Bayesian Prediction, Entropy, and Option Pricingx

Abstract

This paper studies the performance of the Foster-Whiteman (1999) procedure for using a Bayesian predictive distribution for the future price of an asset to compute the price of a European option on that asset. A technical contribution of the paper is the description of a sequential importance sampling procedure for implementing an informative prior that reflects and rewards past option-pricing success. The risk-neutralization of the predictive distribution is accomplished by Stutzer's (1996) constrained KLIC-minimizing change of measure. The procedure is used in weekly pricing of July and November options on soybeans on the Chicago Board of Trade from 1993–1997, and produces option prices that mimic market prices much more closely than those of the Black model or those produced by risk-neutralizing a nonparametric predictive. © 2006, SAGE Publications. All rights reserved.