“Feeling Stressed?” A Critical Analysis of the Regulatory Prescribed Stress Tests for Financial Services in the UK
Bibliographic record
Abstract
This paper captures a qualitative review of the regulatory prescribed stress tests for UK financial services designed by the Bank of England and the Prudential Regulation Authority (PRA)/Financial Conduct Authority (FCA) after the Global Financial Crisis. It presents a critical analysis of the use of stress testing as part of supervisory practices for UK banking institutions and insurance undertakings, commenting on their qualitative characteristics, after looking at the regulatory prescribed stress tests from three key categories: the macroeconomic scenarios for banks, denoted as the bank stress tests (BST), the insurance stress tests (IST), and the biennial exploratory scenarios (BES). In this study, five trends describing regulatory prescribed stress are identified: (1) the regulatory collaboration, (2) cross-industry stress tests, (3) exploratory scenarios, (4) reporting and disclosure requirements, and (5) the underlying modelling capabilities and tools. The associated challenges of (A) governance, (B) frequency, (C) individual disclosures, (D) data and modelling, and (E) capabilities and skillset from participating institutions underpinning these stresses are highlighted, shaping the policy recommendations for future exercises. These address the gaps identified from existing stress tests towards the effective prudential supervision of UK financial services, based on each scenario category, for improvements and advances to practices.
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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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".