Assessing basis risk in index-based longevity swap transactions
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Bibliographic record
Abstract
Abstract In this paper, we carry out an investigation on modelling basis risk and measuring risk reduction in a longevity hedge constructed by index-based longevity swaps. We derive the fitting procedures of the M7-M5 and common age effect+Cohorts models and define the level of longevity risk reduction. Based on a wide range of hedging scenarios of pension plans, we find that the risk reduction levels are often around 50%–80% for a large plan, while the risk reduction estimates are usually smaller than 50% for a small plan. Moreover, index-based hedging looks more effective under a more precise hedging scheme. We also perform a detailed sensitivity analysis on the hedging results. The most important modelling features are the behaviour of simulated future variability, portfolio size, speed of reaching coherence, data size and characteristics, simulation method, and mortality structural changes.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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