{"ID":2840269,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14980","arxiv_id":"2511.14980","title":"Selective Forgetting in Option Calibration: An Operator-Theoretic Gauss-Newton Framework","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2511.14980","url_pdf":"https://arxiv.org/pdf/2511.14980v1","authors":"[\"Ahmet Umur Özsoy\"]","published":"2025-11-18T23:47:49Z","proceeding":"q-fin.MF","tasks":"[\"q-fin.MF\",\"cs.LG\"]","methods":"[]","has_code":false}