Costa, ManonIdRef, Gadat, SébastienIdRef and Huang, LorickIdRef (2025) CV@R penalized portfolio optimization with biased stochastic mirror descent. Finance and Stochastics. (In Press)

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Identification Number : 10.48550/arXiv.2402.11999

Abstract

This article studies and solves the problem of optimal portfolio allocation with CV@R penalty when dealing with imperfectly simulated financial assets. We use a Stochastic biased Mirror Descent to find optimal resource allocation for a portfolio whose underlying assets cannot be generated exactly and may only be approximated with a numerical scheme that satisfies suitable error bounds, under a risk management constraint. We establish almost sure asymptotic properties as well as the rate of convergence for the averaged algorithm. We then focus on the optimal tuning of the overall procedure to obtain an optimized numerical cost. Our results are then illustrated numerically on simulated as well as real data sets

Item Type: Article
Language: English
Date: 2025
Refereed: Yes
Place of Publication: Berlin
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 17 Nov 2023 10:11
Last Modified: 06 May 2025 08:58
OAI Identifier: oai:tse-fr.eu:128711
URI: https://publications.ut-capitole.fr/id/eprint/48376

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