THE RELATIONSHIP BETWEEN NON-PERFORMING LOANS AND MACROECONOMIC FACTORS IN TURKEY

Year 2021, Volume: 8 Issue: 2, 609 - 629, 27.07.2021

Deniz Sevinç

https://doi.org/10.30798/makuiibf.691534

Cited By: 6

Abstract

Non-performing loans are expressed as the first warning of a possible break in the financial system; it is expressed as an early signal of a possible financial or economic crisis. At the same time, changes in macroeconomic indicators as a result of developments in the economy also affect the payments of loan holders; impact the non-performing loan ratio (NPL). The main purpose of this study is to investigate how macroeconomic factors affect the non-performing loan ratio. According to the estimation results of the ARDL Model estimated with quarterly data covering the period 2005Q1-2019Q3, economic growth and inflation decreased the NPL ratio; It was determined that unemployment and exchange rate increased the non-performing loan ratio. According to Toda-Yamamoto causality test findings, there is a causality relationship from all variables to non-performing loans; The existence of a bi-directional causality relationship with unemployment rate has been determined.

Keywords

Non-Performing Loans, Growth, Inflation, Unemployment, Exchange Rate, ARDL, Toda-Yamamoto

TÜRKİYE'DE TAKİPTEKİ BANKA KREDİLERİ İLE MAKROEKONOMİK FAKTÖRLER ARASINDAKİ İLİŞKİ

Abstract

Takipteki banka kredileri, finansal sistemde yaşanabilecek bir kırılmanın ilk uyarıcısı; olası bir finansal veya ekonomik krizin erken sinyali olarak ifade edilmektedir. Aynı zamanda ekonomide yaşanan gelişmeler neticesinde makroekonomik göstergelerde meydana gelen değişmeler de kredi sahiplerinin ödemelerini etkilemekte; takipteki kredi oranını etkilemektedir. Bu çalışmanın temel amacı, makroekonomik faktörlerin takipteki kredi oranını nasıl etkilediğini araştırmaktır. 2005Q1-2019Q3 dönemini kapsayan çeyreklik verilerle tahmin edilen ARDL Modeli tahmin sonuçlarına göre ekonomik büyüme ve enflasyonun takibe düşen kredi oranı azalttığı; işsizlik ve döviz kurunun ise takipteki kredi oranını artırdığı belirlenmiştir. Toda-Yamamoto nedensellik testi bulgularına göre, tüm değişkenlerden takipteki kredilere doğru bir nedensellik ilişkisi olduğu; işsizlik oranı ile çift yönlü nedensellik ilişkisinin varlığı tespit edilmiştir.

Keywords

takipteki krediler, ekonomik büyüme, enflasyon, işsizlik, döviz kuru, ARDL, Toda-Yamamoto

