Hexagon-net: heterogeneous cross-view aligned graph attention networks for implied volatility surface prediction

Liang, Kaiwei, Liu, Ruirui, Huang, Huichou, Ruf, Johannes ORCID: 0000-0003-3616-2194, Zhao, Peilin and Wu, Qingyao (2025) Hexagon-net: heterogeneous cross-view aligned graph attention networks for implied volatility surface prediction. In: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. (In Press)

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Abstract

Implied Volatility Surface (IVS) prediction is critical for options hedging, portfolio management, and risk control. These applications encounter significant challenges, including imbalanced data distributions and inherent uncertainties in forecasting. Recent advances relying on deep learning have led to significant progress in addressing these issues. However, several key problems have not yet been solved: (i) Moneyness and maturities of traded options change over time. Therefore, proper spatio-temporal alignment is an essential prerequisite for the downstream forecasting exercise. (ii) Different regions of the IVS are unevenly informed because of liquidity constraints and therefore should not be modeled uniformly. (iii) The complex interconnections among data points in the IVS from various perspectives—such as the well-known ‘smirk’ patterns across dimensions—are neither explicitly addressed nor effectively captured by existing models. To address these issues, we propose a novel end-to-end heterogeneous cross(x)-view aligned graph attention network (Hexagon-Net), which aligns historical IVS data, learns distinctive IVS patterns, propagates predictive information, and forecasts future IVS movements simultaneously. Extensive experiments on stock index options datasets demonstrate that Hexagon-Net significantly and consistently outperforms the previous approaches in IVS modeling and deep learning. Additionally, we present further experiments—such as ablation studies, sensitivity analyses, and alternative configurations—to explore the reasons behind its superior performance.

Item Type:	Book Section
Additional Information:	© 2025 The Author(s)
Divisions:	Data Science Institute Mathematics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	02 Jun 2025 08:57
Last Modified:	02 Jun 2025 09:09
URI:	http://eprints.lse.ac.uk/id/eprint/128236

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