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Record W4416259796 · doi:10.32782/infrastruct86-11

CHARACTERISTICS OF MODELS FOR ASSESSING THE FINANCIAL STABILITY OF BANKS: DOMESTIC AND FOREIGN EXPERIENCE

2025· article· W4416259796 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMarket Infrastructure · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicBanking, Crisis Management, COVID-19 Impact
Canadian institutionsnot available
Fundersnot available
KeywordsSystemic riskAsset (computer security)Market liquidityContext (archaeology)Stress testFinancial stabilityFinancial crisisRecapitalizationStock exchangeCapital adequacy ratio

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.285
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it