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Alternative tilts for nonparametric option pricing

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  • M. Ryan Haley
  • Todd B. Walker

Abstract

This study generalizes the nonparametric approach to option pricing of Stutzer, M. (1996) by demonstrating that the canonical valuation methodology introduced therein is one member of the Cressie–Read family of divergence measures. Alhough the limiting distribution of the alternative measures is identical to the canonical measure, the finite sample properties are quite different. We assess the ability of the alternative divergence measures to price European call options by approximating the risk‐neutral, equivalent martingale measure from an empirical distribution of the underlying asset. A simulation study of the finite sample properties of the alternative measure changes reveals that the optimal divergence measure depends upon how accurately the empirical distribution of the underlying asset is estimated. In a simple Black–Scholes model, the optimal measure change is contingent upon the number of outliers observed, whereas the optimal measure change is a function of time to expiration in the stochastic volatility model of Heston, S. L. (1993). Our extension of Stutzer's technique preserves the clean analytic structure of imposing moment restrictions to price options, yet demonstrates that the nonparametric approach is even more general in pricing options than originally believed. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:983–1006, 2010

Suggested Citation

  • M. Ryan Haley & Todd B. Walker, 2010. "Alternative tilts for nonparametric option pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(10), pages 983-1006, October.
  • Handle: RePEc:wly:jfutmk:v:30:y:2010:i:10:p:983-1006
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    Cited by:

    1. Omid M. Ardakani, 2022. "Option pricing with maximum entropy densities: The inclusion of higher‐order moments," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1821-1836, October.
    2. Haley, M. Ryan & McGee, M. Kevin, 2011. ""KLICing" there and back again: Portfolio selection using the empirical likelihood divergence and Hellinger distance," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 341-352, March.
    3. 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.
    4. Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle: survey and outlook," Annals of Finance, Springer, vol. 14(3), pages 289-329, August.
    5. Jamie Alcock & Godfrey Smith, 2017. "Non-parametric American option valuation using Cressie–Read divergences," Australian Journal of Management, Australian School of Business, vol. 42(2), pages 252-275, May.
    6. Smith, Godfrey, 2013. "Simulated testing of nonparametric measure changes for hedging European options," Finance Research Letters, Elsevier, vol. 10(2), pages 93-101.
    7. Yu, Xisheng, 2021. "A unified entropic pricing framework of option: Using Cressie-Read family of divergences," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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