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<p>In this study, we explore the efficacy of various methodologies and co-movement measures for modeling the co-movement of cross-commodity prices, using macroeconomic variables. Applying Vector Autoregression (VAR), VAR with exogenous variables (VARX), multiple regressions, and Random Forest regressions, alongside Pearson correlations and Gerber statistics, we analyze the price co-movement of 20 key commodities over the period from mid-2003 to early 2023. Our results reveal that VAR and VARX models notably outperform Random Forests and multiple regressions, achieving <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(R^2\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mi>R</mi> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation> values of up to 89%. Although Random Forests marginally outperform multiple regressions, they are still inferior to VAR-based approaches. Furthermore, we observe that the inclusion of Gerber statistics (differently from the Pearson correlation metric) enhances the effectiveness of VAR and VARX models, while their influence on Random Forests and multiple regression models remains indeterminate. This research contextualizes and contributes to the existing literature on commodity price dynamics, providing insights into the relative strengths of various modeling approaches and setting the stage for future methodological advancements, especially in the applications of Machine Learning (ML) in the field.</p>

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Modeling commodity price co-movement: building on traditional time series models and exploring applications of machine learning algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月2日

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  • Luca L. Kozian,
  • Marcos R. Machado,
  • Joerg R. Osterrieder

摘要

In this study, we explore the efficacy of various methodologies and co-movement measures for modeling the co-movement of cross-commodity prices, using macroeconomic variables. Applying Vector Autoregression (VAR), VAR with exogenous variables (VARX), multiple regressions, and Random Forest regressions, alongside Pearson correlations and Gerber statistics, we analyze the price co-movement of 20 key commodities over the period from mid-2003 to early 2023. Our results reveal that VAR and VARX models notably outperform Random Forests and multiple regressions, achieving \(R^2\) R 2 values of up to 89%. Although Random Forests marginally outperform multiple regressions, they are still inferior to VAR-based approaches. Furthermore, we observe that the inclusion of Gerber statistics (differently from the Pearson correlation metric) enhances the effectiveness of VAR and VARX models, while their influence on Random Forests and multiple regression models remains indeterminate. This research contextualizes and contributes to the existing literature on commodity price dynamics, providing insights into the relative strengths of various modeling approaches and setting the stage for future methodological advancements, especially in the applications of Machine Learning (ML) in the field.