Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929568780124160 |
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| author | Citterio, Matteo D'Errico, Marco Visentin, Gabriele |
| author_facet | Citterio, Matteo D'Errico, Marco Visentin, Gabriele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23275 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks Citterio, Matteo D'Errico, Marco Visentin, Gabriele Risk Management Machine Learning 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. |
| title | Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks |
| topic | Risk Management Machine Learning |
| url | https://arxiv.org/abs/2410.23275 |