@techreport{NBERw10579,
title = "Maximum Likelihood Estimation of Stochastic Volatility Models",
author = "Yacine Ait-Sahalia and Robert Kimmel",
institution = "National Bureau of Economic Research",
type = "Working Paper",
series = "Working Paper Series",
number = "10579",
year = "2004",
month = "June",
doi = {10.3386/w10579},
URL = "http://www.nber.org/papers/w10579",
abstract = {We develop and implement a new method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by the implied volatility of a short dated at-the-money option. We find that the approximation results in a negligible loss of accuracy. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine model of Heston (1993) and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.},
}