Neural Networks for Delta Hedging

TL;DR

This paper investigates the use of various deep neural network architectures for delta hedging in financial markets, aiming to improve upon traditional models like Black-Scholes by combining neural methods with classical approaches.

Contribution

It introduces NNHedge, a comprehensive deep learning framework for developing and evaluating neural network-based hedging strategies, and compares multiple neural architectures for this task.

Findings

Neural networks can effectively model delta hedging strategies.

Combining traditional models with neural networks improves hedging performance.

NNHedge streamlines the development and assessment of neural hedging models.

Abstract

The Black-Scholes model, defined under the assumption of a perfect financial market, theoretically creates a flawless hedging strategy allowing the trader to evade risks in a portfolio of options. However, the concept of a "perfect financial market," which requires zero transaction and continuous trading, is challenging to meet in the real world. Despite such widely known limitations, academics have failed to develop alternative models successful enough to be long-established. In this paper, we explore the landscape of Deep Neural Networks(DNN) based hedging systems by testing the hedging capacity of the following neural architectures: Recurrent Neural Networks, Temporal Convolutional Networks, Attention Networks, and Span Multi-Layer Perceptron Networks. In addition, we attempt to achieve even more promising results by combining traditional derivative hedging models with DNN based approaches. Lastly, we construct \textbf{NNHedge}, a deep learning framework that provides seamless pipelines for model development and assessment for the experiments.