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dc.contributor.authorYan, Yangen_US
dc.contributor.authorShang, Dajingen_US
dc.contributor.authorLinton, Oliveren_US
dc.date.accessioned2012-09-24en_US
dc.date.accessioned2012-10-16T13:08:38Z-
dc.date.available2012-10-16T13:08:38Z-
dc.date.issued2012en_US
dc.identifier.pidoi:10.1920/wp.cem.2012.2512en_US
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_US
dc.language.isoengen_US
dc.publisherCentre for Microdata Methods and Practice Londonen_US
dc.relation.ispartofseriescemmap working paper CWP25/12en_US
dc.subject.jelC14en_US
dc.subject.jelC22en_US
dc.subject.jelG22en_US
dc.subject.ddc330en_US
dc.subject.keywordEmpirical Likelihooden_US
dc.subject.keywordEmpirical processen_US
dc.subject.keywordGARCHen_US
dc.subject.keywordQuantileen_US
dc.subject.keywordValue-at-Risken_US
dc.subject.keywordExpected Shortfallen_US
dc.titleEfficient estimation of conditional risk measures in a semiparametric GARCH modelen_US
dc.typeWorking Paperen_US
dc.identifier.ppn726297488en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

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