The forecasted default probabilities and the estimated asset correlations can be aggregated to the forecasted default rate distribution. The forecasted default rate distribution can be interpreted as a loss distribution if the exposure at default and the loss given default equal one.
Chart 9 compares the forecasted default rate distribution of model 1 without macroeconomic variables and model 2 with macroeconomic variables. Table 10 shows the respective mean forecasted default rate and the quantiles of the forecasted default rate distribution.
0 0.04 0.08 0.12 0.16 0 0.005 0.01 0.015 0.02 0.025 default rate fo reca sted pro b a b ility
model 1 model 2 real default rate
Chart 9: Forecasted default rate distribution, model 1 and model 2
Mean forecasted default rate 0,95-Q. 0,99-Q. 0,999-Q.
Model 1 0.0134 0.0167 0.0187 0.0217
Model 2 0.0146 0.0170 0.0183 0.0201
Table 10: Mean forecasted default rate and quantiles of forecasted default rate distribution, model 1 and model 2
which leads to a lower variance of the forecasted default rate. Hence, the portfolio credit risk is forecasted more accurately. Similar results are observed for model 3 and model 4 when multiple risk segments are assumed.
Chart 10 compares the forecasted default rate distribution of one risk segment model 1 and multiple risk segment model 3. Table 11 shows the respective mean forecasted default rate and the quantiles of the forecasted default rate distribution:
0 0.04 0.08 0.12 0.16 0 0.005 0.01 0.015 0.02 0.025 default rate fo reca sted pro b a b ility
model 1 model 3 real default rate Chart 10: Forecasted default rate distribution, model 1 and model 3
Mean forecasted default rate 0,95-Q. 0,99-Q. 0,999-Q.
Model 1 0.0134 0.0167 0.0187 0.0217
Model 3 0.0131 0.0163 0.0181 0.0206
Table 11: Mean forecasted default rate and quantiles of forecasted default rate distribution, model 1 and model 3
The forecasted default rate distribution of model 1 is broader than that of model 3. Since default rate distributions generally broaden with a higher mean forecasted default rate, we cannot conclude that the assumption of multiple risk segments leads to more accurate credit portfolio risk forecasts. An examination of further periods is advisable.
5
Summary
The present paper describes an alternative methodology for forecasting credit portfolio risk. We showed within this framework that
• individual default probabilities can be forecasted and asset (or default) correlations can be estimated, given the values of risk drivers that are observable in the point of time the forecasts are made.
• the inclusion of variables which are correlated with the business cycle improves the forecasts of default probabilities. The variance of the forecasted default rate decreases, i.e. the uncertainty of the forecasts is diminished.
• asset and default correlations depend on the factors used to model default probabilities. The better the point-in-time calibration of the estimated default probabilities, the smaller the estimated correlations. Thus, correlations and default probabilities should always be estimated simultaneously.
Appendix
• Descriptive statistics firm-specific risk drivers
Ratio Mean Median Standarddev. Min Max
ART 34.5973 30.7526 25.8769 0 100 APT 34.8201 25.9981 30.1343 0 110 CFT 6.5248 4.7671 9.3863 -15 25 CRR 12.2592 10.0036 16.2009 -25 50 ETA 12.1587 9.7701 20.6234 -35 60 ITT 48.1980 38.0972 44.0308 0 160 RIE 387.7100 218.4480 486.0730 -650 1,400 TTT 0.0076 0.0074 0.0011 0.0062 0.0096
Table 12: Data set Deutsche Bundesbank - summary statistics of firm-specific risk drivers
ART APT CFT CRR ETA ITT RIE TTT
ART 1.0000 0.2212 -0.1255 -0.0949 -0.0400 0.0860 -0.0333 0.0675 APT 1.0000 -0.0757 -0.1780 -0.2418 0.2460 -0.2225 -0.0584 CFT 1.0000 0.7423 0.1812 -0.2627 0.3303 -0.1666 CRR 1.0000 0.1476 -0.2923 0.5290 -0.0227 ETA 1.0000 -0.0351 0.3423 -0.0588 ITT 1.0000 -0.1924 0.0121 RIE 1.0000 -0.0174 TTT 1.0000
Table 13: Data set Deutsche Bundesbank - Pearson correlations between firm-specific risk drivers
• Descriptive statistics macroeconomic risk drivers
Variable Mean Median Standarddev. Min Max
BCI 88.311 85.028 7.483 82.29 103.36
GOC 0.0013 0.0054 0.0206 -0.0270 0.0369
Insolvency rate 0.0072 0.0073 0.0013 0.0050 0.0084
UER 9.0537 9.3259 1.8706 6.3000 11.4830
Table 14: Data set Deutsche Bundesbank - summary statistics of macroeconomic risk drivers
BCI GOC Insolvency Rate UER
BCI 1.0000 0.7734 -0.8278 -0.7685
GOC 1.0000 -0.7851 -0.7662
Insolvency Rate 1.0000 0.6347
UER 1.0000
Table 15: Data set Deutsche Bundesbank - Pearson correlations between macro- economic risk drivers
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