Solvency Regulation—An Assessment of Basel III for Banks and of Planned Solvency III for Insurers
Bibliographic record
Abstract
Basel III, regulating the solvency of banks, is to be fully implemented by 2027 while Solvency III directed at insurers is being prepared. In view of past experience, it will be closely modelled after Basel III. This raises two questions. (i) Will Basel III and Solvency III be more successful than their predecessors? (ii) Is it appropriate to continue regulating the solvency of banks and insurers in the same way? The first question is motivated by an earlier finding that Basel I and II risked inducing more rather than less risk-taking by banks, which also holds for Solvency I and II w.r.t. insurers. The methodology applied was to determine the slope of an endogenous perceived efficiency frontier (EPEF) in (μ^,σ^)-space derived from banks’ and insurers’ optimal adjustment to exogenous changes, in expected returns dμ¯ and volatility dσ¯ on the capital market. Both Basel I and II and Solvency I and II neglected the impact of these developments on banks’ and insurers’ EPEF. This neglect had the effect of steepening the EPEF, causing senior management to opt for an increased rather than reduced value of σ^, and hence a lower solvency level. This issue is resolved by Basel III (Principle 5), which requires banks to take developments in the capital market into account in the formulation of their business strategies designed to ensure solvency. In combination with increased capital requirements, this is shown to result in a reduced slope of their EPEF and hence a reduced risk exposure. However, planned Solvency III may cause the EPEF of highly capitalized insurance companies to become steeper, with a concomitant decrease in their risk-taking and an increase of their solvency level. The second question, concerning the appropriateness of the uniformity of solvency regulation directed at banks and insurers, arises because the parameters determining the slope of the respective EPEF are found to crucially differ. Therefore, the uniformity of Basel and Solvency norms creates the risk of a mistaken regulatory focus.
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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 | 0.038 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".