On the long-term equilibrium of mortality rates among multiple populations
Bibliographic record
Abstract
As human life expectancy continues to increase, longevity risk has become a major concern for pension plan sponsors and annuity providers. To hedge the risk, longevity-linked securities have been developed. Since these securities often have payoffs linked to mortality rates of multiple populations, it is important to investigate the relationship between them. In this thesis, we use England and Wales (EW) and Canadian mortality data for illustration. We consider the long-term equilibrium between the mortality indexes of the two populations through cointegration analysis. Our test shows that structural change occurred in the equilibrium. To capture changes in both equilibrium and autoregression structure, we adopt the Threshold Vector Error Correction Model (TVECM). We find that the TVECM model provides adequate fit to our data. This model is further applied to pricing an illustrative longevity bond. Our numerical results indicate that the changes in the long-term equilibrium have a significant impact on longevity bond prices.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".