Arbeitspapier
Estimating probabilities of default with support vector machines
This 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.
- Sprache
-
Englisch
- Erschienen in
-
Series: SFB 649 Discussion Paper ; No. 2007,035
- Klassifikation
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Bankruptcy; Liquidation
Neural Networks and Related Topics
- Thema
-
Bankruptcy
Company rating
Default probability
Support vector machines
Kreditwürdigkeit
Konkurs
Prognoseverfahren
Support Vector Machine
Theorie
Deutschland
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Härdle, Wolfgang Karl
Moro, Rouslan A.
Schäfer, Dorothea
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
- (wo)
-
Berlin
- (wann)
-
2007
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Härdle, Wolfgang Karl
- Moro, Rouslan A.
- Schäfer, Dorothea
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Entstanden
- 2007
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