An Analysis of Portfolios of Insured Debts
Bibliographic record
Abstract
This article analyzes multi-year risk management decisions in portfolios of insured debts or credit insurance. This is done by investigating risk reduction through portfolio diversification, increased insuring capacity and changes in contracts maturities. We propose a contingent-claims model that includes many realistic features such as coupon payments, stochastic interest rate and stochastic cash flows volatility. We distinguish between two types of portfolios: ‘closed’ and ‘opened’. We find that for a given riskiness level of insurer9s capital, an optimal value of credit insurance can be obtained by appropriate risk diversification and/or increased insurer9s capital. Our simulation results show that for insurers with high risk exposure, portfolio risk diversification is more effective than increasing insuring capacity. For a creditworthy insurer, increasing the size of the insurer’s capital can lead to significant improvement in the value of the credit insurance portfolio. This suggests that alternative risk transfer techniques, which provide synthetic (or contingent) capital to the insurer, should be considered in an integrated risk management. <b>TOPICS:</b>Risk management, credit risk management, portfolio management/multi-asset allocation
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".