7. Nonperforming Loans and Financial Stability
- Raphael Espinoza, Ghada Fayad, and Ananthakrishnan Prasad
- Published Date:
- November 2013
The global crisis exposed the vulnerabilities of the banks in the GCC countries to varying degrees. The favorable macroeconomic environment in the years preceding the global crisis had been conducive to high credit growth and lower nonperforming loans (NPLs) of banks. Although the direct exposure of the GCC banks to the subprime market was low, the global financial crisis and the fall in oil prices after the collapse of Lehman Brothers triggered a spiral of falling asset prices and liquidity and credit tightening. This interaction weakened the financial system’s balance sheets and prompted government intervention in the financial sector. In 2009, NPLs increased sharply and credit stagnated, raising worries that the recovery could be slowed down by credit constraints.
The low levels of NPLs in the GCC before the crisis were to a large extent the result of the good economic fortune of the region, and the downturn in the Gulf economies meant that credit risk could worsen. NPLs had reached very high levels in the GCC before the boom years and NPL ratios in double digits were not uncommon.1 We present in Table 7.1 some summary statistics on NPLs in the GCC banking system, based on a Bankscope database that covers around eighty banks in the GCC (see Table 7.3 for coverage).
|NPL ratio in 2008 (unweighted)||2.9||5.5||1.2||1.7||1.4||2.4|
|NPL ratio (historical data)|
|OLS||FE||Arellano-Bond 2-step collapsed||System GMM collapsed||System GMM fwd. orth. collapsed|
|(expenses/avg. assets) -1||0.0483*||0.0868*||0.154**||0.114**||0.106**|
|loans growth -2||0.104**||0.0946*||0.0993*||0.137||0.145|
|non-oil GDP growth||−1.948***||−0.974||−1.893**||−2.156*||−2.090*|
|interest rate -1||0.0241*||0.0535**||0.0044||−0.0053||0.0001|
|Number of banks||79||67||79||79|
|No. of instruments||34||38||38|
|Hansen test p-value||0.27||0.48||0.52|
|A-B AR(1) test p-value||0.00||0.00||0.00|
|A-B AR(2) test p-value||0.17||0.18||0.19|
|t-statistics in brackets|
The GCC countries had experienced particularly high levels of NPLs in the 2000–2 period, when low oil prices and deflated stock markets hurt liquidity and balance sheets (Figure 7.1).
Figure 7.1.NPL ratio and economic activity in the GCC
Source: Bankscope and authors’ calculations
Although impaired loans fluctuated with the macroeconomic conditions, banks’ individual situations mattered as well. Figure 7.2 shows for Bahrain and Oman (and the same is true across the GCC) that although in good times NPLs are low across the board, in bad times, NPLs increase much faster for banks with higher initial levels of NPLs.
Figure 7.2.Bank heterogeneity and the business cycle
Note: Periods of lowest growth in shaded area
Source: Bankscope and authors’ calculations
Financial soundness indicators (FSIs) post-crisis show that the banking sector appears now generally sound, but there are risks associated with banks’ direct and indirect exposure to real estate and stock markets. As FSIs are available with a lag, they tend however to be backward-looking and the average masks the distribution across banks. Nonetheless, stress tests for some GCC countries (e.g. Kuwait, see IMF 2010) suggest that the banking system is resilient to various credit and market events and it would take a significant increase in NPLs before the need arises for recapitalization of any bank in these countries.2
The crisis highlighted the importance of linking the macroeconomic conditions to the health of the banking system. The main goal of macroeconomic stress tests, which have become more common with the financial crisis, is to identify structural vulnerabilities in the financial system in order to assess its resilience to shocks (Drehmann 2008), in particular losses in the loan books. Credit risk increases as the economic situation deteriorates and interest payments rise, a result found in many credit risk models (see for instance IMF 2006).
This chapter focuses on the relationship between macroeconomic variables and NPLs (credit risk) in GCC banks’ books. This is to the best of our knowledge the first attempt to model NPLs in the GCC countries, using bank-level data. This additional level of disaggregation strengthens the accuracy of estimation and allows a discussion of the impact of macro-variables and of bank-specific characteristics. It also allows a discussion of meaningful here nonlinearities, in particular the finding that banks with higher levels of NPLs are also more sensitive to macroeconomic shocks. The model estimates elasticities that are a key input for stress-testing banks’ balance sheets in the GCC.
