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dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.contributor.authorMoro, Rouslan A.en_US
dc.contributor.authorSchäfer, Dorotheaen_US
dc.date.accessioned2008-02-19en_US
dc.date.accessioned2009-07-23T14:44:52Z-
dc.date.available2009-07-23T14:44:52Z-
dc.date.issued2007en_US
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_US
dc.language.isoengen_US
dc.publisherSFB 649, Economic Risk Berlinen_US
dc.relation.ispartofseriesSFB 649 discussion paper 2007,035en_US
dc.subject.jelC14en_US
dc.subject.jelG33en_US
dc.subject.jelC45en_US
dc.subject.ddc330en_US
dc.subject.keywordBankruptcyen_US
dc.subject.keywordCompany ratingen_US
dc.subject.keywordDefault probabilityen_US
dc.subject.keywordSupport vector machinesen_US
dc.subject.stwKreditwürdigkeiten_US
dc.subject.stwKonkursen_US
dc.subject.stwPrognoseverfahrenen_US
dc.subject.stwSupport Vector Machineen_US
dc.subject.stwTheorieen_US
dc.subject.stwDeutschlanden_US
dc.titleEstimating probabilities of default with support vector machinesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn558556450en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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