Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/32175 
Kompletter Metadatensatz
DublinCore-FeldWertSprache
dc.contributor.authorVoev, Valerien
dc.date.accessioned2007-04-26-
dc.date.accessioned2010-05-14T12:00:40Z-
dc.date.available2010-05-14T12:00:40Z-
dc.date.issued2007-
dc.identifier.piurn:nbn:de:bsz:352-opus-32379en
dc.identifier.urihttp://hdl.handle.net/10419/32175-
dc.description.abstractModelling and forecasting the covariance of financial return series has always been a challenge due to the so-called curse of dimensionality. This paper proposes a methodology that is applicable in large dimensional cases and is based on a time series of realized covariance matrices. Some solutions are also presented to the problem of non-positive definite forecasts. This methodology is then compared to some traditional models on the basis of its forecasting performance employing Diebold-Mariano tests. We show that our approach is better suited to capture the dynamic features of volatilities and covolatilities compared to the sample covariance based models.en
dc.language.isoengen
dc.publisher|aUniversity of Konstanz, Center of Finance and Econometrics (CoFE) |cKonstanzen
dc.relation.ispartofseries|aCoFE Discussion Paper |x07/01en
dc.subject.ddc330en
dc.subject.stwVarianzanalyseen
dc.subject.stwZeitreihenanalyseen
dc.subject.stwKapitalertragen
dc.subject.stwPrognoseverfahrenen
dc.subject.stwTheorieen
dc.titleDynamic modeling of large dimensional covariance matrices-
dc.type|aWorking Paperen
dc.identifier.ppn527906778en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:cofedp:0701-

Datei(en):
Datei
Größe
294.59 kB





Publikationen in EconStor sind urheberrechtlich geschützt.