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Estimating Implied PDFs From American Options on Futures: A New Semiparametric Approach

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  • Dimitris Flamouris
  • Daniel Giamouridis

Abstract

This article develops a new methodology for estimating implied probability density functions for futures prices from American options. The restricting Black–Scholes assumption of a lognormal distribution for the underlying asset is relaxed with the use of the more flexible distributional form of an Edgeworth series expansion around a lognormal distribution. The model is applied to the crude oil market. The results provide strong evidence that the market consensus can be accurately reflected in the risk‐neutral densities recovered from observed option prices. The recovered distributions are tested and found to differ significantly from a single lognormal distribution. In addition, the recovered distributions are more robust than those recovered with a model, which assumes a mixture of two lognormal distributions. © 2002 John Wiley & Sons, Inc. Jrl Fut Mark 22:1–30, 2002

Suggested Citation

  • Dimitris Flamouris & Daniel Giamouridis, 2002. "Estimating Implied PDFs From American Options on Futures: A New Semiparametric Approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 22(1), pages 1-30, January.
    • Handle: RePEc:wly:jfutmk:v:22:y:2002:i:1:p:1-30
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    Cited by:

    1. Arismendi, Juan & Genaro, Alan De, 2016. "A Monte Carlo multi-asset option pricing approximation for general stochastic processes," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 75-99.
    2. Datta, Deepa Dhume & Londono, Juan M. & Ross, Landon J., 2017. "Generating options-implied probability densities to understand oil market events," Energy Economics, Elsevier, vol. 64(C), pages 440-457.
    3. 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.
    4. Arturo Leccadito & Pietro Toscano & Radu S. Tunaru, 2012. "Hermite Binomial Trees: A Novel Technique For Derivatives Pricing," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(08), pages 1-36.
    5. Juan Arismendi, 2014. "A Multi-Asset Option Approximation for General Stochastic Processes," ICMA Centre Discussion Papers in Finance icma-dp2014-03, Henley Business School, University of Reading.
    6. J. C. Arismendi & Marcel Prokopczuk, 2016. "A moment-based analytic approximation of the risk-neutral density of American options," Applied Mathematical Finance, Taylor & Francis Journals, vol. 23(6), pages 409-444, November.
    7. Sofiane Aboura & Didier Maillard, 2016. "Option Pricing Under Skewness and Kurtosis Using a Cornish–Fisher Expansion," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(12), pages 1194-1209, December.
    8. Bogdan Negrea & Bertrand Maillet & Emmanuel Jurczenko, 2002. "Revisited Multi-moment Approximate Option," FMG Discussion Papers dp430, Financial Markets Group.

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