Abstract

Portfolio credit risk measurement is greatly affected by data constraints, especially when focusing on loans given to unlisted firms. Standard methodologies adopt convenient but not necessarily properly specified parametric distributions, or they simply ignore the effects of macroeconomic shocks on credit risk. Aiming to improve the measurement of portfolio credit risk, we propose the joint implementation of two new methodologies, namely the Conditional Probability of Default (CoPoD) and the Consistent Information Multivariate Density Optimizing (CIMDO) methodologies. The CoPoD methodology incorporates the effects of macroeconomic shocks into credit risk, recovering robust estimators when only short time series of loans exist. The CIMDO methodology recovers portfolio multivariate densities (on which portfolio credit risk measurement relies) with improved specifications, when only partial information about borrowers is available. Implementation is straightforward and can be very useful in stress testing exercises, as illustrated by that carried out in the International Monetary Fund’s Denmark Financial Sector Assessment Program.

Contributor Notes

This chapter was previously published as IMF Working Paper No. 06/283 (Segoviano and Padilla, 2006). The authors would like to convey their special thanks to the Danish authorities and Kenneth J. Pedersen of the Danish National Bank for their exemplary cooperation. The authors would also like to thank Professor Charles Goodhart of the Financial Markets Group at the London School of Economics, Kal Wajid, Tonny Lybek, other colleagues at the IMF, and Masazumi Hattori of the Bank of Japan for very helpful comments; Kexue Liu for support on the coding; and Brenda Sylvester and Graham Colin-Jones for excellent editorial assistance.
Author: Ms. Li L Ong