Jialin Yu

The liquidity premium theory of interest rates predicts that the Treasury yield curve steepens with inflation uncertainty as investors demand larger risk premiums to hold long-term bonds. By using the dispersion of inflation forecasts to measure this uncertainty, we find the opposite. Since the prices of long-term bonds move more with inflation than short-term ones, investors also disagree and speculate more about long-maturity payoffs with greater uncertainty. Shorting frictions, measured by using Treasury lending fees, then lead long maturities to become overpriced and the yield curve to flatten. We estimate this inflation-betting effect using time variation in inflation disagreement and Treasury supply. Received September 3, 2014; editorial decision August 5, 2016 by Editor Andrew Karolyi

In 2005–2008, over a dozen put warrants traded in China went so deep out of the money that they were almost certain to expire worthless. Nonetheless, each warrant was traded more than three times each day at substantially inflated prices. This bubble is unique in that the underlying stock prices make warrant fundamentals publicly observable and that warrants have predetermined finite maturities. This sample allows us to examine a set of bubble theories. In particular, our analysis highlights the joint effects of short-sales constraints and heterogeneous beliefs in driving bubbles and confirms several key findings of the experimental bubble literature. (JEL G12, G13, O16, P34)

Using recent advances in the econometrics literature, we disentangle from high frequency observations on the transaction prices of a large sample of NYSE stocks a fundamental component and a microstructure noise component. We then relate these statistical measurements of market microstructure noise to observable characteristics of the underlying stocks and, in particular, to different financial measures of their liquidity. We find that more liquid stocks based on financial characteristics have lower noise and noise-to-signal ratio measured from their high frequency returns. We then examine whether there exists a common, market-wide, factor in high frequency stock-level measurements of noise, and whether that factor is priced in asset returns.

We study the asset pricing implications of learning in an environment in which the true model of the world is a multivariate one, but agents update only over the class of simple univariate models. Thus, if a particular simple model does a poor job of forecasting over a period of time, it is discarded in favor of an alternative simple model. The theory yields a number of distinctive predictions for stock returns, generating forecastable variation in the magnitude of the value‐glamour return differential, in volatility, and in the skewness of returns. We validate several of these predictions empirically.

We introduce lack‐of‐recall of past transactions as an alternative assumption to anonymity in a model where trade is centralized. In environments where there is an intertemporal lack‐of‐double‐coincidence of wants problem and lack‐of‐commitment, lack‐of‐recall can give rise to monetary equilibria that dominate nonmonetary outcomes in terms of welfare.