Exchange rates and commodity prices: measuring causality at multiple horizons
Bibliographic record
Abstract
Different causal mechanisms have been proposed to link commodity prices and exchange rates, with opposing implications. We examine these causal relationships empirically, using data on three commodities (crude oil, gold, copper) and four countries (Canada, Australia, Norway, Chile), over the period 1986–2015. To go beyond pure significance tests of Granger non-causality and provide a relatively complete picture of the links, measures of the strength of causality for different horizons and directions are estimated and compared. Since low-frequency data may easily fail to capture important features of the relevant causal links, daily and some 5-minute data are exploited. Both unconditional and conditional (given general stock market conditions and short-term interest rates) causality measures are considered, and allowance for “dollar effects” is made by considering non-U.S. dollar exchange rates. We identify clear causal patterns: (1) there is evidence of Granger-causality between commodity prices and exchange rates in both directions across multiple horizons, but the statistical evidence and measured intensity of the effects are much stronger in the direction of commodity prices to exchange rates, especially at horizon one: the ratios of causality measures in two different directions can be quite high; (2) causality is stronger at short horizons, and becomes weaker as the horizon increases; (3) conditioning on equity prices (the S&P500) does not change the patterns of causality measures found in the unconditional cases; (4) the main results are robust to eliminating U.S.-dollar denomination effects and including a short-term interest rate as the conditioning variable. In contrast with earlier results on the non-predictability of exchange rates, we find that the macroeconomic/trade-based mechanism plays a central role in exchange-rate dynamics, despite the financial feature of these markets.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".