Efficient Hedging Methodology Applied to Equity-Linked Life Insurance
Bibliographic record
Abstract
In this paper we study efficient hedging and its applications to the pricing of equitylinked life insurance contracts. We devote our attention to the pure endowment contracts with a flexible guarantee. In our setting, these insurance instruments are based on two risky assets of the market controlled by the Black-Scholes model during the contract period. The first asset is responsible for the maximal size of future profit while the second provides a flexible guarantee for the insured. \nThe insurance company is considered as a hedger of a maximum of two risky assets as a contingent claim in this market. The contract is exercised if the insured is still alive at the maturity time and cannot be perfectly hedged in view of a positive survival probability of a client. To provide an appropriate risk-management in connection of such a contract, the company should exploit some imperfect hedging forms. Here we propose the use of efficient hedging with a power loss function. \nSpecifying developments in this area, we create the pricing methodology for the insurance contracts under consideration in terms of a generalized Margrabe’s formula. \nThe results are illustrated by a numerical actuarial analysis with the indices Russell 2000 \nand Dow Jones Industrial Average.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".