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dc.contributor.authorCorradi, Valentinaen_US
dc.contributor.authorDistaso, Walteren_US
dc.contributor.authorSwanson, Norman R.en_US
dc.date.accessioned2008-05-16en_US
dc.date.accessioned2010-05-14T11:01:39Z-
dc.date.available2010-05-14T11:01:39Z-
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/10419/31280-
dc.description.abstractThe main objective of this paper is to propose a feasible, model free estimator of the predictive density of integrated volatility. In this sense, we extend recent papers by Andersen, Bollerslev, Diebold and Labys (2003), and by Andersen, Bollerslev and Meddahi (2004, 2005), who address the issue of pointwise prediction of volatility via ARMA models, based on the use of realized volatility. Our approach is to use a realized volatility measure to construct a non parametric (kernel) estimator of the predictive density of daily volatility. We show that, by choosing an appropriate realized measure, one can achieve consistent estimation, even in the presence of jumps and microstructure noise in prices. More precisely, we establish that four well known realized measures, i.e. realized volatility, bipower variation, and two measures robust to microstructure noise, satisfy the conditions required for the uniform consistency of our estimator. Furthermore, we outline an alternative simulation based approach to predictive density construction. Finally, we carry out a simulation experiment in order to assess the accuracy of our estimators, and provide an empirical illustration that underscores the importance of using microstructure robust measures when using high frequency data.en_US
dc.language.isoengen_US
dc.publisherDep. of Economics, Rutgers, the State Univ. of New Jersey New Brunswick, NJen_US
dc.relation.ispartofseriesWorking papers // Department of Economics, Rutgers, the State University of New Jersey 2006,20en_US
dc.subject.jelC22en_US
dc.subject.jelC53en_US
dc.subject.jelC14en_US
dc.subject.ddc330en_US
dc.subject.keywordDiffusionsen_US
dc.subject.keywordintegrated volatilityen_US
dc.subject.keywordkernelsen_US
dc.subject.keywordmicrostructure noiseen_US
dc.subject.keywordrealized volatility measuresen_US
dc.titlePredictive density estimators for daily volatility based on the use of realized measuresen_US
dc.typeWorking Paperen_US
dc.identifier.ppn566316633en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:Working Papers, Department of Economics, Rutgers University

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