Merging Asset Allocation and Longevity Insurance: An Optimal Perspective on Payout Annuities
Why this work is in the frame
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Bibliographic record
Abstract
The Markowitz mean-variance model is widely accepted as the gold standard for asset allocation on the way to retirement. Unfortunately, this framework only considers the risk and return tradeoff in the financial market. It does not consider the longevity risk people face during retirement. And, while a variety of recent papers in the Journal of Financial Planning have discussed the mechanics and importance of payout (also known as lifetime) annuities, the industry literature currently lacks a coherent and formal model of how much wealth should be allocated in-and-between asset classes within a payout annuity. To fill this gap, our paper revisits the importance of longevity insurance – while discussing the problems with fixed payout annuities-- and then moves on to address the proper asset allocation between conventional financial assets and variable payout annuity products. As in the classical Markowitz framework, our focus is on maximizing a suitably defined objective function in an intuitive, comprehensible, and practical manner. In addition to the usual risk and return information from the financial markets, our modeling framework requires inputs on the relative strength of retiree’s bequest motives, subjective health status, and liquidity restrictions. To illustrate the model, we provide some specific case studies and numerical examples to show how a financial planner can actually apply asset allocation ideas within-and-between payout annuity products and conventional asset classes.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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 it