Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/64788 
Kompletter Metadatensatz
DublinCore-FeldWertSprache
dc.contributor.authorYan, Yangen
dc.contributor.authorShang, Dajingen
dc.contributor.authorLinton, Oliveren
dc.date.accessioned2012-09-24-
dc.date.accessioned2012-10-16T13:08:38Z-
dc.date.available2012-10-16T13:08:38Z-
dc.date.issued2012-
dc.identifier.pidoi:10.1920/wp.cem.2012.2512en
dc.identifier.urihttp://hdl.handle.net/10419/64788-
dc.description.abstractThis paper proposes efficient estimators of risk measures in a semiparametric GARCH model defined through moment constraints. Moment constraints are often used to identify and estimate the mean and variance parameters and are however discarded when estimating error quantiles. In order to prevent this efficiency loss in quantile estimation, we propose a quantile estimator based on inverting an empirical likelihood weighted distribution estimator. It is found that the new quantile estimator is uniformly more efficient than the simple empirical quantile and a quantile estimator based on normalized residuals. At the same time, the efficiency gain in error quantile estimation hinges on the efficiency of estimators of the variance parameters. We show that the same conclusion applies to the estimation of conditional Expected Shortfall. Our comparison also leads to interesting implications of residual bootstrap for dynamic models. We find that these proposed estimators for conditional Value-at-Risk and expected shortfall are asymptotically mixed normal. This asymptotic theory can be used to construct confidence bands for these estimators by taking account of parameter uncertainty. Simulation evidence as well as empirical results are provided.en
dc.language.isoengen
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen
dc.relation.ispartofseries|acemmap working paper |xCWP25/12en
dc.subject.jelC14en
dc.subject.jelC22en
dc.subject.jelG22en
dc.subject.ddc330en
dc.subject.keywordEmpirical Likelihooden
dc.subject.keywordEmpirical processen
dc.subject.keywordGARCHen
dc.subject.keywordQuantileen
dc.subject.keywordValue-at-Risken
dc.subject.keywordExpected Shortfallen
dc.titleEfficient estimation of conditional risk measures in a semiparametric GARCH model-
dc.typeWorking Paperen
dc.identifier.ppn726297488en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:ifs:cemmap:25/12en

Datei(en):
Datei
Größe
1.24 MB





Publikationen in EconStor sind urheberrechtlich geschützt.