Selective Forgetting in Option Calibration: An Operator-Theoretic Gauss-Newton Framework
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909913319473152 |
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| author | Özsoy, Ahmet Umur |
| author_facet | Özsoy, Ahmet Umur |
| contents | Calibration of option pricing models is routinely repeated as markets evolve, yet modern systems lack an operator for removing data from a calibrated model without full retraining. When quotes become stale, corrupted, or subject to deletion requirements, existing calibration pipelines must rebuild the entire nonlinear least-squares problem, even if only a small subset of data must be excluded. In this work, we introduce a principled framework for selective forgetting (machine unlearning) in parametric option calibration. We provide stability guarantees, perturbation bounds, and show that the proposed operators satisfy local exactness under standard regularity assumptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14980 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Selective Forgetting in Option Calibration: An Operator-Theoretic Gauss-Newton Framework Özsoy, Ahmet Umur Mathematical Finance Machine Learning Calibration of option pricing models is routinely repeated as markets evolve, yet modern systems lack an operator for removing data from a calibrated model without full retraining. When quotes become stale, corrupted, or subject to deletion requirements, existing calibration pipelines must rebuild the entire nonlinear least-squares problem, even if only a small subset of data must be excluded. In this work, we introduce a principled framework for selective forgetting (machine unlearning) in parametric option calibration. We provide stability guarantees, perturbation bounds, and show that the proposed operators satisfy local exactness under standard regularity assumptions. |
| title | Selective Forgetting in Option Calibration: An Operator-Theoretic Gauss-Newton Framework |
| topic | Mathematical Finance Machine Learning |
| url | https://arxiv.org/abs/2511.14980 |