Credit Rating Score Analysis

SFB 649 Discussion Paper 2016-046

37 Pages Posted: 3 Nov 2016

See all articles by Wolfgang K. Härdle

Wolfgang K. Härdle

Humboldt University of Berlin - Institute for Statistics and Econometrics; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Kok Fai Phoon

SIM University

David Lee Kuo Chuen

Singapore University of Social Sciences (SUSS)

Date Written: November 2, 2016

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.

Keywords: Credit risk, Principal Components Analysis, Credit Rating Score

JEL Classification: C01, G00, G17, G24

Suggested Citation

Härdle, Wolfgang K. and Phoon, Kok Fai and Kuo Chuen, David Lee, Credit Rating Score Analysis (November 2, 2016). SFB 649 Discussion Paper 2016-046. Available at SSRN: https://ssrn.com/abstract=2863115 or http://dx.doi.org/10.2139/ssrn.2863115

Wolfgang K. Härdle (Contact Author)

Humboldt University of Berlin - Institute for Statistics and Econometrics ( email )

Unter den Linden 6
Berlin, D-10099
Germany
+49 30 2093 5631 (Phone)
+49 30 2093 5649 (Fax)

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

Kok Fai Phoon

SIM University ( email )

School of Business
461 Clementi Road
Singapore, 599491
Singapore

David Lee Kuo Chuen

Singapore University of Social Sciences (SUSS) ( email )

461 Clementi Road
Singapore, 599491
Singapore

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