AI-DRIVEN DV01 HEDGING OPTIMIZATION VIA NEURAL SURROGATE MODELING AND STRUCTURED LEAST-SQUARES ACCELERATION
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2025
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| _version_ | 1866902087083753472 |
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| author | He Xiaoxia |
| author_facet | He Xiaoxia |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17865315 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-DRIVEN DV01 HEDGING OPTIMIZATION VIA NEURAL SURROGATE MODELING AND STRUCTURED LEAST-SQUARES ACCELERATION He Xiaoxia DV01; hedging; neural operator; GNN; least squares; sparsity <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> |
| title | AI-DRIVEN DV01 HEDGING OPTIMIZATION VIA NEURAL SURROGATE MODELING AND STRUCTURED LEAST-SQUARES ACCELERATION |
| topic | DV01; hedging; neural operator; GNN; least squares; sparsity |
| url | https://doi.org/10.5281/zenodo.17865315 |