Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/148882
Authors: 
Härdle, Wolfgang
Fai, Phoon-kok
Lee, David Kuo Chuen
Year of Publication: 
2016
Series/Report no.: 
SFB 649 Discussion Paper 2016-046
Abstract: 
We analyse a sample of funds and other securities each assigned a total rating score by an unknown expert entity. The scores are based on a number of risk and complexity factors, each assigned a category (factor score) of Low, Medium, or High by the expert entity. A principal component analysis of the data reveals that based on the chosen risk factors alone we cannot identify a single underlying latent source of risk in the data. Conversely, the chosen complexity factors are clearly related to one or two underlying sources of complexity. For the sample we find a clear positive relation between the first principal component and the total expert score. An attempt to match the securities' expert score by linear projection of their individual factor scores yields a best case correlation between expert score and projection of 0.9952. However, the sum of squared differences is, at 46.5552, still notable.
Subjects: 
Credit risk
Principal Components Analysis
Credit Rating Score
JEL: 
C01
G00
G17
G24
Document Type: 
Working Paper

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