Co-movements and diversification opportunities among Dow Jones Islamic indexes
Bibliographic record
Abstract
Purpose This paper aims to analyse the opportunity of an exclusive investment in the DJ Islamic indexes. The objective is to characterize the links between MENA region index with seven DJ Islamic indexes. Design/methodology/approach A co-movement analysis was conducted to assess whether there is a safe investment during crisis. The VECM verifies the existence of a long run association. The MGARCH-DCC characterizes the dynamic links. The wavelet coherence detects a correlation in a time-frequency domain, which is relevant to set up a diversification strategy based on investment horizons. Findings Despite the existence of a long run association between the Islamic indexes, diversification opportunities are present. The MGARCH-DCC results recommend including the USA, Canada and Emerging Markets indexes with the Mena index to get diversification benefits. The Wavelet coherence confirms these results for 0 to 16 days holding period and more than six-months’ investment horizons. Hence, MENA portfolio managers should not invest in Europe, UK and Emerging Markets indexes. Research limitations/implications This study focused only on the bivariate correlation analysis without taking into consideration multivariate relationships. Future research should use multiple wavelet coherence and explore S&P Shariah indexes. Practical implications This work is important for investors searching for assets governed by sharia rules, who reject resorting to conventional markets, and policy makers dealing with coordination costs. They would be able to formulate strategies based on the different indexes’ relationships. Originality/value This paper enriches the limited stream of literature focusing only on Islamic indexes. Due to the important development of Islamic Finance in each MENA country, the authors shed the light on this Region’s index.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".