Achieving financial stability during a liquidity crisis: a multi-objective approach


Following the financial crisis of 2007, regulators undertook an ample process of redefinition of the necessary objectives to achieve financial stability. Nevertheless, it is highly likely that the achievement of a specific stability goal precludes the possibility of pursuing other stability goals during financial turmoil. Once considered in this way, financial stability can be immediately translated into a multi-objective decision-making problem. In this paper, we analyze some possible trade-offs faced by a regulator in order to preserve financial stability during a liquidity crisis. To this end, we employ a model of liquidity cascades applied to credit networks and we determine the best among different policy options relying on the MOORA (multi-objective optimization on the basis of ratio analysis) method.

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  1. 1.

    As for the Common Core Tier 1, it changed from 2% in 2012 to 3.5% in 2013. Then, it raised to 4.5% (Biondi and Graeff 2020).

  2. 2.

    Usually, Master Agreements are a type of agreement reached amongst parties which define the terms regulating one or more transactions as well as the services characterizing the object of the contract. Within the financial community, bank and sector associations tend to create and present some typified schemes of Master Agreements in order to standardize operators’ behaviors and therefore lower the uncertainty and the bargaining costs of the operations.

  3. 3.

    Indeed, closing out clauses are recognized under the regulatory framework outlined in the Basel Accords (for some examples see: British Bank Association 2002; Directive 2002/47/EC, as amended by Directive 2009/49/EC).

  4. 4.

    As far as we know, only a few contributions investigated the macroprudential implications of OnBS netting. Moreover, all of them considered crises triggered by an asset shock (see Gaffeo et al. 2019b for an overview). This is the first contribution dealing with the impact of OnBS netting on financial stability during a liquidity crisis.

  5. 5.

    The relationship among central counterparties, netting and financial stability is widely known in the literature, among others see Bliss and Kaufman (2006), ECB (2007), Duffie and Zhu (2011), Galbiati and Soramaki (2013).

  6. 6.

    This is the number used in the pioneering work of Nier et al. (2007). We refer to Gaffeo and Molinari (2015) for a discussion aimed at justifying this choice.

  7. 7.

    The balance is stylized in order to be comparable with Lee (2013). For the sake of simplicity, typical balance sheet items such as bonds are not considered. This choice is consistent with the type of Lee’s algorithm we will use, which calculates the liquidity needs of financial intermediaries in the early stages of a bank run.

  8. 8.

    The model is a classic bank run model. For a contextualization of the phenomenon of bank runs and their effects on bank balance sheets (Biondi 2018; Boyer 2013).

  9. 9.

    In this simple example, as in the paper Lee (2013), we use a considerable and unrealistic shock in order to show the dynamics of contagion.

  10. 10.

    See Theorem 2.2 in Hurd (2016, pp. 25–27).

  11. 11.

    The withdrawal of external deposits concerns the moment of the bank run while the withdrawal of interbank deposits concerns the requests for withdrawal of own deposits from one bank to another.

  12. 12.

    The initialization procedure of bank balance sheets for this topology has been presented in Table 1.


  1. Allen, W.A., and G.E. Wood. 2006. Defining financial stability. Journal of Financial Stability 2: 152–172.

    Article  Google Scholar 

  2. Anand, K., P. Gai, S. Kapadia, S. Brennan, and M. Willison. 2013. A network model of financial system resilience. Journal of Economic Behavior and Organization 85: 219–235.

    Article  Google Scholar 

  3. Bargigli, L., and G. Tedeschi. 2014. Interaction in agent-based economics: a survey on the network approach. Physica A: Statistical Mechanics and its Applications 399: 1–15.

    Article  Google Scholar 

  4. Battiston, S., D. Delli Gatti, M. Gallegati, B. Greewald, and J. Stiglitz. 2012. Liaisons dangereuses: Increasing connectivity, risk sharing, and systemic risk. Journal of Economic Dynamics and Control 36: 1121–1141.

    Article  Google Scholar 

  5. Bech, M.L., C.T. Bergstrom, M. Rosvall, and R.J. Garratt. 2015. Mapping change in the overnight money market. Physica A: Statistical Mechanics and its Applications 424: 44–51.

    Article  Google Scholar 

  6. Biondi, Y. 2018. Banking, money and credit: A systemic perspective. Accounting Economics and Law: A Convivium 8: 1–26.

    Google Scholar 

  7. Biondi, Y. and I.J. Graeff. 2020. Between prudential regulation and shareholder value: An empirical perspective on bank shareholder equity (2001–2017). Accounting, Economics, and Law: A Convivium, forthcoming.

  8. Biondi, Y., and F. Zhou. 2019. Interbank credit and the money manufacturing process: A systemic perspective on financial stability. Journal of Economic Interaction and Coordination 14: 437–468.

    Article  Google Scholar 

  9. Bliss, R.R., and G.G. Kaufman. 2006. Derivatives and systemic risk: Netting, collateral, and closeout. Journal of Financial Stability 2: 55–70.

