Predictability of Relative Forecast Performance, Forecast Combination and Shrinkage: An Application to Forecasting Aggregate Equity Return Volatility by Conditioning on Economic Variables

Abstract Forecast combination methods’ importance and usefulness in out-of-sample prediction of economic and financial variables are broadly acknowledged. Recent research highlights the importance of two components in improving point forecast accuracy: the initial selection of point forecasts to combine and the subsequent imposition of additional shrinkage. Motivated by empirical results that indicate the benefits of integrating these two procedures when combining point forecasts, this study contributes by comprehensively extending the dynamic forecast rotation method and proposing a forecast combination technique that implements point forecast selection and additional shrinkage in a parsimonious fashion. I apply the suggested technique to the well-known topic of forecasting aggregate equity return volatility out-of-sample by conditioning on economic variables. I demonstrate its superior performance both from a statistical and from an economic viewpoint.