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.
@article{arxiv.2410.23275,
title = {Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks},
author = {Matteo Citterio and Marco D'Errico and Gabriele Visentin},
journal= {arXiv preprint arXiv:2410.23275},
year = {2024}
}