arxiv_ml 95% Match Research Paper Quantitative Analysts,Risk Managers,Regulators,Machine Learning Researchers in Finance 3 weeks ago

Computing Systemic Risk Measures with Graph Neural Networks

graph-neural-networks › graph-learning

📄 Abstract

Abstract: This paper investigates systemic risk measures for stochastic financial networks of explicitly modelled bilateral liabilities. We extend the notion of systemic risk measures from Biagini, Fouque, Fritelli and Meyer-Brandis (2019) to graph structured data. In particular, we focus on an aggregation function that is derived from a market clearing algorithm proposed by Eisenberg and Noe (2001). In this setting, we show the existence of an optimal random allocation that distributes the overall minimal bailout capital and secures the network. We study numerical methods for the approximation of systemic risk and optimal random allocations. We propose to use permutation equivariant architectures of neural networks like graph neural networks (GNNs) and a class that we name (extended) permutation equivariant neural networks ((X)PENNs). We compare their performance to several benchmark allocations. The main feature of GNNs and (X)PENNs is that they are permutation equivariant with respect to the underlying graph data. In numerical experiments we find evidence that these permutation equivariant methods are superior to other approaches.

Authors (3)

Lukas Gonon

Thilo Meyer-Brandis

Niklas Weber

Submitted

September 30, 2024

arXiv Category

q-fin.CP

arXiv PDF

Key Contributions

Proposes using permutation equivariant neural networks, specifically Graph Neural Networks (GNNs) and (X)PENNs, to compute systemic risk measures and optimal random allocations in stochastic financial networks. This leverages GNNs' ability to handle graph-structured data and permutation equivariance for financial network analysis.

Business Value

Provides financial institutions and regulators with advanced tools to better understand, quantify, and manage systemic risk, contributing to financial stability and informed decision-making.

Paper Metadata

Innovation Type

Application of GNNs

Deployment Feasibility

Moderate, requires expertise in GNNs and financial modeling.

Limitations Addressed

Computational challenges in approximating systemic risk measures and optimal allocations in complex financial networks.

Performance Gains

Demonstrates the effectiveness of GNNs and (X)PENNs for approximating systemic risk measures and optimal allocations.

Technical Tags

systemic riskfinancial networksgraph neural networks (GNNs)permutation equivariant networks(X)PENNsmarket clearingbailout capitaloptimal allocationstochastic networksbilateral liabilities

Research Topics

Financial Risk ManagementGraph Neural NetworksNetwork AnalysisSystemic Risk

Methods & Architectures

Graph Neural Networks (GNNs)(Extended) Permutation Equivariant Neural Networks ((X)PENNs)Market clearing algorithm simulationNumerical approximation Graph Neural Networks (GNNs)(Extended) Permutation Equivariant Neural Networks ((X)PENNs)

Applications & Tasks

Financial Markets Risk Management Systemic Stability Quantifying systemic riskApproximating optimal capital allocationModeling complex financial networks Computing systemic risk measuresFinding optimal random allocationsEstimating minimal bailout capital

Datasets & Benchmarks

Benchmarks

Benchmark allocations

Accuracy of systemic risk approximationPerformance compared to benchmarks

Related Fields

FinanceGraph TheoryMachine LearningNetwork Science

Keywords

systemic riskfinancial networkgraph neural networkGNNpermutation equivariantrisk measurebailoutmarket clearingfinancenetwork analysis

Academic Context

#Financial Risk Management#Graph Neural Networks#Network Analysis#Systemic Risk

Commercial Potential

Potential Products

Systemic risk analysis softwareTools for financial network modeling

Target Industries

FinanceBankingInsuranceRegulatory Bodies

Use Case Examples

Assessing the contagion risk in a banking systemDetermining optimal capital buffers to ensure financial stabilitySimulating the impact of defaults in a complex financial network

Competitive Edge

Applies and adapts Graph Neural Networks to the specific problem of systemic risk measurement in financial networks, offering a data-driven approach.

Market Opportunity

Significant market for financial risk management tools.

Resource Requirements

Compute Needs

Moderate to high, for training GNNs on network data.

Data Requirements

Data representing financial network structure (bilateral liabilities).

Deployment Constraints

Requires accurate representation of the financial network and reliable GNN implementation.

Scalability

Scalability of GNNs to large financial networks is a key advantage.

Production Readiness

Maturity Level

Research

Time to Market

Medium, requires integration into financial risk platforms.

Patent Potential

Moderate, for specific GNN architectures or training methods for financial risk.

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