Time varying connectedness and volatility spillover among Shariah compliant indices
Bibliographic record
Abstract
Shariah-compliant investing is a subset of the conventional financial market; it follows certain faith-based principles and is also seen as a subset of ESG investing. This study seeks to identify the net transmitters and receivers of volatility within the Shariah compliant equity markets of the US, Europe, Canada, Japan, the UK, Asia Pacific, and South Africa. Using several shariah-compliant Dow Jones Islamic Market (DJIM) indices as well as the shariah-compliant JSE Top 40 index, data is collected from Bloomberg for the period of 22 September 2003 to 31 March 2023. The study employs the dynamic connectedness approach to investigate time-varying interdependence and volatility spillover among the indices during periods of economic crises. The results show that during economic crises, the volatility spillover between the shariah-compliant indices decreases significantly. Therefore, we conclude that shariah compliant indices decouple from each other during economic crises. Additional results show that the DJIM US index is the dominant net transmitter of volatility, while the DJIM Japan index is the main receiver of volatility spillover within the network. This study contributes to the academic literature by including an index from the South African market, incorporating a new methodology, and finally extending the period to incorporate the Russia-Ukraine conflict. The results have implications for individual and institutional investors active in the ESG environment and for the development of shariah-compliant investing.
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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 | 0.000 | 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".