Forecasting Copper Prices with Dynamic Averaging and Selection Models

We use data from the London Metal Exchange (LME) to forecast monthly copper returns using the recently proposed dynamic model averaging and selection (DMA/DMS) framework, which incorporates time varying parameters as well as model averaging and selection into one unifying framework. Using a total of 18 predictor variables that include traditional fundamental indicators such as excess demand, inventories and the convenience yield, as well as indicators related to global risk appetite, momentum, the term spread, and various other financial series, we show that there exists a considerable predictive component in copper returns. Covering an out-of-sample period from May 2002 to June 2014 and employing standard statistical evaluation criteria we show that the out-of-sample R2 (relative to a random walk benchmark) can be as high as 18.5 percent for the DMA framework. Time series plots of the cumulative mean squared forecast errors and time varying coefficients show further that firstly, a large part of the improvement in the forecasts is realised during the peak of the financial crisis period at the end of 2008, and secondly that the importance of the most relevant predictor variables has changed substantially over the out-of-sample period. The coefficients of the SP500, the VIX, the yield spread, the TED spread, industrial production and the convenience yield predictors are most heavily affected, with the TED spread and yield spread coefficients even changing signs over this period. Our predictability results remain valid for forecast horizons up to 6 months ahead, but are weaker and smaller than at the one month horizon.