Parametric pricing of higher order moments in S&P500 options

V. L. Martin, G. M. Martin and Guay Lim
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V. L. Martin: Department of Economics, University of Melbourne, Australia, Postal: Department of Economics, University of Melbourne, Australia
G. M. Martin: Department of Econometrics and Business Statistics, Monash University, Australia, Postal: Department of Econometrics and Business Statistics, Monash University, Australia
Authors registered in the RePEc Author Service: Vance Lindsay Martin and Gael Margaret Martin

Journal of Applied Econometrics, 2005, vol. 20, issue 3, 377-404

Abstract: 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.

Date: 2005
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Citations: View citations in EconPapers (33)

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Working Paper: Parametric Pricing of Higher Order Moments in S&P500 Options (2002)
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DOI: 10.1002/jae.762

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