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

37 Pages Posted: 30 Sep 2020

See all articles by Ren-Raw Chen

Ren-Raw Chen

Fordham University - Gabelli School of Business

Date Written: August 14, 2020

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

Suggested Citation

Chen, Ren-Raw, An Artificial Intelligence Approach to the Valuation of American-style Derivatives: A Use of Particle Swarm Optimization (August 14, 2020). Available at SSRN: https://ssrn.com/abstract=3673898 or http://dx.doi.org/10.2139/ssrn.3673898

Ren-Raw Chen (Contact Author)

Fordham University - Gabelli School of Business ( email )

113 West 60th Street
Bronx, NY 10458
United States

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