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Commodity Connectedness

In: Monetary Policy and Global Spillovers: Mechanisms, Effects and Policy Measures

Author

Listed:
  • Francis X. Diebold

    (University of Pennsylvania)

  • Laura Liu

    (Federal Reserve Board)

  • Kamil Yilmaz

    (Koç University)

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Commodity Connectedness," Central Banking, Analysis, and Economic Policies Book Series, in: Enrique G. Mendoza & Ernesto Pastén & Diego Saravia (ed.),Monetary Policy and Global Spillovers: Mechanisms, Effects and Policy Measures, edition 1, volume 25, chapter 4, pages 097-136, Central Bank of Chile.
    • Handle: RePEc:chb:bcchsb:v25c04pp097-136
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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