Systemic Risk Analysis on Reconstructed Economic and Financial Networks

Area of Science:

• Complex systems analysis
• Network science
• Statistical physics applications

Background:

• Real-world complex systems, particularly economic and financial networks, suffer from limited data availability due to privacy concerns.
• This data scarcity hinders accurate assessment of systemic resilience against financial shocks, crises, and cascade failures.

Purpose of the Study:

• To develop an innovative method for reconstructing partially accessible complex systems, specifically economic and financial networks.
• To enable accurate estimation of systemic risk in privacy-protected environments.

Main Methods:

• Reconstruction of network structure using intrinsic node properties and limited connection data.
• Calibration of an inference procedure based on statistical physics principles.
• Generation of network ensembles to estimate real system properties via ensemble averaging.

Main Results:

• The method demonstrates remarkable robustness despite significant information limitations.
• Accurate estimation of systemic risk properties is achievable even with partial network data.
• Successful validation on both synthetic and empirical network datasets.

Conclusions:

• The developed method provides a valuable tool for analyzing privacy-protected economic and financial systems.
• It overcomes the challenge of limited information in complex network modeling.
• Enables better insights into systemic risk assessment in sensitive domains.