Actuarial Modeling and Analysis of the Hong Kong Life Annuity Scheme
Bibliographic record
Abstract
Abstract The Hong Kong Mortgage Corporation (HKMC) Limited, which was established in March 1997 and is wholly owned by the government of the Hong Kong Special Administrative Region, has a major mission to develop and provide different financial retirement instruments to Hong Kong residents to help address the income poverty of retirees. In June 2017, HKMC Annuity Limited, a wholly-owned subsidiary of the HKMC was incorporated to implement a new life annuity scheme which would be launched by mid-2018 to cater for the needs of cash-rich Hong Kong old age residents. The objective of the scheme is to provide an additional financial retirement planning option with minimum credit risk and a certain level of liquidity to the elderly by turning lump-sum premiums into lifetime streams of monthly income at a reasonable and stable return rate. In this paper, we establish an actuarial framework to model this life annuity scheme. The framework enables us to estimate the monthly annuity payments one might receive for a certain amount of investment. It also allows us to analyze the risk entailed in the product, thus shedding light on how the underlying risk can be managed through product design. Our findings will help potential subscribers to understand the scheme and decide whether this scheme should be included in their retirement investment portfolios when it is launched in 2018.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".