Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism

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. This paper considers the discrete points of the implied volatility smile surface as an overall prediction target, extracts the daily, weekly, and monthly option implied volatility as input features and establishes a set of LSTM-Attention deep learning systems. Using the dropout mechanism in training reduces the risk of over-fitting. For the prediction results, we use arbitrage-free smoothing to form the final implied volatility smile surface. This article uses the S&P 500 option market to conduct an empirical study. The research shows that the error curve of the LSTM-attention prediction system converges, and the prediction of the implied volatility surface is more accurate than other predicting system. According to the implied volatility surface of the 3-year rolling forecast, the BS formula is used to pricing the option contract, and then a time spread strategy and a butterfly spread strategy are constructed respectively. The experimental results show that the two strategies constructed using the predicted implied volatility surfaces have higher returns and sharp 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