32 Pages Posted: 20 Sep 2017 Last revised: 18 Jan 2018
Date Written: June 27, 2017
We use variance decompositions from high-dimensional vector autoregressions to characterize connectedness in 19 key commodity return volatilities, 2011-2016. We study both static (full-sample) and dynamic (rolling-sample) connectedness. We summarize and visualize the results using tools from network analysis. The results reveal clear clustering of commodities into groups that match traditional industry groupings, but with some notable differences. The energy sector is most important in terms of sending shocks to others, and energy, industrial metals, and precious metals are themselves tightly connected.
Keywords: network centrality, network visualization, pairwise connectedness, total directional connect- edness, total connectedness, vector autoregression, variance decomposition, LASSO
JEL Classification: G1, C3
Suggested Citation: Suggested Citation