From a monotone probabilistic scheme to a probabilistic max-plus algorithm for solving Hamilton-Jacobi-Bellman equations

TL;DR

This paper introduces a new probabilistic max-plus algorithm for solving Hamilton-Jacobi-Bellman equations that is monotone under weak assumptions, enabling applications to high-dimensional problems like option pricing with multiple stocks.

Contribution

A novel probabilistic scheme that ensures monotonicity under weak conditions, extending the applicability of max-plus methods to more general and higher-dimensional PDEs.

Findings

Applicable to strongly elliptic PDEs with bounded coefficients

Successfully tested on a 5-stock option pricing problem

Improves convergence and flexibility over previous methods

Abstract

In a previous work (Akian, Fodjo, 2016), we introduced a lower complexity probabilistic max-plus numerical method for solving fully nonlinear Hamilton-Jacobi-Bellman equations associated to diffusion control problems involving a finite set-valued (or switching) control and possibly a continuum-valued control. This method was based on the idempotent expansion properties obtained by McEneaney, Kaise and Han (2011) and on the numerical probabilistic method proposed by Fahim, Touzi and Warin (2011) for solving some fully nonlinear parabolic partial differential equations. A difficulty of the algorithm of Fahim, Touzi and Warin is in the critical constraints imposed on the Hamiltonian to ensure the monotonicity of the scheme, hence the convergence of the algorithm. Here, we propose a new "probabilistic scheme" which is monotone under rather weak assumptions, including the case of strongly elliptic PDE with bounded coefficients. This allows us to apply our probabilistic max-plus method in more general situations. We illustrate this on the evaluation of the superhedging price of an option under uncertain correlation model with several underlying stocks and changing sign cross gamma, and consider in particular the case of 5 stocks leading to a PDE in dimension 5.