Identifying policy determinants of bank default risk during the COVID-19 pandemic: empirical evidence from the U.S. and Canada
Bibliographic record
Abstract
This study examines the policy determinants of bank default risk, proxied by credit default swap (CDS) spreads, using quarterly data from the U.S. and Canada for the period 2020 to 2022. The analysis considers four determinant groups: the COVID-19 pandemic shock, aggressive monetary and fiscal policy responses, macroprudential-related bank financial fundamentals, and macroeconomic and market conditions. Utilizing General Least Squares (GLS) and dynamic panel models with system General Method of Moments (GMM), the findings reveal that rising COVID-19 deaths significantly widen CDS spreads. Expansionary monetary policies reduce bank default risk, though the effectiveness under conventional monetary policy is reduced by increasing COVID-19 deaths. In contrast, expansionary fiscal policies and inflation broaden CDS spreads. The combined analysis highlights policy stances as dominant factors, with macroprudential-related fundamentals having minimal impact. The findings are robust using both 5-year and 10-year CDS measures. This study has significant policy implications during health and economic crises. For financial stability, policymakers should prioritize reducing pandemic-related deaths, utilize expansionary monetary policies with health support strategies, balance fiscal support to avoid excessive government spending, and implement measures to control inflation. It is essential to enhance coordination between healthcare, fiscal, and monetary authorities for effective policy implementation.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".