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Parametric pricing of higher order moments in S&P500 options

  • V. L. Martin

    (Department of Economics, University of Melbourne, Australia)

  • G. M. Martin

    (Department of Econometrics and Business Statistics, Monash University, Australia)

  • G. C. Lim

    (Department of Economics, University of Melbourne, Australia)

A general parametric framework based on the generalized Student t-distribution is developed for pricing S&P500 options. Higher order moments in stock returns as well as time-varying volatility are priced. An important computational advantage of the proposed framework over Monte Carlo-based pricing methods is that options can be priced using one-dimensional quadrature integration. The empirical application is based on S&P500 options traded on select days in April 1995, a total sample of over 100,000 observations. A range of performance criteria are used to evaluate the proposed model, as well as a number of alternative models. The empirical results show that pricing higher order moments and time-varying volatility yields improvements in the pricing of options, as well as correcting the volatility skew associated with the Black-Scholes model. Copyright © 2004 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/jae.762
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File URL: http://qed.econ.queensu.ca:80/jae/2005-v20.3/
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 20 (2005)
Issue (Month): 3 ()
Pages: 377-404

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Handle: RePEc:jae:japmet:v:20:y:2005:i:3:p:377-404
DOI: 10.1002/jae.762
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