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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
    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.
    3. Viral V. Acharya & Lasse H. Pedersen & Thomas Philippon & Matthew Richardson, 2017. "Measuring Systemic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 2-47.
    4. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
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    6. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    7. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    8. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    9. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
    10. Julien Chevallier & Florian Ielpo, 2013. "The Economics of Commodity Markets," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02879507, HAL.
    11. Tobias Adrian & Markus K. Brunnermeier, 2016. "CoVaR," American Economic Review, American Economic Association, vol. 106(7), pages 1705-1741, July.
      • Tobias Adrian & Markus K. Brunnermeier, 2008. "CoVaR," Staff Reports 348, Federal Reserve Bank of New York.
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    13. Fernández, Andrés & González, Andrés & Rodríguez, Diego, 2018. "Sharing a ride on the commodities roller coaster: Common factors in business cycles of emerging economies," Journal of International Economics, Elsevier, vol. 111(C), pages 99-121.
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    15. repec:fip:fedhpr:y:2010:i:may:p:65-71 is not listed on IDEAS
    16. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    17. Gazi I. Kara & Mary Tian & Margaret Yellen, 2015. "Taxonomy of Studies on Interconnectedness," FEDS Notes 2015-07-31, Board of Governors of the Federal Reserve System (U.S.).
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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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