Dynamic interdependence of major currencies and the US dollar: a wavelet coherence approach
Bibliographic record
Abstract
Abstract This research employs a wavelet coherence technique to analyze the dynamic inter-connectedness between the US dollar and five major global currencies: Japanese Yen, Canadian Dollar, Euro, British Pound, and Australian Dollar. Daily frequency data from 01/01/2/2019 to 30/08/2024 are used. These currencies are highly correlated and integrated over the long run. The British Pound and Australian Dollar sometimes lead global currency markets despite the US dollar’s dominance. These findings contradict that the US dollar dominates and show that global currency connections are reciprocal. Safe-haven currencies like the Japanese Yen have more significant negative correlations with the US dollar during economic instability, giving investors hedging possibilities. The research shows how macroeconomic variables and global market circumstances shape currency interdependencies and provides insights for investors, policymakers, and risk managers. Recommendations for additional study include investigating geopolitical implications on currency dynamics and expanding the investigation to developing economies. These results have substantial implications for controlling currency risk and creating international monetary policy.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".