References

• Abid, L., Ouertani, M. J. ve Ghorbel, S. (2014). Macroeconomic and Bank Specific Determinants of Household’s Non-Performing Loans in Tunisia: a Dynamic Panel Data. Procedia Economics and Finance, 13, 58-68. https://doi.org/10.1016/S2212-5671(14)00430-4.
• Al-Khazali, O. M. ve Mirzaei, A. (2017). The impact of oil price movements on bank non-performing loans: Global evidence from oil-exporting countries. Emerging Markets Review, 31, 193-208. http://dx.doi.org/10.1016/j.ememar.2017.05.006.
• Bankalarca Kredilerin ve Diğer Alacakların Niteliklerinin Belirlenmesi ve Bunlar İçin Ayrılacak Karşılıklara İlişkin Usul ve Esaslar Hakkında Yönetmelik. (2006). Ankara: Resmi Gazete (26333 sayılı). Erişim Adresi: http://www.resmigazete.gov.tr/eskiler/2006/11/20061101.htm.
• Beck, R., Jakubik, P. ve Piloiu, A. (2013). Non-Performing Loans: What Matters in Addition to the Economic Cycle? ECB Working Paper No: 1515). European Central Bank. https://ssrn.com/abstract=2214971.
• De Bock, R. ve Demyanets, A. (2012). Bank Asset Quality in Emerging Markets: Determinants and Spillovers (IMF Working Paper No: 12/71), International Monetary Fund. https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Bank-Asset-Quality-in-Emerging-Markets-Determinants-and-Spillovers-25766.
• Dimitrios, A., Helen, L. ve Mike, T. (2016). Determinants of non-performing loans: Evidence from Euro-area countries. Finance Research Letters, 18, 116-119. https://doi.org/10.1016/j.frl.2016.04.008.
• Espinoza, R. A. ve Prasad, A. (2010). Nonperforming Loans in the GCC Banking System and Their Macroeconomic Effects (IMF Working Paper No: 10/224), International Monetary Fund, 1-24. https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Nonperforming-Loans-in-the-GCC-Banking-System-and-their-Macroeconomic-Effects-24258.
• Genç, E. ve Şaşmaz, M. Ü. (2016). Takipteki Banka Kredilerinin Makroekonomik Belirleyicileri: Ticari Krediler Örneği. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 36, 119-129. http://dergisosyalbil.selcuk.edu.tr/susbed/article/view/1293.
• Ghosh, A. (2015). Banking-industry specific and regional economi determinants of non-performing loans: Evidence from US states. Journal of Financial Stability, 20, 93-104. https://doi.org/10.1016/j.jfs.2015.08.004.
• Ghosh. A. (2017). Sector-specific analysis of non-performing loans in the US banking system and their macroeconomic impact. Journal of Economics and Business, 93, 29-45. https://doi.org/10.1016/j.jeconbus.2017.06.002.
• Granger, C.W.J. (1969), Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424-438. https://doi.org/10.2307/1912791.
• Grigoli, F., Mansilla, M. ve Saldias, M. (2018). Macro-financial linkages and heterogeneous non-performing loans projections: An application to Ecuador. Journal of Banking & Finance, 97, 130-141. https://doi.org/10.1016/j.jbankfin.2018.09.023.
• Jakubik, P. ve Reininger, T. (2013). Determinants of of Nonperforming Loans in Central, Eastern and Southeastern Europe. Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), 3, 48-66. https://ideas.repec.org/a/onb/oenbfi/y2013i3b3.html.
• Klein, N. (2013). Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic Performance (IMF Working Paper No: 13/72). International Monetary Fund. https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Non-Performing-Loans-in-CESEE-Determinants-and-Impact-on-Macroeconomic-Performance-40413.
• Konstantakis, K. N., Michaelides, P. G. ve 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. https://doi.org/10.1016/j.physa.2015.12.163.
• Macit, F. (2012). What Determines The Non-Performing Loans Ratio: Evidence from Turkish Commercial Banks. CEA Journal of Economics, 7(1), 33-40. https://journal.cea.org.mk/index.php/ceajournal/article/view/108.
• Macit, F. ve Keçeli, B. (2012). Takipteki Kredi Oranını Etkileyen Faktörler: Türkiye'de Katılım Bankaları Örneği. Avrasya İncelemeleri Dergisi, 1 (1), 193-207. http://istanbul.dergipark.gov.tr/iuavid/issue/23607/251322.
• Messai, A. S. ve Jouni, F. (2013). Micro and Macro Determinants of Non-performing Loans. International Journal of Economics and Financial Issues, 3(4), 852-860. http://econjournals.com/index.php/ijefi/article/view/517.
• Radivojevic, N., Cvijanovic, D. Sekulic, D., Pavlovic, D., Jovic, S. ve Maksimovic, G. (2019). Econometric model of non-performing loans determinants. Physica A: Statistical Mechanics and its Applications, 520, 481-488. https://doi.org/10.1016/j.physa.2019.01.015.
• Ranjan, R. ve 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. https://www.rbi.org.in/upload/Publications/PDFs/60610.pdf#page=87.
• Reinhart, C. M. ve Rogoff, K. S. (2011). From Financial Crash to Debt Crisis. American Economic Review, 101(5), 1676–1706. https://doi.org/10.1257/aer.101.5.1676.
• Rinaldi, L. ve Sanchis-Arellano, A. (2006). Household Debt Sustainability: What Explains Household Non-Performing Loans? An Empirical Analysis (ECB Working Paper No: 570). European Central Bank. https://ssrn.com/abstract=872528.
• Shu, C. (2002). The Impact of Macroeconomic Environment on the Asset Quality of Hong Kong's Banking Sector. Hong Kong Monetary Authority.
• Skaricha, B. (2014). Determinants of non-performing loans in Central and Eastern European countries. Financial Theory and Practice, 38 (1) 37-59. https://doi.org/10.3326/fintp.38.1.2.
• Sorge, M. (2004). Stress-Testing Financial Systems: An Overview of Current Methodologies (BIS Working Paper No. 165). Bank for International Settlements. http://dx.doi.org/10.2139/ssrn.759585.
• Tanınmış Yücememiş, B. ve Sözer, İ. A. (2011). Bankalarda Takipteki Krediler: Türk Bankacılık Sektöründe Takipteki Kredilerin Tahminine Yönelik Bir Model Uygulaması. Finansal Araştırmalar ve Çalışmalar Dergisi, 3(5), 43-56. http://dergipark.gov.tr/marufacd/issue/505/4587.
• Tekşen, Ö. ve Çelik, M. (2018). Kredi Türlerinin Krediler Oranına Etkisi: Varlık Temelli Krediler Yüksek Takipteki Kredi Oranları için Bir Kalkan Mı? Muhasebe ve Finansman Dergisi, 79, 95-110. https://dx.doi.org/10.25095/mufad.438778.
• Toda, H. Y. ve Yamamoto, T. (1995). Statistical Inference in Vector Autoregressions With Possibly Integrated Processes. Journal of Econometrics, 66(1-2), 225-250. https://doi.org/10.1016/0304-4076(94)01616-8.
• Us, V. (2017). Dynamics of non-performing loans in the Turkish banking sector by an ownership breakdown: The impact of the global crisis. Finance Research Letters, 20, 109-117. http://dx.doi.org/10.1016/j.frl.2016.09.016.
• Vogiazas, S. D., & Nikolaidou, E. (2011). Investigating the Determinants of Nonperforming Loans in the Romanian Banking System: An Empirical Study with Reference to the Greek Crisis. Economics Research International, Sayı 2011, 1-13. http://dx.doi.org/10.1155/2011/214689.
• Vogiazas, S. D., & Nikolaidou, E. (2014). Credit Risk Determinants for the Bulgarian Banking System. International Advances in Economic Research, 20(1), 87-102. https://doi.org/10.1007/s11294-013-9444-x.
• Yüksel, S. (2016). Bankaların Takipteki Krediler Oranını Belirleyen Faktörler: Türkiye İçin Bir Model Önerisi. Bankacılar Dergisi, 98, 41-56. https://www.tbb.org.tr/Content/Upload/dergiler/dosya/73/Bankacilar_Dergisi_98.Sayi.pdf.