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Parametric pricing of higher order moments in S&P500 options
Author(s) -
Lim G. C.,
Martin G. M.,
Martin V. L.
Publication year - 2004
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.762
Subject(s) - econometrics , volatility (finance) , valuation of options , skew , implied volatility , economics , parametric statistics , monte carlo method , black–scholes model , computer science , mathematics , statistics , telecommunications
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.