TY  - UNPD
A1  - Diebold, Francis X.
A1  - Liu, Laura
A1  - Yılmaz, Kamil
T1  - Commodity connectedness
T2  - Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 575
N2  - 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.
T3  - CFS working paper series - 575 
KW  - network centrality
KW  - network visualization
KW  - pairwise connectedness
KW  - total directional connect- edness
KW  - total connectedness
KW  - vector autoregression
KW  - variance decomposition
KW  - LASSO
Y1  - 2017
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/43861
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-438617
UR  - https://ssrn.com/abstract=3038826
IS  - June 27, 2017
PB  - Center for Financial Studies
CY  - Frankfurt, M.
ER  -