Fore­cast­ing Cop­per Prices with Dy­namic Av­er­ag­ing and Se­lec­tion Mod­els

Data from the London Metal Exchange (LME) are used to forecast monthly copper returns using the recently proposed dynamic model averaging and selection (DMA/DMS) methodology which incorporates time varying parameters as well as time varying model averaging and selection into a unifying framework. Using a total of 18 predictor variables that include traditional fundamental indicators such as excess demand and inventories as well as indicators related to global risk appetite, momentum, the term spread, and various other financial series such as exchange rates and stock prices, 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 (RW) benchmark can be as high as 18 percent with the DMA framework, and as high as 9:6 percent when using a simpler time-varying parameter model. A visual assessment of the cumulative MSFEs shows further that a substantial part of the improvement in the forecast (relative to the RW model) is realised during the peak of the financial crisis period at the end of 2008.