We propose a dynamic asset pricing model in which two assets with identical cash flows can trade at different prices not only because of differences in liquidity but counterparty risk. Counterparty risk reduces lenders or borrowers’ willingness to supply funds and collateral, incentives to shortsell and lend, and the likelihood for new bonds to be on special, thereby narrowing on-/off-the-run spreads and affecting asset prices in spot markets. Consistent with this prediction, we find that on-/off-the-run spreads are low when counterparty risk is high and this relationship is much stronger during the financial crisis.

We propose a dynamic equilibrium model with stochastic interest rates in which agents hold heterogeneous valuations for the same asset and take on positions against each other. The model shows that interest rate uncertainty and investor heterogeneity are key determinants of price dispersion. Higher search intensity reduces price dispersion, while raising volume, leading to a negative volatility-volume relation. The sensitivity of volatility to volume is high when liquidity is low, interest rate variations are high and investors’ valuations are more heterogeneous. Evidence supports our model’s predictions and shows that search frictions play an important role in driving the volatility-volume relation.

Using a dynamic selection model, we obtain consistent and unbiased estimates of risk and returns for infrequently traded bonds and conduct the first comprehensive asset pricing test of municipal bonds using the multifactor approach. Correction for sample selection and infrequent trading problems results in substantially higher beta estimates. Besides conventional risk factors, illiquidity and taxes are important for the pricing of municipal bonds. Importantly, bond returns contain a significant liquidity risk premium. Failing to account for sample selection bias leads to erroneous inference on the magnitude of systematic risk and substantial underestimation of risk premiums.

The authors propose a rational signaling model to investgate the information content of dividends. The model provides a direct test of the relation between unexpected dividend and earnings changes. In identifying the component of unexpected dividend changes, the authors suggest an expectations framework that accounts for the process of dividend adjustment to firms' permanent earnings. A nonlinear regression method is used to estimate the model and test the rationality and signaling hypotheses. Consistent with Paul Healy and Krishna Palepu's (1988) findings, the results show that dividends reflect past, current, and future earnings information.

This paper investigates the role of credit risk in the relationship between short-selling activity and future stock returns. We find that the predictive power of short interest for future returns is concentrated in the worst-rated stocks. Low-grade stocks with the largest short interest decrease outperform those with the largest short interest increase by 1.09 percent in the following month. This return spread is robust to controls for cross-sectional effects and firm characteristics, and is much more pronounced during periods of high investor sentiment and low liquidity. Distressed firms with large short interest increases experience a worse performance subsequently.

This paper investigates the roles of illiquidity and credit risk in determining the relations between price volatility of a bond and its trading frequency and trade size based on a large transaction dataset from October 2004 to June 2012. We find a positive relation between volatility and trading frequency and a negative relation between volatility and trade size. Consistent with the prediction of the search-based theory, the relations are much stronger for illiquid and risky bonds. Furthermore, both liquidity and credit risk become more important in times of stress and their effects are reinforcing. Results strongly suggest that search frictions and credit risk are important factors driving the relation between volatility and trading activity in the corporate bond market.

This paper extends the macroeconomic frailty model to include sectoral frailty factors that capture default correlations among firms in a similar business. We estimate sectoral and macroeconomic frailty factors and their effects on default intensity using the data for Japanese firms from 1992 to 2010. We find strong evidence for the presence of sectoral frailty factors even after accounting for the effects of observable covariates and macroeconomic frailty on default intensity. The model with sectoral frailties performs better than that without. Results show that accounting for the sources of unobserved sectoral default risk covariations improves the accuracy of default probability estimation.

The structural approach offers an integrated framework to deal with yield spreads and default probability simultaneously. However, structural models perform poorly in predicting corporate bond spreads. It is unclear whether this poor performance is caused by characteristics of individual models, missing factors, or different calibration procedures. This study evaluates the performance of four structural models by incorporating two important factors, personal taxes and the liquidity factor, and calibrating these models to data. To ensure our results are not contingent on the calibration method, we further apply the maximum likelihood estimation method to a large sample of individual bonds. Results consistently show that the ability of structural models to predict spreads improves considerably when personal taxes and liquidity are taken into account. Our findings suggest that the poor performance of standard structural models is more likely due to missing factors than the characteristics of individual models or the calibration procedure.

This paper examines volume and volatility dynamics by accounting for market activity measured by the time duration between two consecutive transactions. A time-consistent vector autoregressive (VAR) model is employed to test the dynamic relationship between return volatility and trades using intraday irregularly spaced transaction data. The model is used to identify the informed and uninformed components of return volatility and to estimate the speed of price adjustment to new information. It is found that volatility and volume are persistent and highly correlated with past volatility and volume. The time duration between trades has a negative effect on the volatility response to trades and correlation between trades. Consistent with microstructure theory, shorter time duration between trades implies higher probability of news arrival and higher volatility. Furthermore, bid–ask spreads are serially dependent and strongly affected by the informed trading and inventory costs.