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Estimating probabilities of default with support vector machines
Härdle, Wolfgang (contributor); Moro, Rouslan (contributor) - 2007
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...
Persistent link: https://www.econbiz.de/10003633940
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Graphical data representation in bankruptcy analysis
Härdle, Wolfgang (contributor);  … - 2006
Graphical data representation is an important tool for model selection in bankruptcy analysis since the problem is highly non-linear and its numerical representation is much less transparent. In classical rating models a convenient representation of ratings in a closed form is possible reducing...
Persistent link: https://www.econbiz.de/10003324316
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Estimating probabilities of default with support vector machines
Härdle, Wolfgang (contributor);  … - 2007
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...
Persistent link: https://www.econbiz.de/10003608864
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Localising forward intensities for multiperiod corporate default
Prastyo, Dedy Dwi; Härdle, Wolfgang - 2014
Using a local adaptive Forward Intensities Approach (FIA) we investigate multiperiod corporate defaults and other delisting schemes. The proposed approach is fully datadriven and is based on local adaptive estimation and the selection of optimal estimation windows. Time-dependent model...
Persistent link: https://www.econbiz.de/10010403045
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Predicting bankruptcy with support vector machines
Härdle, Wolfgang (contributor);  … - 2005
Persistent link: https://www.econbiz.de/10003036386
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Default risk calculation based on predictor selection for the Southeast Asian industry
Härdle, Wolfgang; Prastyo, Dedy Dwi - 2013
default prediction provides a better insight into fundamentals of credit risk analysis. Default prediction and default …, and Thailand. The relevant default predictors over the countries reveal that credit risk analysis is sample specific. A …
Persistent link: https://www.econbiz.de/10009779289
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Exploring credit data
Müller, Marlene; Härdle, Wolfgang - 2002
Credit scoring methods aim to assess the default risk of a potential borrower. This involves typically the calculation … of a credit score and the estimation of the probability of default. One of the standard approaches is logistic … discriminant analysis, also referred to as logit model. This model maps explanatory variables for the default risk to a credit …
Persistent link: https://www.econbiz.de/10009627282
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The Default Risk of Firms Examined with Smooth Support Vector Machines
Härdle, Wolfgang; Lee, Yuh-Jye; Schäfer, Dorothea; … - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2007
In the era of Basel II a powerful tool for bankruptcy prognosis isvital for banks. The tool must be precise but also easily adaptable tothe bank's objections regarding the relation of false acceptances (TypeI error) and false rejections (Type II error). We explore the suitabil-ity of Smooth...
Persistent link: https://www.econbiz.de/10005860752
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Estimating Probabilities of Default With Support Vector Machines
Härdle, Wolfgang; Moro, Rouslan; Schäfer, Dorothea - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2007
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...
Persistent link: https://www.econbiz.de/10005861009
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Learning machines supporting bankruptcy prediction
Härdle, Wolfgang; Moro, Rouslan; Hoffmann, Linda - 2010
In many economic applications it is desirable to make future predictions about the financial status of a company. The focus of predictions is mainly if a company will default or not. A support vector machine (SVM) is one learning method which uses historical data to establish a classification...
Persistent link: https://www.econbiz.de/10003973650