IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v61y2023i3d10.1007_s10614-019-09937-4.html
   My bibliography  Save this article

An Evolutionary Game to Study Banks–Firms Relationship: Monitoring Intensity and Private Benefit

Author

Listed:
  • Giovanni Villani

    (University of Bari)

  • Marta Biancardi

    (University of Foggia)

Abstract

The paper analyzes a dynamic evolutionary game between banks and firms whose interaction has always been characterized by conflictual relationships. Banks would like that the funding is spent to achieve the objectives of the projects submitted, whereas firms would allocate these loans to obtain private benefits. Following replicator dynamics, we show that banks and firms have predator-prey interactions of the Lotka–Volterra type. Misbehaving firms who seek private benefits are “predators” and banks are their “prey”. We analyze the dynamics emerging from the model and we prove that the stability of equilibria depending on the fundamental parameters which describe the banks–firms interaction. In addition, we compare equilibria in terms of Pareto efficiency computing welfare through the average profits with some numerical applications. Finally, we propose a stochastic replicator dynamics approach in order to assume a perturbation in the population growth rate and we suppose as endogenous the monitoring intensity.

Suggested Citation

  • Giovanni Villani & Marta Biancardi, 2023. "An Evolutionary Game to Study Banks–Firms Relationship: Monitoring Intensity and Private Benefit," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1075-1093, March.
  • Handle: RePEc:kap:compec:v:61:y:2023:i:3:d:10.1007_s10614-019-09937-4
    DOI: 10.1007/s10614-019-09937-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-019-09937-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10614-019-09937-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ricardo Azevedo Araujo, 2010. "An evolutionary game theory approach to combat money laundering," Journal of Money Laundering Control, Emerald Group Publishing Limited, vol. 13(1), pages 70-78, January.
    2. Choi, Jay Pil & Stefanadis, Christodoulos, 2015. "Monitoring, cross subsidies, and universal banking," International Journal of Industrial Organization, Elsevier, vol. 43(C), pages 48-55.
    3. Fudenberg, D. & Harris, C., 1992. "Evolutionary dynamics with aggregate shocks," Journal of Economic Theory, Elsevier, vol. 57(2), pages 420-441, August.
    4. Igor V. EVSTIGNEEV & Thorsten HENS & Klaus Reiner SCHENK-HOPPÉ, 2015. "Evolutionary Behavioural Finance," Swiss Finance Institute Research Paper Series 15-16, Swiss Finance Institute.
    5. Carletti, Elena, 2004. "The structure of bank relationships, endogenous monitoring, and loan rates," Journal of Financial Intermediation, Elsevier, vol. 13(1), pages 58-86, January.
    6. Rabah Amir & Igor Evstigneev & Klaus Schenk-Hoppé, 2013. "Asset market games of survival: a synthesis of evolutionary and dynamic games," Annals of Finance, Springer, vol. 9(2), pages 121-144, May.
    7. repec:hhs:iuiwop:487 is not listed on IDEAS
    8. Cabrales, Antonio, 2000. "Stochastic Replicator Dynamics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(2), pages 451-481, May.
    9. D. Friedman, 2001. "Towards evolutionary game models of financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 177-185.
    10. M. Dewatripont & E. Maskin, 1995. "Credit and Efficiency in Centralized and Decentralized Economies," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 62(4), pages 541-555.
    11. Jorgen W. Weibull, 1997. "Evolutionary Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262731215, April.
    12. Stamatios Katsikas & Vassili Kolokoltsov & Wei Yang, 2016. "Evolutionary Inspection and Corruption Games," Games, MDPI, vol. 7(4), pages 1-25, October.
    13. Angelo Antoci & Pier Sacco, 1995. "A public contracting evolutionary game with corruption," Journal of Economics, Springer, vol. 61(2), pages 89-122, June.
    14. Ross Cressman, 2003. "Evolutionary Dynamics and Extensive Form Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262033054, April.
    15. Tsebelis, George, 1989. "The Abuse of Probability in Political Analysis: The Robinson Crusoe Fallacy," American Political Science Review, Cambridge University Press, vol. 83(1), pages 77-91, March.
    16. Holler, Manfred J, 1993. "Fighting Pollution When Decisions Are Strategic," Public Choice, Springer, vol. 76(4), pages 347-356, August.
