Infectious disease (COVID-19)-related uncertainty and the safe-haven features of bonds markets
Bibliographic record
Abstract
Purpose This study aims to examine the hedge, diversifier and safe-haven properties of bonds against infectious disease-related equity market volatility (IDEMV), like COVID-19. Design/methodology/approach The authors apply wavelet coherence methodology on the daily data of IDEMV and bond market (US, UK, Japan, Switzerland, Canada, Australia, Sweden, China and Europe) indices from 1 January 2000 to 14 February 2021. Findings The results show no significant co-movement between these bond indices and IDEMV, thus confirming that they serve as a hedge against IDEMV. However, during the turbulent period like COVID-19, the authors find that the US, UK, Japan, Switzerland, Canada, Australia, Sweden, China and European bond markets act as safe-haven against IDEMV, whereas the UK, US, Japan and Canadian bond markets demonstrate an in-phase and positive co-movement with IDEMV during COVID-19, suggesting their role as a diversifier. Research limitations/implications The study findings are important for investors and portfolio managers regarding risk management, portfolio diversification and investment strategies. Originality/value The authors contribute to the fast growing body of work on the financial impacts of COVID-19 as well as to ongoing consideration of whether a bond is a safe-haven investment.
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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 | 0.002 | 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".