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2014-01-31Buch DOI: 10.18452/4499
Nonparametric Estimates for Conditional Quantiles of Time Series
dc.contributor.authorFranke, Jürgen
dc.contributor.authorMwita, Peter
dc.contributor.authorWang, Weining
dc.date.accessioned2017-06-16T00:58:09Z
dc.date.available2017-06-16T00:58:09Z
dc.date.created2014-06-19
dc.date.issued2014-01-31
dc.date.submitted2014-01-31
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/5151
dc.description.abstractWe consider the problem of estimating the conditional quantile of a time series fYtg at time t given covariates Xt, where Xt can ei- ther exogenous variables or lagged variables of Yt . The conditional quantile is estimated by inverting a kernel estimate of the conditional distribution function, and we prove its asymptotic normality and uni- form strong consistency. The performance of the estimate for light and heavy-tailed distributions of the innovations are evaluated by a simulation study. Finally, the technique is applied to estimate VaR of stocks in DAX, and its performance is compared with the existing standard methods using backtesting.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subjecttime serieseng
dc.subjectuniform consistencyeng
dc.subjectvalue-at-riskeng
dc.subjectConditional quantileeng
dc.subjectkernel estimateeng
dc.subjectquantile autoregressioneng
dc.subject.ddc310 Statistik
dc.subject.ddc330 Wirtschaft
dc.titleNonparametric Estimates for Conditional Quantiles of Time Series
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100218114
dc.identifier.doihttp://dx.doi.org/10.18452/4499
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages31
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2014
local.edoc.container-issue12
local.edoc.container-year2014
local.edoc.container-erstkatid2195055-6

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