Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks
This provides regulators and policymakers with a tool for systemic risk monitoring in financial networks, though it is incremental as it builds on existing graph neural network methods applied to a specific domain.
The paper tackled the problem of forecasting margin calls in financial networks by introducing a Dynamic Graph Neural Network (DGNN) for conditional multi-step ahead predictions, achieving accurate forecasts of net variation margins up to a 21-day horizon under stress test scenarios.
We introduce a novel Dynamic Graph Neural Network (DGNN) architecture for solving conditional $m$-steps ahead forecasting problems in temporal financial networks. The proposed DGNN is validated on simulated data from a temporal financial network model capturing stylized features of Interest Rate Swaps (IRSs) transaction networks, where financial entities trade swap contracts dynamically and the network topology evolves conditionally on a reference rate. The proposed model is able to produce accurate conditional forecasts of net variation margins up to a $21$-day horizon by leveraging conditional information under pre-determined stress test scenarios. Our work shows that the network dynamics can be successfully incorporated into stress-testing practices, thus providing regulators and policymakers with a crucial tool for systemic risk monitoring.