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Robust-yet-fragile: A simulation model on exposure and concentration at interbank networks

Author

Listed:
  • Bulent Ozel

    () (Department of Economics, Universitat Jaume I, Castellón, Spain)

  • Mario Eboli

    () (Department of Economics, Gabriele d’Annunzio University, Italy)

  • Andrea Toto

    () (Department of Economics, Gabriele d’Annunzio University, Italy)

  • Andrea Teglio

    () (LEE & Department of Economics, Universitat Jaume I, Castellón, Spain; Ca Foscari University of Venice, Italy)

Abstract

This paper presents a layered simulation model and the results from its initial employment. In this study, we focus on financial contagion due to debt exposure and structural concentration at interbank networks. Our results suggest that a medium density of connections in regular networks is already sufficient to induce a ’robust-yet-fragile’ response to insolvency shocks, while the same occurs in star networks only when the centralization is very high. The simulation model enables us to create stock-flow-consistent interbank networks with desired level of network connectivity and centralization. A parsimonious set of network configuration parameters can be employed not only to create stylized network structures with exact connectivity and centralization features but also random core-periphery network representations of a two-tier banking system. Our generic setup decouples the steps of a research on financial contagion. The layers of the simulator covers phases of a research from interbank network configuration to probing the details of a contagion. The presented version enables researchers (i) to create an interbank system of a desired network structure, (ii) to initialize bank balance sheets where the network in previous step can optionally be used as an input, (iii) to configure a controlled or randomized sequence of exogenous shock vectors, (iv) to simulate and inspect detailed process of a single contagion process via tables, graphs and plots generated by the simulator, (v) to design and run automated Monte Carlo simulations, (vi) to analyze results of Monte Carlo simulations via tools from the simulation analysis library.

Suggested Citation

  • Bulent Ozel & Mario Eboli & Andrea Toto & Andrea Teglio, 2018. "Robust-yet-fragile: A simulation model on exposure and concentration at interbank networks," Working Papers 2018/15, Economics Department, Universitat Jaume I, Castellón (Spain).
  • Handle: RePEc:jau:wpaper:2018/15
    as

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    References listed on IDEAS

    as
    1. S. Mishkin, Frederic, 1999. "Financial consolidation: Dangers and opportunities," Journal of Banking & Finance, Elsevier, vol. 23(2-4), pages 675-691, February.
    2. in ’t Veld, Daan & van Lelyveld, Iman, 2014. "Finding the core: Network structure in interbank markets," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 27-40.
    3. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2015. "Systemic Risk and Stability in Financial Networks," American Economic Review, American Economic Association, vol. 105(2), pages 564-608, February.
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    7. Beck, Thorsten & Demirguc-Kunt, Asli & Levine, Ross, 2006. "Bank concentration, competition, and crises: First results," Journal of Banking & Finance, Elsevier, vol. 30(5), pages 1581-1603, May.
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    More about this item

    Keywords

    Contagion; interbank networks; two-tear systems; core-periphery networks;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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