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The implied volatility smile surface is the basis of option pricing, and thedynamic evolution of the option volatility smile surface is difficult topredict. In this paper, attention mechanism is introduced into LSTM, and avolatility surface prediction method combining deep learning and attentionmechanism is pioneeringly established. LSTM's forgetting gate makes it havestrong generalization ability, and its feedback structure enables it tocharacterize the long memory of financial volatility. The application ofattention mechanism in LSTM networks can significantly enhance the ability ofLSTM networks to select input features. The experimental results show that thetwo strategies constructed using the predicted implied volatility surfaces havehigher returns and Sharpe ratios than that the volatility surfaces are notpredicted. This paper confirms that the use of AI to predict the impliedvolatility surface has theoretical and economic value. The research methodprovides a new reference for option pricing and strategy.

Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism