Lifetime ruin minimization: should retirees hedge inflation or just worry about it?
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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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".