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

Ren Raw Chen; Jeffrey Huang; William Huang; Robert Yu

doi:10.3390/jrfm14020057

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

Ren Raw Chen^*
, Jeffrey Huang
, William Huang
, Robert Yu

^*Corresponding author for this work

Fordham University
Financial Markets

Research output: Contribution to journal › Journal Article › peer-review

8 Scopus citations

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.

Original language	English
Article number	57
Journal	Journal of Risk and Financial Management
Volume	14
Issue number	2
DOIs	https://doi.org/10.3390/jrfm14020057
State	Published - 02 2021
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2021 by the authors.

Keywords

American option
Monte Carlo
PSO

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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.",

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AB - 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.

KW - American option

KW - Monte Carlo

KW - PSO

UR - https://www.scopus.com/pages/publications/85131227309

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DO - 10.3390/jrfm14020057

M3 - 文章

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

Abstract

Bibliographical note

Keywords

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