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Financial Regulation in an Agent Based Macroeconomic Model

Author

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

Abstract

Starting from the agent-based decentralized matching macroeconomic model proposed in Riccetti et al. (2012), we explore the effects of banking regulation on macroeconomic dynamics. In particular, we study the overall credit exposure and the lending concentration towards a single counterparty, finding that the portfolio composition seems to be more relevant than the overall exposure for banking stability, even if both features are very important. We show that a too tight regulation is dangerous because it reduces credit availability. Instead, on one hand, too loose constraints could help banks to make money and to increase their net worth, thus making the constraints not binding. However, on the other hand, if bank profits are tied to higher payout ratio (as it really happened along the deregulation phase of the last 20 years), then the financial fragility increases causing a weaker economic environment (e.g., higher mean unemployment rate), a more volatile business cycle, and a higher probability of triggering financial crises. Accordingly, simulation results support the introduction of the Capital Conservation Buffer (Basel 3 reform).

Suggested Citation

  • Riccetti, Luca & Russo, Alberto & Mauro, Gallegati, 2013. "Financial Regulation in an Agent Based Macroeconomic Model," MPRA Paper 51013, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:51013
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    File URL: https://mpra.ub.uni-muenchen.de/51013/1/MPRA_paper_51013.pdf
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    Cited by:

    1. Dosi, G. & Pereira, M.C. & Roventini, A. & Virgillito, M.E., 2017. "When more flexibility yields more fragility: The microfoundations of Keynesian aggregate unemployment," Journal of Economic Dynamics and Control, Elsevier, vol. 81(C), pages 162-186.
    2. repec:spr:italej:v:3:y:2017:i:3:d:10.1007_s40797-017-0060-4 is not listed on IDEAS
    3. Alberto Cardaci & Francesco Saraceno, 2015. "Inequality, Financialisation and Economic Crisis : an Agent-Based Model," Documents de Travail de l'OFCE 2015-27, Observatoire Francais des Conjonctures Economiques (OFCE).
    4. Poledna, Sebastian & Bochmann, Olaf & Thurner, Stefan, 2017. "Basel III capital surcharges for G-SIBs are far less effective in managing systemic risk in comparison to network-based, systemic risk-dependent financial transaction taxes," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 230-246.
    5. Alberto Russo, 2017. "An Agent Based Macroeconomic Model with Social Classes and Endogenous Crises," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 3(3), pages 285-306, November.
    6. Li, Boyao, 2017. "The impact of the Basel III liquidity coverage ratio on macroeconomic stability: An agent-based approach," Economics Discussion Papers 2017-2, Kiel Institute for the World Economy (IfW).
    7. Cardaci, Alberto & Saraceno, Francesco, 2016. "Inequality, Financialisation and Credit Booms - a Model of Two Crises," SEP Working Papers 2016/2, LUISS School of European Political Economy.

    More about this item

    Keywords

    financial regulation; agent-based macroeconomics; business cycle; crisis; unemployment; leverage;

    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
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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