Recent research documents that aggregate stock prices are driven by shocks with persistence levels ranging from daily intervals to several decades. Building on these insights, we introduce a parsimonious equilibrium model in which regime-shifts of heterogeneous durations affect the volatility of...

Asset pricing models such as the conditional CAPM are typically estimated with MLE using a monthly or quarterly horizon with data sampled to match the horizon even though daily data are available. We develop an overlapping data inference methodology (ODIN) that uses all of the data while...

We provide time-series and cross-sectional evidence on the significance of a risk-return tradeoff in the corporate bond market. We find a significantly positive intertemporal relation between expected return and risk in the bond market and the time-series predictability is driven by aggregate...

We study the effect of releasing public information about productivity or monetary shocks when agents learn from nominal prices. While public releases have the benefit of providing new information, they can have the cost of reducing the informational efficiency of the price system. We show that,...

Empirical results based on two different statistical approaches lead to several conclusions about the role of time-varying asset risk assessments in accounting for what, on the basis of many earlier studies, appear to be time-varying differentials in ex ante asset returns. First, both methods...

We develop a stochastic model of nonsynchronous asset prices based on sampling with random censoring. In addition to generalizing existing models of non-trading our framework allows the explicit calculation of the effects of infrequent trading on the time series properties of asset returns....

We develop a flexible semiparametric time series estimator that is then used to assess the causal effect of monetary policy interventions on macroeconomic aggregates. Our estimator captures the average causal response to discrete policy interventions in a macro-dynamic setting, without the need...

When excess returns are used to estimate linear stochastic discount factor (SDF) models, researchers often adopt a normalization of the SDF that sets its mean to 1, or one that sets its intercept to 1. These normalizations are often treated as equivalent, but they are subtly different both in...