research notes in economics - TCMB

No: 2016-27/September 02, 2016

EKONOMİ NOTLARI Determinants of Non-Performing Loans in the Turkish Banking Sector: What Has Changed After the Global Crisis? * Vuslat Us

Abstract:

This paper analyzes the determinants of non-performing loans in the Turkish banking sector before and after the global crisis. The bank-specific determinants include measures on persistence, capital adequacy,

profitability, lending, inefficiency and bank size, while other determinants include the real GDP growth, inflation, exchange rate and the policy rate, which represent macroeconomic and policy-related variables. In addition, the determinants also include a dummy variable for the global crisis. Estimation results suggest that the determinants of non-performing loans have changed after the crisis. Non-performing loans are mostly shaped by bank-specific factors before the crisis, whereas bank-specific factors have a reduced effect after the global crisis. This can be owed to global factors, which certainly had effects on financial conditions. In the upcoming period, the course of global policy normalization may affect financial conditions and hence non-performing loans. This implies challenges and prospects regarding financial stability and the conduct of monetary policy.

Özet:

Bu çalışmada, Türk bankacılık sektöründeki takipteki kredilerin belirleyicileri küresel kriz öncesi ve sonrasında incelenmektedir. Bankaya özgü belirleyiciler olarak atalet, sermaye yeterliliği, kârlılık, kredi arzı, verimsizlik ve banka

büyüklüğü; makroekonomik ve politikaya ilişkin etkenler olarak ise büyüme, enflasyon, döviz kuru ve politika faizi çalışmaya dâhil edilmektedir. Buna ek olarak, küresel krize ilişkin kukla değişkeni de belirleyiciler arasında yer almaktadır. Tahmin sonuçları, takipteki kredilerin belirleyicilerinin kriz sonrasında değiştiğini göstermektedir. Buna göre, kriz öncesi dönemde takipteki krediler büyük oranda bankaya özgü değişkenler tarafından belirlenirken, kriz sonrası dönemde bu değişkenlerin etkileri azalmaktadır. Bunun sebebi, kriz sonrası dönemde finansal koşulların küresel etkenler tarafından şekillenmiş olmasıdır. Önümüzdeki dönemde, küresel politika normalleşmesinin izleyeceği seyrin finansal koşullar ve dolayısıyla takipteki krediler üzerinde etkili olması muhtemel gözükmektedir. Bu durum, finansal istikrar ve para politikasına ilişkin zorluklar ve olasılıklara işaret etmektedir.

The author would like to thank the anonymous referee for providing exceptionally useful suggestions and invaluable feedback. The author would also like to thank the editors for very careful reading and handling of this text. Usual caveats apply regarding errors and omissions. The CBT Research Notes in Economics is a refereed publication prepared by Central Bank of Turkey with the purpose of disseminating results from research on the Turkish economy with a particular emphasis on macroeconomic developments and issues relevant for monetary policy, and thereby contributing to discussions on economic issues in a timely fashion. All views and opinions expressed in the CBT Research Notes in Economics are of the authors and do not necessarily represent the official views of the Central Bank of Turkey and its staff. A written permission must be obtained to reprint the entire text of notes published in CBT Research Notes in Economics series. Ekonomi Notları

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1. Introduction Understanding the determinants of non-performing loans (NPLs) in the Turkish banking sector is necessary to identify key vulnerabilities of financial stability. This is especially important as the global crisis in 2008 induced important changes in the banking sector structure and the course of NPLs. In particular, CBRT (2010), Selçuk (2010) and Afşar (2011) show that NPLs increased sharply after the crisis. Furthermore, Ganioğlu and Us (2014) and Us (2015) demonstrate that the structure of the Turkish banking sector has changed after the crisis. Moreover, the ongoing normalization of global monetary policies may tighten financial conditions, prompting potential challenges and prospects for the NPL dynamics in the Turkish economy. The previous evidence that the structure of the Turkish banking sector has changed after the crisis besides the findings provided by the above studies that report higher NPLs in the same period raise an important question. In particular, how do the determinants of NPLs change before and after the crisis? Addressing this question is the main motivation of this paper, where Louzis et al. (2012), Salas and Saurina (2002), Jiménez and Saurina (2006) and Ranjan and Dhal (2003) provide the empirical basis. The banking sector is a major pillar of the Turkish financial system. In particular, total banking sector assets represent about 87 percent of the financial system in Turkey (CBRT, 2013). This establishes the main motivation for analyzing NPLs, which provide a major feedback for financial stability. In the meantime, the Central Bank of the Republic of Turkey (CBRT) was compelled by the global crisis to observe financial stability as a supplementary objective. Hence, this analysis is also useful for the conduct of monetary policy, which addresses macroprudential concerns.1 Accordingly, this paper examines the effects of bank-specific, macroeconomic and policyrelated variables on NPL dynamics in the Turkish banking sector during 2002Q4-2015Q4. The analysis is conducted for the overall sample and also by sub-periods in order to assess how the determinants of NPLs change before and after the crisis. Furthermore, the study also addresses the issue of persistence by including the lagged dependent variable term among regressors.

