Advertisement

Portfolio Credit Risk

  • Szymon Borak
  • Wolfgang Karl Härdle
  • Brenda López-Cabrera
Chapter
Part of the Universitext book series (UTX)

Abstract

Financial institutions are interested in loss protection and loan insurance. Thus determining the loss reserves needed to cover the risk stemming from credit portfolios is a major issue in banking. By charging risk premiums a bank can create a loss reserve account which it can exploit to be shielded against losses from defaulted debt. However, it is imperative that these premiums are appropriate to the issued loans and to the credit portfolio risk inherent to the bank. To determine the current risk exposure it is necessary that financial institutions can model the default probabilities for their portfolios of credit instruments appropriately. To begin with, these probabilities can be viewed as independent but it is apparent that it is plausible to drop this assumption and to model possible defaults as correlated events.

References

  1. Breiman, L. (1973). Statistics: With a view towards application. Boston: Houghton Mifflin Company.Google Scholar
  2. Cizek, P., Härdle, W., & Weron, R. (2011). Statistical tools in finance and insurance (2nd ed.). Berlin/Heidelberg: Springer.Google Scholar
  3. Feller, W. (1966). An introduction to probability theory and its application (Vol. 2). New York: Wiley.Google Scholar
  4. Franke, J., Härdle, W., & Hafner, C. (2011). Statistics of financial markets (3rd ed.). Berlin/ Heidelberg: Springer.Google Scholar
  5. Härdle, W., & Simar, L. (2012). Applied multivariate statistical analysis (3rd ed.). Berlin: Springer.Google Scholar
  6. Härdle, W., Müller, M., Sperlich, S., & Werwatz, A. (2004). Nonparametric and semiparametric models. Berlin: Springer.Google Scholar
  7. Harville, D. A. (2001). Matrix algebra: Exercises and solutions. New York: Springer.Google Scholar
  8. Klein, L. R. (1974). A textbook of econometrics (2nd ed., 488 p.). Englewood Cliffs: Prentice Hall.Google Scholar
  9. MacKinnon, J. G. (1991). Critical values for cointegration tests. In R. F. Engle & C. W. J. Granger (Eds.), Long-run economic relationships readings in cointegration (pp. 266–277). New York: Oxford University Press.Google Scholar
  10. Mardia, K. V., Kent, J. T., & Bibby, J. M. (1979). Multivariate analysis. Duluth/London: Academic.Google Scholar
  11. RiskMetrics. (1996). J.P. Morgan/Reuters (4th ed.). RiskMetricsTM.Google Scholar
  12. Serfling, R. J. (2002). Approximation theorems of mathematical statistics. New York: Wiley.Google Scholar
  13. Tsay, R. S. (2002). Analysis of financial time series. New York: Wiley.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Szymon Borak
    • 1
  • Wolfgang Karl Härdle
    • 1
  • Brenda López-Cabrera
    • 1
  1. 1.Humboldt-Universität zu Berlin Ladislaus von Bortkiewicz Chair of StatisticsC.A.S.E. Centre for Applied Statistics and Economics School of Business and EconomicsBerlinGermany

Personalised recommendations