Impact of Outliers in Mortality Rates on the Valuation of Life Annuities
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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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".