Complexity in financial markets is slowly overwhelming canonical statistical modelling. With global crises which stemming from contagion effects becoming more frequent, new tools for financial distress transmission c apture are needed. Graph theory , with its branch on minimum spanning trees can...

Current applications of Graph Neural Networks in citywide short-term crash risk prediction have been limited by a gridded representation of space, which restricts the network’s capability to effectively capture the spatial and temporal dependency of crash occurrences. In addition, a grided...

This paper contributes to the financial crisis literature by developing a deep learning model for predicting financial crisis events. We propose a novel gated graph neural network (GGNN) early warning model based on information spillover networks. The spillover network allows us to clarify the...