Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/239473 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-22
Publisher: 
MDPI, Basel
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.
Subjects: 
American option
Monte Carlo
PSO
JEL: 
G12
G13
G4
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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