Neural Networks for Delta Hedging

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 NNHedge, a deep learning framework that provides seamless pipelines for model development and assessment for the experiments.Keywords Black-Scholes • Neural Derivative Hedging • NNHedge 1. Timely evidence that neural networks score lower profit and loss, when taught traditional hedging strategies.2. Observations that neural networks concentrate more on historical data to calculate present-day delta values.3. The open-sourcing of NNHedge, a deep learning framework for neural derivative hedging. 1