1

For further details, see Başçı and Kara (2011).

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2. Determinants of NPLs In order to analyze NPLs, certain bank-specific, macroeconomic and policy-related variables are selected in line with the literature. Accordingly, capital adequacy may have an effect on NPLs. In particular, thinly capitalized banks have higher risk exposure, which raises NPLs (Berger and DeYoung, 1997; Keeton and Morris, 1987; Salas and Saurina, 2002). Yet, more capitalized banks may also have greater NPLs as they may be tempted to increase their capital as a cushion against credit risk (Ahmad and Ariff, 2007). Besides, higher regulatory capital may encourage banks to embark in riskier activities, causing more toxic credit portfolios (Makri et al., 2014; Rime, 2001; Koehn and Santomero, 1980). Profitability may raise NPLs as higher profits are likely to induce risk, which may generate bad loans (García-Marco and Robles-Fernández, 2008). Yet, NPLs may also grow amid lower profitability due to presumably bad management (Louzis et al., 2012). Lending may have a positive effect on NPLs as excessive loan growth may expose banks to higher risk (Keeton and Morris, 1987; Sinkey and Greenwalt, 1991; Salas and Saurina, 2002; Jiménez and Saurina, 2006; Klein, 2013). Yet, NPLs may also be reversely linked to lending due to stringent lending standards (Khemraj and Pasha, 2009; Vithessonthi, 2016). Inefficiency may raise NPLs as higher inefficiency implies poor loan underwriting, monitoring and cost control, which may increase impaired loans (Berger and DeYoung, 1997; Williams, 2004; Espinoza and Prasad, 2010; Podpiera and Weil, 2008; Louzis et al., 2012). On the other hand, lower inefficiency implies higher cost efficiency, which reflects little resources allocated to monitoring risks, thereby causing higher NPLs (Rossi et al., 2008). Bank size is inversely related with NPLs due to greater opportunities to evaluate loans and diversify risk (Hu et al., 2004; Ranjan and Dhal, 2003; Salas and Saurina, 2002; Louzis et al., 2012). However, too-big-to-fail banks may indulge in excessive risk given higher prospects to be rescued by the government in case of a failure, which leads to increased NPLs (Stern and Feldman, 2004). The real GDP growth is inversely linked to NPLs as higher income improves the debt servicing capacity of borrowers, which implies lower bad loans (Salas and Saurina, 2002: Ranjan and Dhal, 2003; Jiménez and Saurina, 2006; Fofack, 2005). Inflation, on the other hand, may trigger NPLs as it reduces real income (Klein, 2013; Fofack, 2005). Yet, inflation may also have a reverse relation with NPLs as higher inflation decreases the real value of the outstanding loan (Nkusu, 2011; Khemraj and Pasha, 2009).

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The depreciation of the exchange rate may raise NPLs. In particular, a depreciated local currency causes higher NPLs in case of FX lending to unhedged borrowers (Kalluci and Kodra, 2010; Moinescu and Codirlaşu, 2012; Fofack, 2005). Yet, Castro (2013) shows that NPLs may also increase due to the appreciation of the domestic currency. Finally, policy rate hikes are expected to drive up NPLs as higher policy rate may induce higher rates on loans, which adversely affect debt servicing ability (Bofondi and Ropele, 2011; Espinoza and Prasad, 2010; Sinkey and Greenwalt, 1991; Berge and Boye, 2007).

3.

Data, Stylized Facts and the Econometric Methodology 3.1. Data Table A1 displays the description and the source of the data. The analysis covers 21

deposit banks. The dependent variable is the NPL ratio, while bank-specific variables capture persistence, capital adequacy, profitability, lending, inefficiency and bank size. Macroeconomic and policy-related determinants include the real GDP growth, inflation, exchange rate and the policy rate. The dataset also includes a dummy variable for the global crisis.

