Assessing the impact of banking efficiency, operations, and regulation on banking performance: Fresh insight using dynamic correlated framework on the data set of Russia and Ukraine
DOI:
https://doi.org/10.15549/jeecar.v8i1.514Keywords:
Nonperforming loans, Cross section Dependency, Banking, PMG, Unit root.Abstract
The purpose of this study is to investigate how banking industry-specific variables like regulation, efficiency, and operations affected nonperforming loans (NPLs) in Ukraine and Russia from 1995 to 2019. This study has employed the robust unit root test and cross-sectional dependencies technique along with a new DCCE approach. The dynamic correlated method is employed as it provides the best results when data suffers from cross-sectional dependencies. The study concludes that loose credit policy and lower profitability help in rising NPLs. However, in the context of macroeconomic variables, volatile interest rates, and exchange rate fluctuations are the main reason for NPLs in Russia and Ukraine.
The research work also highlights the issue of cross-sectional dependencies and provide substantial methods to resolve the problem of cross-sectional dependencies and provide robust results. Findings will help policymakers to recognize the relevance of industry-specific variables in managing NPLs along with other macroeconomic variables.
References
ABD?O?LU, N., & AYTEK?N, S. (2016). Takipteki Kredi Oran?n? Etkileyen Faktörlerin Belirlenmesi: Mevduat Bankalar? Üzerinde Bir Dinamik Panel Veri Uygulamas?. ??letme Ara?t?rmalar? Dergisi, 8(1), 538-555.
Allen, F., Gu, X., & Jagtiani, J. (2020). A Survey of Fintech Research and Policy Discussion. DOI: https://doi.org/10.21799/frbp.wp.2020.21
Beck, T., & Brown, M. (2015). Foreign bank ownership and household credit. Journal of Financial Intermediation, 24(4), 466-486. DOI: https://doi.org/10.1016/j.jfi.2013.10.002
Berger, A.N., and R. DeYoung, (1997). Problem loans and cost efficiency in commercial banks. Journal of Banking & Finance, 21(6): 849-870.Available at: https://doi.org/10.1016/s0378-4266(97)00003-4. DOI: https://doi.org/10.1016/S0378-4266(97)00003-4
Boudriga, A., Taktak, N. B., & Jellouli, S. (2010, September). Bank specific, business and institutional environment determinants of banks nonperforming loans: evidence from mena countries. In Economic Research Forum, Working Paper (Vol. 547, pp. 1-28).
Castro, V. (2013). Macroeconomic determinants of the credit risk in the banking system: The case of the GIPSI. Economic Modelling, 31, 672-683. DOI: https://doi.org/10.1016/j.econmod.2013.01.027
Chaibi, Hasna, and Zied Ftiti, (2015). "Credit risk determinants: Evidence from a cross-country study". Research in International Business and Finance 33: 1–16 DOI: https://doi.org/10.1016/j.ribaf.2014.06.001
Chang Y, (2004). î Bootstrap Unit root tests in panels with cross-sectional dependency î. J Econ 120:263–293 DOI: https://doi.org/10.1016/S0304-4076(03)00214-8
Chudik A, Pesaran MH (2015b). Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors. J Econ 188(2):393–420 DOI: https://doi.org/10.1016/j.jeconom.2015.03.007
Chudik, A, and M. H. Pesaran (2015a). Large panel data models with cross-sectional dependence: A survey. In The Oxford Handbook Of Panel Data, ed. B. H. Baltagi, 2-45. Oxford: Oxford University Press
Espinoza, R., and A. Prasad, (2010). Nonperforming Loans in the GCC Banking Systems and their Macroeconomic Effects, IMF Working Paper 10/224 (Washington: International Monetary Fund). DOI: https://doi.org/10.5089/9781455208890.001
Jimenez, G. & Saurina, J. (2003). "Loan characteristics and credit risk," Proceedings 857, Federal Reserve Bank of Chicago.
Ghosh, A. (2015). Banking-industry specific and regional economic determinants of non-performing loans: Evidence from US states. J. Financ. Stab., 20, 93–104. [CrossRef] DOI: https://doi.org/10.1016/j.jfs.2015.08.004
Hu, J, Li, Y, and Chiu, Y(2004). "Ownership and non-performing loans: Evidence from Taiwan's banks?, The Developing Economies, Vol. 42, No.3, pp. 405-420
Kahia M, Aïssa MSB, Charfeddine, L (2016). Impact of renewable and non-renewable energy consumption on economic growth: new evidence from the MENA Net Oil Exporting Countries (NOECs). Energy 116:102–115 DOI: https://doi.org/10.1016/j.energy.2016.07.126
Keeton, W.R., and C.S. Morris, (1987). Why do banks' loan losses differ? Economic Review, 72(5): 3-21.
