HyperIV: Real-time Implied Volatility Smoothing

Yongxin Yang, Wenqi Chen, Chao Shu, Timothy Hospedales
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:70550-70564, 2025.

Abstract

We propose HyperIV, a novel approach for real-time 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 requirements – make HyperIV particularly valuable for real-time trading applications. We make code available at https://github.com/qmfin/hyperiv.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-yang25d, title = {{H}yper{IV}: Real-time Implied Volatility Smoothing}, author = {Yang, Yongxin and Chen, Wenqi and Shu, Chao and Hospedales, Timothy}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {70550--70564}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/yang25d/yang25d.pdf}, url = {https://proceedings.mlr.press/v267/yang25d.html}, abstract = {We propose HyperIV, a novel approach for real-time 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 requirements – make HyperIV particularly valuable for real-time trading applications. We make code available at https://github.com/qmfin/hyperiv.} }
Endnote
%0 Conference Paper %T HyperIV: Real-time Implied Volatility Smoothing %A Yongxin Yang %A Wenqi Chen %A Chao Shu %A Timothy Hospedales %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-yang25d %I PMLR %P 70550--70564 %U https://proceedings.mlr.press/v267/yang25d.html %V 267 %X We propose HyperIV, a novel approach for real-time 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 requirements – make HyperIV particularly valuable for real-time trading applications. We make code available at https://github.com/qmfin/hyperiv.
APA
Yang, Y., Chen, W., Shu, C. & Hospedales, T.. (2025). HyperIV: Real-time Implied Volatility Smoothing. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:70550-70564 Available from https://proceedings.mlr.press/v267/yang25d.html.

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