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

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

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


Authors

T. He



Abstract

In finance, option pricing is one of the main topics. A basic model for option pricing is the Binomial Tree Model, proposed by Cox, Ross, and Rubinstein in 1979 (CRR). This model assumes that the underlying asset price follows a binomial distribution with a constant upward probability, the so-called risk-neutral probability. In this paper, we propose a novel method based on the binomial tree. Rather than using the risk-neutral probability, we apply Nonparametric Predictive Inference (NPI) to infer imprecise probabilities of movements, reflecting more uncertainty while learning from data. To study its performance, we price the same European options utilizing both the NPI method and the CRR model and compare the results in two different scenarios, firstly where the CRR assumptions are right, and secondly where the CRR model assumptions deviate from the real market. It turns out that our NPI method, as expected, cannot perform better than the CRR in the first scenario, but can do better in the second scenario.

Citation

He, T., Coolen, F., & Coolen-Maturi, T. (2019). Nonparametric Predictive Inference for European Option Pricing based on the Binomial Tree Model. Journal of the Operational Research Society, 70(10), 1692-1708. https://doi.org/10.1080/01605682.2018.1495997

Journal Article Type Article
Acceptance Date Jun 26, 2018
Online Publication Date Feb 22, 2019
Publication Date Aug 1, 2019
Deposit Date Jun 26, 2018
Publicly Available Date Feb 22, 2020
Journal Journal of the Operational Research Society
Print ISSN 0160-5682
Electronic ISSN 1476-9360
Publisher Taylor and Francis Group
Peer Reviewed Peer Reviewed
Volume 70
Issue 10
Pages 1692-1708
DOI https://doi.org/10.1080/01605682.2018.1495997

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Accepted Journal Article (Revised version) (259 Kb)
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Copyright Statement
Revised version This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 22 February 2019 available online:





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