Systematic Risk in Recovery Rates: An Empirical Analysis of US Corporate Credit Exposures

This paper presents an analytical and empirical analysis of 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 also depend on this risk factor and follow a logit?normal distribution. The results are compared with those of two related 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 estimator for the three models. The model parameters are estimated from default frequencies and recovery rates that were extracted from a bond and loan database of Standard&Poor's. We estimate the correlation between recovery rates and the systematic risk factor and determine the impact on economic capital. Furthermore, the impact of measuring recovery rates from market prices at default and from prices at emergence from default is analysed. As a robustness check for the empirical results of the maximum-likelihood estimation method we also employ a method-of-moments. Our empirical results indicate that systematic risk is a major factor influencing recovery rates. The calculation of a default?weighted recovery rate without further consideration of this factor may lead to downward-biased estimates of economic capital. Recovery rates measured from market prices at default are generally lower and more sensitive to changes of the systematic risk factor than are recovery rates determined at emergence from default. The choice between these two measurement methods has a stronger impact on the expected recovery rates and the economic capital than introducing a dependency of recovery rates on systematic risk in the single risk factor model.