    17. Daniel M. Covitz & Erik Heitfield, 1999. "Monitoring, moral hazard, and market power: a model of bank lending," Finance and Economics Discussion Series 1999-37, Board of Governors of the Federal Reserve System (U.S.).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gianfranco Gambarelli & Daniele Gervasio & Francesca Maggioni & Daniel Faccini, 2022. "A Stackelberg game for the Italian tax evasion problem," Computational Management Science, Springer, vol. 19(2), pages 295-307, June.
    2. Sergei Belkov & Igor V. Evstigneev & Thorsten Hens, 2020. "An evolutionary finance model with a risk-free asset," Annals of Finance, Springer, vol. 16(4), pages 593-607, December.
    3. Huw Dixon & Ernesto Somma, "undated". "Coordination and Equilibrium selection in mean defined supermodular games under payoff monotonic selection dynamics," Discussion Papers 99/37, Department of Economics, University of York.
    4. W. C. Abram & K. Noray, 2018. "Political Corruption and Public Activism: An Evolutionary Game-Theoretic Analysis," Dynamic Games and Applications, Springer, vol. 8(1), pages 1-21, March.
    5. Weibull, Jörgen W., 1997. "What have we learned from Evolutionary Game Theory so far?," Working Paper Series 487, Research Institute of Industrial Economics, revised 26 Oct 1998.
    6. A. Antoci & S. Borghesi & G. Iannucci, 2016. "Green licenses and environmental corruption: a random matching model," Working Paper CRENoS 201615, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    7. Angelo Antoci & Simone Borghesi & Gianluca Iannucci, 2021. "(Dis)honest bureaucrats and (non)compliant firms in an evolutionary game," Metroeconomica, Wiley Blackwell, vol. 72(2), pages 321-344, May.
    8. Valentina Corradi & Rajiv Sarin, "undated". "Continuous Approximations of Stochastic Evolutionary Game Dynamics," ELSE working papers 002, ESRC Centre on Economics Learning and Social Evolution.
    9. Ginés Hernández-Cánovas & Pedro Martínez-Solano, 2007. "Effect of the Number of Banking Relationships on Credit Availability: Evidence from Panel Data of Spanish Small Firms," Small Business Economics, Springer, vol. 28(1), pages 37-53, January.
    10. Doris Neuberger & Solvig Räthke, 2009. "Microenterprises and multiple bank relationships: The case of professionals," Small Business Economics, Springer, vol. 32(2), pages 207-229, February.
    11. Kanishka Dam & Prabal Roy Chowdhury, 2020. "Race to collusion: Monitoring and incentive contracts for loan officers under multiple-bank lending," Discussion Papers 20-05, Indian Statistical Institute, Delhi.
    12. Berno Buechel & Eike Emrich & Stefanie Pohlkamp, 2016. "Nobody’s Innocent," Journal of Sports Economics, , vol. 17(8), pages 767-789, December.
    13. Josef Montag, 2013. "A Radical Change in Traffic Law: Effects on Fatalities in the Czech Republic," CERGE-EI Working Papers wp484, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    14. Dam, Kaniṣka & Roy Chowdhury, Prabal, 2021. "Monitoring and incentives under multiple-bank lending: The role of collusive threats," Journal of Economic Theory, Elsevier, vol. 197(C).
    15. Claude Fluet & Paolo G. Garella, 2014. "Debt Rescheduling with Multiple Lenders: Relying on the Information of Others," Economica, London School of Economics and Political Science, vol. 81(324), pages 698-720, October.
    16. Dai, Darong, 2012. "On the Existence of Pareto Optimal Endogenous Matching," MPRA Paper 43125, University Library of Munich, Germany.
    17. Alexander J. Stewart & Joshua B. Plotkin, 2015. "The Evolvability of Cooperation under Local and Non-Local Mutations," Games, MDPI, vol. 6(3), pages 1-20, July.
    18. Khalil, Fahad & Martimort, David & Parigi, Bruno, 2007. "Monitoring a common agent: Implications for financial contracting," Journal of Economic Theory, Elsevier, vol. 135(1), pages 35-67, July.
    19. Balkenborg, Dieter & Hofbauer, Josef & Kuzmics, Christoph, 2016. "Refined best reply correspondence and dynamics," Center for Mathematical Economics Working Papers 451, Center for Mathematical Economics, Bielefeld University.
    20. Jean Rabanal & Daniel Friedman, 2014. "Incomplete Information, Dynamic Stability and the Evolution of Preferences: Two Examples," Dynamic Games and Applications, Springer, vol. 4(4), pages 448-467, December.

    More about this item

    Keywords

    Banks–firms relationship; Monitoring intensity; Replicator dynamics; Inspection games;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C79 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Other

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:61:y:2023:i:3:d:10.1007_s10614-019-09937-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.