Decomposing Financial Risks and Vulnerabilities in Emerging Europe
Author:
Ms. Srobona Mitra
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Delisle Worrell
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Publication Date:
26 Mar 2010
eISBN:
9781589069114
Language:
English
Keywords:
SP; growth country; stability indicator; bank size; insolvency risk; parent bank; balance sheet information; Credit; Credit risk; Bank soundness; Market risk; Foreign banks; Eastern Europe; Baltics
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This paper assesses how various types of financial risk such as credit risk, market risk, and liquidity risk affect banking stability in emerging Europe. It also examines how the quality of supervisory standards may have mitigated the vulnerabilities arising from these risk factors. Using panel data, the paper finds that (1) credit quality is of general concern especially in circumstances where credit growth is accelerating; (2) although higher provisioning could adversely affect profits and returns volatility, good supervisory policies on provisioning mitigate such adverse effects; and (3) highly liquid banks are not necessarily more stable because they might be pursuing activities with more volatile returns, but a well-functioning payments system helps to lower the adverse impact on stability. The paper also corroborates earlier evidence of the positive (negative) effect of financial depth (foreign ownership) on stability.

Abstract

This paper assesses how various types of financial risk such as credit risk, market risk, and liquidity risk affect banking stability in emerging Europe. It also examines how the quality of supervisory standards may have mitigated the vulnerabilities arising from these risk factors. Using panel data, the paper finds that (1) credit quality is of general concern especially in circumstances where credit growth is accelerating; (2) although higher provisioning could adversely affect profits and returns volatility, good supervisory policies on provisioning mitigate such adverse effects; and (3) highly liquid banks are not necessarily more stable because they might be pursuing activities with more volatile returns, but a well-functioning payments system helps to lower the adverse impact on stability. The paper also corroborates earlier evidence of the positive (negative) effect of financial depth (foreign ownership) on stability.

This paper tests for the impact of various financial risks on bank stability in emerging Europe. Risks include credit, liquidity, and market risks; and risks from the macroeconomic environment. Furthermore, the paper investigates the extent to which vulnerabilities might be mitigated by good supervisory and regulatory policies and practices. Financial sector assessment programs (FSAPs) undertaken by the IMF and the World Bank in most of the countries in the region in recent years have generally reported remarkable success in financial reforms after a period of financial turbulence in the early 1990s, as reflected in rapidly improving financial stability indicators and increasing resilience to financial risk exposure. However, based on experience in other parts of the world, there remains a concern that financial supervision and regulation needs to be further upgraded especially since risks from rapid credit growth and potentially unsustainable macroeconomic imbalances could materialize in the future.

The study covers data on banks of the 10 countries that joined the European Union (EU) in 2004 (EU-10), and eight countries in the surrounding region (S-8).1 The S-8 share many financial characteristics with the EU-10, including, in many cases, the large presence of western European foreign banks and financial institutions. They have also witnessed the rapid credit growth seen in EU-10, and they share a concern about the financial sector implications of exchange rate policy. Also included in the study are the three erstwhile noncore EU countries (EU-3) to act as a control group within the sample.2

I. Literature Survey

The empirical test in our study explores commonly discussed risk factors, using an existing risk measure, and incorporating information on the quality of regulation and supervision. Our discussion includes rapid growth in bank credit, exchange rate regime and volatility, the extent of foreign ownership, bank size, macroeconomic stability, and the quality of the regulatory and supervisory framework. This section provides a background for the study and explains the choice of variables included in the empirical test.

A Measure of Risk

An increasingly used measure of bank soundness is the risk of insolvency or distance to default, also referred to as the z-index (Altman and Saunders, 1998; De Nicoló, 2000; and Lin and others, 2005). This index, which is directly related to the probability of loss exceeding equity capital, can be summarized by:

z ≡ μ + k σ ,

where μ is average return on assets (in percent), k is equity capital in percent of assets, and σ is the standard deviation of return on assets as a proxy for returns volatility.3 Statistically speaking, z measures the number of standard deviations a return realization has to fall in order to deplete equity, under the assumption of normality of banks’ returns. A higher level of z corresponds to a greater distance to equity depletion and therefore higher bank stability.

