This paper proposes a new channel through which long-term interest rates transmit to credit spreads. When life insurers carry negative duration gaps, higher rates reduce their liabilities more than assets. Rate increases therefore boost equity and risk-bearing capacity, lowering equilibrium credit spreads. Empirically, I test this channel with bond-level yields and a maturity-based discontinuity in bond ownership. Insurers’ trades confirm the mechanism: after rates rise, insurers shift portfolios towards riskier, high-yield bonds. As rates increase, bonds more heavily held by life insurers experience greater spread reductions. The results show that institutional duration mismatch shapes credit spreads and corporate financing conditions.

This study investigates the asset pricing implications of a newly documented refinement of the disposition effect, characterized by investors being more likely to sell a security when the magnitude of their gains or losses on it increases. I find that stocks with both large unrealized gains and large unrealized losses outperform others in the following month (trading strategy monthly alpha = 0.5-1%, Sharpe ratio = 1.5). This supports the conjecture that these stocks experience higher selling pressure, leading to lower current prices and higher future returns. Overall, this study provides new evidence that investors' trading behavior can aggregate to affect equilibrium price dynamics.

[Through the interaction between financial constraints and R&D, I study two asset-pricing puzzles: mixed evidence on the financial constraints—return relation and the positive R&D-return relation. Unlike capital investment, R&D is more inflexible. A financially constrained R&D-intensive firm is more likely to suspend/discontinue R&D projects. Therefore, R&D-intensive firms' risk increases with their financial constraints. Conversely, constrained firms' risk increases with their R&D intensity. I find a robust empirical relation between financial constraints and stock returns, primarily among R&D-intensive firms. Moreover, R&D predicts returns only among financially constrained firms. This evidence suggests that financial constraints potentially drive the positive R&D-return relation.]

Using a consumption-based asset pricing model with infinite-horizon nonlinear habit formation, Campbell and Cochrane (1999) show that low consumption in surplus of habit should forecast high expected returns. This article argues that the finite-horizon linear habit model also implies an inverse relation between expected returns and surplus consumption. This article also presents empirical evidence, which indicates that expected returns on stocks and bonds vary with surplus consumption implied by the habit models. The volatility of returns and the reward to volatility are also related to surplus consumption. However, less than 30% of the predictable variation of expected returns, using standard lagged information variables, is attributed to surplus consumption.

Research in finance and macroeconomics has routinely employed multiple horizons to test asset return predictability. In a simple predictive regression model, we find the popular scaled test can have zero power when the predictor is not sufficiently persistent. A new test based on implication of the short-run model is suggested and is shown to be uniformly more powerful than the scaled test. The new test can accommodate multiple predictors. Compared with various other widely used tests, simulation experiments demonstrate remarkable finite-sample performance. We reexamine the predictive ability of various popular predictors for aggregate equity premium.

The percentage of S&P 500 firms using multiyear accounting-based performance (MAP) incentives for CEOs increased from 16.5% in 1996 to 43.3% in 2008. The use and design of MAP incentives depend on the signal quality of accounting versus stock performance, shareholder horizons, strategic imperatives, and board independence. After the technology bubble, option expensing, and the publicity of option backdating, firms increasingly use stock-based MAP plans to replace options, resulting in changes in pay structure, but not in pay level. While firms respond to the evolving contracting environment, they consider firm characteristics and shareholder preferences and do not blindly follow the trend.

[We show that new managers who take over mutual fund portfolios sell off inherited momentum losers at higher rates than stocks in any other momentum decile, even after adjusting for concurrent trades in these stocks by continuing fund managers. This behavior is observed regardless of fund characteristics and is stronger when new managers are external hires. The tendency of continuing fund managers to hold on to losers could be consistent with either a behavior bias stemming from an inability to ignore the sunk costs associated with the stocks' past underperformance or a conscious desire to protect their careers by not admitting prior mistakes. Furthermore, we present evidence that selling off loser stocks helps improve fund performance.]

[Based on a multivariate extension of the constrained locally polynomial estimator of Aït-Sahalia and Duarte (2003), we provide one of the first nonparametric estimates of probability densities of LIBOR rates under forward martingale measures and state-price densities (SPDs) implicit in interest rate cap prices. The forward densities and SPDs depend significantly on the slope and volatility of LIBOR rates, and mortgage markets activities have strong impacts on the shape of the forward densities. The SPDs exhibit a pronounced U-shape as a function of future LIBOR rates, suggesting that the state prices are high at both extremely low and high interest rates, which tend to be associated with recessions and periods of high inflation, respectively. Our results provide nonparametric evidence of unspanned stochastic volatility and suggest that the unspanned factors could be partly driven by activities in the mortgage markets.]

We develop a nonparametric specification test for continuous-time models using the transition density. Using a data transform and correcting for the boundary bias of kernel estimators, our test is robust to serial dependence in data and provides excellent finite sample performance. Besides univariate diffusion models, our test is applicable to a wide variety of continuous-time and discrete-time dynamic models, including time-inhomogeneous diffusion, GARCH, stochastic volatility, regime-switching, jump-diffusion, and multivariate diffusion models. A class of separate inference procedures is also proposed to help gauge possible sources of model mis-specification. We strongly reject a variety of univariate diffusion models for daily Eurodollar spot rates and some popular multivariate affine term structure models for monthly U.S. Treasury yields.

This paper proposes “implied stochastic volatility models” designed to fit option-implied volatility data and implements a new estimation method for such models. The method is based on explicitly linking observed shape characteristics of the implied volatility surface to the coefficient functions that define the stochastic volatility model. The method can be applied to estimate a fully flexible nonparametric model, or to estimate by the generalized method of moments any arbitrary parametric stochastic volatility model, affine or not. Empirical evidence based on S&P 500 index options data show that the method is stable and performs well out of sample.