Title: Maximum likelihood estimation of stochastic volatility models
Description: "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"--National Bureau of Economic Research web site.
Subjects: Mathematical models, Monte Carlo method, Prices, Stochastic processes