Credit Growth

In this paper, credit risk is measured by a combination of rapid credit growth and higher loan-loss provisions. Although credit growth is used as an indicator of banking health, rapid credit growth (introduced as a quadratic credit growth term in the econometric model) is an early indicator for build up of credit risk.4 Higher loan-loss provisions is used as an indicator of materialization of credit risk; its interactions with supervisory standards and practices on credit risk management indicate whether better risk management helped in lowering the adverse impact on stability.5 As we note later, higher provisioning could also indicate prudence if a sound and profitable bank decides to boost precautionary reserves rather than distribute profits—in practice, this has not been the case for most countries in our sample.

The risk of a credit boom-and-bust is the subject that has attracted most attention among possible financial risks in European countries. Although credit growth is largely perceived as part of a welcome catch-up process after many years of limited financial intermediation, some policymakers are increasingly concerned about its negative implications on macroeconomic and financial sector soundness. In particular, banks extending credit amidst stiff competition for market share tend to relax lending standards when funding and economic conditions are good. The fear is that this behavior could lead to a credit crunch later on when liquidity pressures evolve and the economic cycle turns. Bad loans erode capitalization of banks, which leads to tightening of standards resulting in a credit crunch.

As of end-December 2006, 16 European countries experienced an annual private sector credit growth exceeding 20 percent, five of which had rates over 50 percent (Figure 1). Countries with a credit growth rate above 20 percent were Central and Eastern European (CEE) countries, except for Ireland (23 percent), Spain (24 percent), and Luxembourg (34 percent). At end-December 2006, the level of financial intermediation in CEE remained low, with a ratio of private sector credit to GDP ranging between 15 percent (Albania) and 64 percent (Latvia), in contrast to an average of almost 130 percent for the euro area.

Figure 1.
Figure 1.

Credit Growth in Europe, end-2006 1

(In percent)

Citation: IMF Staff Papers 2010, 001; 10.5089/9781589069114.024.A002

Note: Year-on-year growth for 2005–06 except for Albania and Poland (2004–05 year-on-year growth). Private sector credit/GDP for 2006: 2005 for Albania, Poland and Slovenia.

A very swift rise in credit may be the outcome of rapid income growth or the development of new credit markets such as housing and mortgage credit. In such circumstances, credit expansion may coexist for some time with low and declining inflation. Credit may also increase rapidly in cases of successful stabilization and significant economic reform, with credible economic policies. In practice, however, credit boom-and-bust cycles have often been associated with the absence of close financial surveillance. Thus, despite seemingly sound fundamentals, most studies generally agree that financial soundness indicators should be carefully monitored for early warnings of distress, that standards of prudential regulation and supervision should be strengthened and their implementation intensified, and that excess demand pressures should be closely analyzed.6

In terms of policy responses, there is also widespread agreement that, should signs of financial instability appear, tightening fiscal policy can be an effective response to slow down credit growth, whereas monetary policy measures, especially in countries with closely managed exchange rates and open capital accounts, have generally proved largely ineffective. Administrative measures and direct policy tools—such as reserve requirements, credit controls, and so on—are sometimes seen to encourage excessive risk-taking by diverting local currency-denominated credit demand to foreign currency sources. To maintain the quality of banks’ loan portfolios, prudential tightening of all credit institutions is the typically recommended policy response, although there is little evidence that such measures help reduce the speed of credit growth. If prudential measures are used to ration credit, there is an incentive to satisfy the excess credit demand through nonbank financial institutions, transferring the risk to nonbank financial institutions, and/or nonfinancial borrowers (see Hilbers and others, 2005).

