Policy Iteration for Learning an Exercise Policy for American Options

摘要

Options are important financial instruments, whose prices are usually determined by computational methods. Computational finance is a compelling application area for reinforcement learning research, where hard sequential decision making problems abound and have great practical significance. In this paper, we investigate reinforcement learning methods, in particular, least squares policy iteration (LSPI), for the problem of learning an exercise policy for American options. We also investigate a method by Tsitsiklis and Van Roy, referred to as FQI. We compare LSPI and FQI with LSM, the standard least squares Monte Carlo method from the finance community. We evaluate their performance on both real and synthetic data. The results show that the exercise policies discovered by LSPI and FQI gain larger payoffs than those discovered by LSM, on both real and synthetic data. Our work shows that solution methods developed in reinforcement learning can advance the state of the art in an important and challenging application area, and demonstrates furthermore that computational finance remains an under-explored area for deployment of reinforcement learning methods.