Efficient pricing and hedging of high-dimensional American options using deep recurrent networks

Abstract

We propose a deep recurrent neural network (RNN) framework for computing prices and deltas of American options in high dimensions. Our proposed framework uses two deep RNNs, where one network learns the continuation price and the other learns the delta for each timestep. Our proposed framework yields prices and deltas for the entire spacetime, not only at a given point (e.g. t = 0). The computational cost of the proposed approach is linear in N, which improves on the quadratic time seen for feedforward networks that price American options. The computational memory cost of our method is constant in N, which is an improvement over the linear memory costs seen in feedforward networks. Our numerical simulations demonstrate these contributions and show that the proposed deep RNN framework is computationally more efficient than traditional feedforward neural network frameworks in time and memory.