A nonparametric algorithm for optimal stopping based on robust optimization

TL;DR

This paper introduces a nonparametric, robust optimization-based algorithm for solving complex stochastic optimal stopping problems, providing near-optimal solutions with polynomial-time methods and practical heuristics, outperforming existing algorithms.

Contribution

It develops a novel approach that characterizes the structure of optimal Markovian stopping rules for robust problems, enabling polynomial-time solutions and effective heuristics.

Findings

Algorithm outperforms state-of-the-art in high-dimensional option pricing

Robust optimization approximates stochastic problems with arbitrary accuracy

Optimal stopping rules have a surprisingly simple, finite-dimensional structure

Abstract

Optimal stopping is a fundamental class of stochastic dynamic optimization problems with numerous applications in finance and operations management. We introduce a new approach for solving computationally-demanding stochastic optimal stopping problems with known probability distributions. The approach uses simulation to construct a robust optimization problem that approximates the stochastic optimal stopping problem to any arbitrary accuracy; we then solve the robust optimization problem to obtain near-optimal Markovian stopping rules for the stochastic optimal stopping problem. In this paper, we focus on designing algorithms for solving the robust optimization problems that approximate the stochastic optimal stopping problems. These robust optimization problems are challenging to solve because they require optimizing over the infinite-dimensional space of all Markovian stopping rules. We overcome this challenge by characterizing the structure of optimal Markovian stopping rules for the robust optimization problems. In particular, we show that optimal Markovian stopping rules for the robust optimization problems have a structure that is surprisingly simple and finite-dimensional. We leverage this structure to develop an exact reformulation of the robust optimization problem as a zero-one bilinear program over totally unimodular constraints. We show that the bilinear program can be solved in polynomial time in special cases, establish computational complexity results for general cases, and develop polynomial-time heuristics by relating the bilinear program to the maximal closure problem from graph theory. Numerical experiments demonstrate that our algorithms for solving the robust optimization problems are practical and can outperform state-of-the-art simulation-based algorithms in the context of widely-studied stochastic optimal stopping problems from high-dimensional option pricing.