Tail expectile-VaR estimation in the semiparametric Generalized Pareto model

Yasser Abbas, Abdelaati Daouia, Boutheina Nemouchi and Gilles Stupfler

No 25-1607, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: Expectiles have received increasing attention as coherent and elicitable market risk measure. Their estimation from heavy-tailed data in an extreme value framework has been studied using solely the Weissman extrapolation method. We challenge this dominance by developing the theory of two classes of semiparametric Generalized Pareto estimators that make more efficient use of tail observations by incorporating the location, scale and shape extreme value parameters: the first class relies on asymmetric least squares estimation, while the second is based on extreme quantile estimation. A comparison with simulated and real data shows the superiority of our proposals for real-valued profit-loss distributions.

Keywords: Expectile, Extreme risk, Generalized Pareto model, Heavy tails, Semiparametric; extrapolation (search for similar items in EconPapers)
JEL-codes: C13 C14 C18 C53 C58 (search for similar items in EconPapers)
Date: 2025-01
New Economics Papers: this item is included in nep-ecm, nep-inv and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.tse-fr.eu/sites/default/files/TSE/docu ... 2025/wp_tse_1607.pdf Working paper (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:130105

Access Statistics for this paper

More papers in TSE Working Papers from Toulouse School of Economics (TSE) Contact information at EDIRC.
Bibliographic data for series maintained by ().