The study conducts this analysis using bankwise data from Bankscope. According to a dynamic panel estimated over 1995–2008 on around eighty banks in the GCC region, the NPL ratio worsens as economic growth becomes lower and interest rates increase. Larger banks and banks with lower expenses would also have lower NPLs. Finally, high credit growth in the past could generate higher NPLs in the future. According to all models, NPLs are very persistent, which would suggest that the response of credit losses to the macroeconomic cycle could take time to materialize, although it would also imply that NPLs would then cumulate to high levels. The model implies that the cumulative effect of macroeconomic shocks over a three-year horizon is indeed large.
Conversely, a deterioration in banks’ balance sheets may feed back into the economy because banks will tighten credit conditions, especially if there remain uncertainties on the valuation of projects and of assets. As in most countries, the impact of the crisis on the GCC was magnified through the bank lending channel. In response to adverse changes in their capital base, banks became more reluctant to lend and some were forced to deleverage. This chapter therefore concludes on the feedback effect of high NPLs on the real economy.
7.2 Determinants of Nonperforming Loans
Our focus in this section will be on the determinants of NPLs. A reader who is interested in the general context and practices of stress-testing can find several other surveys. For example, the special feature of the Financial Stability Report of the European Central Bank (2006) provides a brief introduction into macro stress-testing as well as an overview of EU country-level macro stress-testing practices.3 A detailed introduction to stress-testing and an overview of the related literature is given in Sorge (2004).
Financial system shocks can emanate from firm-specific factors (idiosyncratic shocks) and from macroeconomic imbalances (systemic shocks). Economic conditions directly affect loan losses in banks portfolios (Keeton and Morris 1987) and credit risk because of deteriorated asset prices (Mueller 2000; Anderson and Sundaresan 2000; Collin-Dufresne and Goldstein 2001).4 Credit risk tends to be accumulated during upturns but losses are realized during the contractionary phase of the business cycle (Kent and D’Arcy 2000; Rajan and Dhal 2003).
Economic growth is however not the only factor driving credit risk. Interest rates can have a stronger effect on NPLs than growth (Fuentes and Maquieira 2003), and changes in unemployment, housing prices, and exchange rates also trigger losses, independently from growth (IMF 2006). The money multiplier and reserve adequacy have also been found to matter (Bercoff, Giovanni, and Grimard 2002).
In addition to macroeconomic factors, bank-specific characteristics may signal or cause risky lending. For instance, bank size, capital ratio, and market power have been linked to the NPL ratio of individual banks (Salas and Saurina 2002). Understanding the determinants of risk-taking behavior of banks has been a subject of much attention in the banking literature. Risk-taking tends to be affected by a number of factors, including, among others, moral hazard, agency problems, ownership structure, and regulatory actions.
Because of moral hazard induced by deposit insurance, banks may increase their risk positions and more so as capital declines. But Duan et al. (1992) did not find that risk-shifting was widespread in the US, perhaps because risk-taking driven by moral hazard is limited by regulation and market discipline. The debate on the effect of government intervention on banks’ risk-taking behavior is large and need not be summarized here (Levine 2004 provides a short survey of the literature).
Additional bank specificities are also likely to be correlated with credit risk. For instance, Hughes et al. (1995) model risk preferences and operating efficiency of banks: risk-averse managers’ utility is a function of both profits and risk. In order to improve loan quality, the managers increase monitoring and incur higher costs, affecting the measure of operating efficiency. Therefore, a less efficient bank may in fact hold a low-risk portfolio. Indeed, Hughes et al.’s (1995) empirical test rejects the hypothesis that banks are risk-neutral.
On the other hand, there may be a positive link between bank risk and operating efficiency because risks are costly to manage. Overall, while studies examining the interplay between capital and portfolio risk have been considered in the literature (Shrieves and Dahl 1992; Jacques and Nigro 1997), little work has been forthcoming on the examination of the relationship between capital and credit risk and its interaction with operational efficiency.
