Semi-Robust Risk Minimizing Hedging Strategies
Bibliographic record
Abstract
This thesis explores robust risk-minimizing hedging strategies for contingent claims in incomplete markets with transaction costs, offering a spectrum of tools to balance risk and cost effectiveness. Robust technique applications to finance and insurance have recently gained popularity due to their ability to mitigate model risk. Model risk arises when strategies (or models) become in and out of sync with the market. A model is robust if it can adapt to a wide range of market-dependent factors. However, robust models can be costly and computationally demanding, especially for complex financial and insurance products. Using a multidimensional event tree model, we employ the asymmetric norm as a semi-robust risk measure, integrating asymmetry for customized risk profiles. Three main strategies are developed: a super-replicating approach ensuring full claim coverage at a higher cost, the norm as constraint, which introduces controlled losses to reduce costs, and the norm as objective, minimizing losses directly to enhance capital efficiency. Additionally, self-financing strategies, which require no additional capital injections, offer cost-effective hedging, while portfolio value as state variable strategies allow real-time adjustments, enhancing robustness under volatile conditions. Testing on European call options show that semi-robust strategies - especially norm-constrained and self-financing approaches - maintain low tail risk with minimized cost, demonstrating versatility in adapting to diverse market conditions, investor goals, and risk tolerances while upholding robust risk control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".