Responses of the International Bond Markets to COVID-19 Containment Measures
Bibliographic record
Abstract
Using an international sample during the COVID-19 outbreak, our study gives evidence that COVID-19 containment measures impact volatility in the international bond markets in different ways. We found that the positive effect of increasing new COVID-19 vaccinations markedly mitigates bond market volatility, while non-pharmaceutical government interventions resembling bad news increase volatility in bond markets. Besides this, changes in total COVID-19 cases and total deaths have co-movement and a significant relationship with this volatility. Our results imply that the investors’ responses to the trigger of increased uncertainty seem to differ in a way that depends on bad or good news as a reflection of the possibility of pandemic control and the health of the economy. The mass vaccinations not only signal a lower probability of stringent government responses to the pandemic but also stabilize investors’ behavior and mitigate compliance fears to open a period of safe living with coronavirus. Our findings are still robust when using alternative measures of independent variables and different forecasting models of conditional volatility.
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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 | 0.003 | 0.001 |
| 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".