Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/169386
Authors: 
Diebold, Francis X.
Liu, Laura
Yilmaz, Kamil
Year of Publication: 
2017
Series/Report no.: 
CFS Working Paper Series 575
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.
Subjects: 
network centrality
network visualization
pairwise connectedness
total directional connectedness
total connectedness
vector autoregression
variance decomposition
LASSO
JEL: 
G1
C3
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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