Takipteki Kredilerin Makroekonomik Belirleyicileri: Heterojen Panel Analizi Ile Seçilmiş Ülkelerden Elde Edilen Kanıtlar

Yıl 2021, Cilt: 10 Sayı: 2, 986 - 999, 19.04.2021

Fatih Ayhan Mustafa Tevfik Kartal

https://doi.org/10.33206/mjss.800648

Cited By: 2

Öz

Takipteki Krediler (NPL), bankaların kredi sağlama kapasitesini kısıtladığından, banka tabanlı altyapısı olan ülkelerin finansal sistemlerinde kritik bir konu olarak ele alınabilmektedir. Dolayısıyla bu durum ekonomik büyüme ve kalkınma için gerekli finansman kaynakları sağlanamamasına neden olmaktadır. Bu bağlamda, çalışma seçilmiş 23 ülkede NPL’nin belirleyicilerini araştırmayı amaçlamaktadır. 4 bağımsız belirleyici 2006-2018 arasındaki yıllık verileri ile heterojen panel analizi kullanılarak analiz edilmiştir. Ampirik bulgular, (i) krediler, gayri safi yurtiçi hasıla (GSYİH) ve tasarrufların istatistiksel olarak anlamlı negatif etkiye sahip olduğunu, buna karşılık döviz kurlarının (FER) NPL üzerinde istatistiksel olarak anlamlı olmayan pozitif bir etkiye sahip olduğunu ortaya koymaktadır; (ii) Kredilerdeki % 1'lik artış NPL’yi yaklaşık % 2 oranında azaltmakta; (iii); FER'in bazı ülkelerde ülke bazında istatistiksel olarak önemli etkileri vardır; (iv) krediler, tasarruflar ve GSYİH için negatif katsayılar hem genel panel hem de ülke bazında tutarlıdır.

Anahtar Kelimeler

Takipteki Krediler, Heterojen Panel Analiz, NPL'nin Belirleyicileri

Destekleyen Kurum

Herhangi bir kurumdan destek alınmamıştır.

The Macro Economic Drivers of Non-Performing Loans (NPL): Evidence from Selected Countries with Heterogeneous Panel Analysis

Öz

NPL could be treated as a critical issue in the financial systems of countries of which have bank-based infrastructures since NPL restrict the credit providing capacity of banks. Hence, necessary financing sources for economic growth and development could not be provided. In this context, the study aims to research the drivers of NPL in selected 23 countries. 4 independent drivers and yearly data between 2006 and 2018 are analyzed by using heterogeneous panel analysis. The empirical findings reveal that (i) credits, gross domestic products (GDP), and savings have statistically significant negative effect whereas foreign exchange rates (FER) have a statistically insignificant positive effect on NPL for the overall panel; (ii) 1% increase in credits reduces NPL by approximately 2%; (iii); FER have statistically significant effects in some countries at the country base; (iv) negative coefficients for credits, savings, and GDP are consistent in both overall panel and country base.

