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?:abstract
  • It is commonly accepted that some financial data may exhibit long-range dependence, while other financial data exhibit intermediate-range dependence or short-range dependence. These behaviours may be fitted to a continuous-time fractional stochastic model. The estimation procedure proposed in this paper is based on a continuous-time version of the Gauss–Whittle objective function to find the parameter estimates that minimize the discrepancy between the spectral density and the data periodogram. As a special case, the proposed estimation procedure is applied to a class of fractional stochastic volatility models to estimate the drift, standard deviation and memory parameters of the volatility process under consideration. As an application, the volatility of the Dow Jones, S&P 500, CAC 40, DAX 30, FTSE 100 and NIKKEI 225 is estimated. (xsd:string)
?:contributor
?:dateModified
  • 2008 (xsd:gyear)
?:datePublished
  • 2008 (xsd:gyear)
?:doi
  • 10.1016/j.jeconom.2008.09.035 ()
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?:hasFulltext
  • true (xsd:boolean)
is ?:hasPart of
?:inLanguage
  • en (xsd:string)
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?:issueNumber
  • 1 (xsd:string)
?:linksDOI
?:linksURN
is ?:mainEntity of
?:name
  • Econometric estimation in long–range dependent volatility models: theory and practice (xsd:string)
?:provider
?:publicationType
  • Zeitschriftenartikel (xsd:string)
  • journal_article (en)
?:sourceInfo
  • GESIS-SSOAR (xsd:string)
  • In: Journal of Econometrics, 147, 2008, 1, 72-83 (xsd:string)
rdf:type
?:url
?:urn
  • urn:nbn:de:0168-ssoar-201031 ()
?:volumeNumber
  • 147 (xsd:string)