Exchange Rate Strategy

Exchange rate volatility is used as an indicator of market risk in this paper. Eastern European banks often receive funds in foreign currency from foreign parents, and extend loans in foreign currency. Banks with large open foreign currency positions could be subject to large valuation changes affecting profits. Thus exchange rate strategies followed by country authorities could create large fluctuations in exchange rates that could adversely affect bank soundness. The literature has focused on the sustainability of exchange rate strategies rather than the implications for financial stability (Gulde, Kähkönen, and Keller, 2000; Burgess, Fabrizio, and Xiao, 2003; Backé and others, 2004) or whether exchange rate was over- or undervalued (De Haan, Berger, and van Fraasen, 2001; Egert and Lahrèche-Révil, 2003; IMF, 2005). Stable and sustainable exchange rate regimes are a necessary, though not sufficient, condition for financial stability. Supervisory policies limiting excessive market risk-taking behavior help bank stability.

Foreign Ownership

Are foreign banks more stable? Foreign direct investment in financial institutions may have helped to integrate countries’ financial markets into the global financial system, bringing significant benefits of efficiency and stability, but it may also have highlighted country risk and financial vulnerability.7 Naaborg and others (2003) concluded that foreign bank entry was among the most striking features of European transition countries, with foreign banks accounting for over half the number and two-thirds the assets of their banking systems within less than 10 years (Table 1). These foreign banks, most of which are owned by reputable western European bank groups, have increased stability and efficiency by revamping the banking sector in many CEE countries and re-establishing public confidence in their financial system. However, De Nicoló and Loukoianova (2007) have shown that the risk profiles of foreign banks are higher than private domestic banks. The presence of these banks has also introduced new challenges for host country supervisors who must assess the risks that may arise from a change in the parent institution’s strategy or risk appetite that are managed by the parent’s centralized risk management on a group-wide basis.8

Table 1.

Bank Ownership Structure in Selected Central and Eastern European Countries, 2003

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Source: Bankscope.

Includes foreign bank branches.

Macroeconomic Stability

Financial vulnerability and resilience depend largely on the soundness of macroeconomic policy, as reflected in stable inflation and GDP growth rates, with sustainable debt and fiscal and external balances.9 Since the emerging market crises of the 1990s, many studies have confirmed a strong correlation between rising macroeconomic vulnerabilities, including large external deficits and debt and financial risks (Kaminsky, Lizondo, and Reinhart, 1998). In particular, large current account deficits make emerging countries vulnerable to sudden reversals of capital flows, as these deficits tend to be financed through foreign bank funding rather than domestic saving and investment decisions, as is the case in rich countries (Blanchard, 2007; and Calvo, 1998).

When foreign investors stop rolling over domestic debt, the resulting financing gaps have required either a depreciation of the exchange rate or a drawdown of reserves with or without higher interest rates. Pressures on the exchange rate affected bank portfolios, as holders of foreign currency or variable interest rate debt found it difficult to make repayments (Roubini and Setser, 2004). The impact of such shocks can be amplified by balance sheet mismatches and the extent to which the inflows have been channeled into the nontradables vs. the tradables sectors. If inflows have been absorbed primarily by nontradables, concerns about a country’s debt sustainability further raise financing costs and, thereby, banks’ liquidity risks, market risks, and credit risks (Sorsa and others, 2007).

The Quality of Regulation and Supervision

Since 2001, the IMF and the World Bank have conducted joint FSAPs in which they assess a country’s financial sector’s ability to withstand shocks and to develop in a sustainable way. An important aspect of these assessments is the capacity of regulatory systems to reduce risks and increase the system’s resilience in case of a disturbance.10 The reports include assessments of country performance in relation to a variety of internationally agreed standards and codes, which typically include the Basel Core Principles of Effective Banking Supervision (BCP) and other codes of good practices, such as the Core Principles for Supervision of Systemically Important Payments Systems (CPSS) and guidelines issued by the International Association of Insurance Supervisors (IAIS) and the International Organization of Securities Commissioners (IOSCO).

In line with earlier studies, we use some of these international standard assessments to compile scores of the overall quality of supervision and regulation.11 Podpiera (2004) found some evidence of a positive impact of compliance with the BCP on banking sector performance. In our paper, we use a similar methodology to calculate a compliance index based on various elements of the BCP and CPSS assessments.12 In particular, from the BCP we use the principles that relate to prudent credit policies and loan-loss provisioning (CPs 7–8); limits on large exposures (CP 9) and connected lending (CP 10); market risk management (CPs 12–13); quality of financial information (CPs 14, 19, and 21); and consolidated supervision (CPs 23–24). From the CPSS, we focus on the principles related to payment systems risk management (CPSS 2–7).

