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...

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...

This paper tests whether it is possible to improve point, quantile and density forecasts of realized volatility by conditioning on macroeconomic and financial variables. We employ quantile autoregressive models augmented with a plethora of macroeconomic and financial variables. Complete subset...