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Clustering and Classification in Option Pricing

Author

Listed:
  • Nikola Gradojevic

    (Lakehead University, The Rimini Center for Economic Analysis)

  • Dragan Kukolj

    (University of Novi Sad)

  • Ramazan Gencay

    (Simon Fraser University, The Rimini Center for Economic Analysis)

Abstract

This paper reviews the recent option pricing literature and investigates how clustering and classification can assist option pricing models. Specifically, we consider non-parametric modular neural network (MNN) models to price the S&P-500 European call options. The focus is on decomposing and classifying options data into a number of sub-models across moneyness and maturity ranges that are processed individually. The fuzzy learning vector quantization (FLVQ) algorithm we propose generates decision regions (i.e., option classes) divided by ÔintelligentÕ classification boundaries. Such an approach improves generaliza- tion properties of the MNN model and thereby increases its pricing accuracy.

Suggested Citation

  • Nikola Gradojevic & Dragan Kukolj & Ramazan Gencay, 2011. "Clustering and Classification in Option Pricing," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 3(2), pages 109-128, October.
    • Handle: RePEc:ren:journl:v:3:y:2011:i:2:p:109-128
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Option Pricing; Clustering; Parametric Methods; Non-parametric Methods; Fuzzy Logic;
    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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