用动态图神经网络预测金融网络中未来21天的保证金变动。
Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks
- 基于动态图神经网络建模金融交易网络演化。
- 在压力测试下实现21天内保证金变化的精准条件预测。
- 适合监管机构用于系统性风险监测与预警。
我们提出一种新型动态图神经网络（DGNN）架构，用于解决时间金融网络中的条件多步预测问题。该模型在模拟的利率互换（IRS）交易网络数据上验证，该网络模拟了真实市场中金融实体动态交易互换合约、网络结构随参考利率条件演变的特征。所提模型可利用预设压力情景下的条件信息，准确预测长达21天的净变动保证金。研究表明，将网络动态纳入压力测试实践可行，为监管机构和政策制定者提供了关键的系统性风险监控工具。
原文摘要 · Abstract (English)
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.
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