Commodity Connectedness

32 Pages Posted: 20 Sep 2017 Last revised: 18 Jan 2018

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: 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

Diebold, Francis X. and Liu, Laura and Yilmaz, Kamil, Commodity Connectedness (June 27, 2017). CFS Working Paper, No. 575, Available at SSRN: or

Francis X. Diebold (Contact Author)

University of Pennsylvania - Department of Economics ( email )

Ronald O. Perelman Center for Political Science
133 South 36th Street
Philadelphia, PA 19104-6297
United States
215-898-1507 (Phone)
215-573-4217 (Fax)


National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Laura Liu

Indiana University Bloomington - Department of Economics ( email )

Wylie Hall
Bloomington, IN 47405-6620
United States

HOME PAGE: http://

Kamil Yilmaz

Koc University

Rumelifeneri Yolu
34450 Sar?yer
Istanbul, 34450

Do you want regular updates from SSRN on Twitter?

Paper statistics

Abstract Views
PlumX Metrics