Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism

TL;DR

This paper introduces a novel deep learning approach combining LSTM and attention mechanisms to improve the prediction of implied volatility smile surfaces, enhancing option pricing accuracy and financial decision-making.

Contribution

It pioneers the integration of attention mechanisms into LSTM for volatility surface prediction, demonstrating improved predictive performance and economic value.

Findings

Predicted volatility surfaces lead to higher returns.

Predicted surfaces increase Sharpe ratios.

The method confirms AI's value in financial forecasting.

Abstract

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.