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.
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| Author: | Francis X. Diebold, Laura Liu, Kamil Yılmaz |
|---|---|
| URN: | urn:nbn:de:hebis:30:3-438617 |
| URL: | https://ssrn.com/abstract=3038826 |
| Parent Title (English): | Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 575 |
| Series (Serial Number): | CFS working paper series (575) |
| Publisher: | Center for Financial Studies |
| Place of publication: | Frankfurt, M. |
| Document Type: | Working Paper |
| Language: | English |
| Year of Completion: | 2017 |
| Year of first Publication: | 2017 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Release Date: | 2017/10/17 |
| Tag: | LASSO; network centrality; network visualization; pairwise connectedness; total connectedness; total directional connect- edness; variance decomposition; vector autoregression |
| Issue: | June 27, 2017 |
| Page Number: | 32 |
| HeBIS-PPN: | 419158677 |
| Institutes: | Wirtschaftswissenschaften / Wirtschaftswissenschaften |
| Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS) | |
| Dewey Decimal Classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
| JEL-Classification: | C Mathematical and Quantitative Methods / C3 Multiple or Simultaneous Equation Models |
| Sammlungen: | Universitätspublikationen |
| Licence (German): |  Deutsches Urheberrecht |
