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

Chen, J.; Coolen, F.P.A.; Coolen-Maturi, T.

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


Authors

J. Chen



Abstract

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.

Citation

Chen, J., Coolen, F., & 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), 1678-1691. https://doi.org/10.1080/01605682.2019.1643682

Journal Article Type Article
Acceptance Date Jul 10, 2019
Online Publication Date Aug 5, 2019
Publication Date 2019
Deposit Date Jul 12, 2019
Publicly Available Date Aug 5, 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 1678-1691
DOI https://doi.org/10.1080/01605682.2019.1643682

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