Systematic Risk in Recovery Rates - an Empirical Analysis of U.S. Corporate Credit Exposures

This paper empirically analyses a parsimonious model framework that accounts for a dependence of bond and bank loan recoveries on systematic risk. We extend the single risk factor model by assuming that the recovery rates follow a logit-normal distribution. The results are compared with two other extended models, suggested in Frye (2000) and Pykhtin (2003), which pose the assumption of a normal and a log-normal distribution of recovery rates.We provide estimators of the parameters of the asset value process and their standard errors in closed form. For the parameters of the recovery rate distribution we also provide closed form solutions of a feasible maximum-likelihood ML-estimator for the three extended models.The correlation between the recovery rate and the systematic risk factor is estimated in all three extended models from default frequencies and recovery rates extracted from a bond and loan database of Standardamp;Poor's. The implications for economic capital are explored if systematic risk in recovery rates is ignored.Furthermore, the impact of measuring recovery rates from market prices at default and from prices at emergence is analysed. As a robustness check for the empirical results of the ML-estimation method we also employ a method-of-moments.Our empirical results indicate that systematic risk is an important factor that influences recovery rates. Ignoring this risk component by the calculation of a default-weighted recovery rate may lead to downward biased estimates of economic capital. Measuring recovery rates just after default or at emergence seems to have a stronger impact on recovery rates and the economic capital than extending the one factor model to capture systematic risk in recovery rates. Recovery rates measured at default are in general lower and more sensitive to changes of the systematic risk factor than recovery rates at emergence