Resilience Amidst Turbulence: Unraveling COVID-19’s Impact on Financial Stability through Price Dynamics and Investor Behavior in GCC Markets
Bibliographic record
Abstract
Conducted within the backdrop of the COVID-19 pandemic, the paper rigorously investigates the influence of this global crisis on the foreign stock markets of diverse GCC countries. Our research employs a multifaceted approach, combining an adjusted correlation test across six distinct stock markets over a substantial timeframe—from January 2, 2001, to April 31, 2021. Employing sophisticated methodologies including FIEGARCH (1.1), DCC-MGARCH(1,1), and Switching-Markov analyses, we intricately scrutinize the impact of the pandemic on these markets. Our comprehensive analysis uncovers compelling evidence demonstrating the pandemic’s profound effects across the majority of GCC countries’ markets. Notably, these markets exhibit an increased vulnerability to the negative repercussions induced by the COVID-19 crisis. The implications stemming from these findings are far-reaching, particularly in the realm of financial policy-making, risk assessment, asset valuation, and portfolio management strategies. Understanding the heightened susceptibility of these markets during financial downturns is crucial for policymakers, investors, and portfolio managers, empowering them with critical insights to navigate and formulate informed strategies amidst such challenging times.
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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 | 0.007 |
| 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 | 0.001 |
| 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".