Option pricing with modular neural networks

Authors:
Nikola Gradojevic;Ramazan Gençay;Dragan Kukolj
Affiliations:
Faculty of Business Administration, Lakehead University, Thunder Bay, ON, Canada;Department of Economics, Simon Fraser University, Burnaby, BC, Canada;Faculty of Engineering, University of Novi Sad, Novi Sad, Serbia
Venue:
IEEE Transactions on Neural Networks
Year:
2009

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Abstract

This paper investigates a nonparametric modular neural network (MNN) model to price the S&P-500 European call options. The modules are based on time to maturity and moneyness of the options. The option price function of interest is homogeneous of degree one with respect to the underlying index price and the strike price. When compared to an array of parametric and nonparametric models, the MNN method consistently exerts superior out-of-sample pricing performance. We conclude that modularity improves the generalization properties of standard feedforward neural network option pricing models (with and without the homogeneity hint).