Has the Degree of Financial Integration, Measured by Correlations and Betas Between GCC Equity Indices and Global Benchmarks, Undergone A Structural Shift Following Major Economic Shocks, Such as the 2014 Oil Price Collapse and the COVID-19 Pandemic?
Bibliographic record
Abstract
This paper examines the evolution of correlations between major Gulf Cooperation Council (GCC) equity indices, namely TASI of Saudi Arabia, DFM of Dubai, and QE of Qatar, and global benchmarks, specifically the S&P 500 and MSCI World, from January 2005 to June 2025. Using regressions on monthly returns, the study evaluates correlations and betas across three subperiods: the pre-oil crash period from 2005 to 2014, the post-oil crash period from 2014 to 2020, and the post-COVID period from 2020 to 2025. Results show that TASI and DFM became more correlated with global indices after 2014, but the evidence does not establish causality. The increase may have reflected broader reforms such as liberalization of foreign ownership rules, the inclusion of Saudi Arabia in MSCI Emerging Markets, and enhanced transparency. Post-COVID, correlations plateaued for TASI and declined for DFM, likely due to renewed domestic trading dominance and regional economic divergence from global recovery patterns. By contrast, QE exhibited persistently weak and statistically insignificant linkages with global benchmarks across all periods. These findings underscore the heterogeneity of GCC markets, the conditional nature of global integration, and implications for diversification, portfolio risk management, and policy design.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".