Structural modeling of the impact of bank nonperforming loans on the banking sector: the Ukrainian experience

  • 186 Views
  • 19 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

The paper aims to develop scientific and methodological approach to assessing the interaction of nonperforming loans of Ukrainian banking institutions, the profitability of the banking sector and its financial stability, which will allow a more detailed assessment of the directions and degree of mutual influence of these elements. To substantiate this interaction economically and mathematically, structural equation modeling was chosen. Particularly, Statistica was chosen as a software tool to assess the adequacy of the resulting model and determine the level of statistical significance of its parameters. Six key indicators were selected as a research information base, two for each subject of research: indicators of nonperforming loans in the banking sector (the volume of nonperforming loans and the ratio of problem loans excluding capital reserves), profitability indicators of the Ukrainian banking sector (assets profit and rate of return on capital), and indicators of financial stability of the Ukrainian banking sector (regulatory capital-to-risk-weighted assets ratio and liquid assets-to-total assets ratio). For calculations, statistic data of selected indicators for 2005–2019 were used.
As a result of calculations, mathematical data were obtained that accurately described the interaction of nonperforming loans of Ukrainian banking institutions, the profitability of the banking sector and its financial stability. The adequacy of the model was verified based on the following criteria: main summary statistics (ICSF criterion, ICS criterion, discrepancy function, maximum residual cosine), noncentrality fit indices (noncentrality parameter, population noncentrality parameter, Steiger-Lind RMSEA index, McDonald noncentrality index, adjusted population Gamma index), other single sample indices (Akaike information criterion, Schwarz criterion), and a normal probability plot.

view full abstract hide full abstract
    • Figure 1. Path diagram to study the relationship between non-performing loans of banking institutions, profitability and financial stability of the Ukrainian banking sector
    • Figure 2. Checking the adequacy of the model of relationships between NPLs of banking institutions, profitability and financial stability of the Ukrainian banking sector
    • Table 1. Factors for analysis of the relationship between nonperforming loans, profitability and financial stability indicators in Ukraine’s banking sector
    • Table 2. Statistic data to study the relationship between banking institutions’ nonperforming loans, profitability and financial stability of the Ukrainian banking sector for 2005–2019
    • Table 3. Normalized output data for structural equation modeling
    • Table 4. The results of structural modeling of the relationship between nonperforming loans of banking institutions, profitability and financial stability of the Ukrainian banking sector
    • Table 5. Main summary statistics to study the relationship between banking institutions’ nonperforming loans, profitability and financial stability of the Ukrainian banking sector
    • Table 6. Model noncentrality indices of the relationship between NPLs of banking institutions, profitability and financial stability of the Ukrainian banking sector
    • Table 7. Single Sample Indices of the relationship between NPLs of banking institutions, profitability and financial stability of the Ukrainian banking sector
    • Conceptualization
      Eugenia Bondarenko
    • Investigation
      Eugenia Bondarenko
    • Project administration
      Eugenia Bondarenko, Ologunla Emmanuel Sunday
    • Software
      Eugenia Bondarenko, Ologunla Emmanuel Sunday
    • Visualization
      Eugenia Bondarenko, Vita Andrieieva
    • Writing – review & editing
      Eugenia Bondarenko, Ologunla Emmanuel Sunday
    • Funding acquisition
      Eugenia Bondarenko, John O. Aiyedogbon
    • Data curation
      Olena Zhuravka, Ologunla Emmanuel Sunday
    • Methodology
      Olena Zhuravka, John O. Aiyedogbon, Vita Andrieieva
    • Validation
      Olena Zhuravka, John O. Aiyedogbon, Ologunla Emmanuel Sunday
    • Writing – original draft
      Olena Zhuravka, John O. Aiyedogbon, Vita Andrieieva
    • Formal Analysis
      John O. Aiyedogbon, Vita Andrieieva