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Continual learning for implied volatility surfaces under regime shifts

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  • Zhuang, Jirong
  • Wu, Xuan

Abstract

We study online implied volatility surface (IVS) reconstruction from sparse option quotes under volatility regime shifts. Completing a surface from a small set of quotes benefits from cross-regime knowledge that a persistent model accumulates through sequential updates, but naive updating can cause catastrophic forgetting (the progressive loss of previously learned surface structures), degrading accuracy and arbitrage regularity when a past regime recurs. We propose VolNP-SRCL, a continual learning neural process that mitigates forgetting through VIX-stratified experience replay and anchors surface shapes with a SABR-guided regulariser. Experiments on S&P 500 index options (2016–2023) show that VolNP-SRCL achieves competitive IV errors relative to daily-refit and online baselines while yielding near-zero butterfly arbitrage violations and non-negative implied risk-neutral densities.

Suggested Citation

  • Zhuang, Jirong & Wu, Xuan, 2026. "Continual learning for implied volatility surfaces under regime shifts," Finance Research Letters, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:finlet:v:101:y:2026:i:c:s1544612326005751
    DOI: 10.1016/j.frl.2026.110046
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