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.