Intra-Safe Haven Currency Behavior During the Global Financial Crisis
Bibliographic record
Abstract
We investigate intra-safe haven currency behavior during the recent global financial crisis. The currencies we consider are the USD, the JPY, the CHF, the EUR, the GBP, the SEK, and the CAD. We first assess which safe haven currency appreciates the most as market uncertainty increases, i.e. we assess which safe haven currency is the "safest". We then use non-temporal threshold analysis to investigate whether intra-safe haven currency behavior changes, e.g. accelerates or decelerates, as market uncertainty increases. We find that the JPY is the "safest" of safe haven currencies and that only the JPY appreciates as market uncertainty increases regardless of the prevailing level of uncertainty. For all other currencies under study we find significant market uncertainty threshold effects. We extend our analysis to also consider intra-safe haven currency behavior before and after the global financial crisis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".