Nikola Gradojevic () ( Faculty of Business Administration, Lakehead University) Ramazan Gencay () ( Department of Economics, Simon Fraser University) Dragan Kukolj () ( Faculty of Engineering, University of Novi Sad)
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This paper investigates a non-parametric 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 homogenous of degree one with respect to the underlying index price and the strike price. When compared to an array of parametric and non-parametric 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)
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Paper provided by Rimini Centre for Economic Analysis in its series Working Paper Series with number
wp32_09.