Influence of COVID-induced fear on sovereign bond yield
Bibliographic record
Abstract
There is limited literature exploring the relationship between the sentiment of fear and bond markets. This study analyzes the influence of fear generated by the coronavirus on bond markets, particularly on the yield of sovereign bond debt issued by the G7 countries (Germany, Canada, the United States, France, Italy, Japan, and the United Kingdom). To accomplish this, search volumes compiled by Google Trends on the topic of coronavirus were used as a proxy for COVID-induced fear. The results from applying a panel data approach for the period from 1 January 2020 to 30 December 2020, show that this fear positively impacts the 10-year sovereign bond yield. We show that a one-point increase in COVID-induced fear was associated with an increase in the weekly change in the sovereign bond yield of around 0.0007%. Thus, we found that COVID-induced fear was associated with an increase in country risk perception. These findings have important implications for policymakers by demonstrating the importance of searching a balance between health concerns and impacts on the economy to avoid increasing country risk. In addition, the results obtained show that in times of greater fear of the coronavirus, investors can obtain higher returns by investing in safe assets, such as sovereign bonds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".