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Controlling systemic risk: Network structures that minimize it and node properties to calculate it.

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Summary

This study introduces an approximation for evaluating systemic risk in financial networks using only node properties, not interinstitution exposures. It finds that network structures with high systemic risk are assortative, linking risky banks together.

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Area of Science:

  • Financial network analysis
  • Systemic risk modeling

Background:

  • Evaluating systemic risk in financial networks typically requires detailed interinstitution exposure data.
  • Existing methods can be computationally intensive and data-demanding.

Purpose of the Study:

  • To develop an approximate method for systemic risk evaluation using only node properties.
  • To investigate network structures that influence systemic risk amplification.

Main Methods:

  • Adaptation of the DebtRank algorithm to utilize node properties (total assets, liabilities) as inputs.
  • Monte Carlo simulations to explore network topologies and their impact on systemic risk.

Main Results:

  • The proposed approximation effectively captures a significant portion of systemic risk as measured by DebtRank.
  • Scalar assortativity correlates with systemic risk levels: assortative networks (risky banks linked to risky banks) exhibit higher risk.
  • Disassortative networks (risky banks linked to stable banks) show lower systemic risk.

Conclusions:

  • Node properties alone can provide a valuable approximation for systemic risk assessment.
  • Network structure, specifically scalar assortativity, plays a crucial role in amplifying or mitigating systemic risk.