Replication Data for: Macroprudential Intermediation Instruments Policy on Mitigating Risk Management Sharia Bank in Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sharia banks must face various operational risks, including shifting from macroeconomic, regulatory factors. The central bank has set minimum policies that must be met by sharia banking in managing risk management so that bank operations can run consistently and prudently under sharia principles. During the pandemic, the central bank has issued stimuli to maintain stability in the financial services sector through a financing restructuring policy for the increase in defaults in the economic recession in Indonesia. And also issued a policy to regulate the Macroprudential Intermediation Ratio to mitigate the impact of increasing risks on the domestic economy. Previous studies stated that macroprudential policies could reduce banks' risk level, but lack of research on Islamic banks. So this study aims to examine the Effectiveness Macroprudential Intermediation Instruments Policy on Mitigating Risk Management Sharia Bank. Using Vector Autoregression and Impulse Response to capture short and long-term impacts along with a causal relationship from 2015 to 2021. This study indicates that the Macroprudential Intermediation Policy effectiveness affects financing and liquidity risks. There's a causal relationship between the Macroprudential Intermediation Policy and financing risk and vice versa, but not in liquidity risk. The response due to shocks between the Macroprudential Intermediation Policy, financing risk, and liquidity risk are not convergent except in the short-term mismatch ratio. So, managing Effectiveness Macroprudential Intermediation Instruments Policy on Mitigating Risk Management Sharia Bank is vital for Islamic banking, because if a shock occurs in this process, the impact will occur in the long term and can lead to bankruptcy.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it