Dynamic programming equation for the mean field optimal stopping problem

TL;DR

This paper develops a dynamic programming framework for mean field optimal stopping problems involving McKean-Vlasov diffusions, incorporating the law-dependent dynamics and establishing an obstacle problem on Wasserstein space.

Contribution

It introduces a novel dynamic programming equation for mean field stopping problems with law-dependent coefficients, using a new Itô formula for flows of marginal laws.

Findings

The dynamic programming equation is an obstacle problem on Wasserstein space.

Optimal stopping policies require randomization.

Application to mean-variance optimal stopping demonstrates effectiveness.

Abstract

We study the optimal stopping problem of McKean-Vlasov diffusions when the criterion is a function of the law of the stopped process. A remarkable new feature in this setting is that the stopping time also impacts the dynamics of the stopped process through the dependence of the coefficients on the law. The mean field stopping problem is introduced in weak formulation in terms of the joint marginal law of the stopped underlying process and the survival process. This specification satisfies a dynamic programming principle. The corresponding dynamic programming equation is an obstacle problem on the Wasserstein space, and is obtained by means of a general It\^o formula for flows of marginal laws of c\`adl\`ag semimartingales. Our verification result characterizes the nature of optimal stopping policies, highlighting the crucial need to randomized stopping. The effectiveness of our dynamic programming equation is illustrated by various examples including the mean-variance optimal stopping problem.