Critical density for network reconstruction

ArXiv ID: 2305.17285 “View on arXiv”

Authors: Unknown

Abstract

The structure of many financial networks is protected by privacy and has to be inferred from aggregate observables. Here we consider one of the most successful network reconstruction methods, producing random graphs with desired link density and where the observed constraints (related to the market size of each node) are replicated as averages over the graph ensemble, but not in individual realizations. We show that there is a minimum critical link density below which the method exhibits an `unreconstructability’ phase where at least one of the constraints, while still reproduced on average, is far from its expected value in typical individual realizations. We establish the scaling of the critical density for various theoretical and empirical distributions of interbank assets and liabilities, showing that the threshold differs from the critical densities for the onset of the giant component and of the unique component in the graph. We also find that, while dense networks are always reconstructable, sparse networks are unreconstructable if their structure is homogeneous, while they can display a crossover to reconstructability if they have an appropriate core-periphery or heterogeneous structure. Since the reconstructability of interbank networks is related to market clearing, our results suggest that central bank interventions aimed at lowering the density of links should take network structure into account to avoid unintentional liquidity crises where the supply and demand of all financial institutions cannot be matched simultaneously.

Keywords: Network Reconstruction, Interbank Networks, Financial Networks, Graph Ensemble, Liquidity Crises, Interbank Assets/Liabilities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper involves heavy statistical mechanics, random graph theory, and scaling analysis, indicating high mathematical complexity. It relies primarily on theoretical modeling and empirical data for validation but lacks the backtest-ready implementation details typical of quantitative finance strategies.

  flowchart TD
    A["Research Goal:<br/>Identify Critical Density for Network Reconstruction"] --> B["Methodology:<br/>Analyze Reconstruction Method<br/>using Graph Ensembles"]
    B --> C["Data/Inputs:<br/>Interbank Asset/Liability Distributions<br/>(Theoretical & Empirical)"]
    C --> D["Computational Process:<br/>Simulate Networks &<br/>Measure Constraint Deviations"]
    D --> E{"Outcome:"}<br/>E --> F["Unreconstructable Phase<br/>Sparse/Homogeneous Networks<br/>High Constraint Deviation"]
    E --> G["Reconstructable Phase<br/>Dense or Heterogeneous/Core-Periphery<br/>Constraints Matched"]
    F & G --> H["Final Finding:<br/>Network Structure Matters for<br/>Liquidity Interventions"]