Lifetime ruin minimization: should retirees hedge inflation or just worry about it?
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Inflation for retirees is different from and mostly higher than the macro-economic (average) inflation rate for the entire population. In the U.S.A, for example, the Consumer Price Index for the Urban population (CPI-U) calculated and reported by the Bureau of Labor Statistics (BLS) has a lesser known cousin called the CPI-E (for the elderly) in which the sub-component weights are based on the consumption patterns of Americans above the age of 62. This suggests that Inflation-Linked Bond Funds (ILBFs) – whose individual component bond adjustments are based on broad population (CPI-U) inflation – might not be the best hedge for individual retirees’ cost of living. But then again, broad shocks to inflation are likely to impact both indices. So, motivated by the question – is it good enough? – the current paper uses lifetime ruin minimization (LRM) techniques to investigate the optimal allocation between an ILBF and a nominal investment fund for a retiree facing an exogenous liability. Our model trades off the benefit of an imperfect hedge against the cost of lower investment growth. However, our numerical results suggest that although ILBFs can be a large part of the optimal retirement portfolio, it should be treated as just another asset class in the broad optimization problem as opposed to a special or unique category.
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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 | 0.000 | 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) | 0.000 | 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