Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/25177 
Kompletter Metadatensatz
DublinCore-FeldWertSprache
dc.contributor.authorFranke, Jürgenen
dc.contributor.authorStockis, Jean-Pierreen
dc.contributor.authorTadjuidje, Josephen
dc.date.accessioned2007-03-07-
dc.date.accessioned2009-07-23T14:44:31Z-
dc.date.available2009-07-23T14:44:31Z-
dc.date.issued2007-
dc.identifier.urihttp://hdl.handle.net/10419/25177-
dc.description.abstractWe consider the problem of estimating the conditional quantile of a time series at time t given observations of the same and perhaps other time series available at time t - 1. We discuss sieve estimates which are a nonparametric versions of the Koenker-Bassett regression quantiles and do not require the specification of the innovation law. We prove consistency of those estimates and illustrate their good performance for light- and heavy-tailed distributions of the innovations with a small simulation study. As an economic application, we use the estimates for calculating the value at risk of some stock price series.en
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2007,005en
dc.subject.jelC14en
dc.subject.jelC45en
dc.subject.ddc330en
dc.subject.keywordconditional quantileen
dc.subject.keywordtime seriesen
dc.subject.keywordsieve estimateen
dc.subject.keywordneural networken
dc.subject.keywordqualitative threshold modelen
dc.subject.keyworduniform consistencyen
dc.subject.keywordvalue at risken
dc.subject.stwZeitreihenanalyseen
dc.subject.stwMaßzahlen
dc.subject.stwSchätztheorieen
dc.subject.stwValue at Risken
dc.subject.stwBörsenkursen
dc.subject.stwTheorieen
dc.titleQuantile sieve estimates for time series-
dc.type|aWorking Paperen
dc.identifier.ppn525376372en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

Datei(en):
Datei
Größe
1.68 MB





Publikationen in EconStor sind urheberrechtlich geschützt.