A real-time quantile-regression approach to forecasting gold returns under asymmetric loss

We propose a real-time quantile-regression approach to analyze whether widely studied macroeconomic and financial variables help to forecast out-of-sample gold returns. The real-time quantile-regression approach accounts for model uncertainty, model instability, and the possibility that a forecaster has an asymmetric loss function. Forecasts are computed and evaluated using the same asymmetric loss function. When the loss function implies that an underestimation is somewhat more costly than an overestimation of the same size, the forecasts computed using the real-time quantile-regression approach outperform forecasts implied by an autoregressive benchmark model.