Commodity Connectedness

Francis Diebold, Laura Liu and Kamil Yilmaz ()

No 23685, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: G1 (search for similar items in EconPapers)
Date: 2017-08
New Economics Papers: this item is included in nep-net
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Citations: View citations in EconPapers (42)

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Related works:
Chapter: Commodity Connectedness (2018)
Working Paper: Commodity Connectedness (2017)
Working Paper: Commodity connectedness (2017)
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