3.2. Stylized Facts Figure A1 displays the evolution of the NPL ratio and bank-specific explanatory variables, while Table A2 shows the descriptive statistics. Accordingly, the NPL ratio declines radically before the crisis, particularly during 2002-2004, due to the implementation of the banking sector reform. This causes a major improvement in banks’ balance sheets by also causing a plunge in the NPL ratio, which continues until 2008.2 However, the global crisis deteriorates the NPL ratio by inducing a significant surge. In particular, the NPL ratio climbs up swiftly as of the second half of 2008 and reaches a peak in the first quarter of 2009. This coincides with the period when the adverse effects of the global crisis are manifested heavily on the Turkish economy. On the other hand, as the crisis-driven negative effects are eliminated, the NPL ratio assumes a gradual downtrend. In fact, the NPL ratio decreases until mid-2011, declining even below the pre-crisis levels. Yet, as of the second half of 2011, the NPL ratio starts to re-surge, albeit marginally. This may be due to the sovereign debt crisis in Europe, which had unfavorable effects on the Turkish economy.

2

For further details, see Ganioğlu and Us (2014).

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The NPL ratio remains rather elevated and hits 3 percent in the third quarter of 2012. Despite posting a slight subsequent decline, the ratio remains steady around 3 percent afterwards. This can be attributed to the relatively tight global financial conditions fueled by the taper tantrum, which also had reflections on the Turkish economy. In fact, the NPL ratio reaches 3.1 percent in the last quarter of 2015, reaching the highest level recorded since the third quarter of 2011. The surge in NPLs may be owed to the slowdown in the economic activity, which has adverse effects on especially consumer and SME loans.3 In sum, this analysis shows that NPLs changed drastically over time. Before the crisis, the NPL ratio is mostly shaped by the banking sector reform, which rehabilitates banks’ balance sheets. However, after the crisis, the NPL ratio is determined mostly by macroeconomic factors amid the changing global financial conditions that pose risks to domestic financial stability.

3.3. Econometric Methodology The empirical framework is in the form of a general equation, which may help to infer how the determinants of NPLs have changed before and after the crisis. Accordingly; ,

Where

,

=

+

,

+

,

+

is the dependent variable of bank at time ;

specific explanatory variables of bank i at time

− 1;

,

+

,

is the matrix of bank, −1

4

macroeconomic and policy-related variables at time − 1 ; and

,

,

is the matrix of is the NPL ratio of

bank at time − 1, which captures the persistence. As for the parameters, term; while

is the intercept

shows the degree of persistence, which is expected to be positive and less than unity; and

are the corresponding coefficient vectors.

with mean 0 and variance

is the idiosyncratic error term

. Subscripts i and t range from 1 to N and 1 to T, respectively,

where N is the number of banks and T is the number of periods in the dataset. The above equation is estimated using panel estimation techniques via fixed effects model, which takes into account the heterogeneity across banks by allowing variable intercepts. Alternatively, the equation is estimated via Generalized Method of Moments (GMM) to eliminate potential biases that may arise due to the dynamic nature of the model. Hence, system GMM is adopted as proposed by Arellano and Bover (1995) and Blundell and Bond (1998), which is a joint estimation of the above equation in levels and in first differences.

3 4

NPLs have been rising abruptly for consumer and SME loans (BRSA, 2015). The explanatory variables enter the equation with one lag to handle endogeneity.

