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Artificial Neural Network Enhanced Parametric Option Pricing

Author

Listed:
  • Panayiotis C. Andreou

    (University of Cyprus)

  • Chris Charalambous

    (University of Cyprus)

  • Spiros H. Martzoukos

Abstract

In this paper we explore ways that alleviate problems of nonparametric (artificial neural networks) and parametric option pricing models by combining the two. The resulting enhanced network model is compared to standard artificial neural networks and to parametric models with several historical and implied parameters. Empirical results using S\&P 500 index call options strongly support our approach.

Suggested Citation

  • Panayiotis C. Andreou & Chris Charalambous & Spiros H. Martzoukos, 2006. "Artificial Neural Network Enhanced Parametric Option Pricing," Computing in Economics and Finance 2006 118, Society for Computational Economics.
    • Handle: RePEc:sce:scecfa:118
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    File URL: http://repec.org/sce2006/up.27825.1139911662.pdf
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    Cited by:

    1. Christian L. Dunis & Jason Laws & Andreas Karathanassopoulos, 2011. "Modelling and trading the Greek stock market with mixed neural network models," Applied Financial Economics, Taylor & Francis Journals, vol. 21(23), pages 1793-1808, December.

    More about this item

    Keywords

    Option pricing; implied volatilities; implied parameters; artificial neural networks; optimization;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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