Global Monetary Conditions Versus Country-Specific Factors in the Determination of Emerging Market Debt Spreads
Bibliographic record
Abstract
We offer evidence in this paper that US interest rate policy has an important influence in the determination of credit spreads on emerging market bonds over US benchmark treasuries, and therefore on their cost of capital. Our analysis improves upon the existing literature and understanding by addressing the dynamics of market expectations in shaping views on interest rate and monetary policy changes and by recognizing nonlinearities in the link between US interest rates and emerging market bond spreads, as the level of interest rates affects the market's perceived probability of default and the solvency of emerging market borrowers. For a country with a moderate level of debt, repayment prospects would remain good in the face of an increase in US interest rates, so there would be little increase in spreads. A country close to the borderline of solvency would face a steeper increase in spreads. Simulations of a 200 basis points (bps) increase in US short-term interest rates (ignoring any change in the US 10 year Treasury rate) show an increase in emerging market spreads ranging from 6 bps to 65 bps, depending on debt/GDP ratios.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".