Uç değer için düzeltilmiş lee-carter modelinin tam hayat anüite hesaplamalarindaki ölüm tahmininde kullanımı
Bibliographic record
Abstract
Annuity and its pricing are very critical to the insurance companies for their financial liabilities. Companies aim to adjust the prices of annuity by choosing the forecasting model that fits best to their historical data. While doing it, there may be outliers in the historical data influencing the model. These outliers can be arisen from environmental conditions and extraordinary events such as weak health system, outbreak of war, occurrence of a contagious disease. These conditions and events impact mortality of populations and influence the life expectancy. So, using future mortality estimates that are not generated by the model that includes all of these factors, can influence on the financial strength of the life insurance industry. Therefore, these outliers should be taken into account as well while forecasting mortality rates and calculating annuity prices. Although there are many discrete and stochastic models that can be used to forecast mortality rates, the most widely known and used of these is Lee-Carter model [18]. Fundamentally, Lee-Carter model uses some time-varying parameters and age-specific components. The parameter, which is inspired and used by many other researchers, is the mortality index κt , that Lee and Carter take as the basis in their model. Once, mortality index is forecasted correctly, then death probabilities of individuals and the prices of annuity can be estimated. In case when there exist extremes in the mortality rates, outlier-adjusted model developed by Chan [7] can be used. This approach implements some iteration integrated in original LeeCarter model to find better model that fits to historical data. In this thesis, we aim to find out whether there is a difference between models that consider mortality jumps and models that do not take into account jumps effects in terms of annuity pricing. Finally, we test the annuity vii price fluctuations among different countries and come to conclusion on the effects of different models on country characteristics. For this comparison, Canada as a developed country with high longevity risk and Russia as an emerging country with jumps in its mortality history are considered. In addition to Canada and Russia, data of UK, Japan and Bulgaria are analyzed to provide ease of interpretation in terms of country characteristics. The results of this thesis support the usages of outlieradjusted models for specific countries in term of annuity pricing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".