Multi periods mean-DCVaR optimization: a Recursive Neural Network resolution
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908967965294592 |
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| author | Lelong, Jérôme Maume-Deschamps, Véronique Thevenot, William |
| author_facet | Lelong, Jérôme Maume-Deschamps, Véronique Thevenot, William |
| contents | We study a discrete-time multi-period portfolio optimization problem under an explicit constraint on the Deviation Conditional Value-at-Risk (DCVaR), defined as the excess of Conditional Value-at-Risk over expected terminal wealth. The objective is to maximize expected return subject to a global tail-risk constraint, leading to a time-inconsistent precommitment problem. We propose a recurrent neural-network-based approach to approximate the optimal precommitment policy, which accommodates path-dependent risk constraints and highdimensional state dynamics without relying on dynamic programming. The explicit constraint formulation allows for exact penalty methods and provides a transparent notion of feasibility. The methodology is validated in a classical complete-market financial model and extended to a multi-period portfolio allocation problem in (re)insurance, capturing the long-term risk dynamics of insurance liabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14439 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi periods mean-DCVaR optimization: a Recursive Neural Network resolution Lelong, Jérôme Maume-Deschamps, Véronique Thevenot, William Portfolio Management We study a discrete-time multi-period portfolio optimization problem under an explicit constraint on the Deviation Conditional Value-at-Risk (DCVaR), defined as the excess of Conditional Value-at-Risk over expected terminal wealth. The objective is to maximize expected return subject to a global tail-risk constraint, leading to a time-inconsistent precommitment problem. We propose a recurrent neural-network-based approach to approximate the optimal precommitment policy, which accommodates path-dependent risk constraints and highdimensional state dynamics without relying on dynamic programming. The explicit constraint formulation allows for exact penalty methods and provides a transparent notion of feasibility. The methodology is validated in a classical complete-market financial model and extended to a multi-period portfolio allocation problem in (re)insurance, capturing the long-term risk dynamics of insurance liabilities. |
| title | Multi periods mean-DCVaR optimization: a Recursive Neural Network resolution |
| topic | Portfolio Management |
| url | https://arxiv.org/abs/2604.14439 |