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Clustering and Classification in Option Pricing


  • 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)


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, Rimini Centre for Economic Analysis, 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

    1. Pan, Jun, 2002. "The jump-risk premia implicit in options: evidence from an integrated time-series study," Journal of Financial Economics, Elsevier, vol. 63(1), pages 3-50, January.
    2. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 1997. " Empirical Performance of Alternative Option Pricing Models," Journal of Finance, American Finance Association, vol. 52(5), pages 2003-2049, December.
    3. Ait-Sahalia, Yacine & Duarte, Jefferson, 2003. "Nonparametric option pricing under shape restrictions," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 9-47.
    4. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 2000. "Pricing and hedging long-term options," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 277-318.
    5. Ramazan Gencay & Aslihan Salih, 2003. "Degree of Mispricing with the Black-Scholes Model and Nonparametric Cures," Annals of Economics and Finance, Society for AEF, vol. 4(1), pages 73-101, May.
    6. Chiarella, Carl & El-Hassan, Nadima & Kucera, Adam, 1999. "Evaluation of American option prices in a path integral framework using Fourier-Hermite series expansions," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1387-1424, September.
    7. Andreas Behr & Ulrich Pötter, 2009. "Alternatives to the normal model of stock returns: Gaussian mixture, generalised logF and generalised hyperbolic models," Annals of Finance, Springer, vol. 5(1), pages 49-68, January.
    8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    9. Garcia, Rene & Gencay, Ramazan, 2000. "Pricing and hedging derivative securities with neural networks and a homogeneity hint," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 93-115.
    10. Carr, Peter & Wu, Liuren, 2004. "Time-changed Levy processes and option pricing," Journal of Financial Economics, Elsevier, vol. 71(1), pages 113-141, January.
    11. Yoshida, Yuji, 2003. "The valuation of European options in uncertain environment," European Journal of Operational Research, Elsevier, vol. 145(1), pages 221-229, February.
    12. Haerdle,Wolfgang & Stoker,Thomas, 1987. "Investigations smooth multiple regression by the method of average derivatives," Discussion Paper Serie A 107, University of Bonn, Germany.
    13. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    14. Nikola Gradojevic & Ramazan Gencay & Dragan Kukolj, 2009. "Option Pricing with Modular Neural Networks," Working Paper series 32_09, Rimini Centre for Economic Analysis.
    15. Ole E. Barndorff-Nielsen & Neil Shephard, 2001. "Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 167-241.
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    More about this item


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

    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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