Banking system stability in crisis periods: The impact of the banking regulator independence

  • Received June 24, 2023;
    Accepted September 22, 2023;
    Published September 28, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/bbs.18(3).2023.18
  • Article Info
    Volume 18 2023, Issue #3, pp. 221-234
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This work is licensed under a Creative Commons Attribution 4.0 International License

Local and global financial crises are caused by a wide range of geopolitical, macro-financial, and socio-economic determinants. The purpose of this study is to assess the role of central bank independence in preventing financial crises and mitigating their consequences. Two hypotheses were tested. A measure of the banking regulator independence is the CWN index of the central bank independence. The hypotheses were tested on data from 53 countries suffering from financial crises over the last 40 years (the sample includes both developed and developing countries from different continents). The tools of nonlinear logit regression (modeling the probability of loss of financial stability due to a financial crisis, considering different levels of the banking regulator independence) and panel regression with random effects (modeling the influence of the banking regulator independence on banking activities during crisis periods) were used for calculations. The study did not confirm that a high level of central bank independence is a necessary condition for preventing the occurrence of financial crises in the national economy. On the contrary, the likelihood of financial instability was found to be higher in countries with more independent central banks. Thus, during crisis periods, an increase in the CWN index by 1 ensures an increase in the regulatory capital adequacy parameter by an average of 0.28%, a decrease in return on assets by 0.59%, and an increase in the share of non-performing loans by 1.69%.

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    • Figure 1. Research approach for assessing the role of central bank independence in leveling financial crises
    • Table 1. Results of logit regression modeling of the probability of a period of financial stability in the country depending on the development parameters of the central bank
    • Table 2. Results of modeling the probability map of financial stability in the country under the influence of the central bank’s functioning parameters
    • Table 3. Results of logit regression modeling of the probability of a period of financial stability in the country depending on the functioning parameters of the banking system
    • Table 4. Results of modeling the probability map of financial stability in the country under the influence of parameters of the functioning of the banking system
    • Table 5. Results of logit regression modeling of the probability of a period of financial stability in the country depending on the parameters of commercial activity of banks
    • Table 6. Results of modeling the probability map of financial stability in the country under the influence of the parameters of commercial activity of banks
    • Table 7. Results of assessing central bank independence in crisis periods on the adequacy of regulatory capital of banks in 53 countries of the world
    • Table 8. Results of evaluating the independence of central banks in crisis periods on the indicator of financial stability of banks in 53 countries of the world
    • Table 9. Results of assessing the independence of central banks in crisis periods on the level of non-performing loans in the loan portfolio of banks in 53 countries of the world
    • Table 10. Results of assessing the independence of central banks in crisis periods on the level of profitability of bank assets in 53 countries of the world
    • Table 11. Results of assessing the independence of central banks in crisis periods by the level of net interest margin of banks in 53 countries of the world
    • Conceptualization
      Atik Kerimov, Viktoria Dudchenko
    • Funding acquisition
      Atik Kerimov
    • Project administration
      Atik Kerimov
    • Writing – review & editing
      Atik Kerimov
    • Data curation
      Azer Babayev
    • Resources
      Azer Babayev
    • Software
      Azer Babayev, Yaryna Samusevych
    • Visualization
      Azer Babayev
    • Formal Analysis
      Viktoria Dudchenko, Yaryna Samusevych
    • Methodology
      Viktoria Dudchenko, Yaryna Samusevych
    • Supervision
      Viktoria Dudchenko
    • Writing – original draft
      Viktoria Dudchenko, Yaryna Samusevych
    • Validation
      Yaryna Samusevych