Default Correlations and Large-Portfolio Credit Analysis∗
نویسندگان
چکیده
A factor model for short-term probabilities of default and other corporate exits is proposed for generating default correlations while permitting missing data. The 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 simulationconvolution algorithm. We apply them on a global sample of 40,560 exchange-listed firms and focus on three large portfolios (the US, Eurozone-12 and ASEAN-5). Our results show 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.
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