Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks

TL;DR

This paper presents a new Dynamic Graph Neural Network model for accurately forecasting margin calls in financial networks under stress scenarios, aiding systemic risk assessment.

Contribution

The paper introduces a novel DGNN architecture tailored for conditional multi-step ahead forecasting in evolving financial networks, integrating network dynamics into stress testing.

Findings

Accurately forecasts margin variations up to 21 days ahead.

Successfully models dynamic network topology changes.

Enhances stress-testing practices for systemic risk monitoring.

Abstract

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.