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Credit Rating Score Analysis

  • Wolfgang Karl Härdle
  • K. F. Phoon
  • D. K. C. Lee
Chapter
Part of the Statistics and Computing book series (SCO)

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.

Reference

  1. Härdle, W. K., & Simar, L. (2015). Applied multivariate statistical analysis (4th ed.). Berlin: Springer.Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Wolfgang Karl Härdle
    • 1
    • 2
  • K. F. Phoon
    • 3
  • D. K. C. Lee
    • 4
  1. 1.C.A.S.E.-Center for Applied Statistics and EconomicsHumboldt-Universität zu BerlinBerlinGermany
  2. 2.School of Economics, 6th LevelResearch fellow in Sim Kee Boon Institute for Financial Economics, Singapore Management UniversitySingaporeSingapore
  3. 3.School of BusinessSingapore University of Social SciencesSingaporeSingapore
  4. 4.Singapore University of Social SciencesSingaporeSingapore

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