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<p>While the futures–spot price relationship is well established in commodity markets, the transmission of price signals to the retail level remains an "<i>incomplete bridge</i>,” particularly under varying speculative regimes. Traditional empirical approaches often fail to capture the nonlinear and heterogeneous dynamics of this process, typically providing a single Average Treatment Effect (ATE) that masks the distortions caused by market frictions. This study addresses this gap by developing a novel causal machine learning (CML) framework. Leveraging double machine learning (DML), we isolate the causal link between futures and retail prices by "<i>partialing out</i>” high-dimensional confounding variables, effectively distinguishing the informational signal from the market "<i>noise</i>” identified in recent literature. We illustrate this framework using the US frozen concentrated orange juice (FCOJ) market as a functional laboratory for concentrated and volatile ”<i>soft</i>” commodities. Our results reveal a non-monotonic relationship: while moderate speculation enhances price discovery (CATE <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\approx\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>≈</mo> </math></EquationSource> </InlineEquation> 1), both low and high speculative intensity impair signal propagation. Crucially, we find that excessive speculation leads to ”<i>informational decoupling</i>,” where increased statistical uncertainty in the CATE reflects a coordination failure in firm-level pricing and procurement decisions. These findings challenge the assumption that speculation consistently enhances market efficiency and could provide a robust, scalable, data-driven analytical tool for future supply-chain research and policy discussions on commodity market regulation.</p>

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Speculation and retail price transmission in the frozen concentrated orange juice market: a causal machine learning analysis

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  • 发表日期：

    2026年3月4日

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  • Alessio Abeltino,
  • Andrea Pannone,
  • Andrea Bernardini

摘要

While the futures–spot price relationship is well established in commodity markets, the transmission of price signals to the retail level remains an "incomplete bridge,” particularly under varying speculative regimes. Traditional empirical approaches often fail to capture the nonlinear and heterogeneous dynamics of this process, typically providing a single Average Treatment Effect (ATE) that masks the distortions caused by market frictions. This study addresses this gap by developing a novel causal machine learning (CML) framework. Leveraging double machine learning (DML), we isolate the causal link between futures and retail prices by "partialing out” high-dimensional confounding variables, effectively distinguishing the informational signal from the market "noise” identified in recent literature. We illustrate this framework using the US frozen concentrated orange juice (FCOJ) market as a functional laboratory for concentrated and volatile ”soft” commodities. Our results reveal a non-monotonic relationship: while moderate speculation enhances price discovery (CATE \(\approx\) ≈ 1), both low and high speculative intensity impair signal propagation. Crucially, we find that excessive speculation leads to ”informational decoupling,” where increased statistical uncertainty in the CATE reflects a coordination failure in firm-level pricing and procurement decisions. These findings challenge the assumption that speculation consistently enhances market efficiency and could provide a robust, scalable, data-driven analytical tool for future supply-chain research and policy discussions on commodity market regulation.