Review of the functions of state banks in the system of ensuring the financial stability of the banking system of Ukraine

Keywords: banking system, state banks, financial stability, efficiency, data analysis

Abstract

Taking into account the extremely important role of state banks for the development of the country (today, they are, in fact, create the banking market), there is a need to re-orient their activity content from generating systemic risk to generating systemic stability.

Among the key components of financial stability highlighted by the central banks of the countries and which, using the tools of macro-prudential regulation, ensure the financial stability of the banking system, a special place is given to the ability of the latter to smoothly perform its functions. The effectiveness of such implementation is determined to be the prior condition for such operational continuity.

For this purpose, methodical support is proposed for revising the functionality of Ukrainian state banks in terms of their efficiency, determined by the DEA model (Data Envelopment Analysis) with input parameters of customer funds, operating expenses, reserves for credit risks, and output parameters of interest income.

The calculations of the performance indicators of state banks by the DEA model were made using DEAOS (Data Envelopment Analysis Online Software).

The values of the efficiency indicator were calculated, the ranking of banks was conducted, and the optimal values of input and output parameters for inefficient banks were given.

Recommendations were made to improve the efficiency of their activities: balancing the volume of customer funds with the volume of active operations; reducing operating expenses and reserves for credit risks by increasing the quality of loan portfolios.

In conclusion, by using the DEA model it is possible not only to determine the measure of the efficiency of state-owned banks in the financial market, but also to make management decisions regarding the adjustment of the main indicators of their activities. This, ultimately, will contribute to raising the level of financial stability not only of state banks, but also of the entire banking system of Ukraine.

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References

Pogorelenko N.P. (2018). Do pytannia pro poniatiinyi aparat finansovoi stabilnosti bankivskoi systemy. Bulletin of the UBS, 3, 66-89 (in Ukr.).

Emrouznejad A.(2010). Measurement of productivity index with dynamic DEA. International Journal of Operational Research, 8 (2), 247–260.

Khodabakhshi M. (2014). The global Malmquist productivity index under the optimistic pessimistic approach of DEA. International Journal of Operations Research, Vol.11, No.4, 131-137.

Shewell P. (2016). Data envelopment analysis in performance measurement: a critical analysis of the literature. Problems and Perspectives in Management, 14(3-3), 705-713.

Porembski M. (2005). Visualizing efficiency and reference relations in data envelopment analysis with an application to the branches of a German bank. Journal of Productivity Analysis, 23 (2), 203–221.

Wu D. (2006) Efficiency analysis of cross-region bank branches using fuzzy data envelopment analysis. Applied Mathematics and Computation, 181, 271–281.

McEachern D. (2007). Intra- and Inter-Country Bank Branch Assessment Using DEA. Journal of Productivity Analysis, 27 (2), 123–136.

Gaganis C. (2009). Estimating and analyzing the efficiency and productivity of bank branches: Evidence from Greece. Managerial Finance, 35, 202–218.

Paradi J. C. (2010). Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega, 39 (1), 99–109.

Miencha I.O. (2015). Efficiency Measurement of Kenyan Commercial Banks. Mediterranean Journal of Social Sciences, Vol. 6 (4), 621–631.

Kočišová K.(2014). Application of Data Envelopment Analysis to Measure Cost. Revenue and Profit Efficiency, 94(3), 47–57.

Paradi, J. C. (2004). Commercial branch performance evaluation and results communication in a Canadian bank – a DEA application. European Journal of Operational Research, 156 (3), 719–735.

Chmutova I. M. (2011). External rating management bank methodм DEA (Data Envelopment Analysis). Problemy ekonomiky, 2, 75–79 (in Ukr.).

Camanho A. S. (2008). A generalisation of the Farrell cost efficiency measure applicable to non-fully competitive settings. International Journal of Management Science, 36, 147–162.

Gaganis C. (2009). Estimating and analyzing the efficiency and productivity of bank branches: Evidence from Greece. Managerial Finance, 35, 202–218.

Lin T. T. (2009). Application of DEA in Analyzing a Bank’s Operating Performance. Expert system with application, 36 (5), 8883–8891.

McEachern D. (2007). Intra- and Inter-Country Bank Branch Assessment Using DEA. Journal of Productivity Analysis, 27 (2), 123–136.

Porembski M. (2005). Visualizing efficiency and reference relations in data envelopment analysis with an application to the branches of a German bank. Journal of Productivity Analysis, 23 (2), 203–221.

Sherman H. D. (2006). Do bank mergers have hidden or foregone value? Realized and unrealized operating synergies in one bank merger. European Journal of Operational Research, 168, 253–268.

Wu D. (2006). Efficiency analysis of cross-region bank branches using fuzzy data envelopment analysis. Applied Mathematics and Computation, 181, 271–281.

Yang Z. (2009). Bank branch operating efficiency: a DEA approach. Proceedings of the International MultiConference of Engineers and Computer Scientists, 2087–2092.

Sowlati T. (2004). Establishing the practical frontier in data envelopment analysis. Omega, 32, 261–272.

Staub R. B.(2009). Evolution of bank efficiency in Brazil: a DEA approach. Central Bank do Brazil: a Working Paper Series, 200, 48.

Chansarn S. (2008). The relative efficiency of commercial banks in Thailand: DEA approach. International Research Journal of Finance and Economics, 18, 53–68.

Daley J. (2009). Measuring bank efficiency: tradition or sophistication? Cardiff Economics Working Paper,24, 1–10.

Hall M. J. (2008). B. Environmental factors affecting Hong Kong banking: a post-Asian financial crisis efficiency analysis. Hong Kong Institute for Monetary Research Working Paper, 12, 1–35.

Mirchev L.(2009). The Bulgarian banking system and the EU single financial market: measuring the level of integration using DEA. Working Paper of the 26th Symposium on Money, Banking and Finance, 1–24.

Nigmonov A.(2010). Bank Performance and Efficiency in Uzbekistan. Eurasian Journal of Business and Economics, 3(5), 1–25.

Sathye M. (2003). Efficiency of Banks in a Developing Economy: The Case of India. European Journal of Operational Research, 3, 662–671.

Staub R. B. (2009). Evolution of bank efficiency in Brazil: a DEA approach. Central Bank do Brazil: a Working Paper Series, 200, 48.

Sufian F. (2010). Modeling banking sector efficiency: a DEA and time series approach. Ekonomika, 89, 111–119.

Tripe D. (2008). Bank branch performance assessment: including customer satisfaction measures. 13th Finsia-Melbourne Centre for Financial Studies Banking and Finance Conference,1–19.

Yue P. (1992). Data envelopment analysis and commercial bank performance: a primer with applications to Missouri banks. The Federal Reserve Bank of St. Louis Review, 1, 31–45.

Ponomarenko V. (2017). Benchmarking of bank performance using the life cycle concept and the DEA approach. Banks and Bank Systems,Vol. 12(3), 74–86.

Dolhikh V. (2013). Non-parametric estimations of the efficiency of the Ukrainian banking system in 2005-2012. Visnyk NBU, 2, 29-35 (in Ukr.).

Data Envelopment Analysis Online Software. URL: https://www.deaos.com/help.aspx?name=overview

Published
2019-01-15
How to Cite
Погореленко, Н. П. (2019). Review of the functions of state banks in the system of ensuring the financial stability of the banking system of Ukraine. Bulletin of V. N. Karazin Kharkiv National University Economic Series, (95), 30-39. https://doi.org/10.26565/2311-2379-2018-95-04