TÜRKİYEDE KAMU, YABANCI VE ÖZEL SERMAYELİ BANKALARIN SERMAYE YETERLİLİK ORANLARININ KREDİ ARZI ÜZERİNE ETKİSİ: BULANIK REGRESYON ÖRNEĞİ
Abstract
Bankaların kredi performanslarını ölçmede sıklıkla kullandıkları oranların bazıları sermaye yeterlilik oranlarıdır. Ticari amaç için faaliyet gösteren tüm banka veya banka olmayan finansal kuruluşlar bu oranları sıklıkla kullanırlar. Bu çalışmada Türkiye’de faaliyette bulunan sermaye yapısı farklılık gösteren bankaların sermaye yeterlilik oranlarını kredi arzı üzerine etkisi incelenmiştir. Bağımlı değişken Kredi arzı (KR), bağımsız değişkenler likidite durumu (L-TP), çekirdek sermaye yeterlilik rasyosu (SYRC) ve sermaye yeterliliği standart rasyosu (SYR) kullanılmıştır. Çözüm yöntemi olarak bulanık regresyon kullanılarak bağımlı ve bağımsız değişkenlerin alt ve üst sınır kat sayıları elde edilmiştir. Elde edilen katsayılar ile bağımlı değişkenin gerçek değerlerinin tahmin aralıklarına düştüğü görülmüştür. Çekirdek sermaye yeterlilik rasyosu ve sermaye yeterliliği standart rasyosunun kredi ve banka türlerine göre geniş aralıklarda tahmin edildiği saptanmıştır.
Supporting Institution
Sivas Cumhuriyet Üniversitesi Bilimsel Araştırma Projeleri (CÜBAP)
Project Number
ZARAVDYO-001
References
- Fungáčová, Z., Herrala, R., & Weill, L. (2012). The influence of bank ownership on credit supply: evidence from the recent financial crisis. In 29th International Conference of the French Finance Association (AFFI).
- Gül, R. E. İ. S., & KÖTÜOĞLU, R. (2016) Türk Bankacılık Sektörünün Sermaye Yeterliliği Davranışı. Yönetim ve Ekonomi Araştırmaları Dergisi, 14(3 (Özel Sayı)), 101-110.
- Hammes, W., & Shapiro, M. (2001). The implications of the new capital adequacy rules for portfolio management of credit assets. Journal of Banking & Finance, 25(1), 97-114.
- Herrera, S., Hurlin, C., & Zaki, C. (2012). Why don't banks lend to Egypt's private sector?. The World Bank.
- Jacobs, D., & Rayner, V. (2012). The role of credit supply in the Australian economy. Reserve Bank of Australia.
- Jheng, T. J., Latiff, A. R. A., Keong, O. C., & Chue, T. (2018). The Relationship between Capital Adequacy Ratio and Stock Price of Banking Institutions: Evidence from Malaysia. Management, 5(3), 67-87.
- Jochem, A., & Reitz, S. (2017). The role of global financial conditions for credit supply in EMU periphery countries. Applied Economics Letters, 24(10), 727-731.
- Kalifa, W., & Bektaş, E. (2018). The impacts of bank-specific and macroeconomic variables on the capital adequacy ratio: evidence from Islamic banks. Applied Economics Letters, 25(7), 477-481.
- Khashei, M., Hejazi, S. R., & Bijari, M. (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy sets and systems, 159 (7), 769-786.
- Modarres, M., Nasrabadi, E., & Nasrabadi, M. M. (2005). Fuzzy linear regression models with least square errors. Applied Mathematics and Computation, 163 (2), 977-989.
- Mousavi, S. J., Ponnambalam, K., & Karray, F. (2007). Inferring operating rules for reservoir operations using fuzzy regression and ANFIS. Fuzzy Sets and Systems, 158 (10), 1064-1082.
- Mumtaz, H., Pinter, G., & Theodoridis, K. (2018). What do VARs tell US about the impact of a credit supply shock?. International Economic Review, 59(2), 625-646.
- Nasrabadi, M. M., Nasrabadi, E., & Nasrabady, A. R. (2005). Fuzzy linear regression analysis: a multi-objective programming approach. Applied Mathematics and Computation, 163 (1), 245-251.
