Extended HJB Equation for Mean-Variance Stopping Problem: Vanishing Regularization Method

TL;DR

This paper introduces a vanishing regularization approach to analyze the mean-variance optimal stopping problem, deriving an extended HJB equation system and establishing equilibrium strategies through a novel game-theoretic framework.

Contribution

It develops a new regularization method and extended HJB equations to solve the time-inconsistent mean-variance stopping problem, providing a rigorous link to equilibrium strategies.

Findings

Derived coupled extended HJB equations for regularized problem

Proved existence of classical solutions for small time horizons

Formal recovery of equilibrium conditions via vanishing regularization

Abstract

This paper studies the time-inconsistent MV optimal stopping problem via a game-theoretic approach to find equilibrium strategies. To overcome the mathematical intractability of direct equilibrium analysis, we propose a vanishing regularization method: first, we introduce an entropy-based regularization term to the MV objective, modeling mixed-strategy stopping times using the intensity of a Cox process. For this regularized problem, we derive a coupled extended Hamilton-Jacobi-Bellman (HJB) equation system, prove a verification theorem linking its solutions to equilibrium intensities, and establish the existence of classical solutions for small time horizons via a contraction mapping argument. By letting the regularization term tend to zero, we formally recover a system of parabolic variational inequalities that characterizes equilibrium stopping times for the original MV problem. This system includes an additional key quadratic term--a distinction from classical optimal stopping, where stopping conditions depend only on comparing the value function to the instantaneous reward.