Application of deep learning to option hedging strategy

Abstract

The emergence of the digital information age has sparked considerable interest in predictive analytics within the realm of finance. Predicting uncertainty lies at the heart of Financial Engineering, where crafting investment or trading strategies using suitable prediction methods can optimize profits. This paper provides a novel work that approves the application of computer deep learning neural network has ability to predict volatility of the Black-Scholes model and its derivatives. Employing a data-driven methodology, deep learning neural networks are utilized for volatility prediction, investigating their potential to enhance the accuracy of stock volatility forecasts and options pricing. Subsequently, the forecasted outcomes are leveraged for designing and executing hedging strategies, leading to the creation of three delta hedging approaches based on the anticipated stock volatility. The effectiveness of deep learning neural networks in volatility prediction is demonstrated, showcasing their ability to dynamically capture volatility shifts and outperform traditional models in forecasting accuracy.

Keywords

• Deep learning
• Neural networks
• Financial engineering
• Quantitative finance
• Risk quantification