01891cam a22002537 4500001000700000003000500007005001700012008004100029100002500070245012700095260006600222490004200288500001500330520085300345530006101198538007201259538003601331690005601367700002001423710004201443830007701485856003801562856003701600w10579NBER20180419192217.0180419s2004 mau||||fs|||| 000 0 eng d1 aAit-Sahalia, Yacine.10aMaximum Likelihood Estimation of Stochastic Volatility Modelsh[electronic resource] /cYacine Ait-Sahalia, Robert Kimmel. aCambridge, Mass.bNational Bureau of Economic Researchc2004.1 aNBER working paper seriesvno. w10579 aJune 2004.3 aWe 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. aHardcopy version available to institutional subscribers. aSystem requirements: Adobe [Acrobat] Reader required for PDF files. aMode of access: World Wide Web. 7aG0 - General2Journal of Economic Literature class.1 aKimmel, Robert.2 aNational Bureau of Economic Research. 0aWorking Paper Series (National Bureau of Economic Research)vno. w10579.4 uhttp://www.nber.org/papers/w1057941uhttp://dx.doi.org/10.3386/w10579