ExpectHill estimation, extreme risk and heavy tails - MISTIS Accéder directement au contenu
Article Dans Une Revue Journal of Econometrics Année : 2021

ExpectHill estimation, extreme risk and heavy tails

Résumé

Risk measures of a financial position are, from an empirical point of view, mainly based on quantiles. Replacing quantiles with their least squares analogues, called expectiles, has recently received increasing attention. The novel expectile-based risk measures satisfy all coherence requirements. We revisit their extreme value estimation for heavy-tailed distributions. First, we estimate the underlying tail index via weighted combinations of top order statistics and asymmetric least squares estimates. The resulting expectHill estimators are then used as the basis for estimating tail expectiles and Expected Shortfall. The asymptotic theory of the proposed estimators is provided, along with numerical simulations and applications to actuarial and financial data.
Fichier principal
Vignette du fichier
main_DGS_JE.pdf (1.91 Mo) Télécharger le fichier
suppl_DGS_JE.pdf (4.4 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01856212 , version 1 (10-08-2018)
hal-01856212 , version 2 (02-03-2020)

Identifiants

Citer

Abdelaati Daouia, Stéphane Girard, Gilles Stupfler. ExpectHill estimation, extreme risk and heavy tails. Journal of Econometrics, 2021, 221 (1), pp.97-117. ⟨10.1016/j.jeconom.2020.02.003⟩. ⟨hal-01856212v2⟩
489 Consultations
589 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More