A Nonparametric Algorithm for Optimal Stopping Based on Robust Optimization

Abstract

Optimal stopping is a fundamental class of stochastic dynamic optimization problems with numerous applications in finance and operations management. In “A nonparametric algorithm for optimal stopping based on robust optimization,” the author introduces an approach based on robust optimization for computing near-optimal solutions for computationally demanding stochastic optimal stopping problems with known probability distributions. Through this new use of robust optimization, the paper develops new algorithms for solving stochastic optimal stopping problems that are practical and can outperform state-of-the-art simulation-based algorithms in the context of pricing high-dimensional financial options.