Abstract

We propose an algorithm for solving optimal stopping problems, and we provide theoretical results concerning convergence and approximation error. This algorithm can be viewed as a "neuro-dynamic programming" method for optimal stopping problems. The applicability of the algorithm is illustrated through a computational case study involving the pricing of a path-dependent financial derivative security that gives rise to an optimal stopping problem with a one-hundred-dimensional state space.

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