Understanding Dynamic Conditional Correlations between Commodities Futures Markets
Q42, Q11, C32
Multivariate GARCH, Dynamic Conditional Correlations, Future Markets, Commodities
Energy Scenarios and Policy
We estimate dynamic conditional correlations between 10 commodities futures returns in energy, metals and agriculture markets over the period 1998-2014 with a DCC-GARCH model. We look at the factors influencing those correlations, adopting a pooled mean group (PMG) estimator. Macroeconomic variables are significantly correlated with agriculture-energy and metals-energy dynamic conditional correlations; while financial variables are relevant in the agriculture-energy correlations and poorly significant in the metals-energy ones. Speculative activity is generally not statistically significant. Correlations started increasing in the years before the financial crisis and decreased at the end of our period of analysis.
Suggested citation: Behmiri, N. B., M. Manera, M. Nicolini, (2016), ‘Understanding Dynamic Conditional Correlations between Commodities Futures Markets’, Nota di Lavoro 17.2016, Milan, Italy: Fondazione Eni Enrico Mattei