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Estimating risk-neutral density with parametric models in interest rate markets

Author

Listed:
  • Frank Fabozzi
  • Radu Tunaru
  • George Albota

Abstract

The departure in modelling terms from the log-normal distribution for option pricing has been largely driven by empirical observations on skewness. In recent years, the Weibull and generalized beta distributions have been used to fit the risk-neutral density from option prices. In this article, we also propose the use of the generalized gamma distribution for recovering the risk-neutral density. In terms of complexity, this distribution, having three parameters, falls between the Weibull and generalized beta distributions. New option pricing formulas for European call and put options are derived under the generalized gamma distribution. The empirical evidence based on a set of interest rate derivatives data indicates that this distribution is capable of producing the same type of performance as the Weibull, generalized beta, and Burr3 distributions. In addition, we analyze the effect of July 2005 bombings in London on interest rate markets under the best fitting distribution. Our results indicate that there was very little impact on the volatility of these markets.

Suggested Citation

  • Frank Fabozzi & Radu Tunaru & George Albota, 2009. "Estimating risk-neutral density with parametric models in interest rate markets," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 55-70.
    • Handle: RePEc:taf:quantf:v:9:y:2009:i:1:p:55-70
    DOI: 10.1080/14697680802272045
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    References listed on IDEAS

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    1. Mrs. Oana M Croitoru & Mr. R. B. Johnston, 2005. "The Impact of Terrorism on Financial Markets," IMF Working Papers 2005/060, International Monetary Fund.
    2. 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.
    3. 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.
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    Citations

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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. Lina M. Cortés & Javier Perote & Andrés Mora-Valencia, 2017. "Implicit probability distribution for WTI options: The Black Scholes vs. the semi-nonparametric approach," Documentos de Trabajo de Valor Público 15923, Universidad EAFIT.
    3. Xixuan Han & Boyu Wei & Hailiang Yang, 2018. "Index Options And Volatility Derivatives In A Gaussian Random Field Risk-Neutral Density Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 1-41, June.
    4. Fabozzi, Frank J. & Leccadito, Arturo & Tunaru, Radu S., 2014. "Extracting market information from equity options with exponential Lévy processes," Journal of Economic Dynamics and Control, Elsevier, vol. 38(C), pages 125-141.
    5. Cortés, Lina M. & Mora-Valencia, Andrés & Perote, Javier, 2020. "Retrieving the implicit risk neutral density of WTI options with a semi-nonparametric approach," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    6. J. Arismendi-Zambrano & R. Azevedo, 2020. "Implicit Entropic Market Risk-Premium from Interest Rate Derivatives," Economics Department Working Paper Series n303-20.pdf, Department of Economics, National University of Ireland - Maynooth.
    7. Ming Shann Tsai & Shu Ling Chiang, 2016. "The Valuation Model for a Risky Asset When Its Risky Factors Follow Gamma Distributions," International Review of Finance, International Review of Finance Ltd., vol. 16(3), pages 421-444, September.
    8. Maria Grith & Volker Krätschmer, 2010. "Parametric estimation of risk neutral density functions," SFB 649 Discussion Papers SFB649DP2010-045, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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