Accuracy Measures for American put Option Pricing Algorithms

Abstract

I address the dichotomy between American put option pricing theory and the numerical algorithms designed to estimate American put option prices. The literature has focused only on pricing error. However, early exercise is the essence of American option pricing (exercising) and with it comes the possibility of early exercise error and its opportunity costs. I introduce an economically viable metric that identifies all the errors of American put option pricing algorithms. The accuracy of such algorithms can thereby be fully assessed. A rational option pricing result generalises the usual integral equation and motivates pure pricing error. This provides new intuition for the optimality condition for early exercise. Early exercise error is motivated by comparing discounted expected profits generated by the estimated model vs. the optimal early exercise model. The error measure applies to any put pricing algorithm and any benchmark. We illustrate our total error measure with a new algorithm.