Capital and Liquidity Risks and Financial Stability: Pre, During and Post Financial Crisis Between Islamic and Conventional Banks in GCC Countries, in the Light of Oil Prices Decline
Bibliographic record
Abstract
Purpose: The main purpose of this paper is to investigate the impact of capital ratio and liquidity risks and the effects of the Global Financial Crisis (GFC) periods on financial stability of conventional and Islamic banks before, during, and after GFC in the year of 2008 five GCC countries. To examine empirically the comparison between conventional and Islamic banks based on financial stability and soundness, as well as capitalization and liquidity in the light of the adverse effects of GFC and oil prices declining during the period of (2000-2017).Design/methodology/approach: By using time series data, this study employs Pedroni’s panel cointegration analysis to test the long run relationship between financial stability of conventional banks and Islamic banks as a dependent variable and independent variables including financial crisis under three periods; pre (2006-2007), during (2008-2009) and post (2010-2011) crisis. As well as employing Generalized Least Squares (GLS) to examine the effects between independent variables which are GDP, inflation, financial crisis periods, oil prices fluctuations risk, banking competition, financial sector development, liquidity risk and capital adequacy ratio and dependent variable which is financial stability of conventional and Islamic banks in GCC countries before (2000-2006) during (2006-2009) and after (2010-2017) crisis.Findings: The findings of this research suggest that there is a long run relationship between financial stability of conventional and Islamic banks and capital ratios, liquidity risk and other independent variables. As well as study’s findings support some previous studies and it generally concludes that Islamic banks were performed better during the crisis than conventional banks. Whereas Islamic banks were more stable during crisis as their business model helped to limit the adverse effects of crisis in 2008, they were also more capitalized and less exposure to liquidity risk. Nevertheless, decrease in Islamic banks’ liquidity led some Islamic banks in GCC countries to declare significant losses in 2009.Originality/value: The result of the study contributes towards understanding the determinants of financial stability of both Islamic banks and conventional banks during financial crisis periods. It 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".