  The implied volatility smile surface is the basis of option pricing, and the
dynamic evolution of the option volatility smile surface is difficult to
predict. In this paper, attention mechanism is introduced into LSTM, and a
volatility surface prediction method combining deep learning and attention
mechanism is pioneeringly established. LSTM's forgetting gate makes it have
strong generalization ability, and its feedback structure enables it to
characterize the long memory of financial volatility. The application of
attention mechanism in LSTM networks can significantly enhance the ability of
LSTM networks to select input features. The experimental results show that the
two strategies constructed using the predicted implied volatility surfaces have
higher returns and Sharpe ratios than that the volatility surfaces are not
predicted. This paper confirms that the use of AI to predict the implied
volatility surface has theoretical and economic value. The research method
provides a new reference for option pricing and strategy.
