Commodity Connectedness

30 Pages Posted: 13 Mar 2017

See all articles by Francis X. Diebold

Francis X. Diebold

University of Pennsylvania - Department of Economics; National Bureau of Economic Research (NBER)

Laura Liu

Indiana University Bloomington - Department of Economics

Kamil Yilmaz

Koc University

Multiple version iconThere are 3 versions of this paper

Date Written: March 2, 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

Diebold, Francis X. and Liu, Laura and Yilmaz, Kamil, Commodity Connectedness (March 2, 2017). PIER Working Paper No. 17-003, Jacobs Levy Equity Management Center for Quantitative Financial Research Paper, Available at SSRN: or

Francis X. Diebold (Contact Author)

University of Pennsylvania - Department of Economics ( email )

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National Bureau of Economic Research (NBER)

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Laura Liu

Indiana University Bloomington - Department of Economics ( email )

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Kamil Yilmaz

Koc University ( email )

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+90 212 338 1653 (Fax)

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