Commodity Connectedness

F. Diebold, L. Liu, K. Yilmaz
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引用次数: 58

Abstract

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.
商品的连通性
我们使用高维向量自回归的方差分解来表征2011-2016年19个关键商品收益波动的连通性。我们研究静态(全样本)和动态(滚动样本)连通性。我们使用网络分析工具对结果进行总结和可视化。结果显示,商品明显集聚到与传统行业分类相匹配的群体中,但存在一些显著差异。能源行业对其他行业的冲击最为重要,而能源、工业金属和贵金属本身是紧密相连的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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