Multi periods mean-DCVaR optimization: a Recursive Neural Network resolution

TL;DR

This paper introduces a neural network approach to optimize multi-period portfolios with explicit tail-risk constraints, effectively handling complex, path-dependent risk and high-dimensional dynamics.

Contribution

It presents a novel recurrent neural network method for solving time-inconsistent, constrained portfolio optimization problems without dynamic programming.

Findings

The approach effectively approximates optimal policies under DCVaR constraints.

The method is validated in financial and insurance models.

It handles high-dimensional, path-dependent risk dynamics.

Abstract

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.