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4. Estimation Results Tables A3 and A4 display the stationarity tests, while Table A5 reports the estimation results. Accordingly, the panel unit root tests suggest that all the bank-specific explanatory variables are stationary except for asset size, which is remedied by first-differencing. As for the macroeconomic and policy-related determinants, the unit root tests reject the presence of non-stationarity for all variables. Main findings can be summarized as follows: The estimations for the overall period suggest that the selection of the explanatory variables is plausible and most of the regressors yield statistically significant coefficients, which also have the expected signs. In addition, both the fixed effects and the system GMM5 estimators produce similar results. In particular, the NPL ratio is persistent given the positive and sizeable coefficient of the lagged dependent variable. In addition, lending has a positive and significant coefficient, which hints at increased NPLs in response to higher percentage of loans in assets. Inefficiency, which is captured by other operating expenses to total assets, has a significant coefficient with a positive sign. This signals that higher operating expenses to total assets feed into NPLs, which suggests inefficiency at underwriting and monitoring capabilities. Furthermore, the coefficient of the bank size is positive, which implies that larger banks are subject to more credit risk. On the other hand, capital adequacy and profitability yield positive yet insignificant coefficients. Macroeconomic and policy-related explanatory variables have meaningful coefficients as well. In particular, the negative coefficient of the GDP growth implies that higher income restricts NPLs due to improved debt servicing capacity, while the positive coefficient of the inflation term reinforces the income sensitivity of the NPLs. In other words, a ceteris paribus increase in inflation leads to lower real income, which hinders the repayment of loans. The depreciation of the Turkish lira that can be captured by an increase in exchange rate may trigger impaired loans. Finally, higher policy rates are likely to be transmitted as higher interest rate on loans. This increases the interest burden and raises the likelihood for default. The dummy variable for the global crisis yields a statistically significant and positive coefficient as anticipated. This provides sufficient evidence that the sample would be split. Consequently, the analysis is repeated individually for the pre-crisis (2002Q4-2008Q3) and the post-crisis (2008Q4-2015Q4) periods, which shows that the significance of potential determinants varies before and after the crisis.

5 The p-values of the AR(1) and AR(2) tests show no serial correlation, while Sargan tests justify instrument validity in all specifications.

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In particular, capital adequacy, inefficiency and bank size are significant before the crisis, while GDP growth is significant only after the crisis. Meanwhile, lending, inflation and exchange rate are statistically significant in both sub-periods. Yet, the sign of the coefficient for lending is negative after the crisis, which confirms Khemraj and Pasha (2009) and Vithessonthi (2016) that attribute this reverse link to the adoption of stringent lending standards. As for the persistence of NPLs, the lagged dependent variable is also statistically significant in both subperiods, while policy rate yields insignificant coefficients by sub-period analysis.

5. Conclusion This paper analyzes the determinants of NPLs in the Turkish banking sector and shows that the global crisis has a major impact on the determinants of NPLs. Accordingly, some striking facts can be highlighted as follows: Except for profitability, all the selected explanatory variables have an effect on the dynamics of NPLs, which justifies the choice of potential determinants. However, the significance of these variables changes before and after the global crisis. To be specific, the NPL ratio of Turkish banks is mostly determined by bank-specific variables before the crisis. In this context, capital adequacy, lending, inefficiency and bank size are significant factors, which have an impact on the NPL ratio of Turkish banks, while inflation and exchange rate are the only macroeconomic indicators that affect the NPL ratio in this period. However, after the crisis, bank-specific factors have a lesser effect on the dynamics of NPLs. In particular, lending is the only significant bank-specific variable, which affects the NPL ratio after the global crisis. Meanwhile, the effectiveness of macroeconomic and policy-related factors also varies. Accordingly, the real GDP growth, inflation and exchange rate are influential on the NPL dynamics during this period. In addition, the persistence of the NPL ratio is also a major factor driving the NPL dynamics. This can be confirmed by the significance of the lagged dependent variable in the overall analysis and also in sub-periods. Moreover, the persistence seems to have an increased effect after the global crisis given the relatively higher coefficient of the lagged NPL ratio in the postcrisis estimations. Against this background, the estimation results are in line with the analysis on the evolution of the NPL ratio, which reveals that the NPL dynamics are shaped by the banking sector reform before the crisis, whereas after the crisis, the NPL ratio seems to be determined mostly by global factors that affect macroeconomic conditions.

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Hence, this paper concludes that the determinants of NPLs have changed after the global crisis. Obviously, accurate policy design for minimizing NPLs will continue to be critical to the stability of the financial system. Accordingly, global financial conditions, which will be subject to the pace of global policy normalization, may affect the course of NPLs in the upcoming period. Clearly, these have challenges and prospects regarding financial stability and the conduct of monetary policy. Future research may analyze the effect of global factors in order to have a better understanding of the NPL dynamics after the crisis. These factors may range from indicators on global financial conditions to measures on global risk perception. Potential determinants may also include variables on domestic financial conditions to assess how impaired loans may respond to the degree of tightness in the local economy. Further work may replicate the same analysis individually for each loan category. In particular, macroeconomic and bank-specific variables may impact each loan type differently. This may be due to structural factors creating different incentives for the non-payment of these loans. Hence, a distinction may help policymakers to promote financial stability by directly identifying the loan type that is likely to generate bad loans. Prospective studies may also examine NPLs by a bank ownership breakdown. This evidently requires a thorough understanding of the underlying structural forces driving these ownership-based differences, which, however, is beyond the scope of this paper.

