Estimation of Stochastic Volatility Models with Diagnostics

doi:doi:10.1016/S0304-4076(97)00039-0

dc.contributor.author	Gallant, A. Ronald	en_US
dc.contributor.author	Hsieh, David	en_US
dc.contributor.author	Tauchen, George	en_US
dc.date.accessioned	2010-03-09T15:42:56Z
dc.date.available	2010-03-09T15:42:56Z
dc.date.issued	1995	en_US
dc.identifier.citation	Gallant, A. R., D. Hsieh, and G. Tauchen. "Estimation of Stochastic Volatility Models with Diagnostics." Journal of Econometrics 81.1 (1997): 159-92. Print.
dc.identifier.uri	http://hdl.handle.net/10161/2057
dc.description.abstract	Method of Moments (EMM) is used to fit the standard stochastic volatility model and various extensions to several daily financial time series. EMM matches to the score of a model determined by data analysis called the score generator. Discrepancies reveal characteristics of data that stochastic volatility models cannot approximate. The two score generators employed here are "Semiparametric ARCH" and "Nonlinear Nonparametric". With the first, the standard model is rejected, although some extensions are accepted. With the second, all versions are rejected. The extensions required for an adequate fit are so elaborate that nonparametric specifications are probably more convenient.	en_US
dc.format.extent	465094 bytes
dc.format.mimetype	application/pdf
dc.language.iso	en_US
dc.publisher	Elsevier
dc.relation.isversionof	doi:10.1016/S0304-4076(97)00039-0
dc.subject	Efficient method of moments	en_US
dc.title	Estimation of Stochastic Volatility Models with Diagnostics	en_US
dc.type	Journal Article	en_US
dc.department	Economics