Multivariate Realized Volatility Forecasting with Graph Neural Network

Financial economics and econometrics literature demonstrate that the limit order book data is useful in predicting short-term volatility in stock markets. In this paper, we are interested in forecasting short-term realized volatility in a multivariate approach based on limit order book data and relational stock market networks. To achieve this goal, we introduce Graph Transformer Network for Volatility Forecasting. The model allows combining limit order book features and a large number of temporal and cross-sectional relations from different sources. Through experiments based on about 500 stocks from S&P 500 index, we find a better performance for our model than for other benchmarks.