Commodity connectedness

Francis Diebold, Laura Liu and Kamil Yilmaz ()

No 575, CFS Working Paper Series from Center for Financial Studies (CFS)

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.

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: C3 G1 (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-cta and nep-net
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (42)

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https://www.econstor.eu/bitstream/10419/169386/1/898376807.pdf (application/pdf)

Related works:
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:zbw:cfswop:575

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