High‐water mark fee structure in variable annuities
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper proposes a novel high‐water mark fee structure and investigates its impact on the marketability of variable annuities. To evaluate the welfare effects of holding a variable annuity, we adopt mean‐variance analysis. By also examining the welfare effects of holding two alternative investments, we introduce a quantitative measure, namely a compatible set of risk aversions, to assess the marketability of the variable annuity under a certain fee structure. Comparing the compatible sets and the welfare effects of holding the variable annuity under the high‐water mark fee structure with those under a constant and a state‐dependent fee structure, we find that the high‐water mark fee structure improves the variable annuity's marketability in two aspects: First, it makes the variable annuity preferable to the alternative investments for a broader range of policyholders. Second, when the variable annuity is preferred over the alternative investments, it produces the highest welfare for the policyholder.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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