Kichurchak, M. (2019). Bank deposit activity in Ukraine: Directions and factors of development activation. Journal of Eastern European and Central Asian Research (JEECAR), 6(1), 145-160. https://doi.org/10.15549/jeecar.v6i1.275 DOI: https://doi.org/10.15549/jeecar.v6i1.275
Levin A, Lin CF, Chu CSJ (2002). Unit root test in panel data: Asymptotic and finite sample properties. J Econ 108:1–24 DOI: https://doi.org/10.1016/S0304-4076(01)00098-7
Meo S, Saeed S., Aria. H, Nazar, R(2020). Water resources and tourism development in South Asia: An application of dynamic common correlated effect (DCCE) model, Environmental Science, and Pollution Research, 3, 2020.DOI: 10.1007/s11356-010-08361-8
Nkusu, M., (2011). "Non-performing loans and macro-financial vulnerabilities in advanced economies", IMF Working Paper 11/161. DOI: https://doi.org/10.5089/9781455297740.001
Ozili, P. K. (2018). Impact of digital finance on financial inclusion and stability. Borsa Istanbul Review, 18(4), 329-340. DOI: https://doi.org/10.1016/j.bir.2017.12.003
Perron P (1991). Test consistency with varying sampling frequency. Econometric Theory 7(3):341–368 DOI: https://doi.org/10.1017/S0266466600004503
Pesaran MH (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74(4):967– 1012 DOI: https://doi.org/10.1111/j.1468-0262.2006.00692.x
Pesaran MH (2007). A simple panel unit root test in the presence of cross-section dependence. J Appl Econ 22:265–312 DOI: https://doi.org/10.1002/jae.951
Pesaran MH, Smith R (1995). Estimating long-run relationships from dynamic heterogeneous panels. J Econ 68(1):79–113
Pesaran, M. Hashem (2004). General diagnostic tests for cross-section dependence in panels' IZA Discussion Paper No. 1240
Petkovski, M., Kjosevski, J. (2014). Does Banking Sector Development Promote Economic Growth? An Empirical Analysis for Selected Countries in Central and South-Eastern Europe. Economic Research, 27(1), 55-66. DOI:10.1 080/1331677X.2014.947107 DOI: https://doi.org/10.1080/1331677X.2014.947107
Podpiera, J., Weill, L., (2008). "Bad luck or bad management? Emerging banking market experience," J. Finance. Stab. 4, pp 135–148. DOI: https://doi.org/10.1016/j.jfs.2008.01.005
Rajan, R., & Dhal, S. C. (2003). Non-performing loans and terms of credit of public sector banks in India: An empirical assessment. Reserve Bank of India Occasional Papers, 24(3), 81-121.
Salas, V. and J. Saurina, (2002). Credit risk in two institutional regimes: Spanish commercial and savings banks. Journal of Financial Services Research, 22(3): 203-224.
Shin Y, Pesaran MH, Smith RP (1999). Pooled mean group estimation
Sobolieva-Tereshchenko, O., & Zhukova , Y. (2020). Stress testing the banking systems: Approach of Ukraine. Journal of Eastern European and Central Asian Research (JEECAR), 7(2), 205-218. https://doi.org/10.15549/jeecar.v7i2.358 DOI: https://doi.org/10.15549/jeecar.v7i2.358
Stiglitz, J., & Weiss, A. (1981). Credit Rationing in Markets with Imperfect Information. The American Economic Review, 71(3), 393-410.
Stijepovi?, R. (2014). Recovery and Reduction of Non-Performing Loans – Podgorica Approach. Journal of Central Banking Theory and Practice, 3(3): 101-108. DOI: 10.2478/jcbtp-2014-0017 DOI: https://doi.org/10.2478/jcbtp-2014-0017
Swamy, V. (2012). "Impact of macroeconomic and endogenous factors on non-performing bank assets", The International Journal of Banking and Finance, Vol.9, No.1, pp.27-47 DOI: https://doi.org/10.2139/ssrn.2060753
Syed, A.A. (2020). “Does Banking Efficiency, Regulation, and Operations Affect Banking Performance in South Asia: Dynamic Correlated Model Approach”. Front. Appl. Math. Stat. 6(38). DOI: 10.3389/fams.2020.00038 DOI: https://doi.org/10.3389/fams.2020.00038
Syed, A. A., & Aidyngul, Y. Macro economical and bank?specific vulnerabilities of nonperforming loans: A comparative analysis of developed and developing countries. Journal of Public Affairs, e2414.
Tanaskovi?, S., Jandri?, M. (2015). Macroeconomic and Institutional Determinants of Non-performing Loans. Journal of Central Banking Theory and Practice, 2015/1:47-62. DOI: 10.1515/jcbtp-2015-0004 DOI: https://doi.org/10.1515/jcbtp-2015-0004
Vovchak, O., Reverchuk, S., Rudevska, V., & Khlan, Y. (2019). Bank business modelling and levels of non-performing loans: Perspectives of international risk factors in Ukraine. Journal of Eastern European and Central Asian Research (JEECAR), 6(2), 282-296. https://doi.org/10.15549/jeecar.v6i2.391 DOI: https://doi.org/10.15549/jeecar.v6i2.391
Westerlund J, Edgerton DL (2007). A panel bootstrap cointegration test. Econ Lett 97(3):185–190 DOI: https://doi.org/10.1016/j.econlet.2007.03.003
Westerlund J, Edgerton DL (2008). A simple test for cointegration independent panels with structural breaks. Oxf Bull Econ Stat 70(5): 665–704 DOI: https://doi.org/10.1111/j.1468-0084.2008.00513.x
Finance & Development, International Monetary Fund, March 2019, Vol. 56, No. 1. International Monetary Fund, Global Financial Stability Report, 2020 DOI: https://doi.org/10.5089/9781484398784.022
World Bank, Data Views from 10.03.2014, www.data.worldbank.org/indicator/FB.AST.NPER.ZS
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