Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We introduce two novel indices built from CDS market data capturing the level and uncertainty information embedded in credit spreads aggregated by industry, and study their role in predicting bonds recovery rates. Analyzing 613 defaulted U.S. corporate bond issues from 2006 to 2019 and using a beta regression model, we find the cross-sectional mean and approximate entropy of CDS spreads aggregated at the sector level to be important predictors of the recovery rates distributions. In the classical beta regression model, both regressors are statistically significant and enhance the pseudo-R2 by up to 4%. Notably, a forward model selection procedure includes the sector-level regressor before well-known variables such as the bonds’ coupon rate or the American default rate. In addition, our sector-uncertainty regressor is the only significant uncertainty variable. These findings offer valuable insights for improving credit risk assessment methodologies and identifying key risk indicators of recovery rates before running prediction models.