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Option Pricing with Modular Neural Networks

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Author Info

  • 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)

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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 32_09.

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Date of creation: Jan 2009
Date of revision: Jan 2009
Handle: RePEc:rim:rimwps:32_09

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Related research

Keywords: Option Pricing; Modular Neural Networks; Non-parametric Methods;

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Cited by:
  1. Ortíz Arango Francisco & Cabrera Llanos Agustín Ignacio & López Herrera Francisco, 2013. "Pronóstico de los índices accionarios DAX y S&P 500 con redes neuronales diferenciales," Contaduría y Administración:Revista Internacional, Accounting and Management: International Journal, vol. 58(3), pages 203-225, julio-sep.
  2. Dragan Kukolj & Nikola Gradojevic & Camillo Lento, 2012. "Improving Non-Parametric Option Pricing during the Financial Crisis," Working Paper Series 35_12, The Rimini Centre for Economic Analysis.
  3. Nikola Gradojevic & Dragan Kukolj & Ramazan Gencay, 2011. "Clustering and Classification in Option Pricing," Review of Economic Analysis, Rimini Centre for Economic Analysis, vol. 3(2), pages 109-128, October.

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