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

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

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
• Public URL: https://durham-repository.worktribe.com/output/1327205