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Marco Bardoscia1, Stefano Battiston2, Fabio Caccioli3

  • 1London Institute for Mathematical Sciences, London, United Kingdom.

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Area of Science:

  • Financial Network Analysis
  • Systemic Risk Modeling
  • Economic Instability Dynamics

Background:

  • Traditional default-cascade models have limitations in assessing financial network shocks.
  • The DebtRank algorithm is a key method for estimating shock impacts but requires refinement.
  • Understanding interbank leverage and shock propagation is crucial for financial stability.

Purpose of the Study:

  • To formulate a dynamical "microscopic" theory of instability for financial networks.
  • To generalize the DebtRank formulation using balance sheet identities and shock transfer rules.
  • To analyze the impact of network structure on financial system stability and loss estimation.

Main Methods:

  • Iterating balance sheet identities of individual banks.
  • Assuming a simple rule for the transfer of shocks from borrowers to lenders.
  • Applying a generalized DebtRank framework to a dataset of top European banks (2008-2013).

Main Results:

  • The dynamical theory provides an interpretation of effective dynamics via accounting principles.
  • The generalized DebtRank prevents underestimation of losses on specific network topologies.
  • Network effects amplified a 0.5% external shock by a factor of 3 in normal periods and 6 during the crisis.

Conclusions:

  • The financial network dynamics can be either stable (analytically computable) or unstable (leading to bank defaults).
  • Network structure significantly influences the amplification of exogenous shocks.
  • The findings highlight the critical role of interbank leverage in systemic risk during financial crises.