Understanding the Behavior and Hedging of Segregated Funds Offering the Reset Feature
Bibliographic record
Abstract
Segregated funds have become an extremely popular Canadian investment vehicle. These instruments provide long-term maturity guarantees and often include complex option features. One controversial aspect is the reset feature, which provides the ability to lock in market gains. Recently, regulators have announced that firms offering these products will be subject to new capital requirements. This paper discusses the effects of volatility, interest rates, investor optimality, and product design on the cost of providing a segregated fund guarantee. For each scenario, the authors provide the appropriate management expense ratio (MER) that should be charged and demonstrate the current liability using a given fixed MER. The paper also investigates intuitive reasons that cause the reset feature to require such a dramatic increase in the hedging costs. Finally, an approximate method for handling the reset feature is presented that can be computed very efficiently, provided the correct proportional fee is charged.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".