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dc.contributor.authorChen, Yingen
dc.contributor.authorHärdle, Wolfgang Karlen
dc.contributor.authorPigorsch, Utaen
dc.date.accessioned2009-01-20-
dc.date.accessioned2009-07-23T15:15:14Z-
dc.date.available2009-07-23T15:15:14Z-
dc.date.issued2009-
dc.identifier.urihttp://hdl.handle.net/10419/25319-
dc.description.abstractWith the recent availability of high-frequency Financial data the long range dependence of volatility regained researchers' interest and has lead to the consideration of long memory models for realized volatility. The long range diagnosis of volatility, however, is usually stated for long sample periods, while for small sample sizes, such as e.g. one year, the volatility dynamics appears to be better described by short-memory processes. The ensemble of these seemingly contradictory phenomena point towards short memory models of volatility with nonstationarities, such as structural breaks or regime switches, that spuriously generate a long memory pattern (see e.g. Diebold and Inoue, 2001; Mikosch and Starica, 2004b). In this paper we adopt this view on the dependence structure of volatility and propose a localized procedure for modeling realized volatility. That is at each point in time we determine a past interval over which volatility is approximated by a local linear process. Using S&P500 data we find that our local approach outperforms long memory type models in terms of predictability.en
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2009,003en
dc.subject.jelG17en
dc.subject.jelC14en
dc.subject.jelC51en
dc.subject.ddc330en
dc.subject.keywordLocalized autoregressive modelingen
dc.subject.keywordrealized volatilityen
dc.subject.keywordadaptive procedureen
dc.subject.stwBörsenkursen
dc.subject.stwVolatilitäten
dc.subject.stwAutokorrelationen
dc.subject.stwZeitreihenanalyseen
dc.subject.stwTheorieen
dc.subject.stwSchätzungen
dc.subject.stwUSAen
dc.titleLocalized realized volatility modelling-
dc.type|aWorking Paperen
dc.identifier.ppn590229435en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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