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Option Pricing with Modular Neural Networks

Author

Listed:
  • 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)

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).

Suggested Citation

  • Nikola Gradojevic & Ramazan Gencay & Dragan Kukolj, 2009. "Option Pricing with Modular Neural Networks," Working Paper series 32_09, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:32_09
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    More about this item

    Keywords

    Option Pricing; Modular Neural Networks; Non-parametric Methods;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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