  This paper introduces SpotV2Net, a multivariate intraday spot volatility
forecasting model based on a Graph Attention Network architecture. SpotV2Net
represents assets as nodes within a graph and includes non-parametric
high-frequency Fourier estimates of the spot volatility and co-volatility as
node features. Further, it incorporates Fourier estimates of the spot
volatility of volatility and co-volatility of volatility as features for node
edges, to capture spillover effects. We test the forecasting accuracy of
SpotV2Net in an extensive empirical exercise, conducted with the components of
the Dow Jones Industrial Average index. The results we obtain suggest that
SpotV2Net yields statistically significant gains in forecasting accuracy, for
both single-step and multi-step forecasts, compared to a panel heterogenous
auto-regressive model and alternative machine-learning models. To interpret the
forecasts produced by SpotV2Net, we employ GNNExplainer
\citep{ying2019gnnexplainer}, a model-agnostic interpretability tool, and
thereby uncover subgraphs that are critical to a node's predictions.
