Kernel estimation of extreme regression risk measures

Abstract : The Regression Conditional Tail Moment (RCTM) is the risk measure defined as the moment of order b ≥ 0 of a loss distribution above the upper α-quantile where α ∈ (0, 1) and when a covariate information is available. The purpose of this communication is first to establish the asymptotic properties of the RCTM in case of extreme losses, i.e when α → 0 is no longer fixed, under general extreme-value conditions on their distribution tail. In particular, no assumption is made on the sign of the associated extreme-value index. Second, the asymptotic normality of a kernel estimator of the RCTM is established, which allows to derive similar results for estimators of related risk measures such as the Regression Conditional Tail Expectation/Variance/Skewness. When the distribution tail is upper bounded, an application to frontier estimation is also proposed. The results are illustrated both on simulated data and on a real dataset in the field of nuclear reactors reliability.
Document type :
Conference papers
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01745322
Contributor : Jonathan El Methni <>
Submitted on : Friday, April 6, 2018 - 3:18:59 PM
Last modification on : Thursday, April 11, 2019 - 4:02:09 PM

Identifiers

  • HAL Id : hal-01745322, version 1

Citation

Jonathan El Methni, Laurent Gardes, Stephane Girard. Kernel estimation of extreme regression risk measures. ICOR 2018 - 13th International Conference on Operations Research, Mar 2018, La Havane, Cuba. pp.1. ⟨hal-01745322⟩

Share

Metrics

Record views

190

Files downloads

26