The memory of stochastic volatility models.
A valid asymptotic expansion for the covariance of functions of multivariate normal vectors is applied to approximate autocovariances of time series generated by nonlinear transformation of Gaussian latent variates, and nonlinear functions of these, with special reference to long memory stochastic v...
| Publicado en: | Journal of Econometrics Vol. 101; no. 2; pp. 195 - 219 |
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| Formato: | Artículo |
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Elsevier Science
April 2001
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=513086369&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 513086369 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03044076 ECM jtl: Journal of Econometrics issn: 03044076 maglogo: N pubinfo: dt: April 2001 vid: 101 iid: 2 pid: 1004 pub: Elsevier Science artinfo: ui: 513086369 10.1016/S0304-4076(00)00079-8 ppf: 195 ppct: 24 formats: tig: atl: The memory of stochastic volatility models. aug: au: Robinson, P. M. su: Stochastic processes sug: subj: Stochastic processes ab: A valid asymptotic expansion for the covariance of functions of multivariate normal vectors is applied to approximate autocovariances of time series generated by nonlinear transformation of Gaussian latent variates, and nonlinear functions of these, with special reference to long memory stochastic volatility models, serving to identify the roles played by the underlying Gaussian processes and the nonlinear transformation. Implications for simple stochastic volatility models are examined in detail, with numerical and Monte Carlo calculations, and applications to cyclic behaviour, cross-sectional and temporal aggregation, and multivariate models are discussed. Copyright (c) 2000 Elsevier Science S.A. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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