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Network analysis and calibration of the “leveraged network-based financial accelerator”

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  • Bargigli, Leonardo
  • Gallegati, Mauro
  • Riccetti, Luca
  • Russo, Alberto

Abstract

In this paper we analyze the network structure that endogenously emerges in the credit market of the agent-based model of Riccetti et al. (2011), where two kinds of financial accelerators are at work: the “leverage accelerator” and the “network-based accelerator”. We focus on the properties of network topology and its interplay with the overall economic performance. Moreover, we empirically calibrate the banking network in the model by using Japanese real data.

Suggested Citation

  • Bargigli, Leonardo & Gallegati, Mauro & Riccetti, Luca & Russo, Alberto, 2014. "Network analysis and calibration of the “leveraged network-based financial accelerator”," Journal of Economic Behavior & Organization, Elsevier, vol. 99(C), pages 109-125.
    • Handle: RePEc:eee:jeborg:v:99:y:2014:i:c:p:109-125
    DOI: 10.1016/j.jebo.2013.12.018
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    Cited by:

    1. Luca Riccetti & Alberto Russo & Mauro Gallegati, 2022. "Firm–bank credit network, business cycle and macroprudential policy," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(2), pages 475-499, April.
    2. Wolski, Marcin & van de Leur, Michiel, 2016. "Interbank loans, collateral and modern monetary policy," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 388-416.
    3. Gerard Ballot & Antoine Mandel & Annick Vignes, 2015. "Agent-based modeling and economic theory: where do we stand?," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 199-220, October.
    4. Andre R. Neveu, 2018. "A survey of network-based analysis and systemic risk measurement," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(2), pages 241-281, July.
    5. Yuri Biondi & Feng Zhou, 2019. "Interbank credit and the money manufacturing process: a systemic perspective on financial stability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 437-468, September.
    6. Tedeschi, Gabriele & Recchioni, Maria Cristina & Berardi, Simone, 2019. "An approach to identifying micro behavior: How banks’ strategies influence financial cycles," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 329-346.
    7. P. Tasca & S. Battiston, 2016. "Market procyclicality and systemic risk," Quantitative Finance, Taylor & Francis Journals, vol. 16(8), pages 1219-1235, August.
    8. Riccetti, Luca & Russo, Alberto & Gallegati, Mauro, 2016. "Stock market dynamics, leveraged network-based financial accelerator and monetary policy," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 509-524.
    9. Catullo, Ermanno & Gallegati, Mauro & Russo, Alberto, 2022. "Forecasting in a complex environment: Machine learning sales expectations in a stock flow consistent agent-based simulation model," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    10. Raffaele Giammetti & Alberto Russo & Mauro Gallegati, 2020. "Key sectors in input–output production networks: An application to Brexit," The World Economy, Wiley Blackwell, vol. 43(4), pages 840-870, April.

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    More about this item

    Keywords

    Network; Bankruptcy cascades; Calibration; Leverage;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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