Commodity connectedness

  • 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.

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Author:Francis X. Diebold, Laura Liu, Kamil Yılmaz
URN:urn:nbn:de:hebis:30:3-438617
URL:https://ssrn.com/abstract=3038826
Parent Title (English):Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 575
Series (Serial Number):CFS working paper series (575)
Publisher:Center for Financial Studies
Place of publication:Frankfurt, M.
Document Type:Working Paper
Language:English
Year of Completion:2017
Year of first Publication:2017
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2017/10/17
Tag:LASSO; network centrality; network visualization; pairwise connectedness; total connectedness; total directional connect- edness; variance decomposition; vector autoregression
Issue:June 27, 2017
Page Number:32
HeBIS-PPN:419158677
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
JEL-Classification:C Mathematical and Quantitative Methods / C3 Multiple or Simultaneous Equation Models
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht