Commodity connectedness

Arbeitspapier

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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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CFS Working Paper Series ; No. 575

Classification and Topics

• Classification: Wirtschaft
• Subject: network centrality ; network visualization ; pairwise connectedness ; total directional connectedness ; total connectedness ; vector autoregression ; variance decomposition ; LASSO

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Diebold, Francis X. ; Liu, Laura ; Yilmaz, Kamil
• Event: Veröffentlichung
• (who): Goethe University Frankfurt, Center for Financial Studies (CFS)
• (where): Frankfurt a. M.
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/169386
• URN: urn:nbn:de:hebis:30:3-438617