New Solutions to an Age-Old Problem: Innovative Strategies for Managing Pension and Longevity Risk
Bibliographic record
Abstract
The recent wave of innovation in the pension and longevity risk transfer market is barely a decade old, but more than U.S. $470 billion in global transaction activity has taken place, mainly in the United Kingdom, the United States, Canada, and the Netherlands. The main deals have been buy-outs, buy-ins, and longevity swaps for pension schemes. Similar derisking solutions have spread to the market for insured annuities. But transactions must be simplified, standardized, and made available to all pension schemes, regardless of size. They must also cover younger deferred scheme participants, as well as those in collective schemes where intergenerational risks are important. New investors must be brought in, and one way of doing this is via sidecars. Capital relief is important in reducing the costs of insurance-based solutions, such as those involving tail-risk protection; regulators need to become more comfortable with such deals.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".