Does Financial Instability of Conventional Banks Affect Financial Stability of Islamic Banks in GCC Countries?
Bibliographic record
Abstract
Purpose: The main purpose of this paper is to investigate the relationship between financial stability in Islamic banks and financial stability and soundness in conventional banks for five GCC countries.Design/methodology/approach: By using time series data, this study employs Pedroni’s panel cointegration to test the long-run relationship between financial stability of Islamic banks and financial stability of conventional banks in GCC countries during the period of (2000-2017). Besides, the study also employs Granger causality to test the causal link between stability of two types of banks (Islamic and Conventional). As well as employing Generalized Least Squares (GLS) to examine the effects between independent variables which are financial stability of conventional banks and their profitability, impact of period of financial crisis (2008/2009), oil prices fluctuations, banking concentration and financial sector development and financial stability of Islamic banks (as the dependent variable).Findings: The findings of this research suggest that there is a long-run, significant and positive relationship between the financial stability of conventional banks and its Islamic counterpart. At the same time, the financial stability of conventional banks is found to Granger caused the stability of Islamic banks.Originality/value: The results of the study contribute towards understanding the determinants of the financial stability of both Islamic banks and conventional banks and how they affect each other. This is important for policy ramifications by the Central Banks in GCC in terms of treating both types of banks differently to mitigate against future financial crises.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".