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Implicit Bayesian Inference Using Option Prices

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Abstract

A Bayesian approach to option pricing is presented, in which posterior inference about the underlying returns process is conducted implicitly, via observed option prices. A range of models which allow for conditional leptokurtosis, skewness and time-varying volatility in returns, are considered, with posterior parameter distributions and model probabilities backed out from the option prices. Fit, predictive and hedging densities associated with the different models are produced. Models are ranked according to several criteria, including their ability to fit observed option prices, predict future option prices and minimize hedging errors. In addition to model-specific results, averaged predictive and hedging densities are produced, the weights used in the averaging process being the posterior model probabilities. The method is applied to option price data on the S&P500 stock index. Whilst the results provide some support for the Black-Scholes model, no one model dominates according to all criteria considered.

Suggested Citation

  • Martin, G.M. & Forbes, C.S. & Martin, V.L., 2000. "Implicit Bayesian Inference Using Option Prices," Monash Econometrics and Business Statistics Working Papers 5/00, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2000-5
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2000/wp5-00.pdf
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    References listed on IDEAS

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    1. Fernández, C. & Steel, M.F.J., 1996. "On Bayesian Modelling of Fat Tails and Skewness," Discussion Paper 1996-58, Tilburg University, Center for Economic Research.
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    Cited by:

    1. Gradojevic Nikola, 2016. "Multi-criteria classification for pricing European options," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 123-139, April.
    2. Rombouts, Jeroen V.K. & Stentoft, Lars, 2014. "Bayesian option pricing using mixed normal heteroskedasticity models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 588-605.
    3. V. L. Martin & G. M. Martin & G. C. Lim, 2005. "Parametric pricing of higher order moments in S&P500 options," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 377-404.
    4. C.S. Forbes & G.M. Martin & J. Wright, 2002. "Bayesian Estimation of a Stochastic Volatility Model Using Option and Spot Prices," Monash Econometrics and Business Statistics Working Papers 2/02, Monash University, Department of Econometrics and Business Statistics.
    5. Lim, G.C. & Martin, G.M. & Martin, V.L., 2006. "Pricing currency options in the presence of time-varying volatility and non-normalities," Journal of Multinational Financial Management, Elsevier, vol. 16(3), pages 291-314, July.
    6. Shu Wing Ho & Alan Lee & Alastair Marsden, 2011. "Use of Bayesian Estimates to determine the Volatility Parameter Input in the Black-Scholes and Binomial Option Pricing Models," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 4(1), pages 1-23, December.
    7. Fry-McKibbin, Renée & Martin, Vance L. & Tang, Chrismin, 2014. "Financial contagion and asset pricing," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 296-308.
    8. Anthony D. Hall & Paul Kofman & Steve Manaster, 2001. "Migration of Price Discovery With Constrained Futures Markets," Research Paper Series 70, Quantitative Finance Research Centre, University of Technology, Sydney.

    More about this item

    Keywords

    Bayesian Implicit Inference; Option Pricing Errors; Option Price Prediction; Hedging Errors; Nonnormal Returns Models; GARCH; Bayesian Model averaging.;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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