Download A Dynamic Approach to Analyzing the Performing Loans: Evidence from the

WORKING PAPER NO: 16/12
A Dynamic Approach to Analyzing the
Effect of the Global Crisis on NonPerforming Loans: Evidence from the
Turkish Banking Sector
April 2016
Vuslat US
© Central Bank of the Republic of Turkey 2016
Address:
Central Bank of the Republic of Turkey
Head Office
Research and Monetary Policy Department
İstiklal Caddesi No: 10
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Phone:
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The views expressed in this working paper are those of the
author(s) and do not necessarily represent the official views of the
Central Bank of the Republic of Turkey. The Working Paper Series
are externally refereed. The refereeing process is managed by the
Research and Monetary Policy Department.
A DYNAMIC APPROACH TO ANALYZING THE EFFECT OF THE GLOBAL
CRISIS ON NON-PERFORMING LOANS: EVIDENCE FROM THE TURKISH
BANKING SECTOR
Vuslat US*
ABSTRACT
This paper analyzes the effect of the global crisis on the determinants of non-performing
loans in the Turkish banking sector by using dynamic panel estimation techniques.
Empirical findings suggest that non-performing loans present persistence, which is more
evident after the crisis, while other regressors have also persistent effects in the postcrisis period. Moreover, non-performing loans are mostly shaped by bank-specific
variables before the crisis, whereas, after the crisis, non-performing loans are also driven
by macroeconomic and policy-related variables. In particular, the post-crisis significance
of GDP, policy rate and sovereign debt shows that robust economic activity, tight
monetary policy and strong fiscal balances restrict non-performing loans, thereby
enhancing financial stability. On the other hand, the significance of inflation in both subperiods indicates that commitment to price stability objective is indispensable for limiting
non-performing loans and promoting financial stability. In the period ahead, the speed
and the direction of normalization in global monetary policies may determine the course
of financial conditions, which therefore have implications regarding non-performing loan
dynamics and financial stability.
Keywords: Global crisis, Non-performing loans, Turkish banking sector, Dynamic panel
estimation, Persistence, Financial stability, Price stability, Normalization.
JEL Codes: C23, E44, E52, G10, G21.
* Central Bank of the Republic of Turkey, Research and Monetary Policy Department, Istiklal Cad. No. 10
Ulus/Ankara 06100 Turkey. Phone: +90 (312) 5075423, e-mail: [email protected].
1. Introduction
Analyzing the determinants of non-performing loans (NPLs) is crucial for diagnosing
weaknesses in financial stability. This has gained importance especially after the global
crisis, which has drawn more attention to factors that may trigger banking crisis and to
the consequences of banking system instability (Agnello and Sousa, 2012; Festic et al.,
2011; Nkusu, 2011; Castro, 2013, Reinhart and Rogoff, 2010).
Turkey represents such a case where the global crisis had notable effects on the
banking sector structure and the course of NPLs.1 This evidence necessitates a thorough
understanding of the NPL dynamics and also brings an important question. In particular,
what is the effect of the global crisis on the determinants of NPLs? Answering this
question is crucial given the ongoing normalization of global monetary policies, which
prompts potential challenges and prospects for the Turkish economy via possibly tighter
financial conditions.
This is explored in the spirit of earlier works2 that link NPL dynamics to selected
determinants. Consequently, the effect of the global crisis is assessed by a sub-period
analysis, which shows whether the significance of these determinants changes before and
after the crisis. Given the use of quarterly data due to short span of time in the pre-crisis
and the post-crisis periods, this may raise the issue of persistence in NPLs, which
necessitates adopting dynamic panel estimation techniques.3
The paper proceeds as follows: The next section presents data, the dynamic panel data
estimation and empirical findings, while the last section concludes this paper. All tables
are given in the Appendix.
2. Data, Dynamic Panel Data Estimation and Empirical Findings
2.1. Data
The analysis utilizes quarterly data during 2002Q4 and 2013Q3. Table A1 displays
the data description. The study includes a balanced panel of 21 deposits banks, while
other deposit banks are excluded due to missing data. Bank-specific data are obtained
from the Banks Association of Turkey. Data on macroeconomic and policy-related
variables are compiled from the Central Bank of the Republic of Turkey (CBRT) and the
Undersecretariat of Turkish Treasury.
1
CBRT (2010), Selçuk (2010) and Afşar (2011) show that NPLs increased sharply, while Ganioğlu and Us
(2014) and Us (2015a) report that the structure of the Turkish banking sector has changed after the crisis.
