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A Closed-Form GARCH Option Valuation Model


Steven L. Heston


University of Maryland - Department of Finance

Saikat Nandi


Federal National Mortgage Association (Fannie Mae)


Review of Financial Studies

Abstract:     
This paper develops a closed-form option valuation formula for a spot asset whose variance follows a GARCH(p,q) process that can be correlated with the returns of the spot asset. It provides the first readily computed option formula for a random volatility model that can be estimated and implemented solely on the basis of observables. The single lag version of this model contains Heston's (1993) stochastic volatility model as a continuous-time limit. Empirical analysis on S&P500 index options shows that the out-of-sample valuation errors from the single lag version of the GARCH model are substantially lower than the ad hoc Black-Scholes model of Dumas, Fleming and Whaley (1998) that uses a separate implied volatility for each option to fit to the smirk/smile in implied volatilties. The GARCH model remains superior even though the parameters of the GARCH model are held constant and volatility is filtered from the history of asset prices while the ad hoc Black-Scholes model is updated every period. The improvement is largely due to the ability of the GARCH model to simultaneously capture the correlation of volatility with spot returns and the path dependence in volatility.

JEL Classification: G12, G13

Accepted Paper Series


Date posted: February 28, 2000  

Suggested Citation

Heston, Steven L. and Nandi, Saikat, A Closed-Form GARCH Option Valuation Model. Review of Financial Studies. Available at SSRN: http://ssrn.com/abstract=210708

Contact Information

Steven L. Heston
University of Maryland - Department of Finance ( email )
Robert H. Smith School of Business
Van Munching Hall
College Park, MD 20742
United States
Saikat Nandi (Contact Author)
Federal National Mortgage Association (Fannie Mae) ( email )
3900 Wisconsin Avenue, NW
Washington, DC 20016-2892
United States
(202)752-8421 (Phone)
Feedback to SSRN (Beta)


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