The effects of the European sovereign debt crisis on major currency markets
Bibliographic record
Abstract
Since the European sovereign debt crisis (ESDC), the euro has been weakening, leading currency users to believe that the ESDC has impacted the major currency markets. To examine the basis of the perceptions of currency market participants, we developed a regression model using the relationship between the currency price in terms of the euro and its denominated sovereign bond price. The Australian dollar (AUD), Canadian dollar (CAD), British pound (GBP), Japanese yen (JPY), Swiss franc (CHF) and US dollar (USD) were the sample currencies used in this study. Interestingly, our findings reveal that European sovereign bond investors have three distinct views about the major currency markets: (1) JPY and USD are safe-haven currency and their denominated government bonds are better alternatives in which to invest; (2) AUD- and CAD-denominated government bonds are not trustworthy investments; and (3) GBP- and CHF-denominated bonds are not appropriate investments in the context of the ESDC. This study provides an important lesson for currency users and sovereign bond investors by indicating that the ESDC affected a limited number of currency markets rather than all major currency 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".