2
Greenidge and Grosvenor (2010), Salas and Saurina (2002), Jiménez and Saurina (2006), Ranjan and Dhal
(2003), Louzis et al. (2012), Rinaldi and Sanchis-Arellano (2006), Berge and Boye (2007), Nkusu (2011),
Berger and DeYoung (1997), Podpiera and Weill (2008), Sinkey and Greenwalt (1991), Kwan and
Eisenbeis (1995), Hassan (1993), Brewer et al. (1996), Gallo et al. (1996), Angbazo (1997), Hassan et al.
(1994), Corsetti et al. (1999) and Breuer (2006) are important works along this line.
3
Beck et al. (2015), Chaibi and Ftiti (2015), Klein (2013), Louzis et al. (2012), Abid et al. (2014), Salas
and Saurina (2002), Jiménez and Saurina (2006) and Rinaldi and Sanchis-Arellano (2006) are previous
studies that assume persistence in NPLs. This is because NPLs are not immediately written off from banks’
balance sheets, which implies that lagged NPL terms may have significant impact on current NPLs. This
requires utilizing dynamic panel data estimation, which includes lagged dependent variable as regressor.
1
2. 2 Dynamic Panel Data Estimation
The determinants of NPLs can be captured by the following general equation:
,
=
+
,
+
,
+
+
,
(1)
Where
is the NPL ratio of bank
, is the NPL ratio of bank at time ;
,
at time − 1; , is the matrix of bank-specific, macroeconomic and policy-related
variables of bank at time ; is the constant term; and and are the corresponding
coefficient vectors, where the former is expected to be positive and less than unity.
is
the unobserved individual (bank-specific) effect and , is the idiosyncratic error term,
both following i.i.d. processes with mean 0 and variances
and , respectively.
Traditional panel data estimators like pooled OLS, fixed effects or random effects
produce biased and inconsistent parameter estimates when applied to above specification.
In fact,
. Also, other explanatory variables may
,
is inherently correlated with
not be strictly exogenous, which implies that they are correlated with past and possibly
current realizations of the error term. Furthermore, the specification may be subject to
fixed individual effects besides heteroscedasticity and autocorrelation within individuals.
These biases can be eliminated via the Generalized Method of Moments (GMM) as
proposed by Arellano and Bond (1991), which developed a difference GMM estimator
where the lagged levels of the regressors are used as instruments for the above equation
in first differences. Arellano and Bover (1995) and Blundell and Bond (1998) extended
Arellano and Bond (1991) by differencing the instruments instead of the regressors in
order to make them exogenous to the fixed effects. This leads from the difference GMM
to the system GMM estimator, which is a joint estimation of the equation in levels and in
first differences.
Yet, Roodman (2009) discusses that the difference and the system GMM may result
in instrument proliferation, which causes some asymptotic results and related
specification tests to be misleading. Hence, Roodman (2009) imposes the moment
condition that the covariance between
and , equals 0 for s≥2 to avoid the
,
problem of instrument proliferation and overfitting.
2.3. Empirical Findings
Empirical findings are presented in Table A2. Accordingly, equation (1) is estimated
using Arellano-Bover/Blundell-Bond estimator (system GMM) where the available lags
of the dependent variable and the lagged values of the exogenous regressors are used as
instruments. For robustness, Roodman estimation results are also reported. To eliminate
downward bias due to possible instrument proliferation, the standard errors incorporate
the Windmeijer (2005) correction. The estimation also tests explanatory power of other
regressors in lagged terms and reports the statistically significant results.
2
Estimations are conducted for the overall sample and by sub-periods for the pre-crisis
and the post-crisis periods, which cover 2002Q4-2008Q3 and 2008Q4-2013Q3,
respectively. The consistency of the GMM estimator relies on the validity of moment
conditions, which assume that the error terms are not serially correlated and the
instruments are appropriate. Hence, one should reject the Arellano-Bond first-order serial
correlation and do not reject the second-order serial correlation. Furthermore, Sargan test
results should indicate that overidentifying restrictions are valid. Accordingly, the pvalues of the AR(1) and AR(2) tests show no serial correlation except for the post-crisis
system GMM estimation, while Sargan tests justify instrument validity in all
specifications excluding the post-crisis Roodman estimation.
