Valuation and Risk Management of Some Longevity and P&C Insurance Products
Bibliographic record
Abstract
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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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".