Valuation and Risk Management of Some Longevity and P&C Insurance Products
Notice bibliographique
Résumé
Numerous insurance products linked to risky assets have emerged rapidly in the last couple of decades. These products have option-embedded features and typically involve at least two risk factors, namely interest and mortality risks. The need for models to capture risk factors' behaviours accurately is enormous and critical for insurance companies. The primary objective of this thesis is to develop pricing and hedging frameworks for option-embedded longevity products addressing correlated risk factors. Various methods are employed to facilitate the computation of prices and risk measures of longevity products including those with maturity benefits. Furthermore, in order to be prepared for the implementation of the new International Financial Reporting Standards (IFRS) 17, the thesis's secondary objective is to provide a methodology for computing risk margins under the impending regulatory requirements. This is demonstrated using a property and casualty (P&C) insurance example and taking advantage of P&C data availability.\nTo accomplish the above-mentioned objectives, five self-contained but related research works are undertaken and described as follows. (i) A pricing framework for annuities is constructed, where interest and mortality rates are both stochastic and dependent. The short-rate process and the force of mortality follow the two-factor Hull-White model and Lee-Carter model, respectively. (ii) The framework in (i) is further developed by adopting the Cox-Ingersoll-Ross model for the short-rate process to price guarantee annuity options (GAOs). The change of measure technique together with the comonotonicity theory is utilised to facilitate the computation of GAO prices. (iii) A further modelling framework extension is attained by considering a two-decrement model for GAO's valuation and risk measurement. Interest rate, mortality and lapse risks are assumed correlated and they are all modelled as affine-diffusion processes. Risk measures are calculated via the moment-based density method. (iv) We introduce a regime-switching set up for the valuation of guaranteed minimum maturity benefits (GMMBs). A hidden Markov model (HMM) modulates the evolution of risk processes and the HMM-based filtering technique is employed to generate the risk-factor models' parameter estimates. An analytical expression for GMMB value is derived with the aid of the change of measure technique in combination with a Fourier-transform approach. (v) Finally, a paid-incurred chain method is customised to model Ontario's automobile claim development triangular data set over a 15-year period, and the moment-based density method is applied to approximate the distributions of outstanding claim liabilities. The risk margins are determined through risk measures as prescribed by the IFRS 17. Sensitivity analysis is performed for risk margins using the bootstrap method.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».