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7. Nonperforming Loans and Financial Stability

Author(s):
Raphael Espinoza, Ghada Fayad, and Ananthakrishnan Prasad
Published Date:
November 2013
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7.1 Introduction

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).

Table 7.1.Summary statistics on nonperforming loans, 1995–2008
BahrainKuwaitOmanQatarSaudi ArabiaUAE
No. Banks26151692032
No. Obs1611209984163219
NPL ratio in 2008 (unweighted)2.95.51.21.71.42.4
NPL ratio (historical data)
25th percentile2.13.43.51.31.71.7
Median5.96.76.53.13.14.3
75th percentile14.812.511.311.57.39.6
Source: Bankscope and authors’ calculations
Source: Bankscope and authors’ calculations
Table 7.2.Macroeconomic and firm-specific determinants of NPLs
Model specification(1)(2)(3)(4)(5)
OLSFEArellano-Bond 2-step collapsedSystem GMM collapsedSystem GMM fwd. orth. collapsed
In(NPL/(1-NPL))-10.898***0.676***0.714***0.881***0.865***
[37.31][15.27][12.04][11.50][12.08]
In(equity) -1−0.0543**−0.364***−0.359***−0.102−0.102
[−2.468][−4.439][−3.107][−1.193][−1.221]
(expenses/avg. assets) -10.0483*0.0868*0.154**0.114**0.106**
[1.803][1.931][2.524][2.576][2.141]
loans growth -20.104**0.0946*0.0993*0.1370.145
[2.364][1.709][1.913][1.499][1.490]
non-oil GDP growth−1.948***−0.974−1.893**−2.156*−2.090*
[−2.597][−0.812][−2.337][−1.887][−1.711]
interest rate -10.0241*0.0535**0.0044−0.00530.0001
[1.778][2.197][0.376][−0.163][0.00291]
VIX0.0131***0.0115***0.0119***0.0140***0.0132***
[3.162][2.825][4.228][2.982][3.040]
Constant−0.599**0.488−0.342−0.402
[−2.275][0.997][−0.527][−0.592]
Observations426426347426426
R-squared0.840.69
Number of banks79677979
No. of instruments343838
Hansen test p-value0.270.480.52
A-B AR(1) test p-value0.000.000.00
A-B AR(2) test p-value0.170.180.19
t-statistics in brackets
*** p < 0.01, ** p < 0.05, * p < 0.1, t-statistics in brackets
*** p < 0.01, ** p < 0.05, * p < 0.1, 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

