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Estimating option implied risk-neutral densities using spline and hypergeometric functions

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  • Ruijun Bu
  • Kaddour Hadri

Abstract

We examine the ability of two recent methods -- the smoothed implied volatility smile method (SML) and the density functionals based on confluent hypergeometric functions (DFCH) -- for estimating implied risk-neutral densities (RNDs) from European-style options. Two complementary Monte Carlo experiments are conducted and the performance of the two RND estimators is evaluated by the root mean integrated squared error (RMISE) criterion. Results from both experiments show that the DFCH method outperforms the SML method for the overall quality of the estimated RNDs concerning both accuracy and stability. An application of the two methods to the OTC currency options market is also presented. Copyright Royal Economic Society 2007

Suggested Citation

  • Ruijun Bu & Kaddour Hadri, 2007. "Estimating option implied risk-neutral densities using spline and hypergeometric functions," Econometrics Journal, Royal Economic Society, vol. 10(2), pages 216-244, July.
  • Handle: RePEc:ect:emjrnl:v:10:y:2007:i:2:p:216-244
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    Citations

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    Cited by:

    1. Alexander Veremyev & Peter Tsyurmasto & Stan Uryasev & R. Rockafellar, 2014. "Calibrating probability distributions with convex-concave-convex functions: application to CDO pricing," Computational Management Science, Springer, vol. 11(4), pages 341-364, October.
    2. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, Elsevier.
    3. Guillermo Benavides Perales & Israel Felipe Mora Cuevas, 2008. "Parametric vs. non-parametric methods for estimating option implied risk-neutral densities: the case of the exchange rate Mexican peso – US dollar," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(1), pages 33-52, May.
    4. Malz, Allan M., 2014. "Simple and reliable way to compute option-based risk-neutral distributions," Staff Reports 677, Federal Reserve Bank of New York.
    5. Bu, Ruijun & Cheng, Jie & Hadri, Kaddour, 2016. "Reducible diffusions with time-varying transformations with application to short-term interest rates," Economic Modelling, Elsevier, vol. 52(PA), pages 266-277.
    6. repec:eee:eneeco:v:64:y:2017:i:c:p:440-457 is not listed on IDEAS
    7. Datta, Deepa Dhume & Londono, Juan M. & Ross, Landon J, 2014. "Generating Options-Implied Probability Densities to Understand Oil Market Events," International Finance Discussion Papers 1122, Board of Governors of the Federal Reserve System (U.S.).
    8. Ruijun Bu & Ludovic Giet & Kaddour Hadri & Michel Lubrano, 2009. "Modeling Multivariate Interest Rates using Time-Varying Copulas and Reducible Stochastic Differential Equations," Working Papers halshs-00408014, HAL.
    9. repec:eee:jimfin:v:78:y:2017:i:c:p:1-20 is not listed on IDEAS
    10. Jean-Baptiste Monnier, 2013. "Technical report : Risk-neutral density recovery via spectral analysis," Papers 1302.2567, arXiv.org.
    11. Taboga, Marco, 2016. "Option-implied probability distributions: How reliable? How jagged?," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 453-469.
    12. Ruijun Bu & Ludovic Giet & Kaddour Hadri & Michel Lubrano, 2009. "Modelling Multivariate Interest Rates using Time-Varying Copulas and Reducible Non-Linear Stochastic Differential," Economics Working Papers 09-02, Queen's Management School, Queen's University Belfast.
    13. Rompolis, Leonidas S., 2010. "Retrieving risk neutral densities from European option prices based on the principle of maximum entropy," Journal of Empirical Finance, Elsevier, vol. 17(5), pages 918-937, December.

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