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

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  • Ting He
  • Frank P. A. Coolen
  • Tahani Coolen-Maturi

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 article, 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 utilising 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.

Suggested Citation

  • Ting He & Frank P. A. Coolen & Tahani Coolen-Maturi, 2019. "Nonparametric predictive inference for European option pricing based on the binomial tree model," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1692-1708, October.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:10:p:1692-1708
    DOI: 10.1080/01605682.2018.1495997
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    Cited by:

    1. Ting He, 2020. "Nonparametric Predictive Inference for Asian options," Papers 2008.13082, arXiv.org.
    2. He, Ting, 2023. "An imprecise pricing model for Asian options based on Nonparametric predictive inference," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    3. U Hou Lok & Yuh-Dauh Lyuu, 2022. "A Valid and Efficient Trinomial Tree for General Local-Volatility Models," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 817-832, October.

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