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dc.contributor.authorChao, Shih-Kangen_US
dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.contributor.authorWang, Weiningen_US
dc.date.accessioned2012-01-30en_US
dc.date.accessioned2012-04-05T16:19:35Z-
dc.date.available2012-04-05T16:19:35Z-
dc.date.issued2012en_US
dc.identifier.urihttp://hdl.handle.net/10419/56704-
dc.description.abstractFinancial risk control has always been challenging and becomes now an even harder problem as joint extreme events occur more frequently. For decision makers and government regulators, it is therefore important to obtain accurate information on the interdependency of risk factors. Given a stressful situation for one market participant, one likes to measure how this stress affects other factors. The CoVaR (Conditional VaR) framework has been developed for this purpose. The basic technical elements of CoVaR estimation are two levels of quantile regression: one on market risk factors; another on individual risk factor. Tests on the functional form of the two-level quantile regression reject the linearity. A flexible semiparametric modeling framework for CoVaR is proposed. A partial linear model (PLM) is analyzed. In applying the technology to stock data covering the crisis period, the PLM outperforms in the crisis time, with the justification of the backtesting procedures. Moreover, using the data on global stock markets indices, the analysis on marginal contribution of risk (MCR) defined as the local first order derivative of the quantile curve sheds some light on the source of the global market risk.en_US
dc.language.isoengen_US
dc.publisherSFB 649, Economic Risk Berlinen_US
dc.relation.ispartofseriesSFB 649 discussion paper 2012-006en_US
dc.subject.jelC14en_US
dc.subject.jelC21en_US
dc.subject.jelC22en_US
dc.subject.jelC53en_US
dc.subject.jelG01en_US
dc.subject.jelG10en_US
dc.subject.jelG20en_US
dc.subject.jelG32en_US
dc.subject.ddc330en_US
dc.subject.keywordCoVaRen_US
dc.subject.keywordValue-at-Risken_US
dc.subject.keywordquantile regressionen_US
dc.subject.keywordlocally linear quantile regressionen_US
dc.subject.keywordpartial linear modelen_US
dc.subject.keywordsemiparametric modelen_US
dc.subject.stwRisikomaßen_US
dc.subject.stwRegressionen_US
dc.subject.stwNichtparametrisches Verfahrenen_US
dc.subject.stwTheorieen_US
dc.titleQuantile regression in risk calibrationen_US
dc.typeWorking Paperen_US
dc.identifier.ppn684402408en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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