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Nonparametric Predictive Inference for American Option Pricing based on the Binomial Tree Model

He, T.; Coolen, F.P.A.; Coolen-Maturi, T.

Nonparametric Predictive Inference for American Option Pricing based on the Binomial Tree Model Thumbnail


T. He


In this article, we present the American option pricing procedure based on the binomial tree from an imprecise statistical aspect. Nonparametric Predictive Inference (NPI) is implemented to infer imprecise probabilities of underlying asset movements, reflecting uncertainty while learning from data, which is superior to the constant risk-neutral probability. In a recent article, we applied the NPI method to the European option pricing procedure that gives good results when the investor has non-perfect information. We now investigate the NPI method for American option pricing, of which imprecise probabilities are considered and updated for every one-time-step path. Different from the classic models, this method is shown that it may be optimal to early exercise an American non-dividend call option because our method considers all information that occurs in the future steps. We also study the performance of the NPI pricing method for American options via simulations in two different scenarios compared to the Cox, Ross and Rubinstein binomial tree model (CRR), first where the CRR assumptions are right, and second where the CRR model uses wrong assumptions. Through the performance study, we conclude that the investor using the NPI method tends to achieve good results in the second scenario.

Journal Article Type Article
Acceptance Date Apr 28, 2020
Online Publication Date May 15, 2020
Publication Date 2021
Deposit Date Apr 30, 2020
Publicly Available Date May 15, 2021
Journal Communications in Statistics - Theory and Methods
Print ISSN 0361-0926
Electronic ISSN 1532-415X
Publisher Taylor and Francis Group
Peer Reviewed Peer Reviewed
Volume 50
Issue 20
Pages 4657-4684
Public URL


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