- Pehlivan, N. Y., Paksoy, T., & Chang, C. T. (2010). An Alternative Method for Fuzzy Regression&58; Fuzzy Radial Basis Function Network. International Journal of Lean Thinking, 1 (1), 1-15.
THE EFFECT OF CAPITAL ADEQUACY RATIO OF TURKEY’S PUBLIC, PRİVATE AND FOREINGN-OWNED BANKS ON CREDIT SUPPLY: FUZZY REGRESSION EXAMPLE
Abstract
Capital adequacy ratios are the most
frequently used rate of banks' credit performance. All banks or non-bank
financial institutions operating for commercial purposes often use these rates.
In this study, the effects of bank's capital adequacy ratio on credit supply of
the differing in capital structure operating in Turkey were examined. Dependent
variable loan supply (KR), independent variables liquidity status (L-TP), core
capital adequacy ratio (SYRC) and capital adequacy standard ratio (CAR) were
used. Fuzzy regression was used as the solution method and lower and upper
limit coefficients of dependent and independent variables were obtained. With
the coefficients obtained, it was seen that the actual values of the dependent
variable fell to the estimation intervals. Core capital adequacy ratio and
capital adequacy standard ratio are estimated at wide intervals according to
loan and bank types.
Project Number
ZARAVDYO-001
References
- Fungáčová, Z., Herrala, R., & Weill, L. (2012). The influence of bank ownership on credit supply: evidence from the recent financial crisis. In 29th International Conference of the French Finance Association (AFFI).
- Gül, R. E. İ. S., & KÖTÜOĞLU, R. (2016) Türk Bankacılık Sektörünün Sermaye Yeterliliği Davranışı. Yönetim ve Ekonomi Araştırmaları Dergisi, 14(3 (Özel Sayı)), 101-110.
- Hammes, W., & Shapiro, M. (2001). The implications of the new capital adequacy rules for portfolio management of credit assets. Journal of Banking & Finance, 25(1), 97-114.
- Herrera, S., Hurlin, C., & Zaki, C. (2012). Why don't banks lend to Egypt's private sector?. The World Bank.
- Jacobs, D., & Rayner, V. (2012). The role of credit supply in the Australian economy. Reserve Bank of Australia.
- Jheng, T. J., Latiff, A. R. A., Keong, O. C., & Chue, T. (2018). The Relationship between Capital Adequacy Ratio and Stock Price of Banking Institutions: Evidence from Malaysia. Management, 5(3), 67-87.
- Jochem, A., & Reitz, S. (2017). The role of global financial conditions for credit supply in EMU periphery countries. Applied Economics Letters, 24(10), 727-731.
- Kalifa, W., & Bektaş, E. (2018). The impacts of bank-specific and macroeconomic variables on the capital adequacy ratio: evidence from Islamic banks. Applied Economics Letters, 25(7), 477-481.
- Khashei, M., Hejazi, S. R., & Bijari, M. (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy sets and systems, 159 (7), 769-786.
- Modarres, M., Nasrabadi, E., & Nasrabadi, M. M. (2005). Fuzzy linear regression models with least square errors. Applied Mathematics and Computation, 163 (2), 977-989.
- Mousavi, S. J., Ponnambalam, K., & Karray, F. (2007). Inferring operating rules for reservoir operations using fuzzy regression and ANFIS. Fuzzy Sets and Systems, 158 (10), 1064-1082.
- Mumtaz, H., Pinter, G., & Theodoridis, K. (2018). What do VARs tell US about the impact of a credit supply shock?. International Economic Review, 59(2), 625-646.
- Nasrabadi, M. M., Nasrabadi, E., & Nasrabady, A. R. (2005). Fuzzy linear regression analysis: a multi-objective programming approach. Applied Mathematics and Computation, 163 (1), 245-251.
- Pehlivan, N. Y., Paksoy, T., & Chang, C. T. (2010). An Alternative Method for Fuzzy Regression&58; Fuzzy Radial Basis Function Network. International Journal of Lean Thinking, 1 (1), 1-15.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Articles |
| Authors | |
| Project Number | ZARAVDYO-001 |
| Publication Date | December 23, 2019 |
| Acceptance Date | November 26, 2019 |
| Published in Issue | Year 2019 Volume: 7 Issue: 2 |
