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Record W4416020972 · doi:10.1111/joes.70025

The Monetary Policy–Commodities Nexus: A Survey

2025· article· en· W4416020972 on OpenAlexaff
Martin T. Bohl, Niklas Humann, Pierre L. Siklos

Bibliographic record

VenueJournal of Economic Surveys · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsBalsillie School of International Affairs
Fundersnot available
KeywordsMonetary policyFinancializationLeverage (statistics)Shock (circulatory)CommodityMargin (machine learning)Inflation (cosmology)Central bank

Abstract

fetched live from OpenAlex

ABSTRACT This survey synthesizes evidence on the bidirectional links between commodity markets and monetary policy. On the commodities‐to‐policy side, we review how shocks to energy, food, and metals pass through to inflation, inflation expectations, economic activity, and financial stability in state‐dependent ways that vary by shock type, exposure, and policy regime. We complement the literature with an analysis of central‐bank speeches, showing how officials classify commodity shocks and how these framings map into policy stances. On the policy‐to‐commodities side, we organize evidence on the transmission of monetary policy to commodity markets via financial, real‐economy, and expectations channels, highlighting heterogeneity across policy instruments, commodities, and central banks. We emphasize how financialization tightens cross‐asset linkages, raises leverage and margin sensitivity, and amplifies discount‐rate and risk‐taking mechanisms. Overall, commodities are best treated as policy‐sensitive state variables, not exogenous disturbances, with implications for policy design, central bank communication, and international monetary spillovers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.015
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.259
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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