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

Hualu Shao, Baicheng Zhou and Shaoqing Gong

Finance Research Letters, 2025, vol. 77, issue C

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.

Keywords: Implied volatility; Convolutional neural network; Deep learning; Financial market (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:77:y:2025:i:c:s1544612325003824

DOI: 10.1016/j.frl.2025.107119

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