This paper studies the effects of time-varying Knightian uncertainty (ambiguity) on asset pricing in a Lucas exchange economy. Specifically, it considers a general equilibrium model where an ambiguity-averse agent applies a discount rate that is adjusted not only for the current magnitude of ambiguity but also for the risk associated with its future fluctuations. As such, both the ambiguity level and volatility help to raise the asset premiums and accommodate richer dynamics of asset prices. Based on a novel empirical measure of the ambiguity level, the estimated model can capture the empirical levels of corporate credit spreads and the equity premium while endogenously matching the historical default probability. More importantly, the model-implied credit spread and equity price-dividend ratio perform remarkably in tracking the time variations in their historical counterparts.

What drives short-term credit spreads: credit risk, liquidity risk, or both? We investigate this issue using the structural approach to credit risk modeling and a novel data set of secondary market transaction prices for Chinese commercial papers (CPs). In particular, we propose and test a structural model with jump risk and exogenous market illiquidity under which the predicted yield spreads can be decomposed into a credit component and a liquidity component. We find that credit risk and, especially liquidity risk, are important determinants of short-term yield spreads. Our model-based decomposition results show that, on average, credit risk and market liquidity account for about 25% and 52% of CP yield spreads, respectively. For comparison, we also examine the drivers of the US CP yield spreads using security-level data. We find that credit risk accounts for a small fraction of the observed yield spreads but liquidity contributes a much greater proportion.

Empirical studies of structural credit risk models so far are often based on calibration, rolling estimation, or regressions. This paper proposes a GMM-based method that allows us to estimate model parameters and test model-implied restrictions in a unified framework. We conduct a specification analysis of five representative structural models based on the proposed GMM procedure, using information from both equity volatility and the term structure of single-name credit default swap (CDS) spreads. Our test results strongly reject the Merton (1974) model and two diffusion-based models with a flat default boundary. The other two models, one with jumps and one with stationary leverage ratios, do improve the overall fit of CDS spreads and equity volatility. However, all five models have difficulty capturing the dynamic behavior of both equity volatility and CDS spreads, especially for investment-grade names. On the other hand, these models have a much better ability to explain the sensitivity of CDS spreads to equity returns.

We examine the ability of structural models to predict credit spreads using global default data and security‐level credit spread data in eight developed economies. We find that two representative, pure default‐risk models tend to underpredict the average credit spreads on investment‐grade (IG) bonds, especially their spreads over government bonds, thereby providing evidence for a “global credit spread puzzle.” However, a model incorporating endogenous liquidity in the secondary debt market helps mitigate the puzzle. Furthermore, the model captures certain determinants of corporate bond market frictions across the eight economies and substantially improves the cross‐sectional fit of individual IG credit spreads.