| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w23685 |
| 来源ID | Working Paper 23685 |
| Commodity Connectedness | |
| Francis X. Diebold; Laura Liu; Kamil Yilmaz | |
| 发表日期 | 2017-08-21 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | Financial Economics ; Financial Markets |
| URL | https://www.nber.org/papers/w23685 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/581358 |
| 推荐引用方式 GB/T 7714 | Francis X. Diebold,Laura Liu,Kamil Yilmaz. Commodity Connectedness. 2017. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w23685.pdf（1600KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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