-д хадгалсан:
| Үндсэн зохиолч: | |
|---|---|
| Формат: | Recurso digital |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
Zenodo
2025
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| Нөхцлүүд: | |
| Онлайн хандалт: | https://doi.org/10.5281/zenodo.17865315 |
| Шошгууд: |
Шошго нэмэх
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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Агуулга:
- <p>An AI-assisted framework for accelerating DV01-based interest-rate hedging is presented. Classical bumpand-revalue DV01 computation is computationally intensive when applied to large portfolios or complex pricing engines. The proposed framework integrates a neural operator surrogate for DV01 prediction and a graph neural network (GNN) for learning sparsity patterns in the DV01 matrix. A trust-region reflective (TRF) least-squares solver then exploits surrogate DV01 evaluations and learned sparsity to reduce computational cost while preserving the original hedging formulation. A controlled numerical experiment is further conducted to compare the baseline TRF solver and the proposed hybrid AI-LS method. The results highlight substantial reductions in runtime while maintaining hedging accuracy. </p>