Effects of maturity choices on loan‐guarantee portfolios1
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this paper is to analyse the effects of the maturities of credit‐enhanced debt contracts on the value of an insurer's loan‐guarantee portfolios. Design/methodology/approach The paper proposes a contingent‐claims model and uses as measure of credit insurance risk, the market value of the private guarantee, which accounts for projects' and guarantor's specific risks, correlations as well as financial leverage. Findings The results indicate that in the case of insuring the debts of two parallel projects with different specific risks, one high‐risk and the other low‐risk, the tradeoff between maturities of the guarantees increases with the projects' expected losses, hence the maturity choice decision is crucial for portfolios subject to high expected losses. For a two sequential projects loan‐guarantee portfolio, the paper finds that, regardless of the order of execution of the projects, it is the maturity of the debt supporting the high‐risk project that drives the risk exposure of the portfolio. Practical implications Since the management of portfolios of guarantees is of significant importance to many organizations both domestically and internationally, this paper proposes a simple and tractable model to gauge the impact of maturity choices for loan‐guarantee portfolios. Originality/value This is a first attempt at modeling multiple maturities in the context of portfolios of vulnerable loan guarantees.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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