Impact of Brexit on Islamic stock markets: employing MGARCH-DCC and wavelet correlation analysis
Bibliographic record
Abstract
Purpose This paper aims to study the co-movement dynamics of Islamic equity returns to explain international portfolio diversification opportunities for investors having a heterogeneous stock holding period in light of Brexit. Design/methodology/approach The authors use the following three recent methodologies: the multivariate generalised autoregressive conditional heteroskedastic-dynamic conditional correlations, continuous wavelet transforms and maximum overlap discrete wavelet transform. Dow Jones Islamic country-based indexes are used from 2 September 2013 to 31 December 2019. Findings There is a high correlation between the United Kingdom (UK) Islamic stock market return with the Canadian, USA, Malaysian and Indian implying lesser diversification benefits for the investors. However, the results tend to indicate that UK Islamic stock market investors who have allocated their investment in Sri Lanka, Kuwait, Japan and Turkey have enjoyed diversification benefits. Besides, there is a declining correlation between UK Islamic stock markets and other selected markets aftermath of Brexit. Turkey seems the most volatile stock over the period, appealing to risk-lover investors to gain from price changes. When the shock occurs in the financial sector, the volatility is mean-reverting faster than other markets in Sri Lanka. On the other hand, Malaysia appears to have the least volatility implying a stable financial sector. Research limitations/implications The results tend to shed light on effective portfolio diversification benefits in light of the recent shock (Brexit) between the UK Islamic stock index and other selected indexes that vary from country to country depending on investment horizons. This critically confirms the significance of heterogeneity in investment horizons and provides significant inferences for portfolio diversification strategies. Originality/value To the best of the authors’ knowledge, this study is the first study investigating the Brexit effect on Islamic stocks, guiding Shariah sensitive investors in their diversification strategies, providing information to investors to consider the implications of this incident on Islamic stocks for future shocks.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".