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dc.contributor.authorHärdle, Wolfgang Karlen
dc.contributor.authorMoro, Rouslan A.en
dc.contributor.authorSchäfer, Dorotheaen
dc.date.accessioned2008-02-19-
dc.date.accessioned2009-07-23T14:44:52Z-
dc.date.available2009-07-23T14:44:52Z-
dc.date.issued2007-
dc.identifier.urihttp://hdl.handle.net/10419/25207-
dc.description.abstractThis paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of testing our approach on German Bundesbank data. In particular we discuss the selection of variables and give a comparison with more traditional approaches such as discriminant analysis and the logit regression. The results demonstrate that the SVM has clear advantages over these methods for all variables tested.en
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Collaborative Research Center 649 - Economic Risk |cBerlinen
dc.relation.ispartofseries|aSFB 649 Discussion Paper |x2007,035en
dc.subject.jelC14en
dc.subject.jelG33en
dc.subject.jelC45en
dc.subject.ddc330en
dc.subject.keywordBankruptcyen
dc.subject.keywordCompany ratingen
dc.subject.keywordDefault probabilityen
dc.subject.keywordSupport vector machinesen
dc.subject.stwKreditwürdigkeiten
dc.subject.stwKonkursen
dc.subject.stwPrognoseverfahrenen
dc.subject.stwSupport Vector Machineen
dc.subject.stwTheorieen
dc.subject.stwDeutschlanden
dc.titleEstimating probabilities of default with support vector machines-
dc.type|aWorking Paperen
dc.identifier.ppn558556450en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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