    Article  Google Scholar 

  10. Borio, C. 2003. Towards a macroprudential framework for financial supervision and regulation? BIS Working Paper.

  11. Borio, C. and M. Drehmann. 2009. Towards an operational framework for financial stability: “fuzzy” measurement and its consequences. BIS Working Paper No. 284.

  12. BoE. 2019. Financial stability report, n. 46.

  13. Boyer, R. 2013. The global financial crisis in historical perspective: An economic analysis combining Minsky Hayek, Fisher, Keynes and the regulation approach. Accounting, Economics, and Law: A Convivium 3: 93–139.

    Google Scholar 

  14. Brandi, G., R. Di Clemente, and G. Cimini. 2016. Epidemics of liquidity shortages in interbank markets. arXiv:1610.03259v1.

  15. Brans, J.P., B. Mareschal, and P. Vincke. 1984. Prométhée: A new family of outranking methods in multicriteria analysis. In Proceedings of the IFORS 84 conference, Washington, 408–421.

  16. Brauers, W.K. 2004a. Optimization methods for a stakeholder society, a revolution in economic thinking by multi-objective optimization, Nonconvex Optimization and its Applications, vol. 73. Boston: Kluwer Academic/Springer.

  17. Brauers, W.K. 2004b. Multi-objective optimization for facilities management. Journal of Business Economics and Management 5: 173–182.

    Article  Google Scholar 

  18. Brauers, W.K. 2004c. Multiobjective optimization (MOO) in privatization. Journal of Business Economics and Management 5: 59–66.

    Article  Google Scholar 

  19. Brauers, W.K., and E.K. Zavadskas. 2010. Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy 16: 5–24.

    Article  Google Scholar 

  20. Brauers, W.K., and E.K. Zavadskas. 2012. Robustness of MULTIMOORA: A method for multi-objective optimization. Informatica 23 (1): 1–25.

    Article  Google Scholar 

  21. British Banking Association. 2002. Deposit netting, Policy Brief.

  22. Caccioli, F., P. Barucca, and K. Teruyoshi. 2017. Network models of financial risk. A review. Kobe University, Discussion Paper No.1719.

  23. Caccioli, F., T.A. Catanach, and J.D. Farmer. 2012. Heterogeneity, correlations and financial contagion. Advances in Complex Systems 15: 1250058.

    Article  Google Scholar 

  24. Capponi, A., and P.-C. Chen. 2015. Systemic risk mitigation in financial networks. Journal of Economic Dynamics and Control 58: 152–166.

    Article  Google Scholar 

  25. Cecchetti, S., and P. Disyatat. 2010. Central bank tools and liquidity shortages. FRBNY Economic Policy Review 16: 29–42.

    Google Scholar 

  26. Chankong, V., and Y. Haimes. 2008. Multiobjective decision making. Theory and methodology. Mineola: Dover Publications.

    Google Scholar 

  27. Chinazzi, M., S. Pegoraro, and G. Fagiolo. 2015. Defuse the bomb: Rewiring interbank networks. Sant’Anna School of Advanced Studies LEM Papers Series No.2015/16.

  28. Cifuentes, R., G. Ferrucci, and H.S. Shin. 2005. Liquidity risk and contagion. Journal of the European Economic Association 3: 556–566.

    Article  Google Scholar 

  29. Condorcet, M. de. 1785. Essai sur l’application de l’analyse à la probabilité des décisions rendues à la pluralité des voix. Paris: l’Imprimerie Royale.

  30. Craig, B., and G. von Peter. 2014. Interbank tiering and money center banks. Journal of Financial Intermediation 23: 322–347.

    Article  Google Scholar 

  31. Delli Gatti, D., M. Gallegati, B. Greenwald, A. Russo, and J.E. Stiglitz. 2010. The financial accelerator in an evolving credit network. Journal of Economic Dynamic and Control 34: 1627–1650.

    Article  Google Scholar 

  32. Duffie, D., and H. Zhu. 2011. Does a central clearing counterparty reduce counterparty risk? The Review of Asset Pricing Studies 1: 74–95.

    Article  Google Scholar 

  33. ECB. 2007. The role of central counterparties. Issues related to central counterparty clearing, ECB-FED Chicago conference.

  34. ECB. 2019. Financial stability review, November.

  35. Eisenberg, L., and T. Noe. 2001. Systemic risk in financial systems. Management Science 47: 236–249.

    Article  Google Scholar 

  36. FED. 2019. Financial stability review, November.

  37. Fricke, D., and T. Lux. 2015. The effects of a financial transaction tax in an artificial financial market. Journal of Economic Interaction and Coordination 10: 119–150.

    Article  Google Scholar 

  38. Gabbi, G., G. Iori, S. Jafarey, and J. Porter. 2015. Financial regulations and bank credit to the real economy. Journal of Economic Dynamic and Control 50: 117–143.

    Article  Google Scholar 

  39. Gaffeo, E., L. Gobbi, and M. Molinari. 2019a. Liquidity contagion with a “first-in/first-out” seniority of claims. Economics Bulletin 39: 2572–2579.

