EXCHANGE RATES AND STOCK MARKET DYNAMICS: ISLAMIC VERSUS CONVENTIONAL FINANCIAL SYSTEMS
Bibliographic record
Abstract
This study investigates the changes and persistence in dynamic connectedness between stock market performance and exchange rate fluctuations, comparing conventional and Islamic financial systems. With an eye on a global financial landscape characterized by the integration of capital markets and the adoption of floating exchange rate regimes, it examines the effects of exchange rate variations on the dynamics of the stock market in nine different countries - the United Kingdom, Australia, Japan, Singapore, Canada, China, India, Korea, and South Africa. The study employs daily data spanning from November 2015 to July 2023 and uses a comprehensive analysis of three-step methodology, including nonparametric causality-in-quantiles tests, asymmetric slope Conditional Autoregressive Value-at-Risk (CAViaR), and Time-Varying Parameter Vector Autoregressive (TVP-VAR) Connectedness measure. Our results underline the asymmetric impact of exchange rate fluctuations on stock markets and highlights the distinctive characteristics of Islamic financial markets. Comparing Islamic and conventional stock markets in the context of exchange rate fluctuations, this study not only serves to fill a gap in the existing literature but also emphasizes the significance of currency exchange rate swings for global investors, policymakers, and practitioners trying to understand the intricacies of global financial markets.
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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 | 0.004 |
| 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 | 0.002 |
| 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".