### C The Diebold-Gunther-Tay method

Diebold, Gunther, and Tay (1998) propose a way to evaluating density forecasts. The basic idea is that since under the null hypothesis the forecasts are equal to the true densities (conditioned on past information), applying the cumulative distribution function (the probability integral transform or PIT) to the series of observations should yield a series of iid uniform-$\left[0,1\right]$ variables. Whether the transformed variables are iid uniform can be checked in various ways. Diebold, Gunther, and Tay (1998) suggest plotting histograms and autocorrelation functions to visualize the quality of the density forecasts.

In order to apply the PIT to our predicted recovery rate densities, we create a vector ${y}^{†}$ in which we stack all recovery rate observations. For each element in the vector, we can now create a conditional density forecast from our estimated model.

Applying the cumulative distribution function associated with these density forecasts to the vector ${y}^{†}$ yields a vector of transformed variables. Under the null hypothesis that the density forecasts are correct, the elements of the vector of transformed variables should be an iid uniform series. Serial correlation of the series would indicate that we have not correctly conditioned on the relevant information. A departure from uniformity would indicate that the marginal distributions are inappropriate.

### D Supplementary tables

 Table 1: Recovery Rate Statistics by Year
 This table reports some annual statistics for the data used in the paper. First column figures are issuer-weighted default rates of US bond issuers provided by Moody’s. The other three columns are the number of default events, and the mean and standard deviation for recovery rates in the Altman data.
 Year 1981 0.17% 1 12.00 - 1982 1.08% 12 39.51 14.90 1983 1.02% 5 48.93 23.53 1984 0.98% 11 48.81 17.38 1985 1.01% 16 45.41 21.87 1986 2.07% 24 36.09 18.82 1987 1.65% 20 53.36 26.94 1988 1.52% 30 36.57 17.97 1989 2.43% 41 43.46 28.78 1990 4.14% 76 25.24 22.28 1991 3.55% 95 40.05 26.09 1992 1.85% 35 54.45 23.38 1993 1.13% 21 37.54 20.11 1994 0.80% 14 45.54 20.46 1995 1.25% 25 42.90 25.25 1996 0.77% 19 41.90 24.68 1997 0.89% 25 53.46 25.53 1998 1.60% 34 41.10 24.56 1999 2.61% 102 28.99 20.40 2000 3.43% 120 27.51 23.36 2001 4.98% 157 23.34 17.87 2002 3.33% 112 30.03 17.18 2003 2.36% 57 37.33 23.98 2004 1.28% 39 47.81 24.10 2005 1.12% 33 58.63 23.46

 Table 2: Recovery Rates by Seniority and Industry
 Panel A: Number of observations and the mean and standard deviation of recovery rates in our sample classified by seniority, for the whole sample (all default events), for default events for which we only observe recovery on a single instrument (with only one seniority), and for default events for which we observe recoveries on at least two different seniorities. Panel B: Number of observations and the mean and standard deviation of recovery rates in our sample classified by industry.
 Panel A: Recovery Rates by Seniority Seniority All default events Senior Secured 203 42.08 25.48 Senior Unsecured 366 36.88 23.29 Senior Subordinated 326 32.90 23.77 Subordinated 154 34.51 23.05 Discount 75 21.29 18.48 Default events with single recovery Senior Secured 145 39.29 23.35 Senior Unsecured 239 36.45 22.45 Senior Subordinated 209 34.52 23.30 Subordinated 87 37.86 20.22 Discount 29 21.72 19.67 Default events with multiple recoveries Senior Secured 58 49.04 29.25 Senior Unsecured 127 37.68 24.87 Senior Subordinated 117 30.00 24.42 Subordinated 67 30.16 25.79 Discount 46 21.03 17.91 Panel B: Recovery Rates by Industry Industry Building 10 33.56 36.24 Consumer 149 35.66 22.21 Energy 47 36.47 16.66 Financial 95 35.60 25.54 Leisure 69 41.43 29.40 Manufacturing 395 35.08 23.83 Mining 14 35.52 17.50 Services 65 34.16 28.09 Telecom 169 29.43 20.90 Transportation 66 38.07 23.79 Utility 23 51.34 27.97 Others 22 37.94 19.30