    Google Scholar 

  40. Gaffeo, E., L. Gobbi, and M. Molinari. 2019b. The economics of netting in financial networks. Journal of Economic Interaction and Coordination 14: 595–622.

    Article  Google Scholar 

  41. Gaffeo, E., and M. Molinari. 2015. Interbank contagion and resolution procedures: Inspecting the mechanism. Quantitative Finance 15: 637–652.

    Article  Google Scholar 

  42. Gai, P., A. Haldane, and S. Kapadia. 2011. Complexity, concentration and contagion. Journal of Monetary Economics 58: 453–470.

    Article  Google Scholar 

  43. Galbiati, M. and K. Soramäki. 2013. Central counterparties and the topology of clearing networks. Working Paper No. 480.

  44. Gossen, H.H. 1853. Entwicklung der Gesetze des menschlichen Verkehrs und der daraus flieszenden Regeln für menschliches Handeln, 3rd ed., 1927. Berlin: Prager.

    Google Scholar 

  45. He, J., X. Sui, and S. Li. 2016. An endogenous model of the credit network. Physica A: Statistical Mechanics and its Applications 441: 1–14.

    Article  Google Scholar 

  46. Hurd, T. 2016. Contagion! The spread of systemic risk in financial networks. Springer Nature: Heidelberg.

    Google Scholar 

  47. Hwang, C.L., and K. Yoon. 1981. Multiple attribute decision making, methods and applications. Lecture notes in economics and mathematical systems, vol. 186. Berlin: Springer.

    Google Scholar 

  48. Iori, G., G. De Masi, O. Precup, G. Gabbi, and G. Cardarelli. 2008. A network analysis of the Italian overnight money market. Journal of Economic Dynamics and Control 32: 259–278.

    Article  Google Scholar 

  49. Jeanneau, S. 2014. Financial stability objectives and arrangements—What’s new?. In The role of central banks in macroeconomic and financial stability, ed. M. Mohanty. Basel, BIS Paper No.76.

  50. Kendall, M.G. 1948. Rank correlation methods. London: Griffin.

    Google Scholar 

  51. Lee, S.H. 2013. Systemic liquidity shortages and interbank network structures. Journal of Financial Stability 9: 1–12.

    Article  Google Scholar 

  52. Lux, T. 2016. A model of the topology of the bank firm credit network and its role as channel of contagion. Journal of Economic Dynamics and Control 66: 36–53.

    Article  Google Scholar 

  53. Miettinen, K. 1999. Nonlinear multiobjective optimization. Berlin: Springer.

    Google Scholar 

  54. Miller, D.W., and M.K. Starr. 1969. Executive decisions and operations research, 237–239. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  55. Minkowsky, H. 1911. Gesammelte Abhandlungen. Leipzig: Teubner.

    Google Scholar 

  56. Minkowsky, H. 1896. Geometrie der Zahlen. Leipzi: Teubner.

    Google Scholar 

  57. Mishkin, F. 1991. Anatomy of financial crisis. NBER Working Paper no. 3934.

  58. Nier, E., J. Yang, T. Yorulmazer, and A. Alentorn. 2007. Network models and financial stability. Journal of Economic Dynamics and Control 31: 2033–2060.

    Article  Google Scholar 

  59. Opricovic, S., and G.-H. Tzeng. 2004. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research 156: 445–455.

    Article  Google Scholar 

  60. Pareto, V. 1906. Manuale di Economia Politica, Translation revised by Pareto Himself, Manuel d’économie politique, 2nd edn. Paris, 1927

  61. Roukny, T., C.P. Georg, and S. Battiston. 2014. A network analysis of the evolution of the German interbank market., Deutsche Bundesbank Discussion Paper No.22/2014.

  62. Roy, B., R. Benayoun, and B. Sussman. 1966. ELECTRE. Paris: Société d’economie et de mathématique appliquées.

    Google Scholar 

  63. Saaty, T.L. 1988. The analytic hierarchy process. New York: Mcgraw-Hill.

    Google Scholar 

  64. Song, Rui, Richard Sowers, and Jonathan Jones. 2012. The structure of central counterparty clearing networks and network stability. SSRN: or

  65. Schinasi, G. 2004. Defining financial stability, IMF Working Paper No. 04/187.

  66. Watts, D.J., and S.H. Strogatz. 1998. Collective dynamics of small-world networks. Nature 393: 440–442.

    Article  Google Scholar 

  67. Xiong, W., H. Fu, and Y. Wang. 2017. Money creation and circulation in a credit economy. Physica A: Statistical Mechanics and its Applications 465: 425–443.

    Article  Google Scholar 

  68. Zedda, S., and S. Sbaraglia. 2020. Which interbank net is the safest? Risk Management 22: 65–82.

    Article  Google Scholar 

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Edoardo, G., Lucio, G. Achieving financial stability during a liquidity crisis: a multi-objective approach. Risk Manag (2021).

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  • Financial stability
  • Multi-objective optimization
  • Credit networks
  • Liquidity crunch cascades
  • C63
  • D85
  • G21

JEL classification

  • C63
  • D85
  • G21