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References Afşar, M., 2011, Küresel Kriz ve Türk Bankacılık Sektörüne Yansımaları (in Turkish), Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 6(2): 143‐171. Ahmad, N.H. and M. Ariff, 2007, Multi-country study of bank credit risk determinants, International Journal of Banking and Finance, 5(1): 135-152. Arellano, M. and O. Bover, 1995, Another look at the instrumental variable estimation of errorcomponents models, Journal of Econometrics, 68(1): 29-51. Başçı, E. and H. Kara, 2011, Finansal İstikrar ve Para Politikası (in Turkish), İktisat İşletme ve Finans, 26(302): 9-25. Berge, T.O. and K.G. Boye, 2007, An analysis of bank’s problem loans, Norges Bank Economic Bulletin, 78(2): 65-76. Berger, A.N. and R. DeYoung, 1997, Problem loans and cost efficiency in commercial banks, Journal of Banking & Finance, 21(6): 849-870. Blundell, R. and S. Bond, 1998, Initial conditions and moment restrictions in dynamic panel data models, Journal of Econometrics, 87(1): 115-143. Bofondi, M. and T. Ropele, 2011, Macroeconomic Determinants of Bad Loans: Evidence from Italian Banks, Bank of Italy Occasional Paper No. 89. BRSA, 2015, Türk Bankacılık Sektörü Temel Göstergeleri (in Turkish), available at http://www.bddk.org.tr/WebSitesi/turkce/Raporlar/TBSGG/15175tbs_temel_gostergele r_aralik_2015.pdf. Castro, V., 2013, Macroeconomic determinants of the credit risk in the banking system: The case of the GIPSI, Economic Modelling, 31(c): 672-683. CBRT,

2010, Financial Stability Report, December, available at http://www.tcmb.gov.tr/wps/wcm/connect/93f51ef9-f063-4496-b3ade7e83121eb87/fulltext11.pdf?MOD=AJPERES&CACHEID=93f51ef9-f063-4496-b3ade7e83121eb87. , 2013, Financial Stability Report, May, available at http://www.tcmb.gov.tr/wps/wcm/connect/TCMB+EN/TCMB+EN/Main+Menu/PUBLIC ATIONS/Reports/Financial+Stability+Report/2013/Financial+Stability+ReportMay+2013%2C+Volume+16/.

Dickey, D.A. and W.A. Fuller, 1979, Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74(366): 427-431. Espinoza, R. and A. Prasad, 2010, Nonperforming Loans in the GCC Banking Systems and their Macroeconomic Effects, IMF Working Paper No. 10/224. Fofack, H., 2005, Non-Performing Loans in Sub-Saharan Africa: Causal Analysis and Macroeconomic Implications, World Bank Policy Research Working Paper No. 3769. Ganioğlu, A. and V. Us, 2014, The Structure of the Turkish Banking Sector Before and After the Global Crisis, CBRT Working Paper No. 14/29.

