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Nonparametric predictive inference for American option pricing based on the binomial tree model.

He, T. and Coolen, F.P.A. and Coolen-Maturi, T. (2021) 'Nonparametric predictive inference for American option pricing based on the binomial tree model.', Communications in statistics – theory and methods., 50 (20). pp. 4657-4684.


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.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Publisher statement:This is an Accepted Manuscript of an article published by Taylor & Francis in Communications in statistics – theory and methods on 15 May 2020 available online:
Date accepted:28 April 2020
Date deposited:05 May 2020
Date of first online publication:15 May 2020
Date first made open access:15 May 2021

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