Hedging Mortality/Longevity Risks for Multiple Years
Bibliographic record
Abstract
In this article, we develop strategies of hedging multiyear mortality (longevity) risk for a life insurer (an annuity provider) through purchasing some mortality-linked securities from a financial intermediary. Under the multiyear hedges for a life insurer (an annuity provider) involving two uncertain factors—the mortality rate and the number of life insureds (annuity recipients)—we derive closed-form formulas for the optimal units of purchasing underlying mortality-linked securities. Numerical illustrations show that the downside risk of loss because of mortality (longevity) risk for the life insurer (annuity provider) can be significantly hedged by purchasing the optimal units of mortality-linked securities, and the sample risk can be reduced by increasing the number of life insureds (annuity recipients) at issue. For a financial intermediary, adopting an optimal weight of a portfolio of life and annuity business can reduce extreme losses from the longevity risk but could slightly increase losses from the mortality risk, and the sample risk cannot necessarily be eliminated by increasing the number of life insureds/annuity recipients at issue.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".