Overall, the estimated models are able to explain the dynamics of non-performing
loans in the Turkish banking sector reasonably well where the signs of the estimated
coefficients are compatible with the economic intuition. Lagged NPL is positively
significant in all estimations, which confirms the persistence in NPLs. The relatively
larger size of the post-crisis coefficient shows that the persistence gets even stronger after
the crisis.
Lending is negatively significant in the overall period and also in sub-periods. This
supports Khemraj and Pasha (2009), which observe a negative relation between lending
and NPLs. Even though the post-crisis coefficient is smaller, lending has a more
persistent yet positive impact in this period as implied by the significant lagged term.
Capital adequacy is significant only before the crisis, where the effect is negative,
complying with the moral hazard hypothesis (Berger and DeYoung, 1997). This asserts
that thinly capitalized banks generally take riskier loans, which could potentially lead to
higher NPLs as confirmed by Keeton and Morris (1987) and Salas and Saurina (2002).
Profitability is also significant only before the crisis with a positive impact. This
validates García-Marco and Robles-Fernández (2008), which argue that profitmaximizing policies will be accompanied by higher levels of risk, inducing greater NPLs.
Inefficiency is negatively significant in the overall period and in sub-periods, which
confirms the skimping hypothesis that conjectures increasing number of NPLs for higher
efficiency (Berger and DeYoung, 1997, Rossi et al., 2008). The effect of inefficiency is
weaker after the crisis given the smaller coefficient.
Bank size is also significant in the overall period and in sub-periods. The effect is
negative in compliance with the diversification hypothesis that envisions lower NPLs for
larger banks (Louzis et al., 2012; Joseph et al., 2012; Hu et al., 2004; Salas and Saurina,
2002; Ranjan and Dhal, 2003; Saunders et al., 1990; Chen et al., 1998; Cebenoyan et al.,
1999; Megginson, 2005). Moreover, bank size becomes even more important after the
crisis as implied by the larger coefficient and the significant lagged term, which is yet
positive.
3
As for macroeconomic and policy-related determinants, inflation is extremely
influential on NPL as it is positively significant in all estimations. This indicates that
higher inflation reduces the borrowers’ real income, causing higher NPLs (Rinaldi and
Sanchis-Arellano, 2006; Fofack, 2005, Abid et al., 2014; Klein, 2013). Yet, NPL is less
responsive to inflation after the crisis as the coefficient is considerably smaller in this
period.
GDP is negatively significant in the overall period and also after the crisis. This
supports Salas and Saurina (2002), Dash and Kabra (2010), Jiménez and Saurina (2006),
Khemraj and Pasha (2009) and Fofack (2005), documenting countercyclicality of NPLs
as higher growth expands debt servicing capacity of borrowers. GDP is more persistent
after the crisis as the lagged GDP term is also significant.
Policy rate is negatively significant in the overall analysis and after the crisis. This
suggests that monetary tightening in the post-crisis period4 leads to prudent banking
behavior, which improves the quality of lending, thereby reducing NPLs.
Finally, sovereign debt has a positively significant coefficient in the overall analysis
and after the crisis, which conforms Louzis et al. (2012), Ali and Daly (2010) and Makri
et al. (2014), reporting that higher sovereign debt raises NPLs.
On the other hand, exchange rate has no effect on NPL contradicting with Chaibi and
Ftiti (2015), Kalluci and Kodra (2010), Moinescu and Codirlaşu (2012), Beck et al.
(2013) and Fofack (2005), which assert that depreciation of the local currency may result
in higher NPLs. This may be owed to the fact that FX-loans constitute a smaller
percentage of total loans in the Turkish economy.5
3. Conclusion
This paper examines the effect of the global crisis on the determinants of NPLs in the
Turkish banking sector and shows that NPL dynamics have changed after the crisis. In
particular, NPLs are mostly determined by bank-specific factors before the crisis, while
macroeconomic and policy-related factors are more influential on NPLs after the crisis.
Furthermore, dynamic panel estimation results reveal that NPLs are more persistent after
the crisis, while other variables have also persistent effects on NPLs during this period.
Accordingly, the post-crisis significance of GDP, policy rate and sovereign debt
shows that robust economic activity, tight monetary policy and strong fiscal balances
gained importance after the crisis in limiting non-performing loans and enhancing
financial stability. However, inflation is significant both before and after the crisis, which
implies that maintaining the price stability objective reduces NPLs, thus promoting
financial stability.
4
Us (2015b) provides an overview of the CBRT’s tight monetary policy conduct after the crisis, which also observes financial
stability.