7.3 A Panel Model for GCC Banks
We investigate the determinants of NPL ratios in GCC banks using panel data of individual banks’ balance sheets from Bankscope. Although some of the data goes as far back as 1995, for most of the banks, data was available only from 1998 (the list of banks is available in Table 7.3). As in much of the literature on credit risk, the dependent variable is the logit transformation of the NPL ratio (i.e., log(NPL/(1-NPL)) where NPL is the Nonperforming Loans ratio), as this transformation ensures that the dependent variable spans over the interval] -∞;+∞ [(as opposed to between 0 and 1) and is distributed symmetrically in the GCC (see Figure 7.3).
Figure 7.3.Logit transformation of the NPL ratio
The macroeconomic explanatory variables include non-oil real GDP growth, stock market returns, interest rates, world trade growth, the VIX index (proxying for global risk aversion and tight financing conditions), and a 1997–8 dummy for the Asian crisis. Non-oil real GDP is the appropriate variable to use, for both theoretical and econometric reasons. In the GCC countries, NPLs are driven by the state of the non-oil economy: indeed, the government and large oil and petrochemical companies (whose revenues depend directly on oil) are government-owned in the region and do not default on loans. To the extent that oil revenues spill over to the non-oil economy, via public spending, household revenues, and downstream activity, etc., this effect will be captured well by non-oil real GDP growth. Econometrically, oil prices are constant across GCC countries and therefore bring less country-specific information on the state of the economy.
Unemployment was not used because in the GCC, the importance of the foreign labor force means that unemployment is very stable and very low (Saudi Arabia is an exception as its domestic labor force is larger). Housing prices were not used either owing to paucity of a consistent data series in the GCC. Finally, because the GCC countries peg their currencies to the dollar, there seems to be no reason to include the exchange rate in a model of NPLs. The regressions also control for firm-level variables. In particular, we look at the risk factors suggested by the literature: the capital adequacy ratio, different measures of efficiency (the expenses/asset ratio, the cost/income ratio, and the return on equity), size (we use the logarithm of equity), the lagged net interest margin, and lagged credit growth (deflated by the CPI).
Several econometric specifications of the dynamic panel are estimated, including OLS, fixed effects, difference GMM (Arellano and Bond 1991), and system GMM (Blundell and Bond 1998), which may be a better specification when the auto-regressive coefficient is close to 1 (in which case difference GMM is inefficient). The forward orthogonalization procedure of Arellano and Bover (1995) was also used to reduce observation losses due to differencing. Finally, to reduce the number of instruments, the collapsing method of Holtz-Eakin, Newey, and Rosen (1988) was used. The macroeconomic variables were considered as strictly exogenous (i.e., can be instrumented by itself as a one-column “IV-style” instrument; see Roodman 2006), while the lagged bank-level variables were modeled as predetermined (and need to be instrumented GMM-style in the same way as the lagged dependent variable). The results are presented in Table 7.2.
The number of instruments was kept below forty in all GMM specifications. The Arellano-Bond AR(1) test for autocorrelation of the residuals rejects the hypothesis that the errors are not autocorrelated, which is expected since differencing generates autocorrelation of order 1. The Arellano-Bond AR(2) p-values are above 5 percent. This is needed in order not to reject the hypothesis that the errors in the levels equation are uncorrelated, an assumption that ensures that the orthogonality conditions and the Arellano-Bond specifications are correct. The Hansen test of overidentifying restrictions also suggests that the instruments are appropriate.
Our analysis shows that both macroeconomic variables and bank-specific variables contributed to the build-up in NPLs in the GCC countries. Non-oil GDP and interest rates were dominant in the first category, and the size of capital, credit growth, and efficiency (non-interest expenses/assets) were found to be the significant bank-specific variables.
Starting with firm-specific variables, the NPL ratio exhibits a strong autocorrelation, estimated to be between 0.6 (the fixed-effect model suffers from a downward Nickell-bias) and 0.9 (the OLS estimate is upward-biased). As a result, NPLs should be expected to worsen relatively slowly when affected by a shock, but in the same vein, it would be reasonable to anticipate long-lasting increases in NPLs. A high coefficient on the lagged NPL ratio also implies that the system GMM is a more efficient estimator than the difference GMM, which is why our preferred specification is that presented in Table 7.2, column 4 (or column 5, a specification which is nearly identical).
