Default Correlations and Large-Portfolio Credit Analysis

Abstract

A factor model with sparsely correlated residuals is used to model short-term probabilities of default and other corporate exits while permitting missing data, and serves as the basis for generating default correlations. This novel factor model can then be used to produce portfolio credit risk profiles (default-rate and portfolio-loss distributions) by complementing an existing credit portfolio aggregation method with a novel simulation–convolution algorithm. We apply the model and the portfolio aggregation method on a global sample of 40,560 exchange-listed firms and focus on three large portfolios (the U.S., Eurozone-12, and ASEAN-5). Our results reaffirm the critical importance of default correlations. With default correlations, both default-rate and portfolio-loss distributions become far more right-skewed, reflecting a much higher likelihood of defaulting together. Our results also reveal that portfolio credit risk profiles evaluated at two different time points can change drastically with moving economic conditions, suggesting the importance of modeling credit risks with a dynamic system. Our factor model coupled with the aggregation algorithm provides a useful tool for active credit portfolio management.

KEY WORDS:

• Bottom–up
• Convolution
• Factor
• Missing data

ACKNOWLEDGMENTS

The authors would like to acknowledge the helpful comments received From the seminar participants at Auckland Technology University, Federal Reserve Board, New York University, Princeton University and Bank of Canada, and also at the 2015 International Conference on Financial Engineering & Innovation in Chengdu, China.

This article originated from several discussions with Rowan Douglas and David Simmons of Willis on how the CRI database of Risk Management Institute, National University of Singapore can be intelligently applied to help insurers manage their credit portfolios. This project benefits from RMI-CRI being a member of the Willis Research Network at the time. This research began when Miao was a Willis Research Fellow.