Year: 2017
Type: report
Abstract: We use variance decompositions from high-dimensional vector autoregressions to characterize connectedness in 19 key commodity return volatilities, 2011-2016.We study both static (fullsample) 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.
DOI: 10.3386/w23685
Authors: Francis Diebold, Laura Liu, Kamil Yilmaz
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4237295867