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

Abstract

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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Notes

  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.

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Edoardo, G., Lucio, G. Achieving financial stability during a liquidity crisis: a multi-objective approach. Risk Manag (2021). https://doi.org/10.1057/s41283-021-00067-6

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Keywords

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

JEL classification

  • C63
  • D85
  • G21