Quasi-Maximum Likelihood estimation of Stochastic Volatility models

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dc.contributor.author Ruiz Ortega, Esther
dc.date.accessioned 2009-07-15T10:28:48Z
dc.date.available 2009-07-15T10:28:48Z
dc.date.issued 1994
dc.identifier.bibliographicCitation Journal of Econometrics, 1994, vol. 63, n. 1, p. 289-306
dc.identifier.issn 0304-4076
dc.identifier.uri http://hdl.handle.net/10016/4786
dc.description Publicado además en: Recent developments in Time Series, 2003, vol. 2, ISBN13: 9781840649512, pp. 117-134
dc.description.abstract Changes in variance or volatility over time can be modelled using stochastic volatility (SV) models. This approach is based on treating the volatility as an unobserved vatiable, the logarithm of which is modelled as a linear stochastic process, usually an autoregression. This article analyses the asymptotic and finite sample properties of a Quasi-Maximum Likelihood (QML) estimator based on the Kalman filter. The relative efficiency of the QML estimator when compared with estimators based on the Generalized Method of Moments is shown to be quite high for parameter values often found in empirical applications. The QML estimator can still be employed when the SV model is generalized to allow for distributions with heavier tails than the normal. SV models are finally fitted to daily observations on the yen/dollar exchange rate.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.rights ©Elsevier
dc.subject.other Exchange rates
dc.subject.other Generalized method of moments
dc.subject.other Kalman filter
dc.subject.other Quasi- maximum likelihood
dc.subject.other Stochastic volatility
dc.title Quasi-Maximum Likelihood estimation of Stochastic Volatility models
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1016/0304-4076(93)01569-8
dc.subject.eciencia Estadística
dc.identifier.doi 10.1016/0304-4076(93)01569-8
dc.rights.accessRights openAccess
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