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dc.contributor.authorChen, Yingen_US
dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.contributor.authorPigorsch, Utaen_US
dc.date.accessioned2009-01-20en_US
dc.date.accessioned2009-07-23T15:15:14Z-
dc.date.available2009-07-23T15:15:14Z-
dc.date.issued2009en_US
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_US
dc.language.isoengen_US
dc.publisherSFB 649, Economic Risk Berlinen_US
dc.relation.ispartofseriesSFB 649 discussion paper 2009,003en_US
dc.subject.jelG17en_US
dc.subject.jelC14en_US
dc.subject.jelC51en_US
dc.subject.ddc330en_US
dc.subject.keywordLocalized autoregressive modelingen_US
dc.subject.keywordrealized volatilityen_US
dc.subject.keywordadaptive procedureen_US
dc.subject.stwBörsenkursen_US
dc.subject.stwVolatilitäten_US
dc.subject.stwAutokorrelationen_US
dc.subject.stwZeitreihenanalyseen_US
dc.subject.stwTheorieen_US
dc.subject.stwSchätzungen_US
dc.subject.stwUSAen_US
dc.titleLocalized realized volatility modellingen_US
dc.typeWorking Paperen_US
dc.identifier.ppn590229435en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
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