Option Pricing With Modular Neural Networks
University of Guelph, Department of Economics and Finance; University of Bologna - Rimini Center for Economic Analysis (RCEA)
Simon Fraser University
This paper applies 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. We find that modularity improves the generalization properties of standard feedforward neural network option pricing models (with and without the homogeneity hint), relative to the Black-Scholes model.
Number of Pages in PDF File: 25
Keywords: Option Pricing, Modular Neural Networks, Non-parametric Methods.
JEL Classification: C45, G12
Date posted: January 27, 2007
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