Francis Diebold (),
Laura Liu () and
Kamil Yilmaz ()
PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania
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 connectedness; total connectedness; vector autoregression; variance decomposition; LASSO (search for similar items in EconPapers)
JEL-codes: G1 C3 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2017-03-02, Revised 2017-03-02
New Economics Papers: this item is included in nep-net
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Chapter: Commodity Connectedness (2018)
Working Paper: Commodity Connectedness (2017)
Working Paper: Commodity connectedness (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:17-003
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