Anahtar Kelimeler

Non-Performing Loans, NPL-Drivers, Heterogeneous Panel

Kaynakça

• Alandejani, M., & Asutay, M. (2017). Nonperforming Loans in the GCC Banking Sectors: Does the Islamic Finance Matter? Research in International Business and Finance, (42), 832-854. doı: 10.1016/j.ribaf.2017.07.02
• Baltagi, B. (2008). Econometric Analysis of Panel Data (4th ed.). Hoboken, NJ: John Wiley & Sons.
• Baltagi, B. H., Feng, Q., & Kao, C. (2012). A Lagrange Multiplier Test for Cross-Sectional Dependence in a Fixed Effects Panel Data Model. Journal of Econometrics, 170(1), 164-177. doi: https://doi.org/10.1016/j.jeconom.2012.04.004
• Barseghyan, L. (2010). Non-Performing Loans, Prospective Bailouts, and Japan's Slowdown. Journal of Monetary Economics, 57(7), 873-890.https://doi.org/10.1016/j.jmoneco.2010.08.002
• Beck, R., Jakubik, P., & Piloui, A. (2013). Non-Performing Loans What Matters in Addition to the Economic Cycle? ECB Working Paper, No. 1515. Frankfurt am Main, Germany: European Central Bank. ISSN 1725-2806 (online)
• Breusch, T., & Pagan, A. (1980). The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics. Review of Economic Studies, 47(1), 239-253. Stable URL: https://www.jstor.org/stable/2297111
• Bilgin, M. H., Gözgör, G., Lau, C. K. M., & Sheng, X. (2018). The Effects of Uncertainty Measures on the Price of Gold. International Review of Financial Analysis, 58, 1-7. Doi: https://doi.org/10.1016/j.irfa.2018.03.009
• Boudriga, A., Taktak, N. B., & Jellouli, S. (2010). Bank Specific, Business and Institutional Environment Determinants of Banks Nonperforming Loans: Evidence from MENA Countries. Dokki, Cairo Egypt: The Economic Research Forum (ERF), Economic Research Forum Working Paper Nu: 547.
• Buncic, D., & Melecky, M. (2013). Macroprudential Stress Testing of Credit Risk: A Practical Approach for Policy Makers. Journal of Financial Stability, 9(3), 347-370. Doi: https://doi.org/10.1016/j.jfs.2012.11.003
• Das, A., & Ghosh, S. (2007). Determinants of Credit Risk in Indian State-Owned Banks: An Empirical Investigation. Published in: Economic Issues, 12(2), 48-66.
• De Bock, R., & Demyanets, A. (2012). Bank Asset Quality in Emerging Markets: Determinants and Spillovers. IMF Working Paper, No. 12/71.
• Dimitrios, A., Helen, L., & Mike, T. (2016). Determinants of Non-Performing Loans: Evidence from Euro-Area Countries. Finance Research Letters, (18), 116-119. Doi: https://doi.org/10.1016/j.frl.2016.04.008
• Distinguin, I., Roulet, C., & Tarazi, A. (2013). Bank Regulatory Capital and Liquidity: Evidence from US and European Publicly Traded Banks. Journal of Banking & Finance, 37(9), 3295-3317. Doi: https://doi.org/10.1016/j.jbankfin.2013.04.027
• Eberhardt, M., & Bond, S. (2009). Cross-Section Dependence in Nonstationary Panel Models: A Novel Estimator. Munich Personal RePEc Archive, MPRA Working Paper, No. 17870. Online at https://mpra.ub.uni-muenchen.de/17870/
• Eberhardt, M., & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Department of Economics . University Discussion Paper Series of Oxford Working Paper, No. 515.
• Erdaş, M. L. (2009). Do Macroeconomic Variables Have a Symmetric or Asymmetric Effect on Non-Performing Loans? Evidence from Turkey. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 6(2), 370-392. Doi: https://doi.org/10.30798/makuiibf.518076
• Espinoza, R. A., & Prasad, A. (2010). Nonperforming Loans in the GCC Banking System and Their Macroeconomic Effects. IMF Working Paper, No: 10/224.
• Farhan, M., Sattar, A., Chaudhry, A. H., & Khalil, F. (2012). Economic Determinants of Non-Performing Loans: Perception of Pakistani Bankers. European Journal of Business and Management, 4(19), 87-99.
• Ghosh, A. (2015). Banking-Industry Specific and Regional Economic Determinants of Non-Performing Loans: Evidence from US States. Journal of Financial Stability, 20, 93-104. Doi: https://doi.org/10.1016/j.jfs.2015.08.004
• Gözgör, G., Demir, E., Belás, J., & Yeşilyurt, S. (2019). Does Economic Uncertainty Affect Domestic Credits? An Empirical Investigation. Journal of International Financial Markets, Institutions and Money, 63, 1-11. Doi: https://doi.org/10.1016/j.intfin.2019.101147
• Jakubik, P., & Reininger, T. (2013). Determinants of Nonperforming Loans in Central, Eastern and Southeastern Europe, Focus on European Economic Integration. Oesterreichische Nationalbank, 3, 48–66.
• Kartal, M. T., İbiş, C., & Çatıkkaş, Ö. (2018). Adequacy of Audit Committees: A Study of Deposit Banks in Turkey. Borsa İstanbul Review, 18(2), 150-165. Doi: https://doi.org/10.1016/j.bir.2018.01.002
