A novel loss function for neural network models exploring stock realized volatility using Wasserstein Distance

This study proposes a novel loss function for neural network models that explores the topological structure of stock realized volatility (RV) data by adding Wasserstein Distance (WD). The study shows that the proposed loss statistically significantly improves the forecast accuracy of neural network models for magnitude-dependent error measures, for example, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), but not necessarily for relative error measures, such as Quasi-likelihood (QLIKE). Additionally, this research provides user-friendly open-source code for researchers and practitioners to implement the proposed loss function efficiently and quickly. •Propose a novel loss function using Wasserstein Distance to explore stock realized volatility.•Enhance neural network models’ forecast accuracy significantly with the proposed loss function.•Show significant topological structure within realized volatility data.•Present user-friendly open-source code to implement the proposed loss function.