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In this paper, a novel algorithm for determining the free exercise boundary for high-dimensional Bermudan option problems is presented. First, a rough estimate of the boundary is constructed on a fine (daily) time grid. This rough estimate is used to generate a more accurate estimate on a coarse time grid (exercise opportunities). Antithetic branching is used to reduce the computational workload. The method is validated by comparing it with other methods of solving the standard Black–Scholes problem. Finally, the method is applied to two cases of Bermudan options with a second stochastic variable: a stochastic interest rate and a stochastic volatility.

American options are important financial products traded in enormous volumes across the world. Therefore, accurate and efficient valuation is of paramount importance for global financial markets. Due to the early exercise feature, the pricing of American options is significantly more complicated than European options, and an analytical closed-form solution is unavailable even for simple dynamic models. Practitioners employ various valuation methods to strike the balance: accurate valuation usually suffers inefficiency, while fast valuation likely leads to inaccuracy. In this paper, we provide an innovative solution to address both the accuracy and efficiency issues of pricing American options by applying quantum reinforcement learning. Meanwhile, the quantum part of the new approach would potentially speed up the calculation dramatically.