Bibliographic record
Abstract
Several of the more complex optimization problems in finance can be characterized as impulse control problems. Impulse control problems can be written as quasi-variational inequalities, which are then solved to determine the optimal control strategy. Since most quasi-variational inequalities do not have analytical solutions, numerical methods are generally used in the solution process. \n \nIn this thesis, the impulse control problem framework is applied to value two complex long-term option-type contracts. Both pricing problems considered are cast as impulse control problems and solved using an implicit approach based on either the penalty method or the operator splitting scheme. \n \nThe first contract chosen is an exotic employee stock option referred to as an infinite reload option. This contract provides the owner with an infinite number of reload opportunities. Each time a reload occurs, the owner pays the strike price using pre-owned company shares and, in return, receives one share for each option exercised and a portion of a new reload option. Numerical methods based on the classic Black-Scholes equation are developed while taking into account contract features such as vesting periods. In addition, the value of an infinite reload option to it's owner is obtained by using a utility maximization approach. \n \nThe second long-term contract considered is a variable annuity with a guaranteed minimum death benefit (GMDB) clause. Numerical methods are developed to determine the cost of the GMDB clause while including features such as partial withdrawals. The pricing model is then used to determine the fair insurance charge which minimizes the cost of the contract to the issuer. Due to the long maturity of variable annuities, non-constant market parameters expressed through the use of regime-switching are included in the GMDB pricing model.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".