Tools
Tools
Costa, Manon
, Gadat, Sébastien and Huang, Lorick
(2022)
CV@R penalized portfolio optimization with biased stochastic mirror descent.
TSE Working Paper, n. 22-1342, Toulouse
Preview |
Text
Download (1MB) | Preview |
Abstract
This article studies and solves the problem of optimal portfolio allocation with CV@R constraints when dealing with imperfectly simulated nancial assets. We use a Stochastic biased Mirror Descent to nd optimal resource allocation for a portfolio whose underlying assets cannot be generated exactly and may only be approximated with a numerical scheme that satises 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: | Monograph (Working Paper) |
|---|---|
| Language: | English |
| Date: | June 2022 |
| Place of Publication: | Toulouse |
| Uncontrolled Keywords: | Stochastic Mirror Descent, Biased observations,, Risk management constraint, Portfolio selection, Discretization |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Institution: | Université Toulouse Capitole |
| Site: | UT1 |
| Date Deposited: | 22 Jun 2022 08:26 |
| Last Modified: | 20 Nov 2023 14:26 |
| OAI Identifier: | oai:tse-fr.eu:127041 |
| URI: | https://publications.ut-capitole.fr/id/eprint/45762 |
Available Versions of this Item
- CV@R penalized portfolio optimization with biased stochastic mirror descent. (deposited 22 Jun 2022 08:26) [Currently Displayed]
