Impact of Outliers in Mortality Rates on the Valuation of Life Annuities
Why this work is in the frame
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Bibliographic record
Abstract
Annuity pricing is essential to insurance companies for their financial liabilities. Therefore, one of the purposes of companies is to adjust the annuity prices using a forecasting model that fits their historical data best. However, historical data may have outliers influencing the model. Extraordinary events such as a weak health system, an outbreak of war, and pandemics like Spanish flu or, more recently, Covid-19may cause outliers resulting in misevaluation of mortality rates. These outliers should be taken into account to preserve the life insurance industry’s financial strength and liability. In this study, we aim to find if there is an impact of mortality outliers in annuity pricing. We analyze the annuity price fluctuations among different countries using two models: Lee-Carter model and Outlier-Adjusted Lee-Carter model. Since the effect of possible outliers in the mortality data may vary according to race, geographic location, economic welfare, and demographic structures, we choose five countries for comparison. Russia and Bulgaria as emerging countries, Canada, Japan, and United Kingdom, as developed countries with high longevity risk, are considered. Moreover, we show the annuity pricing on a simulated diverse portfolio created for the prices of four types of life annuities for a more comprehensive assessment. The results of this study prove the use of outlier-adjusted models for specific countries.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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