FSAPs and reports on standards and codes (ROSCs) for a majority of the countries in our sample, between June 2001 and present, reveal that the regulatory frameworks of almost all European countries were adequately supervised, and many were described as well supervised and regulated (Cihak and Tieman, 2006). In all cases where supervision was only adequate, the FSSAs reported that a process of further strengthening was already underway. Compliance with BCP was generally good, even though there remained a few areas of weaknesses, with respect to lack of transparency of bank ownership, weak governance, and inadequate credit and other risk management policies in some countries.

II. Methodology

The financial risk variables used in this paper are common to those found in similar studies, except for the data on compliance with certain financial supervisory standards, which have rarely been applied in the literature on financial risk.13

The Model

Our model follows in the tradition of studies that focus on the joint effect of a variety of macroeconomic and prudential variables on the vulnerability of financial institutions or the financial system as a whole.14 However, rather than test for financial institution failure, as is typical in these studies, our dependent variable is a measure of insolvency risk, or distance-to-default, of an individual bank—logz_rol, based on the z-index described in Section I. A higher z-index or logz_rol implies greater stability.

We estimate the following model to test for different risk factors that affect logz_rol:

log ⁡ z − ⁢ r ⁢ o ⁢ l i ⁢ t ⁢ j = α + β 1 ⁢ ( S ⁢ i ⁢ z ⁢ e i ⁢ t ⁢ j ) + β 1 ⁢ f ⁢ ( f ⁢ o ⁢ d i ⁢ j * ⁢ S ⁢ i ⁢ z ⁢ e i ⁢ t ⁢ j ) + Σ s = 1 3 ⁢ β B ⁢ R , s ⁢ ( B ⁢ R i ⁢ t ⁢ j s ) + Σ s = 1 3 ⁢ β B ⁢ R ⁢ C , s ⁢ ( B ⁢ R i ⁢ t ⁢ j * s ⁢ C ⁢ P j B ⁢ R ⁢ s ) + β M ⁢ R ⁢ ( M ⁢ R t ⁢ j ) + β M ⁢ R ⁢ C ⁢ ( M ⁢ R t ⁢ j * ⁢ C ⁢ P j M ⁢ R ) + Σ s = 1 3 ⁢ β M ⁢ s ⁢ ( M ⁢ a ⁢ c t ⁢ j ) + ɛ i ⁢ t ⁢ j ⁢ .

The subscript i stands for bank; subscript t for year; j for country. Our dependent variable, logz_rol, is a variation of De Nicolós (2000) indicator of banking stability. Logz_rol is computed as the sum of the average return on assets (in percent) and equity capital (as percent of assets) over the standard deviation of return on assets. To take advantage of as much year-over-year variation as possible, we use a three-year rolling z-index, which is computed by using the three-year centered moving average of return on assets (profitability) plus the three-year moving average of equity to assets (capitalization) over the three-year standard deviation (of return on assets). In centering the moving average, we lose observations for 1997 and 2004 but get a sense of implications of current and past risk-taking on partly future stability. All variables, including the dependent variable, are transformed into natural logarithms.

The list of explanatory variables aims’ to incorporate a wide variety of possible risks as discussed in Section I. The right-hand side variables are grouped into those that describe bank size (Size), including an interaction term with foreign-owned banks (fod*Size); bank-specific risks factors (BRs), country-specific market risk factors (MR), and interaction of each of bank risk factors and market risk factors with the countries’ compliance level with certain core principles of effective banking supervision and payment systems (CP) (see Table 3); and variables describing the macroeconomic environment (Mac) that vary with country and year.15 The bank-specific factors included are credit growth, loan-loss provisions, liquidity, bank size, and foreign ownership. Market risk is measured by exchange rate volatility, and macro-economic risks include the ratio of credit to GDP, trade openness, and the inflation rate.

