An Iteration Algorithm for American Options Pricing Based on Reinforcement Learning

Abstract

In this paper, we present an iteration algorithm for the pricing of American options based on reinforcement learning. At each iteration, the method approximates the expected discounted payoff of stopping times and produces those closer to optimal. In the convergence analysis, a finite sample bound of the algorithm is derived. The algorithm is evaluated on a multi-dimensional Black-Scholes model and a symmetric stochastic volatility model, the numerical results implied that our algorithm is accurate and efficient for pricing high-dimensional American options.