Portfolio diversification opportunities for U.S. Islamic investors with its trading partners when the world catches a cold: A Multivariate-GARCH and wavelet approach
Bibliographic record
Abstract
The goal of this study is to analyse the co-movements and the portfolio diversification \nbetween the Islamic index of U.S. and its top trading partners, namely Canada, China, \nMexico, Japan and Germany, using Morgan Stanley Capital International (MSCI) daily \nreturns data from January 2013 to August 2020. We employed three main techniques: \nmultivariate-GARCH-DCC, CWT and MODWT to analyse whether these markets have any \ndiversification opportunities. Our findings reveal that, first, we observed that the U.S. Islamic index and its trading partners showed increased integration after U.S. implemented its first China-specific tariffs in 2018 and were closely integrated during the Covid-19 pandemic in 2020. Second, CWT results show that investors would gain diversification benefits in China and Mexico under specific investment horizons. Third, the results of MODWT shows Japan Islamic index provide short term diversification opportunity and Mexico Islamic index for longer term investments.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".