Estimation of Sector Weights from Real-World Data

  • Michael Lesko
  • Frank Schlottmann
  • Stephan Vorgrimler
Part of the Springer Finance book series (FINANCE)


We discuss four different approaches to the estimation of sector weights for the CreditRisk+ model from German real-world data. Using a sample loan portfolio, we compare these approaches in terms of the resulting unexpected loss risk figures.


Industry Sector Default Rate Saddlepoint Approximation Systematic Risk Factor Fourth Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    P. Bürgisser, A. Kurth, A. Wagner. Incorporating severity variations into credit risk. Journal of Risk, 3 (4): 5–31, 2001.Google Scholar
  2. 2.
    P. Bürgisser, A. Kurth, A. Wagner, M. Wolf. Integrating correlations. Risk, 12 (7): 57–60, 1999.Google Scholar
  3. 3.
    Credit Suisse Financial Products. CreditRisk+: A credit risk management framework. London, 1997. Available at http: //wv. csfb. com/creditrisk. Google Scholar
  4. 4.
    M. B. Gordy. Saddlepoint approximation of CreditRisk+. Journal of Banking Fl Finance, 26: 1335–1353, 2002.CrossRefGoogle Scholar
  5. 5.
    M. B. Gordy. A comparative anatomy of credit risk models. Journal of Banking el Finance, 24: 119–149, 2000.CrossRefGoogle Scholar
  6. 6.
    M. Lesko, F. Schlottmann, S. Vorgrimler. Fortschritte bei der Schätzung von Risikofaktorgewichten für CreditRisk+. Die Bank, 6: 436–441, 2001.Google Scholar
  7. 7.
    I. Jolliffe. Principal Component Analysis, 2nd edition. Springer-Verlag, Heidelberg, 2002.MATHGoogle Scholar
  8. 8.
    T. Wilde. CreditRisk+. In S. Das, editor, Credit Derivatives and Credit Linked Notes, 2nd edition. John Wiley & Sons, New York, 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Michael Lesko
  • Frank Schlottmann
  • Stephan Vorgrimler

There are no affiliations available

Personalised recommendations