Finite sample properties of a QML estimator of stochastic volatility models with long memory

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dc.contributor.author Pérez, Ana
dc.contributor.author Ruiz Ortega, Esther
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2010-01-13T10:15:10Z
dc.date.available 2010-01-13T10:15:10Z
dc.date.issued 1999-09
dc.identifier.uri http://hdl.handle.net/10016/6360
dc.description.abstract In this paper, we analyse the finite sample properties of a Quasi-Maximum Likelihood (QML) estimator of Long Memory Stochastic Volatility models based on the Whittle approximation of the Gaussian likelihood in the frequency domain. We extend previous studies by including in our Monte Carlo design all the parameters in the model and some more realistic cases. We show that for the parameter values usually encountered in practice, the properties of this estimator are such that inference is not reliable unless the sample size is extremely large. We also discuss a problem of nonidentification in the AutoRegressive Long Memory Stochastic Volatility Model when the volatility has a unit root and we show up its effect on the small sample properties of the QML estimators. The paper finishes with the empirical analysis of daily observations of the IBEX35 index of the Madrid Stock Exchange as an illustration of the problems faced when using this estimator with real time series.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 99-77-29
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Fractional integration
dc.subject.other Heteroscedastic time series
dc.subject.other Quasi-maximum Likelihood estimator
dc.subject.other spectral density
dc.title Finite sample properties of a QML estimator of stochastic volatility models with long memory
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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