The mean–variance (in)efficiency of duration‐based immunization
Bibliographic record
Abstract
Abstract Empirical studies report inconclusive assessment of duration‐based immunization, notably showing that more sophisticated strategies do not outperform immunization relying on Macaulay duration. This article provides a mean–variance framework to explain this puzzle. We characterize the efficient portfolio allocations for a stylized barbell strategy trading off reinvestment risk with discounting risk. We show, in a model‐free setting, that barbell allocations form a convex set in the mean–variance space, and the endpoints of the efficient frontier can switch as time passes, reversing the set of efficient allocations. Consequently, duration‐based immunization, which is not minimum variance, can exhibit temporary inefficiency. This result is numerically illustrated in a one‐factor Gaussian and a two‐factor non‐Gaussian model. Using yield curve scenarios resampled from U.S. data over the 1977–2020 period, we further corroborate our conclusions non‐parametrically, and find that duration‐based immunization is sometimes inefficient.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".