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Selective Forgetting in Option Calibration: An Operator-Theoretic Gauss-Newton Framework

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  • Ahmet Umur Ozsoy

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.

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  • Ahmet Umur Ozsoy, 2025. "Selective Forgetting in Option Calibration: An Operator-Theoretic Gauss-Newton Framework," Papers 2511.14980, arXiv.org.
    • Handle: RePEc:arx:papers:2511.14980
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    File URL: http://arxiv.org/pdf/2511.14980
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