Cointegration and Detectable Linear and Nonlinear Causality : Analysis Using the London Metal Exchange Lead Contract

This paper applies linear and nonlinear Granger causality tests to examine the dynamic relation between London Metal Exchange (LME) cash prices and three possible predictors. The analysis uses matched quarterly inventory, UK Treasury bill interest rates, futures prices and cash prices for the commodity lead traded on the LME. We also examine the effects of cointegration on both linear and nonlinear Granger causality tests. When cointegration is not modeled, we find evidence of both linear and nonlinear causality between cash prices and analyzed predictor variables. However, after controlling for cointegration, we no longer find evidence of significant nonlinear causality. Our results contribute to the empirical literature on commodity price forecasting by highlighting the relationship between cointegration and detectable linear and nonlinear causality. We also illustrate the importance of interest rate and inventory as well as futures price in forecasting cash prices. Our failure to detect significant nonlinearity after controlling for cointegration may also go some way to explaining the reason for the disappointing forecasting performances of many nonlinear models in the general finance literature. It may be that the variables are correct, but the functional form is overly complex and a standard VAR or VECM may often apply