[Observable covariates are useful for predicting default, but several studies question their value for explaining credit spreads. We introduce a discrete-time no-arbitrage model with observable covariates, which allows for a closed-form solution for the value of credit default swaps (CDS). The default intensity is a quadratic function of the covariates, specified such that it is always positive. The model yields economically plausible results in terms of fit, the economic impact of the covariates, and the prices of risk. Risk premiums are large and account for a smaller percentage of spreads for firms with lower credit quality. Macroeconomic and firm-specific information can explain most of the variation in CDS spreads over time and across firms, even with a parsimonious specification. These findings resolve the existing disconnect in the literature regarding the value of observable covariates for credit risk pricing and default prediction.]

ABSTRACT We develop a structural bond valuation model to simultaneously capture liquidity and credit risk. Our model implies that renegotiation in financial distress is influenced by the illiquidity of the market for distressed debt. As default becomes more likely, the components of bond yield spreads attributable to illiquidity increase. When we consider finite maturity debt, we find decreasing and convex term structures of liquidity spreads. Using bond price data spanning 15 years, we find evidence of a positive correlation between the illiquidity and default components of yield spreads as well as support for downward‐sloping term structures of liquidity spreads.

Assessments of the trade-off theory have typically compared the present value of tax benefits to the present value of bankruptcy costs. We verify that this comparison overwhelmingly favors tax benefits, suggesting that firms are under-leveraged. However, when we allow firms to experience even modest (e.g., 1–2% annualized) financial distress costs prior to bankruptcy, the cumulative present value of such costs can easily offset the tax benefits.

ABSTRACT Most extant structural credit risk models underestimate credit spreads—a shortcoming known as the credit spread puzzle. We consider a model with priced stochastic asset risk that is able to fit medium‐ to long‐term spreads. The model, augmented by jumps to help explain short‐term spreads, is estimated on firm‐level data and identifies significant asset variance risk premia. An important feature of the model is the significant time variation in risk premia induced by the uncertainty about asset risk. Various extensions are considered, among them optimal leverage and endogenous default.

Abstract Variables that in theory determine credit spreads have limited explanatory power in existing empirical work on corporate bond data. We investigate the linear relationship between theoretical determinants of default risk and default swap spreads. We find that estimated coefficients for a minimal set of theoretical determinants of default risk are consistent with theory and are significant statistically and economically. Volatility and leverage have substantial explanatory power in univariate and multivariate regressions. A principal component analysis of residuals and spreads indicates limited evidence for a residual common factor, confirming that the theoretical variables explain a significant amount of the variation in the data.

Observable covariates are useful for predicting default, but several studies question their value for explaining credit spreads. We introduce a discrete-time no-arbitrage model with observable covariates, which allows for a closed-form solution for the value of credit default swaps (CDS). The default intensity is a quadratic function of the covariates, specified such that it is always positive. The model yields economically plausible results in terms of fit, the economic impact of the covariates, and the prices of risk. Risk premiums are large and account for a smaller percentage of spreads for firms with lower credit quality. Macroeconomic and firm-specific information can explain most of the variation in CDS spreads over time and across firms, even with a parsimonious specification. These findings resolve the existing disconnect in the literature regarding the value of observable covariates for credit risk pricing and default prediction.

We develop a structural credit risk model, which allows us to price equity/credit indices and their options through the asset dynamics of index constituents. We estimate the model via MLE and find that equity and credit index option prices are well explained out-of-sample. Contrary to recent empirical findings, the two option markets are not inconsistently priced through the lens of our model. Returns on both options, while extreme, do not indicate any evidence of mispricing. Our analysis suggests that jointly addressing the pricing of various instruments requires properly attributing three different sources of systematic risk: asset, variance, and jump risks.