  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.
