Commodity Connectedness

30 Pages Posted: 21 Aug 2017 Last revised: 21 Mar 2021

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: August 2017

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 (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.

Suggested Citation

Diebold, Francis X. and Liu, Laura and Yilmaz, Kamil, Commodity Connectedness (August 2017). NBER Working Paper No. w23685, Available at SSRN: https://ssrn.com/abstract=3023080

Francis X. Diebold (Contact Author)

University of Pennsylvania - Department of Economics ( email )

Ronald O. Perelman Center for Political Science
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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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HOME PAGE: http://https://laurayuliu.com/

Kamil Yilmaz

Koc University ( email )

Rumeli Feneri Yolu
Sariyer
Istanbul, 34450
Turkey
+90 212 338 1458 (Phone)
+90 212 338 1653 (Fax)

HOME PAGE: http://https://sites.google.com/view/kamilyilmaz/

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