HyperIV: Real-time Implied Volatility Smoothing
Yongxin Yang, Wenqi Chen, Chao Shu, Timothy M. Hospedales
摘要
We propose HyperIV, a novel approach for realtime implied volatility smoothing that eliminates the need for traditional calibration procedures. Our method employs a hypernetwork to generate parameters for a compact neural network that constructs complete volatility surfaces within 2 milliseconds, using only 9 market observations. Moreover, the generated surfaces are guaranteed to be free of static arbitrage. Extensive experiments across 8 index options demonstrate that HyperIV achieves superior accuracy compared to existing methods while maintaining computational efficiency. The model also exhibits strong cross-asset generalization capabilities, indicating broader applicability across different market instruments. These key features -rapid adaptation to market conditions, guaranteed absence of arbitrage, and minimal data requirementsmake HyperIV particularly valuable for real-time trading applications. We make code available at https://github.com/qmfin/hyperiv .
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- Implicit Neural Representations with Periodic Activation FunctionsVincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell 等NeurIPS 2020 · 被引用 4,008 次
- Deep Smoothing of the Implied Volatility SurfaceDamien Ackerer, Natasa Tagasovska, Thibault VatterNeurIPS 2020 · 被引用 68 次
- Operator Deep Smoothing for Implied VolatilityRuben Wiedemann, Antoine Jacquier, Lukas GononICLR 2025
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