The capital adequacy ratio was not found to be significant even in the fixed-effect regression and in the GMM specifications, and was dropped from the model. This result matches those found for other countries and suggests that regulation is effectively preventing capital from reaching low levels and influencing risk-taking. Nevertheless, efficiency was found to be significant and with the expected sign. The two alternative measures of efficiency (the cost to income ratio and return on equity) were not significant and were also dropped from the model. The net interest margin was also found to be insignificant. Finally, the past expansion of a bank’s balance sheet was also found in two specifications to worsen NPLs, even after controlling for macroeconomic variables.
Indeed, the macroeconomic conditions were found to be important and with the expected sign in all models. A temporary decrease by 3 percentage points in non-oil GDP growth would increase NPLs by 0.3 to 1.1 percentage points, depending on the initial level of NPLs (the model is nonlinear and therefore one can only interpret the coefficients using marginal effects at different points of the distribution of NPLs). The effect of a 300 basis points increase in interest rates would be similar. Since the AR coefficient of the logit transformation of NPL is high (between 0.6 and 0.9), these shocks cumulate to a large extent (Figure 7.4).
Figure 7.4.Dynamics of NPLs with maintained macroeconomic shocks
Note: Effect of a 3 percentage point fall in non-oil real GDP and a 300 basis points increase in interest rates
World trade growth and the Asian crisis dummy were not found to be significant, but the VIX index was highly significant in all specifications. External financing conditions, in addition to interest rates, seem therefore to matter more than the global trade cycle in driving credit risk in the GCC. We also investigated, using interaction terms, whether banks that have higher expenses or that expanded faster are more sensitive to decreases in activity, but we found no significant effect. It is likely that the nonlinearity embedded in the logit transformation already captures some of these effects since banks that expanded quickly or are inefficient also are likely to start from a higher base of NPLs.5
7.4 Concluding on the Systemic Importance of Credit Risk
Our empirical results support the view that both macro-factors and bank-specific characteristics determine the level of nonperforming loans. In particular, we find strong evidence of a significant inverse relationship between real (non-oil) GDP and nonperforming loans. The study also showed that global financial market conditions have an effect on NPLs of banks. This implies that regulators and central banks in the GCC have to be wary about increasing NPLs during periods of low growth and tight financing. Among bank control factors, efficiency and past expansion of the balance sheet were found to be significant.
As banks’ balance sheets have remained affected in the aftermath of the 2009 recession, worries have been raised that credit growth may remain sluggish—as was indeed the case in past episodes in MENA (Barajas et al. 2011)—hampering the speed of the recovery. Indeed, there are four channels via which banking stress can affect economic growth: (i) individual exposures can spread to the wider financial system, triggering lower economic activity in the financial sector; (ii) credit and market risk lead to higher lending interest rates; (iii) losses may prompt asset sales, which further depress asset prices; and (iv) increased risk aversion leads to tighter credit conditions, resulting in lower credit growth (Kida 2008). Econometric models have confirmed the existence of a feedback between credit losses and the macroeconomy in the US (Keeton 1999) and Europe (Ciccarelli et al. 2010).
An exploratory panel VAR we estimated, using macroeconomic data, also found that this channel is plausible in the GCC.6 The results (Figure 7.5) show that higher interest rates increase NPLs and higher GDP reduces the NPL ratio (row 3, columns 1 and 2). The feedback effect of higher NPLs suffered by the banking sector is shown in the last column of the second row: a one-standard deviation increase in the change in the NPL ratio (an increase by 2 percentage points) reduces GDP growth by around 0.7 percentage point after two years. However, default shocks do not occur often and the forecast error variance decomposition shows that only 5 to 7 percent of the non-oil GDP growth variance can be explained by NPLs shocks. Overall, according to the panel VAR, there could be a strong, albeit short-lived feedback effect from losses in banks’ balance sheets on economic activity.
