COVID-19 and the forward-looking stock-bond return relationship
Bibliographic record
Abstract
The COVID-19 pandemic has caused stock market crashes and collapse of economic activities in many countries. As a result, many investors changed their stock and bond market expectations. This study investigates whether the number of COVID-19 confirmed cases influences the forward-looking stock-bond correlations. We apply a quantile approach that is beneficial to explore non-linear relationships between the forward-looking stock-bond return correlations and the COVID-19 cases. The correlations are estimated using the DCC-GARCH model for 21 financial markets from three regions (North American, Asia-Pacific, and Europe). We present empirical evidence that there are heterogeneous responses across regions and countries. Specifically, the negative stock-bond correlations weaken as the number of COVID-19 cases in the regions of North America (the U.S. and Canada) and Asia-Pacific (Australia and Japan) increases. Our results suggest that the number of COVID-19 cases is not important. Investors sell risky stocks and buy safe Treasury bonds at the beginning of the pandemic, while they adjust their portfolios risk levels when they obtain more information. Our result also highlights that this pattern is not observed in European countries.
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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 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".