Infectious disease (COVID-19)-related uncertainty and the safe-haven features of bonds markets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 it