Selective Forgetting in Option Calibration: An Operator-Theoretic Gauss-Newton Framework
Mathematical Finance
2025-11-20 v1 Machine Learning
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.
Cite
@article{arxiv.2511.14980,
title = {Selective Forgetting in Option Calibration: An Operator-Theoretic Gauss-Newton Framework},
author = {Ahmet Umur Özsoy},
journal= {arXiv preprint arXiv:2511.14980},
year = {2025}
}