Recent Advances in Credit Risk Management

  • Frances Cowell
  • Borjana Racheva
  • Stefan Trück
Conference paper
Part of the Contributions to Economics book series (CE)

In the last decade, the market for credit related products as well as techniques for credit risk management have undergone several changes. Financial crises and a high number of defaults during the late 1990s have stimulated not only public interest in credit risk management, but also their awareness of its importance in today's investment environment. Also the market for credit derivatives has exhibited impressive growth rates. Active trading of credit derivatives only started in the mid 1990s, but since then has become one of the most dynamic financial markets. The dynamic expansion of the market requires new techniques and advances in credit derivative and especially dependence modelling among drivers for credit risk. Finally, the upcoming new capital accord (Basel II) encourages banks to base their capital requirement for credit risk on internal or external rating systems [4]. This regulatory body under the Bank of International Settlements (BIS) becoming effective in 2007 aims to strengthen risk management systems of international financial institutions. As a result, the majority of international operating banks sets focus on an internal-rating based approach to determine capital requirements for their loan or bond portfolios. Another consequence is that due to new regulatory requirements there is an increasing demand by holders of securitisable assets to sell or to transfer risks of their assets.

Recent research suggests that while a variety of advances have been made, there are still several fallacies both in banks' internal credit risk management systems and industry wide used solutions. As [15] point out, the use of the normal distribution for modelling the returns of assets or risk factors is not adequate since they generally exhibit heavy tails, excess kurtosis and skewness. All these features cannot be captured by the normal distribution. Also the notion of correlation as the only measure of dependence between risk factors or asset returns has recently been examined in empirical studies, for example [7]. Using the wrong dependence structure may lead to severe underestimation of the risk for a credit portfolio. The concept of copulas [13] allowing for more diversity in the dependence structure between defaults as well as the drivers of credit risk could be a cure to these deficiencies.


Business Cycle Credit Risk Credit Rating Asset Return Stable Distribution 
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.


  1. 1.
    Alessandrini, F., 1999. Credit Risk, Interest Rate Risk, and the Business Cycle. Journal of Fixed Income 9 (2), 42–53Google Scholar
  2. 2.
    Allen, L., Saunders, A., 2003. A Survey of Cyclical Effects in Credit Risk Measurement Model. BIS Working Paper 126Google Scholar
  3. 3.
    Artzner, P., Delbaen, F., Eber, J.-M., Heath, D., 1999. Coherent measures of risk. Mathematical Finance 9 (3), 203–228CrossRefGoogle Scholar
  4. 4.
    Basel Committee on Banking Supervision, 2001. The new Basel Capital Accord, Second Consultative DocumentGoogle Scholar
  5. 5.
    Belkin, B., Forest, L., Suchower, S., 1998. The Effect of Systematic Credit Risk on Loan Portfolio Value-at-Risk and Loan Pricing. CreditMetrics MonitorGoogle Scholar
  6. 6.
    Belkin, B., Forest, L., Suchower, S., 1998. A one-parameter Representation of Credit Risk and Transition Matrices. CreditMetrics MonitorGoogle Scholar
  7. 7.
    Embrechts, P., McNeil, A., Straumann, D., 1999. Correlation and Dependence in Risk Management: Properties and Pitfalls. In: Risk management: value at risk and beyond, ed. Dempster, MGoogle Scholar
  8. 8.
    Fama, E., 1965. The Behaviour of Stock Market Prices. Journal of Business 38, 34–105CrossRefGoogle Scholar
  9. 9.
    Helwege, J., Kleiman, P., 1997. Understanding aggregate default rates of high-yield bonds. Journal of Fixed Income 7(1), 55–61Google Scholar
  10. 10.
    Kim, J., 1999. Conditioning the Transition Matrix. Risk Credit Risk Special Report, 37–40Google Scholar
  11. 11.
    Mandelbrot, B., 1963. The Variation of certain speculative Prices. Journal of Business 36, 394–419CrossRefGoogle Scholar
  12. 12.
    Nickell, P., Perraudin, W., Varotto, S., 2000. Stability of Rating Transitions. Journal of Banking and Finance 1–2, 203–227CrossRefGoogle Scholar
  13. 13.
    Picone, D., 1959. Fonctions de répartition à n dimensions et leurs marges. Working Paper, Cass Business School 8, 229–231Google Scholar
  14. 14.
    Rachev, S., Martin, R., Racheva, B., Stoyanov, S., 2006. Stable ETL Portfolios and Extreme Risk Management. Working PaperGoogle Scholar
  15. 15.
    Rachev, S., Mittnik, S., 2000. Stable Paretian Models in Finance. Wiley, New YorkGoogle Scholar
  16. 16.
    Rachev, S., Ortobelli, S., Stoyanov, S., Fabozzi, F., Biglova, A., 2006. Desirable Properties of an Ideal Risk Measure in Portfolio Theory. Working PaperGoogle Scholar
  17. 17.
    Samorodnitsky, G., Taqqu, M., 1994. Stable Non-Gaussian Random Processes. Chapman & Hall, New YorkGoogle Scholar
  18. 18.
    Schweizer, B., Sklar, A., 1983. Probabilistic Metric Spaces. North Holland Elsevier, New YorkGoogle Scholar
  19. 19.
    Szegö, G., 2002. Measures of Risk. Journal of Banking and Finance 26(7), 1253–1272CrossRefGoogle Scholar
  20. 20.
    Szegö G., 2004. Risk Measures for the 21st Century. Wiley, ChichesterGoogle Scholar
  21. 21.
    Trück, S., 2008. Forecasting Credit Migration Matrices with Business Cycle Effects —A Model Comparison. European Journal of Finance 14(5), 359–379CrossRefGoogle Scholar
  22. 22.
    Trück, S., Rachev, S., 2005. Credit Portfolio Risk and PD Confidence Sets through the Business Cycle. Journal of Credit Risk 1(4)Google Scholar
  23. 23.
    Wei, J., 2003. A Multi-Factor, Credit Migration Model for Sovereign and Corporate Debts. Journal of International Money and Finance 22, 709–735CrossRefGoogle Scholar
  24. 24.
    Wilson, T., 1997. Measuring and Managing Credit Portfolio Risk. McKinsey & CompanyGoogle Scholar
  25. 25.
    Wilson, T., 1997. Portfolio Credit Risk I/II. Risk 10Google Scholar

Copyright information

© Physica-Verlag Heidelberg 2009

Authors and Affiliations

  1. 1.Morley Fund ManagementLondonEngland
  2. 2.FinAnalytica Inc.SofiaBulgaria
  3. 3.School of Economics and FinanceQueensland University of TechnologyAustralia

Personalised recommendations