Maximum likelihood estimation of stochastic volatility models
By: Yacine Ait-Sahalia, Robert Kimmel.
Contributors: Kimmel, Robert.; National Bureau of Economic Research.
Series: NBER working paper series ;; working paper 10579; Working paper series (National Bureau of Economic Research : Online) ;; working paper no. 10579.
Publisher: National Bureau of Economic Research
Published: 2004
Place of publication: Cambridge, MA
Language: eng
Format: Electronic resource
Subjects: Prices -- Mathematical models.; Monte Carlo method.; Stochastic processes.
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.
Notes: Includes bibliographical references.
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