Figure 7.5.Feedback effect—panel VAR impulse response functions
|Qatar||Ahli Bank QSC||Bahrain||Commercial Bank of Bahrain B.S.C.|
|Qatar||Commercial Bank of Qatar (The) QSC||Bahrain||Gulf International Bank BSC|
|Qatar||Doha Bank||Bahrain||National Bank of Bahrain|
|Qatar||International Bank of Qatar||Bahrain||Shamil Bank of Bahrain B.S.C.|
|Qatar||Qatar Development Bank Q.S.C.||Bahrain||TAIB Bank B.S.C. (2)|
|Qatar||Qatar International Islamic||Bahrain||United Gulf Bank (BSC) EC|
|Qatar||Qatar Islamic Bank SAQ||Kuwait||Al Ahli Bank of Kuwait (KS|
|Qatar||Qatar National Bank||Kuwait||Bank of Kuwait & The Middl|
|Oman||Ahli Bank SAOG||Kuwait||Burgan Bank SAK|
|Oman||Bank Dhofar SAOG||Kuwait||Commercial Bank of Kuwait|
|Oman||Bank Muscat SAOG||Kuwait||Gulf Bank KSC (The)|
|Oman||Bank Muscat SAOG (2)||Kuwait||Industrial Bank of Kuwait|
|Oman||Bank of Oman, Bahrain and Kuwait SAOG||Kuwait||Kuwait Finance House|
|Oman||Commercial Bank of Oman S.A.O.G. (Old)||Kuwait||Kuwait International Bank|
|Oman||Majan International Bank SAOC||Kuwait||National Bank of Kuwait S.A.K.|
|Oman||National Bank of Oman (SAOG)||UAE||Abu Dhabi Commercial Bank|
|Oman||Oman Arab Bank SAOG||UAE||Abu Dhabi Islamic Bank - P (2)|
|Oman||Oman Development Bank SAOG||UAE||Bank of Sharjah|
|Oman||Oman International Bank||UAE||Commercial Bank International P.S.C.|
|Saudi Arabia||Al Rajhi Bank-Al Rajhi Banking & Investment Corporation||UAE||Commercial Bank of Dubai P.S.C.|
|Saudi Arabia||Arab National Bank||UAE||Dubai Bank (2)|
|Saudi Arabia||Bank Al-Jazira||UAE||Emirates Bank International PJSC|
|Saudi Arabia||Bank AlBilad||UAE||Emirates Industrial Bank|
|Saudi Arabia||Banque Saudi Fransi||UAE||Emirates NBD PJSC|
|Saudi Arabia||National Commercial Bank (The)||UAE||First Gulf Bank|
|Saudi Arabia||Riyad Bank||UAE||Invest Bank P.S.C.|
|Saudi Arabia||Samba Financial Group||UAE||Mashreqbank|
|Saudi Arabia||Saudi British Bank (The)||UAE||National Bank of Abu Dhabi|
|Saudi Arabia||Saudi Hollandi Bank||UAE||National Bank of Dubai Public Joint Stock Company|
|Saudi Arabia||Saudi Investment Bank (The)||UAE||National Bank of Fujairah|
|Saudi Arabia||United Saudi Bank||UAE||RAKBANK-National Bank of Ras Al-Khaimah (P.S.C.) (The)|
|Bahrain||Ahli United Bank (Bahrain) B.S.C.||UAE||National Bank of Umm Al-Qaiwain|
|Bahrain||Bahrain International Bank||UAE||Sharjah Islamic Bank|
|Bahrain||Bahrain Islamic Bank B.S.C. (2)||UAE||Union National Bank|
|Bahrain||Bahraini Saudi Bank (The)||UAE||United Arab Bank PJSC|
AndersonR. and SundaresanS.(2000). “A comparative study of structural models of corporate bond yields: An explanatory investigation” Journal of Banking and Finance24: 255–69.
ArellanoM. and BondS.(1991). “Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations” Review of Economic Studies58: 277–97.
ArellanoM. and BoverO.(1995). “Another look at the instrumental variables estimation of error components models” Journal of Econometrics68: 29–51.
AltmanE. I.RestiA. and SironiA.(2002). “The link between default and recovery rates: Effects on the procyclicality of regulatory capital ratios.” Bank for International Settlements Working Paper No. 113.
BarajasA.ChamiA.EspinozaR. and BarajasH. H. (2011). “Further fallout from the global financial crisis: Credit crunch in the ‘periphery’” World Economics12: 153–76.
BercoffJ.di GiovanniJ. and GrimardF.(2002). “Argentinean banks, credit growth and the tequila crisis: A duration analysis.” Unpublished.
