Oil Prices, Sustainability Initiatives, and Stock Market Dynamics: Insights from the MSCI UAE Index
Bibliographic record
Abstract
This study examines the interplay between oil price volatility, sustainability-driven initiatives, and the MSCI UAE Index, highlighting the challenges that oil-dependent economies face in balancing financial stability with sustainability transitions. Using a dataset of 2707 daily observations from 2014 to 2024, we applied linear regression, ARCH, GARCH, and TARCH models to analyze volatility dynamics across two key periods: the 2014–2016 oil price collapse and the 2019–2023 phase marked by the COVID-19 pandemic and increasing sustainability efforts. Our findings indicate that oil price fluctuations significantly impact the MSCI UAE Index, with GARCH models confirming persistent volatility and TARCH models revealing asymmetrical effects, where negative shocks intensify market fluctuations. While the initial sustainability policy announcements contributed to short-term volatility and investor uncertainty, they ultimately fostered market confidence and long-term stabilization. Unlike previous studies focusing solely on oil price volatility in emerging markets, this research integrates sustainability policy announcements into financial modeling, providing novel empirical insights into their impact on financial stability in oil-exporting economies. The findings suggest that stabilization funds, dynamic portfolio strategies, and transparent regulatory policies can mitigate oil price volatility risks and enhance market resilience during sustainability transitions, offering valuable insights for investors, policymakers, and financial institutions navigating the UAE’s evolving economic landscape.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".