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Understanding Dynamic Conditional Correlations between Commodities Futures Markets

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  • Niaz Bashiri Behmiri

    (Fondazione Eni Enrico Mattei, Milan)

  • Matteo Manera

    (University of Milan-Bicocca and Fondazione Eni Enrico Mattei, Milan)

  • Marcella Nicolini

    (University of Pavia and Fondazione Eni Enrico Mattei, Milan)

Abstract

We estimate dynamic conditional correlations between 10 commodities futures returns in energy, metals and agriculture markets over the period 1998-2014 with a DCC-GARCH model. We look at the factors influencing those correlations, adopting a pooled mean group (PMG) estimator. Macroeconomic variables are significantly correlated with agriculture-energy and metals-energy dynamic conditional correlations; while financial variables are relevant in the agriculture-energy correlations and poorly significant in the metals-energy ones. Speculative activity is generally not statistically significant. Correlations started increasing in the years before the financial crisis and decreased at the end of our period of analysis.

Suggested Citation

  • Niaz Bashiri Behmiri & Matteo Manera & Marcella Nicolini, 2016. "Understanding Dynamic Conditional Correlations between Commodities Futures Markets," Working Papers 2016.17, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2016.17
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    Cited by:

    1. Martínez, Beatriz & Torró, Hipòlit, 2018. "Hedging spark spread risk with futures," Energy Policy, Elsevier, vol. 113(C), pages 731-746.

    More about this item

    Keywords

    Multivariate GARCH; Dynamic Conditional Correlations; Future Markets; Commodities;

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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