Understanding the Behaviour and Hedging of Segregated Funds Offering the Reset Feature
Bibliographic record
Abstract
Segregated funds have been an extremely popular Canadian investment vehicle during the past few years. These instruments provide long term maturity guarantees and often include extremely complex option features. One heavily debated feature is the reset feature; the ability to lock in market gains. In typical cases, investors have the ability to do this two or four times per year. The valuation of this embedded optionality has been controversial. Recently, regulators have announced that firms offering these products will be subject to new capital requirements. In this paper we discuss the effects of market parameters, such as volatility and interest rates, on the cost of providing a segregated fund guarantee. We also demonstrate how the level of optimality in investors' use of the reset feature affects the cost of providing such a guarantee. For each scenario, we provide the appropriate management expense ratio (M.E.R.) which should be charged as well as demonstrating the current liability using a given fixed M.E.R. Further, we explore ways of modifying standard contracts so as to reduce the required hedging costs. We also take a closer look at some intuitive reasons why the reset feature requires such a dramatic increase in the hedging costs. Finally, we present an approximate method for handling the reset feature which can be computed very efficiently. This method provides accurate results when the correct proportional fee is being charged.
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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 | 0.002 | 0.017 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".