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On the linkages between energy and agricultural commodity prices: A dynamic time warping analysis

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  • Miljkovic, Dragan
  • Vatsa, Puneet

Abstract

We use dynamic time warping, a non-parametric pattern recognition method, to study interlinkages between major energy and agricultural commodity prices. Cluster analysis is conducted to group commodity prices based on their behavioral likeness by maximizing the differences between groups while minimizing the differences within groups. Two clusters emerge: one comprises the prices of crude oil and six major agricultural commodities, whereas the other contains coal and natural gas prices. Regarding lead-lag associations, oil prices generally lag crop prices; however, there are periods during which the former lead the latter. Furthermore, the duration with which oil prices lead or lag crop prices changes frequently.

Suggested Citation

  • Miljkovic, Dragan & Vatsa, Puneet, 2023. "On the linkages between energy and agricultural commodity prices: A dynamic time warping analysis," International Review of Financial Analysis, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:finana:v:90:y:2023:i:c:s1057521923003502
    DOI: 10.1016/j.irfa.2023.102834
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    More about this item

    Keywords

    Commodity prices; Dynamic time warping; Lead-lag analysis; Pattern recognition; Time-series clustering;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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