A boosting approach to forecasting gold and silver returns: economic and statistical forecast evaluation

We use a boosting algorithm to forecast the returns of gold and silver prices. We then study the implications of using different information criteria to terminate the boosting algorithm in terms of the statistical and economic performance of a forecasting model. Our findings demonstrate that information criteria that select parsimonious forecasting models perform better in statistical terms than information criteria that select relatively complex forecasting models, but this good performance does not necessarily survive an economic performance evaluation.