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

Ren-Raw Chen, Jeffrey Huang, William Huang and Robert Yu
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Ren-Raw Chen: Gabelli School of Business, Fordham University, 45 Columbus Avenue, New York, NY 10019, USA
Jeffrey Huang: Bank SinoPac, Financial Markets, 5F, #306, Bade Road, Section 2, Taipei 104, Taiwan
William Huang: Gabelli School of Business, Fordham University, 45 Columbus Avenue, New York, NY 10019, USA
Robert Yu: Gabelli School of Business, Fordham University, 45 Columbus Avenue, New York, NY 10019, USA

JRFM, 2021, vol. 14, issue 2, 1-22

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 (regarding computation and accuracy) in solving high-dimensional optimization problems and hence, is 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 (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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