|Title||Structural modeling of the impact of bank nonperforming loans on the banking sector: the Ukrainian experience|
Bondarenko, Yevheniia Kostiantynivna
Zhuravka, Olena Serhiivna
Ologunla, Emmanuel Sunday
|Date of Issue||2020|
|License||Creative Commons Attribution 4.0 International License|
|Citation||Eugenia Bondarenko, Olena Zhuravka, John O. Aiyedogbon, OlogunlaEmmanuel Sunday and Vita Andrieieva (2020). Structural modeling of the impactof bank nonperforming loans on the banking sector: the Ukrainian experience.Banks and Bank Systems, 15(2), 67-78. doi:10.21511/bbs.15(2).2020.07|
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 profit-ability 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.
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