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Stock market dynamics, leveraged network-based financial accelerator and monetary policy

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

Abstract

We build an agent-based model with a threefold financial accelerator: (i) leverage—negative shocks on firms' output make banks less willing to loan funds and firms less willing to invest, and hence a credit reduction follows further reducing the output; (ii) stock market—due to lower profit, firms' capitalization on the stock market decreases, thus the distance-to-default diminishes and it reinforces the leverage accelerator; (iii) network—credit network may propagate the initial shock. We find that stock market volatility may damage the real economy if the stock market is too relevant. Our findings have relevant implications for monetary policy.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:reveco:v:43:y:2016:i:c:p:509-524
    DOI: 10.1016/j.iref.2016.01.012
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    1. Delli Gatti, Domenico & Gallegati, Mauro & Greenwald, Bruce & Russo, Alberto & Stiglitz, Joseph E., 2010. "The financial accelerator in an evolving credit network," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1627-1650, September.
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    8. Riccetti, Luca & Russo, Alberto & Gallegati, Mauro, 2013. "Leveraged network-based financial accelerator," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1626-1640.
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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. Bence Mérõ, 2019. "Novel Modelling of the Operation of the Financial Intermediary System – Agent-based Macro Models," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 18(3), pages 83-113.
    3. Rzeszutek, Marcin & Godin, Antoine & Szyszka, Adam & Augier, Stanislas, 2020. "Managerial overconfidence in initial public offering decisions and its impact on macrodynamics and financial stability: Analysis using an agent-based model," Journal of Economic Dynamics and Control, Elsevier, vol. 118(C).
    4. Carlos M. Fernández-Márquez & Matías Fuentes & Juan José Martínez & Francisco J. Vázquez, 2021. "Productivity and unemployment: an ABM approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(1), pages 133-151, January.
    5. Giri, Federico & Riccetti, Luca & Russo, Alberto & Gallegati, Mauro, 2019. "Monetary policy and large crises in a financial accelerator agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 42-58.
    6. Rémi Stellian & Jenny P. Danna‐Buitrago, 2020. "Financial distress, free cash flow, and interfirm payment network: Evidence from an agent‐based model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(4), pages 598-616, October.
    7. Cao, Guangxi & Zhang, Qi & Li, Qingchen, 2017. "Causal relationship between the global foreign exchange market based on complex networks and entropy theory," Chaos, Solitons & Fractals, Elsevier, vol. 99(C), pages 36-44.
    8. Huub Meijers & Önder Nomaler & Bart Verspagen, 2019. "Demand, credit and macroeconomic dynamics. A micro simulation model," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 337-364, March.
    9. Rémi Stellian & Gabriel I. Penagos & Jenny P. Danna-Buitrago, 2021. "Firms in financial distress: evidence from inter-firm payment networks with volatility driven by ‘animal spirits’," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(1), pages 59-101, January.
    10. David Vidal-Tomás & Rocco Caferra & Gabriele Tedeschi, 2022. "The day after tomorrow: financial repercussions of COVID-19 on systemic risk," Review of Evolutionary Political Economy, Springer, vol. 3(1), pages 169-192, April.
    11. Xiao, Hailian & Zhao, Ying & Zhou, Meihua, 2022. "Can financial factors affect corporate debt leverage convergence?," Pacific-Basin Finance Journal, Elsevier, vol. 72(C).
    12. Deborah Noguera & Gabriel Montes-Rojas, 2023. "Minskyan model with credit rationing in a network economy," SN Business & Economics, Springer, vol. 3(3), pages 1-26, March.
    13. Hosszú, Zsuzsanna & Mérő, Bence, 2017. "Hitelciklusok és anticiklikus tőkepuffer egy ágensalapú keynesi modellben [Credit cycles and the counter-cyclical capital buffer in an agent-based Keynesian model]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 457-475.
    14. Deborah Noguera & Gabriel Montes-Rojas, 2022. "Credit-constrained fluctuations and uncertainty in a network economy," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(80), pages 5-52, November.

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    More about this item

    Keywords

    Agent-based modeling; Stock market; Leverage; Financial accelerator; Monetary policy;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G01 - Financial Economics - - General - - - Financial Crises

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