CHARACTERISTICS OF MODELS FOR ASSESSING THE FINANCIAL STABILITY OF BANKS: DOMESTIC AND FOREIGN EXPERIENCE
Bibliographic record
Abstract
The article notes that the world's central banks implement macroprudential policies with an orientation towards financial stability. It is proven that the variety of models reflects the different relationships between types of crises, their forecasting capabilities and impact on the financial stability of the banking system. It is revealed that methods and models are used to implement macroprudential policies that allow identifying sources of instability within the entire system, assessing potential losses for the real sector of the economy in the event of a crisis, and determining the level of impact of destructive factors on the entire banking system. To assess financial stability, the NBU uses the IMF recommended indicators that reflect capital adequacy, asset quality, profit, profitability, liquidity, risk sensitivity, etc. The methods and models used by the ECB, the Bank of England, the Bank of Canada, and the Bank for International Settlements in the context of studying the impact on financial stability are systematized. The Bank of England's use of the RAMSI stress test has provided an applied toolkit for studying shocks to individual institutions and their impact on the spread of the crisis in the banking system as a whole. It is noted that the Bank of Canada's macrofinancial risk assessment framework (MFRAF) is based on taking into account the risks associated with the solvency, liquidity and capital overflow risk of individual institutions. It is noted that the comprehensive indicator of systemic stress developed by the ECB consists of five segments: money, bond, stock and foreign exchange markets, as well as financial intermediaries. The main models of macroprudential supervision of the ECB are: early warning models, macrostress testing models, crisis spread models, and models that reflect the state of systemic instability. The practice of using the Composite Indicator of Systemic Stress (CISS) is based on the assessment of systemic risk and the level of its impact on financial stability, which characterize: real, corporate, financial, external sectors, the state of household finances and financial markets. It has been proven that in the crisis and post-crisis periods, central banks around the world most often used DSGE (development of dynamic stochastic general equilibrium) models.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".