Commodity connectedness - Francis X. Diebold; Laura Liu; Kamil Yilmaz (Goethe University Frankfurt, Center for Financial Studies (CFS), 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.
urn:nbn:de:hebis:30:3-438617
Keywords: G1; C3; 330; network centrality; network visualization; pairwise connectedness; total directional connectedness; total connectedness; vector autoregression; variance decomposition; LASSO