Option Pricing with Modular Neural Networks

Nikola Gradojevic, Ramazan Gencay and Dragan Kukolj ()
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Dragan Kukolj: Faculty of Engineering, University of Novi Sad

Working Paper series from Rimini Centre for Economic Analysis

Abstract: 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).

Keywords: Option Pricing; Modular Neural Networks; Non-parametric Methods (search for similar items in EconPapers)
JEL-codes: C45 G12 (search for similar items in EconPapers)
Date: 2009-01
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Citations: View citations in EconPapers (27)

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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:32_09

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