BANKING SYSTEM OF UKRAINE UNDER THE CONDITIONS OF PANDEMIC AND WAR: SIMULTANEOUS ECONOMETRIC MODEL

Keywords: banking system, bank, assets, credit, macroeconomic modeling, econometric modeling, retail turnover, state budget, export of goods and services

Abstract

The high-quality work of the banking sector creates opportunities for economic development. However, significant turbulence, first due to the COVID-19 pandemic and later due to the full-scale war, affected the state of the banking system of Ukraine. With the beginning of russia's full-scale war in Ukraine, the banking system faced serious difficulties. However, it passed the most difficult stages with dignity and continued to work without failure. We propose the simultaneous model of the banking sector of Ukraine based on quarterly statistical data for 2016-2022. The aggregate assets of banks, the capital and reserves of banks, and the expenses of banks of Ukraine were used as the endogenous variables. Macroeconomic and financial indicators were selected as exogenous variables: bank income, bank loans, state budget expenditures, retail turnover, and export of goods and services. Dependencies between variables are described by three equations. According to the first equation, banks' assets are affected by capital, reserves, income, bank loans, and government budget expenditures. The second equation describes the dependence of banks' capital and reserves on aggregate assets and expenditures of banks, as well as on macroeconomic indicators of retail turnover, export, and state budget expenditures. The third equation examines the impact of bank assets, capital, income, and loans, as well as state budget expenditures, on bank expenditures. High values of R-squared and statistically significant parameters were obtained for all equations. It was found that the assets, capital, and reserves of banks are depended on the volume of retail trade, and the expenses of banks are depended on the incomes of banks, which is quite logical. A close relationship between indicators of banking activity and macroeconomic indicators was also revealed. Taking into account the modeling results, it can be concluded that the implementation of the simultaneous model of the banking system of Ukraine is an essential tool for assessing the relationship between macroeconomic factors and quantitative characteristics of banking activity and their impact in order to ensure the financial stability of the banking system.

References

Begenau J., Landvoigt T. Financial regulation in a quantitative model of the modern banking system. The Review of Economic Studies. 2022. № 89(4). P. 1748–1784.

Tabash M.I., Abdulkarim F.M., Akinlaso M.I., Dhankar R.S. Islamic banking and economic growth: fresh insights from Nigeria using autoregressive distributed lags (ARDL) approach. African Journal of Economic and Management Studies. 2022. № 13(4). С. 582–597.

Вдовин М.Л., Брода А.Р. Статистичне моделювання інвестиційних ризиків в умовах ринку. Глобальні та національні проблеми економіки. 2017. Випуск 17. С. 903–908.

Kyshakevych B., Klymkovych I. Estimation of Z-score for Ukrainian banking system. Scientific Journal of Polonia University. 2018. № 30(5). P. 43–51.

Mints A., Marhasova V., Hlukha H., Kurok R., Kolodizieva T. Analysis of the stability factors of Ukrainian banks during the 2014–2017 systemic crisis using the Kohonen self-organizing neural networks. Banks and Bank Systems. 2019. № 14(3). P. 86.

Kaminskyi A., Nehrey M., Zomchak L. Machine learning methods application for consumer banking. SHS Web of Conferences. 2021. Vol. 107. EDP Sciences, 202.

Obeid R. Early Warning of Bank Failure in the Arab Region: A Logit Regression Approach. Asian Journal of Economics and Empirical Research. 2022. № 9(2). P. 91–99.

Зомчак Л.М., Вдовин М.Л. Прогнозування успішності банківського маркетингу методами логістичної регресії. Інтелект XXI. 2020. № 5. С. 100–104. URL: http://www.intellect21.nuft.org.ua/journal/2020/2020_5/21.pdf

Kuzmenko O.V., Koibichuk V.V. Econometric modeling of the influence of relevant indicators of gender policy on the efficiency of a banking system. Cybernetics and Systems Analysis. 2018. № 54. P. 687–695.

Almaqtari F.A., Al‐Homaidi E.A., Tabash M.I., Farhan N.H. The determinants of profitability of Indian commercial banks: A panel data approach. International Journal of Finance & Economics. 2019. № 24(1). P. 168–185.

Yuan D., Gazi M.A.I., Harymawan I., Dhar B.K., Hossain A.I. Profitability determining factors of banking sector: Panel data analysis of commercial banks in South Asian countries. Frontiers in Psychology. 2022. № 13. 1000412.

Rezgallah H., Özataç N., Katircioğlu S. The impact of political instability on risk‐taking in the banking sector: International evidence using a dynamic panel data model (System‐GMM). Managerial and Decision Economics. 2019. № 40(8). P. 891–906.

İncekara A., Çetinkaya H. Liquidity risk management: A comparative analysis of panel data between Islamic and conventional banking in Turkey. Procedia Computer Science. 2019. № 158. P. 955–963.

Zhu B. The multi-country transmission of sovereign and banking risk: A spatial vector autoregressive approach. Spatial Economic Analysis. 2018. № 13(4). P. 422–441.

Nguyen M., Xuan P., Bui T. Causal relationship between banking system development and real estate market. Management Science Letters. 2020. № 10(1). P. 41–52.

