MétaCan
Menu
Back to cohort
Record W4416020972 · doi:10.1111/joes.70025

The Monetary Policy–Commodities Nexus: A Survey

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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