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Commodity connectedness

Author

Listed:
  • Diebold, Francis X.
  • Liu, Laura
  • Yilmaz, Kamil

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

  • Diebold, Francis X. & Liu, Laura & Yilmaz, Kamil, 2017. "Commodity connectedness," CFS Working Paper Series 575, Center for Financial Studies (CFS).
    • Handle: RePEc:zbw:cfswop:575
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    References listed on IDEAS

    as
    1. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
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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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