We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions...

I consider a consumption based asset pricing model where the consumer does not know if shocks to dividends are stationary (temporary) or non-stationary (permanent). The agent uses a Bayesian learning algorithm with a bias towards recent observations to assign probability to each process. While...

Two broad classes of consumption dynamics - long-run risks and rare disasters - have proven successful in explaining the equity premium puzzle when used in conjunction with recursive preference. We show that bounds a-la Gallant, Hansen and Tauchen (1990) that restrict the volatility of the...

Stock market predictability is of considerable interest in both academic research and investment practice. Ross (2005) provides a simple and elegant upper bound on the predictive regression R-squared that R^2 = (1 R_f)^2 Var(m) for a given asset pricing model with kernel m, where R_f is the...

Models based on factors such as size, value, or momentum are ubiquitous in asset pricing. Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid individual assets, this measure is...

This paper provides global evidence supporting the hypothesis that expected return models are enhanced by the inclusion of variables that describe the evolution of book-to-market-changes in book value, changes in price, and net share issues. This conclusion is supported using data representing...

Asset returns change with fundamentals and other factors, such as technical information and sentiment over time. In modeling time-varying expected returns, this article focuses on the out-of-sample predictability of the aggregate stock market return via extensions of the conventional predictive...

We provide a short and selected review of the vast literature on cross-section predictability. We focus on the state of art methods used to forecast the cross-section of stock returns with major predictors and are primarily interested in the ideas, methods, and their applications. To understand...

We document that the first and third cross-sectional moments of corporate bond returns significantly and positively predict future stock market returns both in- and out-of-sample. The predictability emerges from informed bond trading and gradual diffusion of information. Particularly, the...