 Table 3: Parameter estimates (2)
 Parameter estimates and measures of fit for various models that differ in the combination of variables that influence the hazard rate $\lambda$ and the two parameters of the beta distribution $\alpha$ and $\beta$. Note that with the specification of Shumway (2001), a positive coefficient on a explanatory variable for $\lambda$ implies that $\lambda$ falls when the variable rises. sen2, sen3, sen4, sen5 are seniority dummies for Senior Unsecured, Senior Subordinated, Subordinated and Discount respectively, mult is a dummy that is one for observations corresponding to default events for which we observe multiple recoveries, cycle is the unobserved credit cycle, and lagged def. rate and rec. rates are the previous annual default rate and mean recovery rate respectively. indB and indC are dummies corresponding to industry groups B and C. ${}^{*}$ denotes individual significance at 5%.
 M2a M2b M5 M6 Default Rates $\lambda$ $\lambda$ $\lambda$ $\lambda$ constant 3.36${}^{*}$ 3.85${}^{*}$ 3.36${}^{*}$ 3.41${}^{*}$ cycle 1.04${}^{*}$ 1.05${}^{*}$ 1.03${}^{*}$ lagged def. rate -1.20 Recovery Rates $\alpha$ $\beta$ $\alpha$ $\beta$ $\alpha$ $\beta$ $\alpha$ $\beta$ constant 0 . 40${}^{*}$ 1 . 00${}^{*}$ 0 . 52${}^{*}$ 1 . 48${}^{*}$ 0 . 34${}^{*}$ 1 . 15${}^{*}$ 0 . 38 1 . 53${}^{*}$ sen2 0 . 04 0 . 15 -0 . 02 0 . 10 -0 . 06 -0 . 09 -0 . 07 -0 . 07 sen3 -0 . 18 0 . 00 -0 . 16 -0 . 02 -0 . 35${}^{*}$ -0 . 40${}^{*}$ -0 . 30 -0 . 32 sen4 0 . 34 0 . 38${}^{*}$ 0 . 54 1 . 11${}^{*}$ 0 . 06 0 . 01 -0 . 04 -0 . 11 sen5 -0 . 32 0 . 47 -0 . 23 0 . 32 -0 . 32 0 . 19 -0 . 23 0 . 45 mult -0 . 22 -0 . 56${}^{*}$ -0 . 39 -0 . 54 -0 . 29 -0 . 49 -0 . 26 -0 . 56 mult$×$sen2 -0 . 21 -0 . 26 0 . 00 0 . 03 -0 . 18 -0 . 35 -0 . 25 -0 . 40 mult$×$sen3 -0 . 14 -0 . 08 -0 . 72 -0 . 57 -0 . 28 -0 . 17 -0 . 22 -0 . 05 lagged rec. rate 0 . 37 -0 . 40 cycle 0 . 18 -0 . 65${}^{*}$ 0 . 48${}^{*}$ -0 . 44 0 . 55 -0 . 63 cycle$×$sen2 0 . 08 0 . 15 -0 . 15 0 . 13 -0 . 01 0 . 29 cycle$×$sen3 -0 . 25 -0 . 01 -0 . 26 0 . 25 -0 . 20 0 . 26 cycle$×$sen4 -0 . 46 -0 . 56 -0 . 09 0 . 41 0 . 20 0 . 79 cycle$×$sen5 -0 . 5 -0 . 16 -0 . 46 -0 . 14 -0 . 72 -0 . 61 cycle$×$mult 0 . 67 0 . 31 0 . 65 0 . 20 0 . 73 0 . 35 cycle$×$mult$×$sen2 -0 . 49 -0 . 42 0 . 06 0 . 22 -0 . 03 0 . 10 cycle$×$mult$×$sen3 0 . 76 0 . 80 0 . 52 0 . 42 0 . 34 0 . 20 cycle$×$lagged rec. rate -0 . 39 0 . 44 indB 0 . 29${}^{*}$ 0 . 43${}^{*}$ indC 0 . 09 0 . 40${}^{*}$ Transition Prob. p 0.8487 0.9523 0.8742 0.8232 q 0.7872 0.7634 0.7432 0.6443 Measures of Fit Log Likelihood 131.726 91.214 202.032 193.003 AIC -221.45 -110.43 -322.06 -302.01 BIC -0.1344 0.0695 -0.1395 -0.1118

### References

Diebold, F. X., T. A. Gunther, and A. S. Tay, 1998, “Evaluating Density Forecasts with Applications to Financial Risk Management,” International Economic Review, 39, 863–883.

Shumway, T., 2001, “Forecasting Bankruptcy More Accurately: A Simple Hazard Model,” Journal of Business, 74, 101–124.