An Artificial Intelligence Approach to the Valuation of American-style Derivatives: A Use of Particle Swarm Optimization

Abstract

In this paper, we evaluate American-style, path-dependent derivatives with an artificial intelligence technique. Specifically we use swarm intelligence to find the optimal exercise boundary for an American-style derivative. Swarm intelligence is particularly efficient (computation and accuracy) in solving high-dimensional optimization problems and hence perfectly suitable for valuing complex American-style derivatives (e.g. multiple-asset, path-dependent) which require a high-dimensional optimal exercise boundary.

Keywords: American Option, Monte Carlo, PSO

JEL Classification: G12, G13, G4