Credit rating score analysis - Wolfgang Karl Härdle; Phoon-kok Fai; David Kuo Chuen Lee (Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, 2016)
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.
Keywords: C01; G00; G17; G24; 330; Credit risk; Principal Components Analysis; Credit Rating Score