Flattening the Volatility Smile: A Test of Option Pricing Models

39 Pages Posted: 2 Oct 2001

See all articles by Tom Arnold

Tom Arnold

University of Richmond - E. Claiborne Robins School of Business

Date Written: July 27, 2001

Abstract

By using an over-identified Generalized Method of Moments (GMM) estimation procedure with careful consideration for data biases existing in the previous literature, we estimate parameters for a stochastic volatility jump diffusion (SVJ) model. The estimated parameters indicate a statistically significant highly negative infrequent jump process in the underlying security return distribution consistent with market crashes. When comparing to a stochastic volatility (SV) option pricing model, the SVJ is more robust but not always the superior model. The robustness of the models is further gauged by evaluating the performance up to a year beyond the estimation data. Again, the SVJ model generally (but not always) performs better. stochastic volatility, jump diffusion

Keywords: Options, SPX, Generalized Method of Moments, GMM,

JEL Classification: G12, G13

Suggested Citation

Arnold, Thomas M., Flattening the Volatility Smile: A Test of Option Pricing Models (July 27, 2001). Available at SSRN: https://ssrn.com/abstract=285237 or http://dx.doi.org/10.2139/ssrn.285237

Thomas M. Arnold (Contact Author)

University of Richmond - E. Claiborne Robins School of Business ( email )

1 Gateway Drive
Richmond, VA 23173
United States
804-287-6399 (Phone)
804-289-8878 (Fax)

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