提出可选择性遗忘的期权定价模型校准方法，无需重新训练即可剔除过时数据。
Selective Forgetting in Option Calibration: An Operator-Theoretic Gauss-Newton Framework
- 基于算子理论设计高斯-牛顿框架，实现参数化期权模型的渐进式更新。
- 在标准假设下证明了局部精确性，且具有稳定性和扰动边界保证。
- 适合需要频繁更新或合规删除数据的金融建模场景，如高频交易系统。
期权定价模型的校准需随市场变化反复进行，但现有系统缺乏在不重新训练的情况下移除数据的机制。当报价过时、损坏或需满足删除要求时，传统校准流程必须重建整个非线性最小二乘问题，即便仅需排除少量数据。本文提出一种针对参数化期权校准的可选择性遗忘（机器去学习）的原理性框架。我们提供了稳定性保证、扰动界，并证明在标准正则性假设下，所提出的算子满足局部精确性。
原文摘要 · Abstract (English)
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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