This paper studies the impact of international capital flows on asset prices through risk premia. We investigate whether foreign purchases of U.S. Treasury securities significantly contributed to the decline in excess returns on long-term bonds between 1995 and 2008. We run forecasting...

We propose a quantile regression approach to equity premium forecasting. Robust point forecasts are generated by both fixed and time-varying weighting schemes, thus exploiting the entire distributional information associated with each predictor. Further gains are achieved by incorporating the...

Academic research has extensively used macroeconomic variables to forecast the U.S. equity risk premium, with little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the forecasting ability of technical indicators with that of...

This paper extends the complete subset linear regression framework to a quantile regression setting. We employ complete subset combinations of quantile forecasts in order to construct robust and accurate equity premium predictions. Our recursive algorithm that selects, in real time, the best...

We show that uncertainty of monetary policy (MPU) commands a risk premium in the US Treasury bond market. Using the news based MPU measure in Baker, Bloom, and Davis (2016) to capture monetary policy uncertainty, we find that MPU forecasts significantly and positively future monthly Treasury...

We propose a new method to improve density forecasts of the equity premium using information from options markets. We obtain predictive densities from stochastic volatility (SV) and GARCH models, which we then tilt using the second moment of the risk-neutral distribution implied by options...

This paper investigates the predictive ability of international volatility risk for the daily aggregate Chinese stock market returns. We employ the innovations in implied volatility indices of seven major international markets as our international volatility risk proxies. We find that...

We find that investor attention proxies proposed in the literature collectively have a common component that has significant power in predicting stock market risk premium, both in-sample and out-of-sample. This common component is well extracted by using partial least squares, scaled principal...