Credit Enhancement and Loan Default Risk Premiums
Bibliographic record
Abstract
Abstract Using contingent claims analysis, we study the impact of private guarantees on the default risk premiums or credit spreads of discount loans. Specifically, we analyze the reduction of the default risk premium on a new junior loan by obtaining the numerical estimates under a stochastic interest rate. We demonstrate how the value of credit enhancement relates to the profitability and size of the new junior loan, as well as the leverage, asset risk, and debt seniority of the borrowing firm and the private insurer. The main results show that (a) for the new junior loan, although the benefits of financial insurance are substantial with an AAA‐rated private insurer, in general, default risk premiums can only be reduced to a minute amount; and (b) even with complete insurance coverage from an AAA‐rated private insurer, loan issues command default risk premiums that reflect not only the intrinsic values and risks of the insured and the insurer, but also their covariance. Résumé Nous examinons l'impact des garanties financières privées sur la structure de risque des taux d'intérêt des prêts escomptés en utilisant l'approche d'évaluation de Brennan (1979) et de Stapleton et Subrahmanyam (1984) qui n'impose pas la condition de perte et de gain nul sur les créanciers. Nous étudions également la réduction (ou augmentation) des primes de risque de défaut des dettes sous le régime de taux d'intérêt stochastique. Pour obtenir des résultats de statique comparée, des simulations numériques ont été entreprises. Les résultats confirment les évidences empiriques provenant du marché d'assurance (et de rehaussement de crédit) des obligations. Nous trouvons que les primes de défaut ne peuvent être éliminées complètement par des endosseurs privés donc vulnérables.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".