deep learning approach to american option pricing

This study focuses on pricing the american put option by applying a deep learning-based algorithm under the double heston model. the double heston model is a multi-factor stochastic volatility model that offers more flexibility in modeling the volatility term structure and better empirical fit to option prices compared to one-factor models. the option price derivation under this model leads to a linear complementarity problem. to solve this problem, we utilize the deep galerkin method (dgm), which is a method based on deep learning. our numerical results show the efficiency and accuracy of the algorithm as evidenced by comparing it with the antithetic variable least-square monte carlo (av-lsm) method.