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Prediction of the implied volatility surface–An empirical analysis of the SSE 50ETF option based on CNNs

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  • Shao, Hualu
  • Zhou, Baicheng
  • Gong, Shaoqing

Abstract

With advancements in artificial intelligence, deep learning techniques have been widely used in predicting financial market volatility. This study forecasts the implied volatility of stock options of the top 50 companies listed on the Shanghai Stock Exchange(SSE) using a convolutional neural network (CNN) with a scaled exponential linear unit activation function and no pooling layer. The CNN model is compared to a back-propagation (BP) neural network to evaluate predictive performance. Results show that the CNN model shows superior performance in predicting implied volatility compared to the BP neural network, accurately fitting data patterns as well as smile and term structures.

Suggested Citation

  • Shao, Hualu & Zhou, Baicheng & Gong, Shaoqing, 2025. "Prediction of the implied volatility surface–An empirical analysis of the SSE 50ETF option based on CNNs," Finance Research Letters, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:finlet:v:77:y:2025:i:c:s1544612325003824
    DOI: 10.1016/j.frl.2025.107119
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    References listed on IDEAS

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