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Durham University

Department of Mathematical Sciences

Academic Staff

Publication details for Tahani Coolen-Maturi

He, T., Coolen, F.P.A. & Coolen-Maturi, T. (2019). Nonparametric Predictive Inference for European Option Pricing based on the Binomial Tree Model. Journal of the Operational Research Society 70(10): 1692-1708.

Author(s) from Durham


In finance, option pricing is one of the main topics. A basic model for option pricing is the Binomial
Tree Model, proposed by Cox, Ross, and Rubinstein in 1979 (CRR). This model assumes that the underlying asset price follows a binomial distribution with a constant upward probability, the so-called
risk-neutral probability. In this paper, we propose a novel method based on the binomial tree. Rather
than using the risk-neutral probability, we apply Nonparametric Predictive Inference (NPI) to infer
imprecise probabilities of movements, reflecting more uncertainty while learning from data. To study its
performance, we price the same European options utilizing both the NPI method and the CRR model
and compare the results in two different scenarios, firstly where the CRR assumptions are right, and
secondly where the CRR model assumptions deviate from the real market. It turns out that our NPI
method, as expected, cannot perform better than the CRR in the first scenario, but can do better in the
second scenario.