Clustering and Classification in Option Pricing

This paper reviews the recent option pricing literature and investigates how clustering and classification can assist option pricing models. Specifically, we consider non-parametric modular neural network (MNN) models to price the S&P-500 European call options. The focus is on decomposing and classifying options data into a number of sub-models across moneyness and maturity ranges that are processed individually. The fuzzy learning vector quantization (FLVQ) algorithm we propose generates decision regions (i.e., option classes) divided by ÔintelligentÕ classification boundaries. Such an approach improves generaliza- tion properties of the MNN model and thereby increases its pricing accuracy.