Economic Significance of Commodity Return Forecasts from the Fractionally Cointegrated VAR Model

Abstract

We model and forecast commodity spot and futures prices using fractionally cointegrated vector autoregressive (FCVAR) models generalizing the well-known (non-fractional) CVAR model to accommodate fractional integration. In our empirical analysis to daily data on 17 commodity markets, the fractional model is statistically superior in terms of in-sample fit and out-of-sample forecasting. We analyze economic significance of the forecasts through dynamic (mean-variance) trading strategies, leading to statistically significant and economically meaningful profits in most markets. We generally find that the fractional model generates higher profits on average, especially in the futures markets.

Keywords: commodity markets, economic significance, forecasting, fractional cointegration, futures markets, price discovery, trading rule, vector error correction model

JEL Classification: C32, G11