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dc.contributor.authorChao, Shih-Kangen
dc.contributor.authorHärdle, Wolfgang Karlen
dc.contributor.authorWang, Weiningen
dc.date.accessioned2012-01-30-
dc.date.accessioned2012-04-05T16:19:35Z-
dc.date.available2012-04-05T16:19:35Z-
dc.date.issued2012-
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
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2012-006en
dc.subject.jelC14en
dc.subject.jelC21en
dc.subject.jelC22en
dc.subject.jelC53en
dc.subject.jelG01en
dc.subject.jelG10en
dc.subject.jelG20en
dc.subject.jelG32en
dc.subject.ddc330en
dc.subject.keywordCoVaRen
dc.subject.keywordValue-at-Risken
dc.subject.keywordquantile regressionen
dc.subject.keywordlocally linear quantile regressionen
dc.subject.keywordpartial linear modelen
dc.subject.keywordsemiparametric modelen
dc.subject.stwRisikomaßen
dc.subject.stwRegressionen
dc.subject.stwNichtparametrisches Verfahrenen
dc.subject.stwTheorieen
dc.titleQuantile regression in risk calibration-
dc.typeWorking Paperen
dc.identifier.ppn684402408en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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