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)
New Economics Papers: this item is included in nep-net
Date: 2017-03-02, Revised 2017-03-02
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Chapter: Commodity Connectedness (2018)
Working Paper: Commodity Connectedness (2017)
Working Paper: Commodity connectedness (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:17-003
Access Statistics for this paper
More papers in PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania 133 South 36th Street, Philadelphia, PA 19104. Contact information at EDIRC.
Bibliographic data for series maintained by Administrator ().