5
FX lending to resident individuals was prohibited in 2009, which caused FX-loans to decrease substantially.
4
Empirical findings indicate that accurate policy design is critical to minimizing NPLs.
This includes not only monetary and fiscal policy, but also macroprudential policies,
which aim at improving the stability of the financial system. Obviously, the
implementation of such policies should occur in an environment of strong economic
growth. Meanwhile, regulatory authorities should also focus on microprudential policies,
which pertain to measures of risk management and identification of banks with potential
impaired loans.
In the period ahead, global policy normalization, which will probably affect financial
conditions, may also change the course of NPLs. So, future research may explore the
sensitivity of NPLs to financial conditions, both domestic and global. Prospective
research may also analyze NPLs for each loan type individually. This may help
policymakers to directly identify the loan type that is likely to generate NPLs. Similarly,
further research can be extended by an ownership-breakdown. This may facilitate to
recognize structural factors inducing NPLs, which, however, is beyond the scope of this
paper.
5
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10
Appendix
Table A1. Description of Variables
VARIABLES
DESCRIPTION
Dependent Variable
NPL
Overdue loans to total loans and receivables
Independent Variables
NPL(-1)
Lagged NPL
LENDING
Total loans and receivables to total assets
LENDING(-1)
Lagged lending
CAPITAL ADEQUACY
Shareholders’ equity to total assets
PROFITABILITY
Net profits (loss) to total assets
INEFFICIENCY
Other operating expenses to total assets
BANK SIZE
Total assets to banking sector’s assets
BANK SIZE(-1)
Lagged bank size
INFLATION
Year-on-year change in the consumer price index in logs
GDP
Year-on-year change in the real GDP in logs
GDP(-1)
Lagged GDP
POLICY RATE
CBRT policy rate
SOVEREIGN DEBT
Central government domestic debt stock to GDP
EXCHANGE RATE
Quarter-on-quarter change in USD/TL rate in logs
11
Table A2. Estimation Results
NPL(-1)
LENDING
LENDING(-1)
CAPITAL
ADEQUACY
PROFITABILITY
INEFFICIENCY
BANK SIZE
BANK SIZE(-1)
INFLATION
GDP
GDP(-1)
POLICY RATE
SOVEREIGN
DEBT
EXCHANGE
RATE
CONSTANT
Wald chi2
Prob>chi2
AR(1)
AR(2)
Sargan
No of Obs.
Overall
System
Roodman
GMM
GMM
0.739***
0.785***
(0.037)
(0.034)
-0.035***
-0.018
(0.009)
(0.012)
Pre-Crisis Period
System
Roodman
GMM
GMM
0.763***
0.793***
(0.027)
(0.027)
-0.084**
-0.019
(0.033)
(0.015)
-
-
-
-
-0.160
(0.124)
0.199
(0.153)
-0.079*
(0.043)
-0.151**
(0.075)
-0.133
(0.088)
0.210
(0.171)
-0.068
(0.049)
-0.080*
(0.043)
-0.128*
(0.040)
0.223***
(0.123)
-0.095**
(0.041)
-0.285***
(0.059)
-0.092**
(0.042)
0.172
(0.114)
-0.043
(0.039)
-0.060
(0.048)
-
-
-
-
0.184**
(0.079)
-0.073***
(0.012)
0.149*
(0.078)
-0.072***
(0.011)
0.194**
(0.091)
0.052
(0.076)
0.174*
(0.091)
0.065
(0.069)
-
-
-
-
-0.079***
(0.022)
0.141**
(0.064)
-0.013
(0.010)
0.008
(0.014)
3671.12
0.0000
0.0885
0.7328
0.9236
903
-0.068***
(0.022)
0.088***
(0.031)
-0.008
(0.012)
0.009
(0.013)
21559.75
0.0000
0.064
0.664
0.945
903
-0.068
(0.068)
-0.042
(0.068)
-0.043
(0.028)
0.084**
(0.036)
3400.93
0.0000
0.1096
0.5545
0.9532
483
-0.040
(0.074)
0.006
(0.043)
-0.032
(0.030)
0.014
(0.017)
20120.58
0.0000
0.093
0.522
0.764
483
*,**,*** denote statistical significance for p<0.1, p<0.05 and p<0.01, respectively.
Robust standard errors are in parentheses.
