Parametric pricing of higher order moments in S&P500 options

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 prici...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2005-03, Vol.20 (3), p.377-404
Main Authors: Lim, G. C., Martin, G. M., Martin, V. L.
Format: Article
Language:English
Subjects:
Applied economics
Distribution
Econometrics
Economic models
Financial economics
Forecasting models
Goods and services tax
Market prices
Mathematical methods
Mathematical moments
Methodology
Model testing
Modelling
Monte Carlo simulation
Options contracts
Parametric models
Price volatility
Probability distributions
Skewed distribution
Statistical models
Stochastic models
Stock options
Stock prices
Studies
Volatility
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Summary: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.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.762