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

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Listed:
  • Ren-Raw Chen

    (Gabelli School of Business, Fordham University, 45 Columbus Avenue, New York, NY 10019, USA
    We thank Joe Pimbley for his encouragement and valuable comments that make this paper substantially better.)

  • 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)

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.

Suggested Citation

  • Ren-Raw Chen & Jeffrey Huang & William Huang & Robert Yu, 2021. "An Artificial Intelligence Approach to the Valuation of American-Style Derivatives: A Use of Particle Swarm Optimization," JRFM, MDPI, vol. 14(2), pages 1-22, February.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:2:p:57-:d:491728
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    References listed on IDEAS

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    Cited by:

    1. Tapio Behrndt & Ren-Raw Chen, 2022. "A New Look at the Swing Contract: From Linear Programming to Particle Swarm Optimization," JRFM, MDPI, vol. 15(6), pages 1-20, May.
    2. Farnaz Ghashami & Kamyar Kamyar & S. Ali Riazi, 2021. "Prediction of Stock Market Index Using a Hybrid Technique of Artificial Neural Networks and Particle Swarm Optimization," Applied Economics and Finance, Redfame publishing, vol. 8(3), pages 1-8, December.

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