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A Simple Option‐Pricing Formula

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  • Robert Savickas

Abstract

A simple option‐pricing formula based on the Weibull distribution is introduced. The simplicity of the algebraic form and ease of implementation are comparable to those of Black‐Scholes. Application to S&P 500 options shows that the pricing biases present in the Black‐Scholes model are eliminated. Prices produced by the presented model generally lie within or close to the bid‐ask spread. For long‐term options (over one year), the Weibull formula exhibits significantly higher precision than the Black‐Scholes formula does. While a rigorous comparison of all available models is necessary, the simplicity and precision of the proposed model are its main advantages over the existing models.

Suggested Citation

  • Robert Savickas, 2002. "A Simple Option‐Pricing Formula," The Financial Review, Eastern Finance Association, vol. 37(2), pages 207-226, May.
    • Handle: RePEc:bla:finrev:v:37:y:2002:i:2:p:207-226
    DOI: 10.1111/1540-6288.00012
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    Cited by:

    1. Molina Barreto, Andrés Mauricio & Jiménez Moscoso, José Alfredo, 2014. "Valoración de derivados europeos con mixtura de distribuciones Weibull [Valuation for European derivatives with mixture-Weibull distributions]," MPRA Paper 118572, University Library of Munich, Germany, revised 08 Aug 2014.
    2. Ben Boukai, 2021. "On the RND under Heston's stochastic volatility model," Papers 2101.03626, arXiv.org.
    3. Sheri Markose & Amadeo Alentorn, 2005. "Option Pricing and the Implied Tail Index with the Generalized Extreme Value (GEV) Distribution," Computing in Economics and Finance 2005 397, Society for Computational Economics.
    4. Luiz Vitiello & Ivonia Rebelo, 2015. "A note on the pricing of multivariate contingent claims under a transformed-gamma distribution," Review of Derivatives Research, Springer, vol. 18(3), pages 291-300, October.
    5. Markose, Sheri M & Alentorn, Amadeo, 2005. "The Generalized Extreme Value (GEV) Distribution, Implied Tail Index and Option Pricing," Economics Discussion Papers 3726, University of Essex, Department of Economics.
    6. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    7. Imdade Chitou & Gilles Dufrénot & Julien Esposito, 2021. "Linking Covid-19 epidemic and emerging market OAS: Evidence using dynamic copulas and Pareto distributions," Working Papers halshs-03297198, HAL.
    8. Hang Lin & Lixin Liu & Zhengjun Zhang, 2023. "Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model," Mathematics, MDPI, vol. 11(14), pages 1-32, July.
    9. Li, Yifan & Nolte, Ingmar & Pham, Manh Cuong, 2024. "Parametric risk-neutral density estimation via finite lognormal-Weibull mixtures," Journal of Econometrics, Elsevier, vol. 241(2).
    10. Ben Boukai, 2021. "The Generalized Gamma distribution as a useful RND under Heston's stochastic volatility model," Papers 2108.07937, arXiv.org, revised Aug 2021.

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