Random survival forests models for SME credit risk measurementстатья
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Дата последнего поиска статьи во внешних источниках: 7 декабря 2013 г.
Авторы:
Fantazzini D. ,
Figini S.
Журнал:
Methodology and Computing in Applied Probability
Том:
11
Номер:
1
Год издания:
2009
Издательство:
SPRINGER
Местоположение издательства:
VAN GODEWIJCKSTRAAT 30, DORDRECHT, NETHERLANDS, 3311GZ
Первая страница:
29
Последняя страница:
45
DOI:
10.1007/s11009-008-9078-2
Аннотация:
This paper extends the existing literature on empirical research in the field of credit risk default for Small Medium Enterprizes (SMEs). We propose a non-parametric approach based on Random Survival Forests (RSF) and we compare its performance with a standard logit model. To the authors’ knowledge, no studies in the area of credit risk default for SMEs have used a variety of statistical methodologies to test the reliability of their predictions and to compare their performance against one another. As for the in-sample results, we find that our non-parametric model performs much better that the classical logit model. As for the out-of-sample performances, the evidence is just the opposite, and the logit performs better than the RSF model. We explain this evidence by showing how error in the estimates of default probabilities can affect classification error when the estimates are used in a classification rule. В© Springer Science+Business Media, LLC 2008.
Добавил в систему:
Фантаццини Деан c.