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

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.

Keywords:

• Imprecise probability
• nonparametric predictive inference
• asset trading
• European option trading
• asset risk pricing
• option risk pricing

Disclosure statement

No potential conflict of interest was reported by the authors.