Sample Average Approximation for Portfolio Optimization under CVaR constraint in an (re)insurance context - Optimization and learning for Data Science
Pré-Publication, Document De Travail Année : 2024

Sample Average Approximation for Portfolio Optimization under CVaR constraint in an (re)insurance context

Résumé

We consider optimal allocation problems with Conditional Value-At-Risk (CVaR) constraint. We prove, under very mild assumptions, the convergence of the Sample Average Approximation method (SAA) applied to this problem, and we also exhibit a convergence rate and discuss the uniqueness of the solution. These results give (re)insurers a practical solution to portfolio optimization under market regulatory constraints, i.e. a certain level of risk.
Fichier principal
Vignette du fichier
paper.pdf (898.47 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04733015 , version 1 (11-10-2024)

Identifiants

  • HAL Id : hal-04733015 , version 1

Citer

Jérôme Lelong, Véronique Maume-Deschamps, William Thevenot. Sample Average Approximation for Portfolio Optimization under CVaR constraint in an (re)insurance context. 2024. ⟨hal-04733015⟩
19 Consultations
7 Téléchargements

Partager

More