A two factor long memory stochastic volatility model

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dc.contributor.author Veiga, Helena
dc.date.accessioned 2006-11-09T10:58:08Z
dc.date.available 2006-11-09T10:58:08Z
dc.date.issued 2006-02
dc.identifier.uri http://hdl.handle.net/10016/234
dc.description.abstract In this paper we fit the main features of financial returns by means of a two factor long memory stochastic volatility model (2FLMSV). Volatility, which is not observable, is explained by both a short-run and a long-run factor. The first factor follows a stationary AR(1) process whereas the second one, whose purpose is to fit the persistence of volatility observable in data, is a fractional integrated process as proposed by Breidt et al. (1998) and Harvey (1998). We show formally that this model (1) creates more kurtosis than the long memory stochastic volatility (LMSV) of Breidt et al. (1998) and Harvey (1998), (2) deals with volatility persistence and (3) produces small first order autocorrelations of squared observations. In the empirical analysis, we use the estimation procedure of Gallant and Tauchen (1996), the Efficient Method of Moments (EMM), and we provide evidence that our specification performs better than the LMSV model in capturing the empirical facts of data.
dc.format.extent 700561 bytes
dc.format.mimetype application/pdf
dc.language.iso eng
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 2006-03
dc.title A two factor long memory stochastic volatility model
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.identifier.repec ws061303
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