Do sovereign credit rating events affect the foreign exchange market? Evidence from a treatment effect analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We estimate the effect of sovereign credit rating events on the foreign exchange market. Using entropy balancing—a treatment effect methodology that properly addresses the possible self‐selection and endogeneity biases related to rating events—we find robust evidence that a positive (negative) sovereign credit rating event significantly increases (decreases) on average exchange rates, with a larger magnitude for negative events. This effect remains significant under flexible (but not under fixed) exchange rate regimes, and displays asymmetries related to the size of the rating event: in particular, only negative large (i.e., above one notch) rating events trigger a significant response of exchange rates. Lastly, we unveil important nonlinearities related to the initial value of the rating, suggesting a possible amplification mechanism: the impact of positive (negative) rating events is stronger in absolute value if ratings are initially high (low).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 0.003 |
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 it