Using internet search attention to predict the change of option implied volatility: Analysis based on artificial neural network
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2023-07-25
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Journal Article
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Abstract
In this paper, we use the artificial neural network (ANN) method to investigate the effect of internet searches on the implied volatility of options. Based on the S&P 500 index call options, we establish an ANN model that reveals the relationship between the change of option implied volatility and the index return, option delta, and option maturity. The results show that the estimation accuracy of this model is 15% higher than that of the analytical model proposed by Hull and White (2017). This model is also 40% better than the neural network model of Cao, Chen, and Hull (2020). Then, we introduce 25 indicators of internet search attention, integrate them into a Google trends index (GTX), and use it as a comprehensive index to measure internet search attention. Finally, the change rate of the GTX is added to the ANN model mentioned above to explore the dynamic characteristics of option implied volatility from the perspective of internet search attention. The new model further improves the estimation accuracy by 30%.
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published
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Volume
43 (7)
Pages / Article No.
2055 - 2071
Publisher
Zhongguo Xitong Gongcheng Xuehui
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Subject
implied volatility; deep learning; artificial neural network; internet search attention; Google trends