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Commodity Connectedness

Author

Listed:
  • Francis X. Diebold

    (Department of Economics, University of Pennsylvania)

  • Laura Liu

    (Federal Reserve Bank)

  • Kamil Yilmaz

    (Department of Economics, 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.

Suggested Citation

  • Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2017. "Commodity Connectedness," PIER Working Paper Archive 17-003, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 02 Mar 2017.
    • Handle: RePEc:pen:papers:17-003
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    network centrality; network visualization; pairwise connectedness; total directional connectedness; total connectedness; vector autoregression; variance decomposition; LASSO;
    All these keywords.

    JEL classification:

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

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