Dynamic Connectedness, Spillovers, and Delayed Contagion between Islamic and Conventional Bond Markets: Time‐ and Frequency‐Domain Approach in COVID‐19 Era
Bibliographic record
Abstract
Using the Baruník and Křehlík spillover index, the study examines the dynamic connectedness and spillovers between Islamic and conventional (G6) bond markets to reveal the time‐ and frequency‐domain dynamics of the two asset classes under different market conditions. From August 22, 2012, through September 17, 2021, the daily bond yield indices for Islamic and G6 markets were employed. The findings reveal that volatility spillovers between and within Islamic and/or G6 bond markets are time‐ and frequency‐dependent, although conventional bonds are more volatile than Islamic bonds during Black Swan periods. Across all time horizons, USA, UK, and Canada are the biggest producers of shocks to the Islamic and G6 markets, with Pakistan being the lowest shocks transmitter. During the European debt crisis, Brexit, and COVID‐19 periods, the results underscore delayed contagious spillovers emanating from USA, Canada, and UK. With both the Islamic and G6 bond markets, short‐term spillovers are more important than long‐term spillovers. Investors should use their understanding of market trends and volatility to hedge their holdings against poorer asset returns when volatility spillover is more severe during market turmoil. Spillovers should be closely monitored by policymakers, since they jeopardise cross‐market linkages.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".