This paper proposes a predictive approach to estimate macroeconomic tail risk dynamics over the long run (1876-2015). Our approach circumvents the scarcity of large macroeconomic crises by using observable predictive variables in a large international panel. This method does not require to use...

We propose a novel method to estimate risk-neutral quantiles that uses sorting to minimize an objective function given by a convex combination of call and put option prices over the range of available strike prices. We demonstrate that this new method significantly improves the accuracy of...

This paper investigates whether the HML, the SMB along with the short-term reversal, the long-term reversal and the momentum factors exhibit both in-sample and out-of-sample forecasting ability for the US stock returns. Our findings suggest that these factors contain significantly more...

The paper examines statistical and economic evidence of out-of-sample bond return predictability for a real-time Bayesian investor who learns about parameters, hidden states, and predictive models over time. We find some statistical evidence using information contained in forward rates. However,...

A model of portfolio return dynamics is considered in which the price of risk is permitted to be heterogeneous. In doing this, a novel method is proposed that delivers improved out-of-sample forecasts of portfolio returns. The main innovation is the use of a set of predictors that account for...

Stocks with increases in idiosyncratic risk tend to earn low subsequent returns for a few months. However, high idiosyncratic risk stocks eventually earn persistently high returns. These results are consistent with positively priced idiosyncratic risk and temporary underreaction to idiosyncratic...