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On nonparametric predictive inference for asset and European option trading in the binomial tree model

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  • Junbin Chen
  • Frank P. A. Coolen
  • Tahani Coolen-Maturi

Abstract

This article introduces a novel method for asset and option trading in a binomial scenario. This method uses nonparametric predictive inference (NPI), a statistical methodology within imprecise probability theory. Instead of inducing a single probability distribution from the existing observations, the imprecise method used here induces a set of probability distributions. Based on the induced imprecise probability, one could form a set of conservative trading strategies for assets and options. By integrating NPI imprecise probability and expectation with the classical financial binomial tree model, two rational decision routes for asset trading and for European option trading are suggested. The performances of these trading routes are investigated by computer simulations. The simulation results indicate that the NPI based trading routes presented in this article have good predictive properties.

Suggested Citation

  • Junbin Chen & Frank P. A. Coolen & Tahani Coolen-Maturi, 2019. "On nonparametric predictive inference for asset and European option trading in the binomial tree model," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1678-1691, October.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:10:p:1678-1691
    DOI: 10.1080/01605682.2019.1643682
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    Cited by:

    1. He, Ting, 2023. "An imprecise pricing model for Asian options based on Nonparametric predictive inference," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    2. 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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