Safe haven currencies: A dependence-switching copula approach
Bibliographic record
Abstract
This paper presents a unique approach to investigating the safe haven properties of five major currencies: the US dollar, the Japanese yen, the Swiss franc, the euro, and the British pound. Unlike other studies, we employ a flexible dependence-switching copula model to examine the joint tail dependence between these currencies and global market risk. This innovative method allows us to directly measure the strength of safe haven currencies when they are most relevant. Using daily data from 1999 to 2024, our empirical results show that the US dollar remains a safe haven currency during periods of heightened global risk aversion. Moreover, the safe haven behavior of the yen persists even in the presence of the US dollar’s appreciation. The Swiss franc exhibits safe haven characteristics, albeit less pronounced than the US dollar, and the euro and the pound demonstrate the weakest safe haven behavior. In addition, we find that the safe haven status of a currency fluctuates over time, with the US dollar being the strongest safe haven currency during periods of major global market turmoil.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".