Hedging Segregated Fund Guarantees
Bibliographic record
Abstract
Segregated funds have become a very popular investment instrument in Canada. Segregated funds are essentially mutual funds which have been augmented with additional insurance features which provide a guarantee on the initial principal invested after a specified time horizon. They are similar in many respects to variable annuities in the United States. However, segregated funds often have complex embedded optionality. For instance, many contracts provide a reset provision. This allows investors to increase their guarantee level as the value of the underlying mutual fund goes up. These contracts typically also offer features such as mortality benefits, where the guarantee is paid off immediately upon death of the investor. Furthermore, because the payment for the guarantee is usually amortized over the life of the contract, there are additional complications due to investor lapsing. In this work we describe hedging strategies which allow underwriting companies to reduce their risk exposure to these contracts. The hedging techniques incorporate the strengths of both actuarial and financial approaches. In particular, we look at some of the difficulties which arise due to the fact that in many cases the underwriting company is not able to take short positions in the underlying mutual fund. An alternative is to hedge using other actively traded securities, such as index participation units, stock index options, or stock index futures contracts. However, due to the mismatch between the hedging instrument and the segregated fund contract being hedged, there is additional basis risk. We investigate the performance of these types of hedging strategies using stochastic simulation techniques.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".