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García-Marco, T. and M.D. Robles-Fernández, 2008, Risk-taking behavior and ownership in the banking industry: The Spanish evidence, Journal of Economics and Business, 60(4): 332-354. http://evds.tcmb.gov.tr/. http://www.tbb.org.tr. Hu, J.L., Y. Li and Y.H. Chiu, 2004, Ownership and nonperforming loans: Evidence from Taiwan’s banks, The Developing Economies, 42(3): 405-420. Im, K.S., M.H. Pesaran and Y. Shin, 2003, Testing for unit roots in heterogeneous panels, Journal of Econometrics, 115(1): 53-74. Jiménez, G. and J. Saurina, 2006, Credit cycles, credit risk, and prudential regulation, International Journal of Central Banking, 2(2): 65-98. Kalluci, I. and O. Kodra, 2010, Macroeconomic Determinants of Credit Risk: The case of Albania, in A. Fullani (ed.), Economic Policies in SEE: Design, Performance and Challenges, Bank of Albania. Keeton, W.R. and C.S. Morris, 1987, Why Do Banks’ Loan Losses Differ?, Federal Reserve Bank of Kansas City, Economic Review, 72(5): 3-21. Khemraj, T. and S. Pasha, 2009, The determinants of non-performing loans: An econometric case study of Guyana, MPRA Paper No. 53128. Klein, N., 2013, Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic Performance, IMF Working Paper No. 13/72. Koehn, M. and A. Santomero, 1980, Regulation of bank capital and portfolio risk, Journal of Finance, 35(5): 1235-1244. Levin, A., C.F. Lin and C.S.J. Chu, 2002, Unit root tests in panel data: Asymptotic and finitesample properties, Journal of Econometrics, 108(1): 1-24. Louzis, D.P., A.T. Vouildis and V.L. Metaxas, 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): 10121027. Makri, V., A. Tsagkanos and A. Bellas, 2014, Determinants of Non-Performing Loans: The Case of Eurozone, Panoeconomicus, 61(2): 193-206. Moinescu, B. and A. Codirlaşu, 2012, Assessing the Sectoral Dynamics of Non-performing Loans: Signs from Financial and Real Economy, Theoretical and Applied Economics, XIX(2): 69-80. Nkusu, M., 2011, Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies, IMF Working Paper No. 11/161. Phillips, P.C.B. and P. Perron, 1988, Testing for a Unit Root in Time Series Regression, Biometrika, 75(2): 335-346. Podpiera, J. and L. Weill, 2008, Bad Luck or Bad Management? Emerging Banking Market Experience, Journal of Financial Stability, 4(2): 135-148.

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Ranjan, R. and S.C. Dhal, 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. Rime, B., 2001, Capital requirements and bank behaviour: empirical evidence for Switzerland, Journal of Banking & Finance, 25(5): 789-805. Rossi, S., M. Schwaiger and G. Winkler, 2008, Linking Managerial Behaviour to Cost and Profit Efficiency in the Banking Sectors of Central and Eastern European Countries, Kredit und Capital, 41(4): 598-629. 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. Selçuk, B., 2010, Küresel Krizin Türk Finans Sektörü Üzerindeki Etkileri (in Turkish), Ekonomi Bilimleri Dergisi, 2(2): 21-27. Sinkey, J.F. and M.B. Greenwalt, 1991, Loan-Loss Experience and Risk-Taking Behavior at Large Commercial Banks, Journal of Financial Services Research, 5(1): 43-59. Stern, G.H. and R.J. Feldman, 2004, Too Big To Fail: The Hazards of Bank Bailouts, Brookings Institution Press, Washington, DC, USA. Us, V., 2015, Banking Sector Performance in Turkey Before and After the Global Crisis, İktisat İşletme ve Finans, 30(353): 45-74. Vithessonthi, C., 2016, Deflation, bank credit growth, and non-performing loans: Evidence from Japan, International Review of Financial Analysis, 45(2016): 295-305. Williams, J., 2004, Determining Management Behaviour in European Banking, Journal of Banking & Finance, 28(10): 2427-2460.

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Appendix: Table A1. Description of Variables and Sources VARIABLE

DESCRIPTION

SOURCE

Overdue loans to total loans and receivables

http://www.tbb.org.tr

Regulatory capital to risk-weighted assets


Dependent variables NPL/LOANS Independent variables EQUITY/RWASSETS PROFITS/EQUITY