BlundellR. and BondS.(1998). “Initial conditions and moment restrictions in dynamic panel data models” Journal of Econometrics87: 11–143.
CareyM.(1998). “Credit risk in private debt portfolios” Journal of Finance53: 1363–87.
Collin-DufresneP. and GoldsteinR. (2001). “Do credit spreads reflect stationary leverage ratios?” Journal of Finance56: 1929–57.
CiccarelliM.MaddaloniA. and PeydróJ. L. (2010). “Trusting the bankers: A new look at the credit channel of monetary transmission.” European Central Bank Working Paper Series No. 1228.
DrehmannM.(2008). “Stress tests: Objectives, challenges and modeling choices” Riksbank Economic Review2: 60–92.
DrehmannM.(2009). “Macroeconomic stress testing banks: A survey of methodologies” in M.Quagliariello (ed.) Stress Testing the Banking System: Methodologies and Applications. Cambridge: Cambridge University Press37–67.
DuanJ. C.MoreauA. F. and SealeyC. W. (1992). “Fixed-rate deposit insurance and risk-shifting behaviour at commercial banks” Journal of Banking and Finance16: 715–42.
European Central Bank (2006). “Country level macro stress-testing practices” Financial Stability Review (June) 147–54.
FuentesR. and MaquieiraC.(2003). “Institutional arrangements, credit market development and loan repayment in Chile.” School of Business and EconomicsUniversidad de Chile.
Holtz-EakinD.NeweyW. and RosenH. S. (1988). “Estimating vector autoregressions with panel data” Econometrica56: 1371–95.
HughesJ. P.LangW.MesterL. J. and MoonC.-G. (1995). “Recovering technologies that account for generalized managerial preferences: An application to non-risk-neutral banks.” Federal Reserve Bank of Philadelphia Working Paper.
International Monetary Fund (2006). “Spain: Financial sector assessment program. Technical note: Stress-testing methodology and results.” IMF Country Report No. 06/216. Washington DC: International Monetary Fund.
International Monetary Fund (2010). “Kuwait: Financial system stability assessment—update.” IMF Country Report No. 10/239.
KhamisM. and SenhadjiA.(2010). “Impact of the global financial crisis on the Gulf Cooperation Council Countries and challenges ahead: An update.” Washington DC: International Monetary Fund.
JacquesK. and NigroP. (1997). “Risk-based capital, portfolio risk and bank capital: A simultaneous equations approach” Journal of Economics and Business49: 533–47.
KeetonW. R. (1999). “Does faster loan growth lead to higher loan losses?” Federal Reserve Bank of Kansas City Economic Review84 (2): 57–75.
KeetonW. R. and MorrisC. S. (1987). “Why do banks’ loan losses differ?” Federal Reserve Bank of Kansas City Economic Review72 (5): 3–21.
KentC. and D’ArcyP.(2000). “Cyclical prudence: Credit cycles in Australia.” Bank for International Settlements Working Paper No 1.
KidaM.(2008). “A macro stress testing model with feedback effects” Reserve Bank of New Zealand Discussion Paper Series DP2008/08. WellingtonMay.
LevineR.(2004). “The corporate governance of banks: A concise discussion of concepts and evidence.” World Bank Policy Research Working Paper No. 3404.
LoveI. and ZicchinoL.(2006). “Financial development and dynamic investment behavior: Evidence from panel VAR” Quarterly Review of Economics and Finance46: 190–210.
MuellerC.(2000). “A simple multi-factor model of corporate bond prices.” Doctoral dissertationUniversity of Wisconsin-Madison.
RajanR. and DhalS. C. (2003). “Nonperforming loans and terms of credit of public sector banks in India: An empirical assessment” Reserve Bank of India Occasional Papers 24:81–121.
RoodmanD.(2006). “How to do xtabond2: An introduction to ‘Difference’ and ‘System’ GMM in Stata.” Center for Global Development Working Paper No. 103.
SalasV. and SaurinaJ.(2002). “Credit risk in two institutional regimes: Spanish commercial and savings banks” Journal of Financial Services Research22: 203–24.
ShrievesR. and DahlD.(1992). “The relationship between risk and capital in commercial banks” Journal of Banking and Finance16: 439–57.