Skip to Main content Skip to Navigation
Conference papers

Tail risk estimation based on extreme Lp-quantiles

Stéphane Girard 1 Abdelaati Daouia 2 Gilles Stupfler 3, 4
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann
2 GREMAQ - Groupe de recherche en économie mathématique et quantitative
3 School of Mathematical Sciences [Nottingham]
4 GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille
Abstract : The notion of quantiles lies at the heart of extreme-value theory and is one of the basic tools in risk management.The alternative family of expectiles is based on squared rather than absolute error loss minimization. Both quantiles and expectiles can be embedded in the more general class of Lp-quantiles as the minimizers of an asymmetric power loss function. In this talk, new methods will be presented for estimating high Lp-quantiles from heavy-tailed distributions.
Document type :
Conference papers
Complete list of metadata
https://hal.archives-ouvertes.fr/hal-01415533
Contributor : Stephane Girard Connect in order to contact the contributor
Submitted on : Tuesday, December 13, 2016 - 11:37:26 AM
Last modification on : Friday, February 4, 2022 - 3:33:51 AM

Identifiers

  • HAL Id : hal-01415533, version 1

Collections

CNRS | EHESS | INRA | EC-MARSEILLE | INSMI | INRIA | LJK | UNIV-AMU | LJK_PS | UGA | LJK_PS_MISTIS | GREQAM | AMSE | UT1-CAPITOLE | INRAE | INRIA2

Citation

Stéphane Girard, Abdelaati Daouia, Gilles Stupfler. Tail risk estimation based on extreme Lp-quantiles. Statistics workshop Tilburg University, Dec 2016, Tilburg, Netherlands. ⟨hal-01415533⟩

Export

BibTeX TEI DC DCterms EndNote Datacite

Share

Metrics

Record views

778