On Risk Management of Mortality and Longevity Capital Requirement: A Predictive Simulation Approach
Why this work is in the frame
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Bibliographic record
Abstract
In the insurance industry, life insurers are required by regulators to meet capital requirements to avoid insolvency caused by, for example, sudden mortality changes due to the COVID-19 pandemic. To prevent any large movements in this required capital, insurance companies are motivated to establish hedging strategies to mitigate the inherent risk exposures they face. Nonetheless, devising and implementing risk mitigation solutions to risk managing capital requirement is frequently impeded by the computational complexities stemming from the extensive simulations required. In this paper, we delve into a simulation quandary concerning the management of solvency capital risk associated with mortality and longevity. More specifically, we introduce a thin-plate regression spline method as a surrogate alternative to the standard nested simulation approach. Using this efficient simulation method, we further investigate hedging strategies that utilize mortality-linked securities coupled with stochastic mortality dynamics. Our simulation results provide a numerical justification to the market-making of mortality-linked securities in the context of mortality and longevity capital risk management.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it