Does FX Volatility Affect the Distributions of Commodity Futures Returns?
Bibliographic record
Abstract
This paper employs a two-step GARCH-M procedure to study price and volatility spillover effects from a series of foreign exchange rate returns to CME Group commodities in the grains, livestock, and energy complexes. Exchange rates are purported to have supply and demand effects on commodity prices and we hypothesize that this translates into transmissions of distributional shocks from currency returns to commodity returns distributions. The currencies are segregated into three groups: large GDP economies and major US trade partners, emerging economies, and pacific rim countries. Our results show that exchange rates for the EU, Canada, Mexico, Brazil, and Australia have the strongest and broadest transmissions of mean innovations to the observed commodities. These are all either major trade partners with the US and/or major exporters of agricultural commodities. Volatility transmissions are much less pronounced and tend to occur for lower liquidity commodities. These results have implications for models considering asset pricing, price discovery, and hedging applications for commodity returns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".