The recent literature provides conflicting empirical evidence about the pricing of idiosyncratic risk. This paper sheds new light on the matter by exploiting the richness of option data. First, we find that idiosyncratic risk explains 28% of the variation in the risk premium on a stock. Second, we show that the contribution of idiosyncratic risk to the equity premium arises exclusively from jump risk. Third, we document that the commonality in idiosyncratic tail risk is much stronger than that in total idiosyncratic risk documented in the literature. Tail risk thus plays a central role in the pricing of idiosyncratic risk. Received May 15, 2017; editorial decision September 12, 2018 by Editor Stijn Van Nieuwerburgh.

Previous studies identify default rate as the main systematic determinant of bond recovery rates. We revisit this paradigm by investigating the impact of another factor, economic uncertainty. Based on a wide sample of American default issues and relying on beta regression models, well-suited for the bounded, heteroskedastic and skewed sample of recovery rates, we analyze the determinants of recovery rate distributions. We find economic uncertainty to be of paramount importance, as it proves to be the most important systematic determinant of recovery rate distributions, significant for both their mean and dispersion. By contrast, default rate remains a key determinant of the dispersion of these distributions, but not for their means. Considering this evidence is critical to the sound implementation of stochastic recovery rate models used by financial institutions for the computation of regulatory capital.

We examine the ability of observed macroeconomic factors and the possibility of changes in regime to explain the proportion of yield spreads caused by the risk of default in the context of a reduced form model. For this purpose, we extend the Markov-switching risk-free term structure model of Bansal and Zhou (2002) to the corporate bond setting and develop recursive formulas for default probabilities, risk-free and risky zero-coupon bond yields as well as credit default swap premia. The model is calibrated with consumption, inflation, risk-free yields and default data for Aa, A and Baa bonds from the 1987 to 2008 period. We find that our macroeconomic factors are linked with two out of three sharp increases in the spreads during this sample period, indicating that the spread variations can be related to macroeconomic undiversifiable risk.

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.