IDEAS home Printed from https://ideas.repec.org/p/msh/ebswps/2003-6.html
   My bibliography  Save this paper

Pricing Australian S&P200 Options: A Bayesian Approach Based on Generalized Distributional Forms

Author

Listed:
  • David B. Flynn
  • Simone D. Grose

    ()

  • Gael M. Martin

    ()

  • Vance L. Martin

Abstract

A new class of option price models is developed and applied to options on the Australian S&P200 Index. The class of models generalizes the traditional Black-Scholes framework by accommodating time-varying conditional volatility, skewness and excess kurtosis in the underlying returns process. An important property of the more general pricing models is that the computational requirements are practically the same as those associated with the Black-Scholes model, with both methods being based on one-dimensional integrals. Bayesian inferential methods are used to evaluate a range of models nested in the general framework, using observed market option prices. The evaluation is based on posterior distributions estimated for the parameters of the alternative models, as well as posterior model probabilities, out-of-sample predictive performance and implied volatility smiles. The empirical results provide strong evidence that time-varying volatility, leptokurtosis and skewness are priced in Australian stock market options.

Suggested Citation

  • David B. Flynn & Simone D. Grose & Gael M. Martin & Vance L. Martin, 2003. "Pricing Australian S&P200 Options: A Bayesian Approach Based on Generalized Distributional Forms," Monash Econometrics and Business Statistics Working Papers 6/03, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2003-6
    as

    Download full text from publisher

    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2003/wp6-03.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. J. Durbin & S. J. Koopman, 2000. "Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 3-56.
    2. Ray, W. D., 1997. "Hidden Markov and other models for discrete-valued time series : by Iain L. Mac Donald and Walter Zucchini. ISBN 0 412 55850 5. Monographs on Statistics and Applied Probability 70. Chapman and Hall, L," International Journal of Forecasting, Elsevier, vol. 13(4), pages 587-588, December.
    3. Freeland, R. K. & McCabe, B. P. M., 2004. "Forecasting discrete valued low count time series," International Journal of Forecasting, Elsevier, vol. 20(3), pages 427-434.
    4. R. K. Freeland & B. P. M. McCabe, 2004. "Analysis of low count time series data by poisson autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 701-722, September.
    5. Robert C. Jung & A. R. Tremayne, 2003. "Testing for serial dependence in time series models of counts," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(1), pages 65-84, January.
    6. Chib, Siddhartha & Greenberg, Edward & Winkelmann, Rainer, 1998. "Posterior simulation and Bayes factors in panel count data models," Journal of Econometrics, Elsevier, pages 33-54.
    7. Kleibergen, Frank, 2004. "Invariant Bayesian inference in regression models that is robust against the Jeffreys-Lindley's paradox," Journal of Econometrics, Elsevier, vol. 123(2), pages 227-258, December.
    8. Chib, Siddhartha & Winkelmann, Rainer, 2001. "Markov Chain Monte Carlo Analysis of Correlated Count Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 428-435, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stamey, James & Gerlach, Richard, 2007. "Bayesian sample size determination for case-control studies with misclassification," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2982-2992, March.
    2. 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.

    More about this item

    Keywords

    Bayesian Option Pricing; Leptokurtosis; Skewness; Time-Varying Volatility; Option Price Prediction; Implied Volatility Smiles;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:msh:ebswps:2003-6. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dr Xibin Zhang). General contact details of provider: http://edirc.repec.org/data/dxmonau.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.