Oil shocks and the Islamic financial market: Evidence from a causality-in-quantile approach
Bibliographic record
Abstract
This study examines the nonlinear relationship between Islamic stock indices and oil shocks. Nonlinearity is viewed from the prism of nonparametric causality-in-quantile, and oil price is decomposed into demand, supply, and risk. The objective of this study is to examine the causality between sectoral Islamic stocks and oil shocks. Using a dataset for ten sectoral Islamic stock indices, we show that causality between the variables of interest is heterogenous across (i) measures of shocks (i.e., demand, supply, or risk), (ii) types of the sector (i.e., the ten sectors), (iii) state of the market (bear, normal, bull) and (iv) model specifications (mean vs. variance equation). We find that for the US, sectoral returns, demand and risk shocks affect Industrial, Information Technology, and ESG sectors across all quantiles, while supply shocks cause changes across normal market conditions. The US healthcare sector remains insensitive and the communications sector is affected only across extreme quantiles. Each oil shock exhibits a significant causal effect on Asian Pacific and Emerging Islamic markets consistently across all quantiles. Developed and European Islamic markets remain sensitive to risk-related shocks. Policy implications of these results are discussed. • We examine nonlinear relationship between Islamic finance and oil shocks for ten sectoral Islamic finance stocks. • We use nonparametric causality-in-quantile, and decompose oil price into demand, supply, and risk. • We show that causality between the variables of interest is heterogenous across. (i) measures of shocks (i.e., demand, supply, or risk) (ii) types of the sector (i.e., the ten sectors) (iii) state of the market (bear, normal, bull) (iv) model specifications (mean vs. variance equation)
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".