Uç değer için düzeltilmiş lee-carter modelinin tam hayat anüite hesaplamalarindaki ölüm tahmininde kullanımı
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,006 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».