Yakubu I.N., Abokor A.H. Factors determining bank deposit growth in Turkey: an empirical analysis. Rajagiri management journal. 2020. № 14(2). P. 121–132.

Зомчак Л.М., Старчевська І.М. Симультативне моделювання залежності економічного зростання та рівня інфляції України. Науковий вісник Полтавського університету економіки і торгівлі. Серія «Економічні науки». 2022. № 1 (105). С. 78–85.

Державна служба статистики України. URL: http://www.ukrstat.gov.ua/ (дата звернення: 10.06.2023).

Національний банк України. Статистика. URL: https://bank.gov.ua/ua/statistic (дата звернення: 10.06.2023).

Begenau, J., & Landvoigt, T. (2022). Financial regulation in a quantitative model of the modern banking system. The Review of Economic Studies, 89(4), 1748–1784.

Tabash, M.I., Abdulkarim, F.M., Akinlaso, M.I., & Dhankar, R.S. (2022). Islamic banking and economic growth: fresh insights from Nigeria using autoregressive distributed lags (ARDL) approach. African Journal of Economic and Management Studies, 13(4), 582–597.

Vdovyn, M.L., & Broda, A.R. (2017). Statystychne modelyuvannya investytsiynykh ryzykiv v umovakh rynku. Hlobalʹni ta natsionalʹni problemy ekonomiky, 17, 903–908.

Kyshakevych, B., & Klymkovych, I. (2018). Estimation of Z-score for Ukrainian banking system. Scientific Journal of Polonia University, 30(5), 43–51.

Mints, A., Marhasova V., Hlukha H., Kurok R., Kolodizieva T. (2019). Analysis of the stability factors of Ukrainian banks during the 2014–2017 systemic crisis using the Kohonen self-organizing neural networks. Banks and Bank Systems, 14(3), 86.

Kaminskyi A., Nehrey M., Zomchak L. Machine learning methods application for consumer banking. SHS Web of Conferences. Vol. 107. EDP Sciences, 202.

Obeid, R. (2022). Early Warning of Bank Failure in the Arab Region: A Logit Regression Approach. Asian Journal of Economics and Empirical Research, 9(2), 91–99.

Zomchak, L.M., Vdovyn, M.L. (2020) Prohnozuvannya uspishnosti bankivsʹkoho marketynhu metodamy lohistychnoyi rehresiyi. Intelekt XXI, 5, 100–104. Available at: http://www.intellect21.nuft.org.ua/journal/2020/2020_5/21.pdf

Kuzmenko, O.V., & Koibichuk, V.V. (2018). Econometric modeling of the influence of relevant indicators of gender policy on the efficiency of a banking system. Cybernetics and Systems Analysis, 54, 687–695.

Almaqtari, F.A., Al‐Homaidi, E.A., Tabash, M.I., & Farhan, N.H. (2019). The determinants of profitability of Indian commercial banks: A panel data approach. International Journal of Finance & Economics, 24(1), 168–185.

Yuan, D., Gazi, M. A. I., Harymawan, I., Dhar, B. K., & Hossain, A. I. (2022). Profitability determining factors of banking sector: Panel data analysis of commercial banks in South Asian countries. Frontiers in Psychology, 13, 1000412.

Rezgallah, H., Özataç, N., & Katircioğlu, S. (2019). The impact of political instability on risk‐taking in the banking sector: International evidence using a dynamic panel data model (System‐GMM). Managerial and Decision Economics, 40(8), 891–906.

İncekara, A., & Çetinkaya, H. (2019). Liquidity risk management: A comparative analysis of panel data between Islamic and conventional banking in Turkey. Procedia Computer Science, 158, 955–963.

Zhu, B. (2018). The multi-country transmission of sovereign and banking risk: A spatial vector autoregressive approach. Spatial Economic Analysis, 13(4), 422–441.

Nguyen, M., Xuan, P., & Bui, T. (2020). Causal relationship between banking system development and real estate market. Management Science Letters, 10(1), 41–52.

Yakubu, I. N., & Abokor, A. H. (2020). Factors determining bank deposit growth in Turkey: an empirical analysis. Rajagiri management journal, 14(2), 121–132.

Zomchak, L.M., & Starchevsʹka, I.M. (2022). Symulʹtatyvne modelyuvannya zalezhnosti ekonomichnoho zrostannya ta rivnya inflyatsiyi Ukrayiny. Naukovyy visnyk Poltavsʹkoho universytetu ekonomiky i torhivli. Seriya «Ekonomichni nauky», 1(105), 78–85.

State Statistics Service of Ukraine. Available at: http://www.ukrstat.gov.ua/ (accessed: 10.06.2023).

National Bank of Ukraine. Available at: https://bank.gov.ua/ua/statistic (accessed 10.06.2023).

Article views: 116
PDF Downloads: 97
Published
2023-06-27
How to Cite
Komar, M., Zomchak, L., & Peshko, B. (2023). BANKING SYSTEM OF UKRAINE UNDER THE CONDITIONS OF PANDEMIC AND WAR: SIMULTANEOUS ECONOMETRIC MODEL. Economy and Society, (52). https://doi.org/10.32782/2524-0072/2023-52-15
Section
FINANCE, BANKING AND INSURANCE