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.
Highlights
Default correlations are crucial information for many practical applications that involve more than one obligor
The bottom-up approach comprises three steps: (1) simulate future paths of 1-month PDs and probabilities of other exits (POEs) for the target group of obligors to some horizon of interest, and with which default correlations are generated, (2) conditional on the simulated paths, the corresponding default-rate distribution is produced using the convolution algorithm of Duan (2010) whereas the portfolio-loss distribution is generated with a novel simulation-convolution algorithm proposed in this paper, and (3) repeat the simulation many times and average the simulated default-rate and portfolio-loss distributions to obtain the desired portfolio credit risk profiles
We propose a practical method for generating default correlations among obligors of a large credit portfolio
Summary
Default correlations are crucial information for many practical applications that involve more than one obligor. The bottom-up approach comprises three steps: (1) simulate future paths of 1-month PDs and POEs for the target group of obligors to some horizon of interest, and with which default correlations are generated, (2) conditional on the simulated paths, the corresponding default-rate distribution is produced using the convolution algorithm of Duan (2010) whereas the portfolio-loss distribution is generated with a novel simulation-convolution algorithm proposed in this paper, and (3) repeat the simulation many times and average the simulated default-rate and portfolio-loss distributions to obtain the desired portfolio credit risk profiles. The subsample consists of all firms in the RMI-CRI database that satisfy the selection criterion of having at least 60 months of PDs and POEs. Three large portfolios (the US, Eurozone-12 and ASEAN-5) are used to show the critical importance of default correlations. Our results reveal that portfolio credit risk profiles evaluated at two different time points (September 2008 and December 2014) can change drastically with moving economic conditions, suggesting the importance of modeling credit risks with a dynamic system
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