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Non-parametric extraction of implied asset price distributions

Author

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  • Healy, Jerome V.
  • Dixon, Maurice
  • Read, Brian J.
  • Cai, Fang Fang

Abstract

We present a fully non-parametric method for extracting risk neutral densities (RNDs) from observed option prices. The aim is to obtain a continuous, smooth, monotonic, and convex pricing function that is twice differentiable. Thus, irregularities such as negative probabilities that afflict many existing RND estimation techniques are reduced. Our method employs neural networks to obtain a smoothed pricing function, and a central finite difference approximation to the second derivative to extract the required gradients.

Suggested Citation

  • Healy, Jerome V. & Dixon, Maurice & Read, Brian J. & Cai, Fang Fang, 2007. "Non-parametric extraction of implied asset price distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 121-128.
    • Handle: RePEc:eee:phsmap:v:382:y:2007:i:1:p:121-128
    DOI: 10.1016/j.physa.2007.02.013
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    References listed on IDEAS

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    1. Ait-Sahalia, Yacine & Lo, Andrew W., 2000. "Nonparametric risk management and implied risk aversion," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 9-51.
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    6. Healy, J.V. & Dixon, M. & Read, B.J. & Cai, F.F., 2004. "Confidence limits for data mining models of options prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 162-167.
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    9. Bhupinder Bahra, 1997. "Implied risk-neutral probability density functions from option prices: theory and application," Bank of England working papers 66, Bank of England.
    10. Bondarenko, Oleg, 2003. "Estimation of risk-neutral densities using positive convolution approximation," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 85-112.
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    Cited by:

    1. 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.
    2. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2019. "Option Implied Risk-Neutral Density Estimation: A Robust and Flexible Method," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 705-728, August.
    3. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.

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