Time-Frequency Based Dynamics of Decoupling or Integration between Islamic and Conventional Equity Markets
Bibliographic record
Abstract
This paper investigates the decoupling and integration between the region-wise (Asia, Europe, Africa and the Americas) developed and emerging market’s equity pairs of Islamic and conventional stock returns with the focus on multi-horizons. In doing so, daily wavelet and ADCC-based stock returns correlations are estimated to capture the dynamics of time-frequency and the time-domain based correlations, respectively. The findings of this study indicate that at the short-term horizon, the all selected emerging and developed Islamic and conventional equity markets across all regions depict a high positive correlation, suggesting a rejection of the decoupling hypothesis. However, it is accepted for some of the developed markets of the Pacific region (Hong Kong and New Zealand), Europe (Ireland, Denmark and Spain) and emerging markets of Asia (China), Europe (Czech Republic) and Americas (Argentina and Peru) at a medium-term horizon. Moreover, in an examination of the comparative behaviors of the wavelet and ADCC-based Islamic-conventional correlations, the observed transitional behavior has been exemplified as the difference between the time-frequency and time-domain analysis. This study provides fruitful insights for investors who opt for cross-asset allocation and seek maximum portfolio diversification benefits.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".