• Kaufmann, S., & Valderrama, M. T. (2008). Bank Lending in Germany and the UK: Are There Differences between A Bank‐Based and A Market‐Based Country? International Journal of Finance and Economics, 13(3), 266-279. Doi: https://doi.org/10.1002/ijfe.345
• Kılıç Depren, S., & Kartal, M. T. (2020). Prediction on the Volume of Non-Performing Loans (NPL) in Turkey using Multivariate Adaptive Regression Splines (MARS) Approach. International Journal of Finance and Economics, In Press. Doi: https://doi.org/10.1002/ijfe.2126
• Klein, N. (2013). Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic Performance. IMF Working Paper, No. 13/72.
• Konstantakis, K. N., Michaelides, P. G., & Vouldis, A. T. (2016). Non-Performing Loans (NPLs) in a Crisis Economy: Long-run Equilibrium Analysis with a Real Time VEC Model for Greece (2001-2015). Physica A: Statistical Mechanics and Its Applications, 451, 149-161. Doi: https://doi.org/10.1016/j.physa.2015.12.163
• Louzis, D. P., Vouldis, A. T., & Metaxas, V. L. (2012). Macroeconomic and bank-Specific Determinants of Non-Performing Loans in Greece: A Comparative Study of Mortgage, Business and Consumer Loan Portfolios. Journal of Banking & Finance, 36(4), 1012-1027. Doi: https://doi.org/10.1016/j.jbankfin.2011.10.012
• Maddala, G.S., Trost, R.P., Li, H., & Joutz, F. (1997). Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators. Journal of Business & Economic Statistics, 15(1), 90-100.
• Messai, A. S., & Jouini, F. (2013). Micro and Macro Determinants of Non-Performing Loans. International Journal of Economics and Financial Issues, 3(4), 852-860.
• Mohammad, S., Asutay, M., Dixon, R., & Platonova, E. (2020). Liquidity Risk Exposure and Its Determinants in the Banking Sector: A Comparative Analysis between Islamic, Conventional and Hybrid Banks. Journal of International Financial Markets, Institutions and Money, 101196.
• Morgan, P. J., & Pontines, V. (2018). Financial Stability and Financial Inclusion: The Case of SME Lending. The Singapore Economic Review, 63(1), 111-124.
• Nkusu, M. (2011). Non-Performing Loans and Macro financial Vulnerabilities in Advanced Economies. IMF Working Paper, No. 11/161.
• Pesaran, M. H., & Smith, R. (1995). Estimating Long-run Relationships from Dynamic Heterogeneous Panels. Journal of Econometrics, 68(1), 79-113. Doi: https://doi.org/10.1016/0304-4076(94)01644-F
• Pesaran, M. H. (2004). General Diagnostic Tests for Cross Section Dependence in Panels. The University of Cambridge, Faculty of Economics. Cambridge Working Paper, No. 0435.
• Pesaran, M. H. (2007). A Simple Panel Unit Root Test in the Presence of Cross-Sectional Dependence. Journal of Applied Econometrics, 22(2), 265-312. https://doi.org/10.1002/jae.951
• Pesaran, M. H., & Yamagata, T. (2008). Testing Slope Homogeneity in Large Panels. Journal of Econometrics, 142(1), 50-93. Doi: https://doi.org/10.1016/j.jeconom.2007.05.010
• Saba, I., Kouser, R., & Azeem, M. (2012). Determinants of Non-Performing Loans: Case of US Banking Sector. The Romanian Economic Journal, 44(6), 125-136.
• Salas, V., & Saurina, J. (2002). Credit Risk in Two Institutional Regimes: Spanish Commercial and Savings Banks. Journal of Financial Services Research, 22(3), 203-224.
• Saunders, A., & Cornett, M. (2006). Financial Institutions Management: A Risk Management Approach. Fifth Edition. Boston: McGraw-Hill.
• Skarica, B. (2014). Determinants of Non-Performing Loans in Central and Eastern European Countries. Financial Theory and Practice, 38(1), 37-59.
• Swamy, P. (1970). Efficient Inference in a Random Coefficient Regression Model. Econometrica, 38(2), 311-322.
• Tanaskovic, S., &Jandric, M. (2015). Macroeconomic and Institutional Determinants of Non-Performing Loans. Journal of Central Banking Theory and Practice, 4(1), 47-62.
• Touny, M. A., & Shehab, M. A. (2015). Macroeconomic Determinants of Non-Performing Loans: An Empirical Study of Some Arab Countries. American Journal of Economics and Business Administration, 7(1), 11-22. DOI: 10.3844/ajebasp.2015.11.22
• Umar, M., & Sun, G. (2018). Determinants of Non-Performing Loans in Chinese Banks. Journal of Asia Business Studies, 12(3), 273-289. DOI 10.1108/JABS-01-2016-0005
• Vithessonthi, C. (2016). Deflation, Bank Credit Growth, and Non-Performing Loans: Evidence from Japan. International Review of Financial Analysis, (45), 295-305. Doi : https://doi.org/10.1016/j.irfa.2016.04.003
• WB. (2020). Open Data, https://data.worldbank.org/indicator, 06.30.2020.
• Zeng, S. (2012). Bank Non-Performing Loans (NPLS): A Dynamic Model and Analysis in China. Modern Economy, (3), 100-110. Doi: http://dx.doi.org/10.4236/me.2012.31014 P