Arbeitspapier
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: CFS Working Paper Series ; No. 575
- Klassifikation
-
Wirtschaft
- Thema
-
network centrality
network visualization
pairwise connectedness
total directional connectedness
total connectedness
vector autoregression
variance decomposition
LASSO
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Diebold, Francis X.
Liu, Laura
Yilmaz, Kamil
- Ereignis
-
Veröffentlichung
- (wer)
-
Goethe University Frankfurt, Center for Financial Studies (CFS)
- (wo)
-
Frankfurt a. M.
- (wann)
-
2017
- Handle
- URN
-
urn:nbn:de:hebis:30:3-438617
- Letzte Aktualisierung
- 10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Diebold, Francis X.
- Liu, Laura
- Yilmaz, Kamil
- Goethe University Frankfurt, Center for Financial Studies (CFS)
Entstanden
- 2017
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