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Bayesian Prediction, Entropy, and Option Pricingx

Author

Listed:
  • F. Douglas Foster

    (Professor of Finance, University of New South Wales, Sydney, NSW 2052.)

  • Charles H. Whiteman

    (Senior Associate Dean; Director, Economic Research Institute; and Stanley M. Howe Chair in Leadership, Tippie College of Business, The University of Iowa.)

Abstract

This paper studies the performance of the Foster-Whiteman (1999) procedure for using a Bayesian predictive distribution for the future price of an asset to compute the price of a European option on that asset. A technical contribution of the paper is the description of a sequential importance sampling procedure for implementing an informative prior that reflects and rewards past option-pricing success. The risk-neutralization of the predictive distribution is accomplished by Stutzer's (1996) constrained KLIC-minimizing change of measure. The procedure is used in weekly pricing of July and November options on soybeans on the Chicago Board of Trade from 1993–1997, and produces option prices that mimic market prices much more closely than those of the Black model or those produced by risk-neutralizing a nonparametric predictive.

Suggested Citation

  • F. Douglas Foster & Charles H. Whiteman, 2006. "Bayesian Prediction, Entropy, and Option Pricingx," Australian Journal of Management, Australian School of Business, vol. 31(2), pages 181-205, December.
    • Handle: RePEc:sae:ausman:v:31:y:2006:i:2:p:181-205
    DOI: 10.1177/031289620603100202
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    References listed on IDEAS

    as
    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. Stutzer, Michael, 1996. "A Simple Nonparametric Approach to Derivative Security Valuation," Journal of Finance, American Finance Association, vol. 51(5), pages 1633-1652, December.
    3. Dejong, David N. & Whiteman, Charles H., 1996. "Modeling Stock Prices without Knowing How to Induce Stationarity," Econometric Theory, Cambridge University Press, vol. 12(4), pages 739-740, October.
    4. Foster, F Douglas & Viswanathan, S, 1993. "The Effect of Public Information and Competition on Trading Volume and Price Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 6(1), pages 23-56.
    5. F. Douglas Foster & Charles H. Whiteman, 1999. "An Application of Bayesian Option Pricing to the Soybean Market," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(3), pages 722-727.
    6. Barone-Adesi, Giovanni & Whaley, Robert E, 1987. "Efficient Analytic Approximation of American Option Values," Journal of Finance, American Finance Association, vol. 42(2), pages 301-320, June.
    7. DeJong, David N. & Ingram, Beth F. & Whiteman, Charles H., 2000. "A Bayesian approach to dynamic macroeconomics," Journal of Econometrics, Elsevier, vol. 98(2), pages 203-223, October.
    8. Stutzer, Michael, 1995. "A Bayesian approach to diagnosis of asset pricing models," Journal of Econometrics, Elsevier, vol. 68(2), pages 367-397, August.
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    Cited by:

    1. Leung, Melvern & Fung, Man Chung & O’Hare, Colin, 2018. "A comparative study of pricing approaches for longevity instruments," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 95-116.
    2. 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.
    3. Kogure, Atsuyuki & Kurachi, Yoshiyuki, 2010. "A Bayesian approach to pricing longevity risk based on risk-neutral predictive distributions," Insurance: Mathematics and Economics, Elsevier, vol. 46(1), pages 162-172, February.

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