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

Chen, J. and Coolen, F.P.A. and Coolen-Maturi, T. (2019) 'On nonparametric predictive inference for asset and European option trading in the binomial tree model.', Journal of the Operational Research Society., 70 (10). pp. 1678-1691.


This paper introduces a novel method for asset and option trading in a binomial scenario. This method uses nonparametric predictive inference (NPI), a statistical methodology within im- precise 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 nancial 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 com- puter simulations. The simulation results indicate that the NPI based trading routes presented in this paper have good predictive properties.

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 Journal of the Operational Research Society on 5 August 2019 available online:
Date accepted:10 July 2019
Date deposited:12 July 2019
Date of first online publication:05 August 2019
Date first made open access:05 August 2020

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