The existing publications demonstrate that the limit order book data is useful in predicting short-term volatility in stock markets. Since stocks are not independent, changes on one stock can also impact other related stocks. In this paper, we are interested in forecasting short-term realized volatility in a multivariate approach based on limit order book data and relational data. To achieve this goal, we introduce Graph Transformer Network for Volatility Forecasting. The model allows to combine limit order book features and an unlimited 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.

翻译：现有出版物表明,定购单簿数据有助于预测股票市场的短期波动性。由于股票不是独立的,一个股票的变化也会影响其他相关股票。在本文中,我们有兴趣根据定购单簿数据和关系数据,以多变方式预测短期已实现的波动性。为实现这一目标,我们引入了波动预测图变器网络。该模型可以将定购单功能和来自不同来源的无限时间和跨部门关系结合起来。通过基于500个S & P 500指数的试验,我们发现我们的模型比其他基准的模型表现更好。