12
Post-Crisis Period
System
Roodman
GMM
GMM
0.893***
0.946***
(0.030)
(0.016)
-0.034***
-0.040***
(0.011)
(0.011)
0.034***
0.039***
(0.010)
(0.011)
-0.020
-0.009
(0.016)
(0.013)
0.065
0.018
(0.043)
(0.032)
-0.064**
-0.037*
(0.028)
(0.020)
-0.506***
-0.428***
(0.149)
(0.125)
0.469***
0.410***
(0.148)
(0.124)
0.053**
0.048**
(0.021)
(0.020)
-0.043***
-0.036***
(0.010)
(0.011)
-0.021***
-0.023***
(0.007)
(0.007)
-0.025**
-0.011*
(0.012)
(0.020)
0.058**
0.029
(0.026)
(0.015)
0.007
0.008
(0.006)
(0.006)
-0.008
-0.004
(0.010)
(0.006)
8843.40
28496.25
0.0000
0.0000
0.0014
0.001
0.7694
0.843
0.1256
0.078
420
420
Central Bank of the Republic of Turkey
Recent Working Papers
The complete list of Working Paper series can be found at Bank’s website
(http://www.tcmb.gov.tr).
Forecasting Turkish Real GDP Growth in a Data Rich Environment
(Bahar Şen Doğan, Murat Midiliç Working Paper No. 16/11 March 2016)
Does Multiplicity of Equilibria Arise in the Eaton-Gersovitz Model of Sovereign Default?
(Yasin Kürşat Önder Working Paper No. 16/10 March 2016)
Revisiting Capital Structure of Non-financial Public Firms in Turkey
(Ramazan Karaşahin, Doruk Küçüksaraç Working Paper No. 16/09 March 2016)
Faiz Koridoru ve Banka Faizleri: Parasal Aktarım Mekanizmasına Dair Bazı Bulgular
(Mahir Binici, Hakan Kara, Pınar Özlü Çalışma Tebliği No. 16/08 Mart 2016)
Search by Firms and Labor Market Policies
(Gönül Şengül Working Paper No. 16/07 March 2016)
Life Satisfaction and Keeping up with Other Countries
(Ozan Ekşi, Neslihan Kaya Ekşi Working Paper No. 16/06 February 2016)
The Impact of the ECB’s Conventional and Unconventional Monetary Policies on Stock Markets
(Reinder Haitsma, Deren Ünalmış, Jakob de Haan Working Paper No. 16/05 February 2016)
Liquidity Management of Non-Financial Firms: Cash Holdings and Lines of Credit Evidence from Turkey
(Yavuz Arslan, Yunus Emrah Bulut, Tayyar Büyükbaşaran, Gazi Kabaş Working Paper No. 16/04 February 2016)
A Hedonic House Price Index for Turkey
(Timur Hülagü, Erdi Kızılkaya, Ali Gencay Özbekler, Pınar Tunar Working Paper No. 16/03 February 2016)
In Pursuit of Understanding Markups in Restaurant Services Prices
(Mustafa Utku Özmen Working Paper No. 16/02 January 2016)
Immigration and Prices: Quasi-Experimental Evidence from Syrian Refugees in Turkey
(Binnur Balkan Konuk, Semih Tümen Working Paper No. 16/01 January 2016)
Tüketici Güvenini Belirleyen Unsurlar Üzerine Ampirik Bir Çalışma: Türkiye Örneği
(Tuğrul Gürgür , Zübeyir Kılınç Working Paper No. 15/38 December 2015)
Imported Intermediate Goods and Product Innovation: Evidence from India
(Murat Şeker, Daniel Rodriguez-Delgado, Mehmet Fatih Ulu Working Paper No. 15/37 December 2015)
Liquidity Crises, Liquidity Lines and Sovereign Risk
(Yasin Kürşat Önder Working Paper No. 15/36 December 2015)
Quantifying the Effects of Loan-to-Value Restrictions: Evidence from Turkey
(Yavuz Arslan, Gazi Kabaş, Ahmet Ali Taşkın Working Paper No. 15/35 December 2015)
Compulsory Schooling and Early Labor Market Outcomes in a Middle-Income Country
(Huzeyfe Torun Working Paper No. 15/34 November 2015)
“I Just Ran four Million Regressions” for Backcasting Turkish GDP Growth
(Mahmut Günay Working Paper No. 15/33 November 2015)
Has the Forecasting Performance of the Federal Reserve’s Greenbooks Changed over Time?
(Ozan Ekşi ,Cüneyt Orman, Bedri Kamil Onur Taş Working Paper No. 15/32 November 2015)