymptotic Gaussian Inference about extreme expectiles. Statistics and Computing, vol. 34 (n° 130). document_url: https://publications.ut-capitole.fr/id/eprint/49539/1/wp_tse_1444.pdf