Risk‐Based Capital and Credit Insurance Portfolios
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.
Bibliographic record
Abstract
This paper analyzes the risk‐management practices of a vulnerable credit insurer by studying the effects of time‐varying correlations, asset risks and loan maturities on the risk‐based capital that backs credit insurance portfolios. Since asset correlations may change over a business cycle, we have analyzed these effects by means of a one‐factor Gaussian stochastic model as part of an extended contingent claims analysis. Our results show the need to account for cyclical changes to correlations in the pricing of credit insurance. When compared with the reserve of risk‐based capital recommended by the Basel II Internal Ratings‐Based (IRB) approach, our model provides a better capital buffer against extreme credit losses, especially in times of recession and/or in a risky business environment. Using a risk‐adjusted performance metric (RAPM), we find insurers perform better when insuring relatively short‐term loans. We also make several policy recommendations on creating a reserve of risk‐based capital to protect against possible loan losses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it