Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/3244 
Kompletter Metadatensatz
DublinCore-FeldWertSprache
dc.contributor.authorLux, Thomasen
dc.contributor.authorKaizoji, Taiseien
dc.date.accessioned2009-01-28T14:29:19Z-
dc.date.available2009-01-28T14:29:19Z-
dc.date.issued2004-
dc.identifier.urihttp://hdl.handle.net/10419/3244-
dc.description.abstractWe investigate the predictability of both volatility and volume for a large sample of Japanese stocks. The particular emphasis of this paper is on assessing the performance of long memory time series models in comparison to their short-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions of up to 100 days ahead. In most respects, the long memory models (ARFIMA, FIGARCH and the recently introduced multifractal models) dominate over GARCH and ARMA models. However, while FIGARCH and ARFIMA also have a number of cases with dramatic failures of their forecasts, the multifractal model does not suffer from this shortcoming and its performance practically always improves upon the na?ve forecast provided by historical volatility. As a somewhat surprising result, we also find that, for FIGARCH and ARFIMA models, pooled estimates (i.e. averages of parameter estimates from a sample of time series) give much better results than individually estimated models.en
dc.language.isoengen
dc.publisher|aKiel University, Department of Economics |cKielen
dc.relation.ispartofseries|aEconomics Working Paper |x2004-05en
dc.subject.jelG12en
dc.subject.jelC53en
dc.subject.jelC22en
dc.subject.ddc330en
dc.subject.keywordLong memory modelsen
dc.subject.keywordVolumeen
dc.subject.keywordVolatilityen
dc.subject.keywordForecastingen
dc.subject.stwBörsenkursen
dc.subject.stwVolatilitäten
dc.subject.stwBörsenumsatzen
dc.subject.stwPrognoseverfahrenen
dc.subject.stwZeitreihenanalyseen
dc.subject.stwSchätzungen
dc.subject.stwAktienmarkten
dc.subject.stwJapanen
dc.titleForecasting volatility and volume in the Tokyo stock market: The advantage of long memory models-
dc.typeWorking Paperen
dc.identifier.ppn388943122en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:cauewp:1936en

Datei(en):
Datei
Größe
592.15 kB





Publikationen in EconStor sind urheberrechtlich geschützt.