We show that in a model with equity and debt financing, the specification of the borrowing constraint is crucial to generate empirically plausible responses of macro variables and asset prices to financial shocks. The interaction between financial frictions and labor demand, as in Jermann and...

This paper proposes a data-based measure of model performance to discriminate among competing asset pricing models of return predictability. I form a set of variance bounds on pricing kernels based on different systems for predicting asset returns. For a given asset pricing model, I define the...

We exploit differences in institutional and macroeconomic environments to shed light on what drives variation in the aggregate earnings-returns relation over time within the U.S. and across countries. We find that both intertemporal and cross-country variation in the aggregate earnings-returns...

This paper empirically explores the short- and long-run effects of fiscal and monetary policies on US stock returns and tests the validity of market efficiency. The results support the presence of a strong long-run (equilibrium) relation binding stock prices with fiscal (but not monetary)...

Ever since the Great Recession, central banks have supplemented their traditional policy tool of setting the short-term interest rate with massive buyouts of assets to extend lines of credit and jolt flagging demand. As with many new policies, there have been a range of reactions from...

Monetary policy shocks have a large impact on stock prices during narrow time windows centered around press releases by the FOMC. We use spatial autoregressions to decompose the overall effect of monetary policy shocks into a direct effect and a network effect. We attribute 50 to 85 percent of...

This paper studies whether and how the central bank should prick asset price bubbles, if the effect of interest rate policy on bubbles can significantly vary across periods. For this purpose, I first construct a financial accelerator model with an agent-based financial market that can...

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with exogenous predictors. Unlike existing Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms, our approach is not based on a structural form representation of the...