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Option pricing based on the generalized lambda distribution

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  • Charles J. Corrado

Abstract

This article proposes the generalized lambda distribution as a tool for modeling nonlognormal security price distributions. Known best as a facile model for generating random variables with a broad range of skewness and kurtosis values, the generalized lambda distribution has potential financial applications, including Monte Carlo simulations, estimations of option‐implied state price densities, and almost any situation requiring a flexible density shape. A multivariate version of the generalized lambda distribution is developed to facilitate stochastic modeling of portfolios of correlated primary and derivative securities. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:213–236, 2001

Suggested Citation

  • Charles J. Corrado, 2001. "Option pricing based on the generalized lambda distribution," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 21(3), pages 213-236, March.
    • Handle: RePEc:wly:jfutmk:v:21:y:2001:i:3:p:213-236
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    Cited by:

    1. David Mauler & James McDonald, 2015. "Option Pricing and Distribution Characteristics," Computational Economics, Springer;Society for Computational Economics, vol. 45(4), pages 579-595, April.
    2. 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.
    3. Marin Bozic, 2010. "Pricing Options on Commodity Futures: The Role of Weather and Storage," Working Papers 1003, The Institute of Economics, Zagreb.
    4. 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.
    5. Vijverberg, Chu-Ping C. & Vijverberg, Wim P.M. & Taşpınar, Süleyman, 2016. "Linking Tukey’s legacy to financial risk measurement," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 595-615.
    6. Marcos Massaki Abe & Eui Jung Chang & Benjamin Miranda Tabak, 2007. "Forecasting Exchange Rate Density Using Parametric Models: the Case of Brazil," Brazilian Review of Finance, Brazilian Society of Finance, vol. 5(1), pages 29-39.
    7. Canan G. Corlu & Alper Corlu, 2015. "Modelling exchange rate returns: which flexible distribution to use?," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1851-1864, November.
    8. Stephanie Danielle Subramoney & Knowledge Chinhamu & Retius Chifurira, 2021. "Value at Risk estimation using GAS models with heavy tailed distributions for cryptocurrencies," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 10(4), pages 40-54, October.
    9. Yuzhi Cai, 2021. "Estimating expected shortfall using a quantile function model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4332-4360, July.
    10. Bogdan Negrea & Bertrand Maillet & Emmanuel Jurczenko, 2002. "Revisited Multi-moment Approximate Option," FMG Discussion Papers dp430, Financial Markets Group.
    11. José L. Vilar-Zanón & Olivia Peraita-Ezcurra, 2019. "A linear goal programming method to recover risk neutral probabilities from options prices by maximum entropy," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(1), pages 259-276, June.
    12. Shi-jie Jiang & Mujun Lei & Cheng-Huang Chung, 2018. "An Improvement of Gain-Loss Price Bounds on Options Based on Binomial Tree and Market-Implied Risk-Neutral Distribution," Sustainability, MDPI, vol. 10(6), pages 1-17, June.

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