Understanding Systemic Risk in the Banking Sector: A MacroFinancial Risk Assessment Framework
Bibliographic record
Abstract
The MacroFinancial Risk Assessment Framework (MFRAF) models the interconnections between liquidity and solvency in a financial system, with multiple institutions linked through an interbank network. The MFRAF integrates funding liquidity risk as an endogenous outcome of the interactions between solvency risk and the liquidity profiles of banks, which is a complementary approach to the new Basel III Liquidity Coverage Ratio framework for Canada. The calibration exercise presented in the article highlights the vulnerability of leveraged institutions to the combination of low cash holdings and excessive dependence on short-term debt funding. As well, by quantifying the trade-offs among higher capital ratios for banks, increased liquid assets or fewer short-term liabilities in reducing risks in the banking system, the MFRAF illustrates that a regulatory framework that properly controls for systemic risk should consider these three factors in a comprehensive manner.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".