A Direct Approach in the Pricing Analysis and Risk Role Matching of a Guaranteed Annuity Option Under Correlated Risks
Bibliographic record
Abstract
A guaranteed annuity option (GAO) converts an insured’s fund value into a life annuity subject to a guaranteed minimum rate at the policy’s maturity. This type of insurance product is contingent on policyholder’s survival, and it is therefore sensitive to both investment and longevity risks. An adequate quantification of the impact of the underlying variables, including their correlation, in the pricing methodology is necessary to ensure the issuer’s solvency. A pricing framework for GAO that addresses the stochasticity and correlation of these two risks is considered. In comparison to previous approaches of GAO valuation, this proposed method directly evaluates the conditional expectation without resorting to any probability measure changes. We provide an accessible parameter estimation and examination of GAO’s sensitivity to the parameters of the combined models. The accuracy of our estimated parameters is verified and an empirical demonstration making use of actual mortality and financial data are included.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".