Appendix
Table 7.3.Bankscope coverage in the GCC
CountryBankCountryBank
QatarAhli Bank QSCBahrainCommercial Bank of Bahrain B.S.C.
QatarCommercial Bank of Qatar (The) QSCBahrainGulf International Bank BSC
QatarDoha BankBahrainNational Bank of Bahrain
QatarInternational Bank of QatarBahrainShamil Bank of Bahrain B.S.C.
QatarQatar Development Bank Q.S.C.BahrainTAIB Bank B.S.C. (2)
QatarQatar International IslamicBahrainUnited Gulf Bank (BSC) EC
QatarQatar Islamic Bank SAQKuwaitAl Ahli Bank of Kuwait (KS
QatarQatar National BankKuwaitBank of Kuwait & The Middl
OmanAhli Bank SAOGKuwaitBurgan Bank SAK
OmanBank Dhofar SAOGKuwaitCommercial Bank of Kuwait
OmanBank Muscat SAOGKuwaitGulf Bank KSC (The)
OmanBank Muscat SAOG (2)KuwaitIndustrial Bank of Kuwait
OmanBank of Oman, Bahrain and Kuwait SAOGKuwaitKuwait Finance House
OmanCommercial Bank of Oman S.A.O.G. (Old)KuwaitKuwait International Bank
OmanMajan International Bank SAOCKuwaitNational Bank of Kuwait S.A.K.
OmanNational Bank of Oman (SAOG)UAEAbu Dhabi Commercial Bank
OmanOman Arab Bank SAOGUAEAbu Dhabi Islamic Bank - P (2)
OmanOman Development Bank SAOGUAEBank of Sharjah
OmanOman International BankUAECommercial Bank International P.S.C.
Saudi ArabiaAl Rajhi Bank-Al Rajhi Banking & Investment CorporationUAECommercial Bank of Dubai P.S.C.
Saudi ArabiaArab National BankUAEDubai Bank (2)
Saudi ArabiaBank Al-JaziraUAEEmirates Bank International PJSC
Saudi ArabiaBank AlBiladUAEEmirates Industrial Bank
Saudi ArabiaBanque Saudi FransiUAEEmirates NBD PJSC
Saudi ArabiaNational Commercial Bank (The)UAEFirst Gulf Bank
Saudi ArabiaRiyad BankUAEInvest Bank P.S.C.
Saudi ArabiaSamba Financial GroupUAEMashreqbank
Saudi ArabiaSaudi British Bank (The)UAENational Bank of Abu Dhabi
Saudi ArabiaSaudi Hollandi BankUAENational Bank of Dubai Public Joint Stock Company
Saudi ArabiaSaudi Investment Bank (The)UAENational Bank of Fujairah
Saudi ArabiaUnited Saudi BankUAERAKBANK-National Bank of Ras Al-Khaimah (P.S.C.) (The)
BahrainAhli United Bank (Bahrain) B.S.C.UAENational Bank of Umm Al-Qaiwain
BahrainBahrain International BankUAESharjah Islamic Bank
BahrainBahrain Islamic Bank B.S.C. (2)UAEUnion National Bank
BahrainBahraini Saudi Bank (The)UAEUnited Arab Bank PJSC
BahrainBBK B.S.C.
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1Nonperforming loans increased in most GCC countries in 2009 to 3.9 percent in Bahrain, 9.7 percent in Kuwait, 2.8 percent in Oman, 1.7 percent in Qatar, 3.3 percent in Saudi Arabia, and 4.6 percent in the UAE. (Khamis and Senhadji 2010).
2Recent IMF stress tests conducted by staff for Kuwait and Bahrain show that the respective banking systems are adequately capitalized and that it would take a significant increase in NPLs before the need arises for recapitalization of any bank (see IMF Article IV reports for Kuwait and Bahrain).
3Macro stress-testing refers to a range of techniques used to assess the vulnerability of a financial system to exceptional but plausible macroeconomic shocks.
4It is important to bear in mind for macro stress-testing that not only credit exposures but also default probabilities and recovery rates may change in the simulated macro stress scenario, compared to estimates derived from a benign sample period. In fact, Sorge (2004) documents several empirical studies that provide evidence of the sensitivity of default probabilities and recovery rates to macroeconomic variables. For example, Carey (1998) provides evidence of significant differences in default rates and loss severity between “good” and “bad” years. Altman et al. (2002) document the increase in default rates and decrease in recovery rates in the US during the recession of 1990–1 and the downturn of 2001–2, and contrasts it with the low levels recorded during the expansion years 1993–8. In this chapter, we leave the issue of recovery rates aside as we did not have access to recovery rates data for the GCC.
5Results are overall similar when looking at post-2001 data, with the coefficients robust to the smaller sample. Lagged credit growth becomes significant in all specifications, suggesting that balance sheet expansion drives future NPLs, but non-oil growth loses significance in the GMM specifications as data covers a smaller part of the business cycle.
6The variables in the VAR are the interest rate, the log of non-oil real GDP, and the NPL ratio. The Levin-Lin-Chu test could not reject the presence of a unit root in the sample. The variables were demeaned using the Helmert procedure as in Love and Zicchino (2006). The identification procedure is based on a Choleski decomposition with the interest rate ordered first, followed by non-oil real GDP, and the NPL ratio ordered last. This ordering is predicated by the pegged exchange rate regime (interest rates follow dollar rates and are mostly unaffected by domestic conditions) and the assumption that causality initially runs from growth to NPLs. In particular, the Choleski decomposition assumes that the NPL ratio cannot instantaneously affect non-oil GDP.

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