I develop an interest rate model with separate factors driving innovations in bond yields and their covariances. It features a flexible and tractable affine structure for bond covariances. Maximum likelihood estimation of the model with panel data on swaptions and discount bonds implies pricing errors for swaptions that are almost always lower than half of the bid–ask spread. Furthermore, market prices of interest rate caps do not deviate significantly from their no‐arbitrage values implied by the swaptions under the model. These findings support the conjectures of Collin‐Dufresne and Goldstein (2003) , Dai and Singleton (2003) , and Jagnnathan, Kaplin, and Sun (2003) .

We study an important recent series of buyback auctions conducted by the U.S. Treasury in retiring $67.5 billion of its illiquid off‐the‐run debt. The Treasury was successful in buying back large amounts of illiquid debt while suffering only a small market‐impact cost. The Treasury included the most‐illiquid bonds more frequently in the auctions, but tended to buy back the least‐illiquid of these bonds. Although the Treasury had the option to cherry pick from among the bonds offered, we find that the Treasury was actually penalized for being spread too thinly in the buybacks.

We model visibility bias in the social transmission of consumption behavior. When consumption is more salient than nonconsumption, people perceive that others are consuming heavily, and infer that future prospects are favorable. This increases aggregate consumption in a positive feedback loop. A distinctive implication is that disclosure policy interventions can ameliorate undersaving. In contrast with wealth‐signaling models, information asymmetry about wealth reduces overconsumption. The model predicts that saving is influenced by social connectedness, observation biases, and demographic structure, and provides new insight into savings rates. These predictions are distinct from other common models of consumption distortions.

In the presence of sentiment fluctuations, arbitrageurs may engage in different strategies leading to dispersed sentiment exposures. We find that hedge funds in the top decile ranked by sentiment beta outperform those in the bottom decile by 0.59% per month on a risk‐adjusted basis, with the spread being larger among skilled funds. We also find that about 10% of hedge funds have sentiment timing skill that positively correlates with fund sentiment beta and contributes to fund performance. Our findings show that skilled hedge funds can earn high returns by predicting and exploiting sentiment changes rather than betting against mispricing.