Optimal Control of Conditional Value-at-Risk in Continuous Time

TL;DR

This paper develops an efficient dynamic programming-based method for optimal control problems involving Conditional Value-at-Risk in continuous time, overcoming time-inconsistency issues and extending to broader risk metrics.

Contribution

It introduces a bilevel optimization approach with convexity conditions, enabling gradient-based solutions without state-space lifting, and extends to general risk measures.

Findings

Provides a Hamilton-Jacobi-Bellman framework for CVaR control

Develops convergent approximation schemes for broader applicability

Demonstrates application to portfolio optimization under CVaR constraints

Abstract

We consider continuous-time stochastic optimal control problems featuring Conditional Value-at-Risk (CVaR) in the objective. The major difficulty in these problems arises from time-inconsistency, which prevents us from directly using dynamic programming. To resolve this challenge, we convert to an equivalent bilevel optimization problem in which the inner optimization problem is standard stochastic control. Furthermore, we provide conditions under which the outer objective function is convex and differentiable. We compute the outer objective's value via a Hamilton-Jacobi-Bellman equation and its gradient via the viscosity solution of a linear parabolic equation, which allows us to perform gradient descent. The significance of this result is that we provide an efficient dynamic programming-based algorithm for optimal control of CVaR without lifting the state-space. To broaden the applicability of the proposed algorithm, we propose convergent approximation schemes in cases where our key assumptions do not hold and characterize relevant suboptimality bounds. In addition, we extend our method to a more general class of risk metrics, which includes mean-variance and median-deviation. We also demonstrate a concrete application to portfolio optimization under CVaR constraints. Our results contribute an efficient framework for solving time-inconsistent CVaR-based sequential optimization.