Dynamic connectedness between conventional and Islamic stock markets: a quantile network analysis
Bibliographic record
Abstract
Purpose This study aims to examine the quantile connectedness between Islamic and conventional stock markets in a set of eight countries, including developed and developing economies, over a period from August 29, 2014 to September 19, 2024. Design/methodology/approach This study uses the quantile-based connectedness approach, as introduced by Ando et al. (2022), to explore the dynamic connectedness between Islamic and conventional stock markets. Findings The results of this study highlight a significant increase in connectedness between Islamic and conventional stock markets in both the upper and lower quantiles. This study also observes that Islamic indices are markedly responsive to market volatility, typically exhibiting a net recipient of shocks. Conversely, the Japanese Islamic stock market evinces a consistent net transmitter profile across diverse market conditions. Conventional indices display a more diversified behavioral pattern, with the Canadian stock market emerging as the primary net transmitter across different market scenarios. Furthermore, this study reveals pronounced asymmetry in the transmission of shocks and volatility during bearish market periods compared to bullish periods. Research limitations/implications This study addresses an important gap in the financial literature by highlighting the notion of connectedness between Islamic and conventional stock markets, particularly during crises. These findings offer crucial implications for investors and policymakers, helping them to anticipate and manage risk and make informed investment and portfolio diversification decisions. However, it is important to note that many other factors, such as economic, political and regulatory factors, will also play an important role in this connectedness between conventional and Islamic stock markets. Originality/value This study makes a noteworthy contribution to the current financial literature in three ways. Initially, it innovates the analysis of quantile connectedness between the traditional and Islamic stock markets. In addition, it examines connectedness under a range of market including bearish, stable and bullish conditions. By scrutinizing diverse quantiles, this study presents a more profound understanding of this correlation, emphasizing variations and potential asymmetries among the different market states. Such an enhanced analysis is of particular importance in assessing potential risk transmission channels and vulnerability of investments, whether Islamic or conventional. It should be noted that this study analyzes daily data during a period of significant economic and financial events, including the COVID-19 crisis in 2020 and the Russia–Ukraine conflict in 2022.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".