Net profits (loss) to shareholders’ equity


LOANS/ASSETS

Total loans and receivables to total assets


OTHEREXP/ASSETS

Other operating expenses to total assets


ASSETS/GDP

Total assets to the GDP


DCRISIS

Dummy variable for global crisis


Year-on-year change in the real GDP in logs

http://evds.tcmb.gov.tr

GDP INFLATION

Year-on-year change in the consumer price index in logs http://evds.tcmb.gov.tr

EXCHANGE

Quarter-on-quarter change in USD/TL rate in logs


POLICYRATE

Change in policy rate


Figure A1. Evolution of Bank-Specific Variables NPL/LOANS

EQUITY/RWASSETS

PROFITS/EQUITY

0.10

0.10

0.05

0.05

0.00

0.00

0.00

0.00

0.00

0.00 2002Q4 2003Q4

2002Q4 2003Q4 2004Q4 2005Q4 2006Q4

LOANS/ASSETS

OTHEREXP/ASSETS

2015Q4

0.05

2013Q4 2014Q4

0.05

2012Q4

0.10

2011Q4

0.10

2010Q4

0.20

2008Q4 2009Q4

0.20

2007Q4

0.10

2006Q4

0.10

2005Q4

0.15

2004Q4

0.15

2015Q4

0.30

2011Q4 2012Q4 2013Q4 2014Q4

0.30

2008Q4 2009Q4 2010Q4

0.15

2007Q4

0.15

2015Q4

0.20

2011Q4 2012Q4 2013Q4 2014Q4

0.20

2007Q4 2008Q4 2009Q4 2010Q4

0.40

2006Q4

0.40

2003Q4 2004Q4 2005Q4

0.20

2002Q4

0.20

ASSETS/GDP

0.03

0.15

0.15

0.20

0.20

0.02

0.02

0.10

0.10

0.00

0.00

0.00

0.00

0.05

0.05 2014Q4 2015Q4

0.03

2012Q4 2013Q4

0.40

2010Q4 2011Q4

0.40

2008Q4 2009Q4

0.20

2006Q4 2007Q4

0.20

2004Q4 2005Q4

0.05

2002Q4 2003Q4

0.05

2015Q4

0.60

2013Q4 2014Q4

0.60

2011Q4 2012Q4

0.25

2009Q4 2010Q4

0.25

2007Q4 2008Q4

0.06

2005Q4 2006Q4

0.06

2002Q4 2003Q4 2004Q4

0.80

2002Q4 2003Q3 2004Q2 2005Q1 2005Q4 2006Q3 2007Q2 2008Q1 2008Q4 2009Q3 2010Q2 2011Q1 2011Q4 2012Q3 2013Q2 2014Q1 2014Q4 2015Q3

0.80

Notes: Shaded region denotes the post-crisis period. Series are weighted by the relative size of each bank in the respective series. Source: http://www.tbb.org.tr, Author’s calculations.

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Table A2. Descriptive Statistics

NPL/ LOANS EQUITY/ RWASSETS PROFITS/ EQUITY LOANS/ ASSETS OTHEREXP/ ASSETS ASSETS/ GDP GDP

INFLATION

EXCHANGE

POLICYRATE

Overall Pre-Crisis Post-Crisis Overall Pre-Crisis Post-Crisis Overall Pre-Crisis Post-Crisis Overall Pre-Crisis Post-Crisis Overall Pre-Crisis Post-Crisis Overall Pre-Crisis Post-Crisis Overall Pre-Crisis Post-Crisis Overall Pre-Crisis Post-Crisis Overall Pre-Crisis Post-Crisis Overall Pre-Crisis Post-Crisis

MEAN

MEDIAN

MIN

MAX

0.050 0.063 0.038 0.201 0.241 0.165 0.074 0.081 0.068 0.538 0.451 0.615 0.024 0.029 0.019 0.133 0.099 0.164 0.047 0.059 0.036 0.093 0.114 0.075 0.011 -0.003 0.023 -0.007 -0.012 -0.003

0.033 0.032 0.034 0.159 0.169 0.156 0.068 0.078 0.059 0.593 0.475 0.642 0.020 0.023 0.018 0.055 0.039 0.074 0.051 0.068 0.040 0.083 0.091 0.076 0.010 -0.018 0.021 -0.003 -0.005 -0.001

0.000 0.000 0.006 0.036 0.036 0.120 -1.786 -1.786 -0.146 0.001 0.001 0.214 0.004 0.004 0.004 0.000 0.000 0.002 -0.159 -0.072 -0.159 0.043 0.069 0.043 -0.086 -0.086 -0.052 -0.083 -0.083 -0.037

0.946 0.946 0.124 3.161 3.161 0.344 0.618 0.618 0.339 0.878 0.810 0.878 0.274 0.274 0.065 0.611 0.448 0.611 0.119 0.113 0.119 0.275 0.275 0.100 0.241 0.241 0.102 0.034 0.034 0.027

STANDARD DEVIATION 0.072 0.102 0.021 0.162 0.226 0.037 0.106 0.141 0.059 0.170 0.179 0.117 0.020 0.025 0.010 0.157 0.119 0.179 0.049 0.037 0.055 0.045 0.058 0.014 0.060 0.070 0.045 0.019 0.023 0.013

Table A3. Panel Unit Root Tests

NPL/ LOANS EQUITY/ RWASSETS PROFITS/ EQUITY LOANS/ ASSETS OTHEREXP/ ASSETS ASSETS/ GDP

IPS

LLC

Critical Value (1 percent)



-3.377

-23.441

-1.930

-1.810

-1.750

-3.046

-12.937

-1.930

-1.810

-1.750

-6.148

-19.337

-1.930

-1.810

-1.750

-2.405

-8.768

-1.930

-1.810

-1.750

-7.003

-23.801

-1.930

-1.810

-1.750

-0.717

-1.802

-1.930

-1.810

-1.750

Notes: The panel unit root tests are based on the assumption of a panel mean with no time trend. IPS and LLC denote Levin-Lin-Chu and ImPesaran-Shin tests (Levin et al., 2002; Im et al., 2003).

