Does the Malaysian Sovereign sukuk market offer portfolio diversification opportunities for global fixed-income investors? Evidence from wavelet coherence and multivariate-GARCH analyses
Bibliographic record
Abstract
Understanding the co-movement among asset returns is a critical issue in finance, as investors can minimize risk through diversification. International investors seek alternative asset classes to diversify their portfolio. Therefore, it would be meaningful to investigate whether sukuk (Islamic bond) offer any advantages in terms of global diversification. In this context, we examined the volatilities and correlations of sovereign bond indexes in developed countries, such as the US, Canada, Germany, the UK, Australia, and Japan, and the Thomson Reuters BPA Malaysia Sukuk Index, using wavelet coherence and multivariate-GARCH analyses. The data cover the period January 2010–December 2015. The results of the study significantly highlight that wavelet coherence illustrates lower co-movement between returns on developed market bond index (the US, the UK, Australia, Canada, Germany, and Japan) with returns on the Malaysian sukuk index during the sample period. Moreover, the Malaysian sukuk market has negative unconditional correlation with the US and Canadian bond markets, which is a good sign of diversification benefits. This study reveals attractive opportunities in terms of diversification benefits, with credit quality and sharia-compliant financial sector exposure for investors who want to invest in fixed-income securities.
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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 | 0.001 |
| 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".