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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.06707 |
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| _version_ | 1866913741967196160 |
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| author | Huge, Brian Savine, Antoine |
| author_facet | Huge, Brian Savine, Antoine |
| contents | We extend the scope of differential machine learning and introduce a new breed of supervised principal component analysis to reduce dimensionality of Derivatives problems. Applications include the specification and calibration of pricing models, the identification of regression features in least-square Monte-Carlo, and the pre-processing of simulated datasets for (differential) machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06707 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Axes that matter: PCA with a difference Huge, Brian Savine, Antoine Computational Finance We extend the scope of differential machine learning and introduce a new breed of supervised principal component analysis to reduce dimensionality of Derivatives problems. Applications include the specification and calibration of pricing models, the identification of regression features in least-square Monte-Carlo, and the pre-processing of simulated datasets for (differential) machine learning. |
| title | Axes that matter: PCA with a difference |
| topic | Computational Finance |
| url | https://arxiv.org/abs/2503.06707 |