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No: 2016-27/September 02, 2016

Table A4. Time Series Unit Root Tests

GDP INFLATION

ADF

PP

-2.692 -6.016

-2.982 -8.952

Critical Value (1 percent) -3.592 -3.574



EXCHANGE

-6.429

-6.429

-3.563

-2.919

-2.597

POLICYRATE

-3.351

-3.250

-3.565

-2.920

-2.598

Notes: Unit root test are based on the assumption of a constant with no time trend. ADF and PP denote Augmented Dickey-Fuller and PhillipsPerron statistics (Dickey and Fuller, 1979; Phillips and Perron, 1988).

Table A5. Estimation Results

NPL/ LOANS(-1) EQUITY/ RWASSETS(-1) PROFITS/ EQUITY(-1) LOANS/ ASSETS(-1) OTHEREXP/ ASSETS(-1) ∆ASSETS/ GDP DCRISIS GDP(-1) INFLATION(-1) EXCHANGE(-1) POLICYRATE CONSTANT F-statistics

FIXED EFFECTS 0.810*** (0.041) 0.036 (0.022) 0.001 (0.004) 0.020 (0.014) 0.116*** (0.034) 0.055** (0.022) 0.006*** (0.002) -0.034*** (0.011) 0.062* (0.035) 0.026*** (0.006) 0.122* (0.069) -0.018 (0.012) 5309.06 (0.000)

Wald chi-squared

-

AR(1) AR(2) Sargan Number of Observations

-

Overall SYSTEM GMM 0.819*** (0.014) 0.013 (0.008) 0.003 (0.007) 0.012** (0.006) 0.089*** (0.034) 0.037 (0.031) 0.006*** (0.002) -0.040*** (0.011) 0.062*** (0.020) 0.028*** (0.008) 0.108*** (0.039) -0.009** (0.004) 10775.69 (0.000) 0.0294 0.7725 0.8396 1092

Pre-Crisis Period FIXED SYSTEM EFFECTS GMM 0.792*** 0.726*** (0.052) (0.027) 0.047** 0.014 (0.021) (0.013) -0.005 -0.006 (0.006) (0.013) 0.061* 0.028* (0.035) (0.017) 0.148** 0.072 (0.057) (0.062) 0.107* 0.080 (0.049) (0.059)

Post-Crisis Period FIXED SYSTEM EFFECTS GMM 0.794*** 0.827*** (0.027) (0.020) -0.002 -0.003 (0.013) (0.010) 0.004 0.004 (0.006) (0.006) -0.010* -0.011*** (0.005) (0.004) 0.026 0.006 (0.029) (0.030) 0.015 0.025 (0.016) (0.016)

-

-

-

-

0.029 (0.102) 0.117** (0.053) 0.029 (0.037) 0.132 (0.101) -0.048 (0.029) 5922.52 (0.000)

-0.002 (0.044) 0.179*** (0.037) 0.049* (0.027) 0.140 (0.086) -0.023*** (0.011)

-0.028*** (0.006) -0.013 (0.018) 0.021*** (0.004) -0.011 (0.021) 0.014*** (0.004) 347.41 (0.000)

-0.027*** (0.005) 0.026* (0.014) 0.019*** (0.004) -0.017 (0.023) 0.011*** (0.004)

-

2377.14 (0.000) 0.0497 0.7719 0.6753

-

504

4284.36 (0.000) 0.0036 0.4709 0.1168 588

Notes: ∆ denotes the difference operator. *, **, *** denote statistical significance at 1, 5 and 10 percent, respectively. Robust standard errors, probability for Wald chi-squared and F-statistics are reported in parenthesis.

For questions and comments: Editor, Research Notes in Economics TCMB İdare Merkezi, Anafartalar Mah. İstiklal Cad, No: 10, Kat:15, 06050, Ulus Altındağ Ankara/TURKEY E-mail: [email protected]

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