IDEAS home Printed from https://ideas.repec.org/a/ipf/finteo/v31y2007i3p249-278.html
   My bibliography  Save this article

A Mathematical Model and Programme Support for Determination of the Values of the Marginal Reserve Requirement as Instrument of Monetary Policy

Author

Listed:
  • Darko Pongrac

    (Croatian National Bank, Zagreb)

  • Kristina Soric

    (Faculty of Economics, Zagreb)

  • Visnja Vojvodic Rosenzweig

    (Faculty of Economics, Zagreb)

Abstract

This paper studies the problem of interdependence between central bank and commercial bank goals. The basic central bank task is to achieve and to maintain price stability. Croatian external debt has been increasing for years and so the activities of the Croatian National Bank are designed to correct this situation. In order to stop the further increase of the external debt, the Croatian National Bank uses several monetary policy instruments, among which is the marginal reserve requirement. On the other hand, the goal of commercial banks is to maximise profits. Banks take loans from abroad at a lower interest rate and invest this money in Croatia at a higher interest rate, thus fulfilling their goal. In order to obtain the desired effects of the marginal reserve requirement, its optimal percentage value should be determined. This problem is modelled as a bi-level mixed 0-1 programming problem. The objective of the leader (Croatian National Bank) is to minimize the increase in household loans by setting different percentages of the reserve requirements for loans extended to households and for those granted to enterprises. The objective of the followers (banks) is to maximize profits. In order to solve this NP-hard problem a heuristic is proposed. In order to verify the model, the paper ends with simulations and the presentation of computational results.

Suggested Citation

  • Darko Pongrac & Kristina Soric & Visnja Vojvodic Rosenzweig, 2007. "A Mathematical Model and Programme Support for Determination of the Values of the Marginal Reserve Requirement as Instrument of Monetary Policy," Financial Theory and Practice, Institute of Public Finance, vol. 31(3), pages 249-278.
  • Handle: RePEc:ipf:finteo:v:31:y:2007:i:3:p:249-278
    as

    Download full text from publisher

    File URL: http://www.ijf.hr/eng/FTP/2007/3/pongrac.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Buyang Cao & Fred Glover, 1997. "Tabu Search and Ejection Chains---Application to a Node Weighted Version of the Cardinality-Constrained TSP," Management Science, INFORMS, vol. 43(7), pages 908-921, July.
    2. Ellison, Martin & Valla, Natacha, 2001. "Learning, uncertainty and central bank activism in an economy with strategic interactions," Journal of Monetary Economics, Elsevier, vol. 48(1), pages 153-171, August.
    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. Ellison, Martin, 2006. "The learning cost of interest rate reversals," Journal of Monetary Economics, Elsevier, vol. 53(8), pages 1895-1907, November.
    2. Alberto Locarno, 2012. "Monetary policy in a model with misspecified, heterogeneous and ever-changing expectations," Temi di discussione (Economic working papers) 888, Bank of Italy, Economic Research and International Relations Area.
    3. Mewael F. Tesfaselassie & Eric Schaling & Sylvester Eijffinger, 2011. "Learning about the Term Structure and Optimal Rules for Inflation Targeting," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(8), pages 1685-1706, December.
    4. Schaling, E., 2003. "Learning, Inflation Reduction and Optimal Monetary Policy," Discussion Paper 2003-74, Tilburg University, Center for Economic Research.
    5. Svensson, Lars E. O. & Williams, Noah, 2006. "Bayesian and adaptive optimal policy under model uncertainty," CFS Working Paper Series 2007/11, Center for Financial Studies (CFS).
    6. Lars E.O. Svensson & Noah Williams, 2009. "Optimal Monetary Policy under Uncertainty in DSGE Models: A Markov Jump-Linear-Quadratic Approach," Central Banking, Analysis, and Economic Policies Book Series, in: Klaus Schmidt-Hebbel & Carl E. Walsh & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series (ed.),Monetary Policy under Uncertainty and Learning, edition 1, volume 13, chapter 3, pages 077-114, Central Bank of Chile.
    7. Maria Demertzis & Nicola Viegi, 2006. "Aiming for the Bull's Eye: Uncertainty and Inertia in Monetary Policy," Computing in Economics and Finance 2006 150, Society for Computational Economics.
    8. Tesfaselassie, M.F. & Schaling, E., 2010. "Managing disinflation under uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 34(12), pages 2568-2577, December.
    9. Spagat, Michael & Rosal, Joao Mauricio, 2002. "Structural Uncertainty and Central Bank Conservatism: The Ignorant Should Keep Their Eyes Shut," CEPR Discussion Papers 3568, C.E.P.R. Discussion Papers.
    10. Lars E. O. Svensson & Noah Williams, 2008. "Optimal monetary policy under uncertainty: a Markov jump-linear-quadratic approach," Review, Federal Reserve Bank of St. Louis, vol. 90(Jul), pages 275-294.
    11. Ehrmann, Michael & Ellison, Martin & Valla, Natacha, 2003. "Regime-dependent impulse response functions in a Markov-switching vector autoregression model," Economics Letters, Elsevier, vol. 78(3), pages 295-299, March.
    12. Robert Tetlow & Peter von zur Muehlen, 2004. "Avoiding Nash Inflation: Bayesian and Robus Responses to Model Uncertainty," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 7(4), pages 869-899, October.
    13. Svensson, Lars E.O., 2010. "Inflation Targeting," Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 22, pages 1237-1302, Elsevier.
    14. Matteo Cacciatore & Dmitry Matveev & Rodrigo Sekkel, 2022. "Uncertainty and Monetary Policy Experimentation: Empirical Challenges and Insights from Academic Literature," Discussion Papers 2022-9, Bank of Canada.
    15. Miller, Marcus & Thampanishvong, Kannika, 2003. "Learning to Forget? Contagion and Political Risk in Brazil," CEPR Discussion Papers 3785, C.E.P.R. Discussion Papers.
    16. Colin Osterman & César Rego, 2016. "A k-level data structure for large-scale traveling salesman problems," Annals of Operations Research, Springer, vol. 244(2), pages 583-601, September.
    17. Alberto Locarno, 2007. "Imperfect Knowledge, Adaptive Learning, and the Bias Against Activist Monetary Policies," International Journal of Central Banking, International Journal of Central Banking, vol. 3(3), pages 47-85, September.
    18. repec:jss:jstsof:23:c02 is not listed on IDEAS
    19. Tesfaselassie, Mewael F., 2008. "Central bank learning and monetary policy," Kiel Working Papers 1444, Kiel Institute for the World Economy (IfW Kiel).
    20. Vito Polito & Peter Spencer, "undated". "UK Macroeconomic Volatility and the Welfare Costs of Inflation," Discussion Papers 11/21, Department of Economics, University of York.
    21. Hauk, Esther & Lanteri, Andrea & Marcet, Albert, 2021. "Optimal policy with general signal extraction," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 54-86.

    More about this item

    Keywords

    monetary policy instruments; commercial banks credit activity; marginal reserve requirement; bi-level mixed 0-1 programming problem; NP-hard problem; heuristic;
    All these keywords.

    JEL classification:

    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

    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:ipf:finteo:v:31:y:2007:i:3:p:249-278. 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: Martina Fabris (email available below). General contact details of provider: https://edirc.repec.org/data/ijfffhr.html .

    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.