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...

Descripción completa

Detalles Bibliográficos
Publicado en:Journal of Econometrics Vol. 101; no. 2; pp. 195 - 219
Autor principal: Robinson, P. M.
Formato: Artículo
Publicado: Elsevier Science April 2001
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