Table 2.

Correlation Coefficients of z-Index with Other Bank Performance Indicators

(Pooled Sample)

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Note: An “*” indicates significance at the 5 percent level.

Log z_rol is the rolling z-index or the logarithm of (return on assets + equity on assets)/3-year standard deviation of return on assets.

Table 3.

Variables Description

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IFS = IMF, International Financial Statistics; WEO = IMF, World Economic Outlook; BS = Bankscope. Standards and codes: Assessment grades:

4 = observed (in CPSS) or compliant (in BCP); 3 = broadly observed (in CPSS) or largely compliant (in BCP); 2 = partly observed (in CPSS) or materially noncompliant (in BCP); 1 = nonobserved (in CPSS) or non-complaint (in BCP) Note: A “1” in front of a variable denotes its natural log—lx = ln(x). An ‘s’ after the name of the variable denotes its square—xs = x*x or lxs = ln(x)*ln(x).

The effect of various risks and risk mitigating factors on bank stability is estimated by means of pooled OLS with heteroskedasticity-corrected (White) standard errors clustered by country. A log-log specification is chosen so that the estimated coefficients can be interpreted as elasticities.16 Furthermore, to see which component of logz_rol is influenced by the various risk factors, we also run the same model with the individual components of logz_rol—profitability, equity over assets, and return-volatility—as dependent variables. Finally, to test for robustness, given substantial regional variation in the indicators, we run a simplified version of the model over pooled regional sub-samples.

Two caveats are in order. First, the specification of our model is not designed to infer causal relationships between bank stability and the various risk factors. Rather, the purpose of this paper is to identify statistically significant conditional correlations between these variables. In other words, the aim of our study is to investigate whether the presence of stronger banks is associated with, say, a stricter prudential and regulatory framework. Our results do not allow us to infer whether this stricter prudential framework has caused banks to become stronger or whether stronger banks prefer to operate in an environment with a stricter prudential and regulatory framework.

Second, in many countries of our sample, a significant portion of total loans is either denominated in foreign currency (dollars or euros) or indexed to the euro. As a result, it would be important to control for the impact of dollarization and euroization on financial stability, and to examine how exchange rate volatility may affect credit or liquidity risk directly (through open foreign currency positions on banks’ balance sheets) or indirectly (through banks’ exposures to borrowers that may not be able to repay their debts denominated in foreign exchange). Unfortunately, neither the currency breakdowns of banks’ loan portfolios nor information on borrowers’ ability to withstand an exchange rate shock are readily available, making it very difficult to analyze this type of risk.

Still, the z-index correlates well with other bank characteristics. The correlation coefficients of logz_rol with selected performance indicators are shown in Table 2. Higher stability is associated with higher efficiency (interest expense, cost, and personnel expenses). Observations with higher net loans (relative to assets) are associated with higher stability but those with higher revenues are not.

Data Coverage

The paper uses annual data from Bankscope over the period 1997–2004. For the 21 countries included in our three groups (EU-3, EU-10, and S-8), we selected all commercial banks available in Bankscope for which data were available up to (at least) 2003. This yielded a total of 334 banks. Branches and subsidiaries of multinational banks are consolidated on a national basis—that is, various subsidiaries of a foreign bank in different countries are reported as separate entities. The construction of the z-index necessitates dropping 1997 and 2004.

Explanatory Variables

Bank-Specific Risks

Banks’ risks are captured by credit risk and liquidity risk, and their interactions with the countries’ compliance with certain supervisory standards.17 A summary of various risks and risk-mitigating factors is given in Table A1; the discussion below draws on this table. See Table 3 for details on the variables used in the econometric exercise.

Credit Risk

Credit risk from banks’ loan portfolios (in both local and foreign currency) is the main vulnerability of banks in EU-10 + S-8 region, as identified in several FSAP reports.18 This is especially true in the case of a credit boom, which may hide the potential for future nonperforming loans (NPLs). There may also be indirect exchange rate-related credit risk on loans made in foreign currency (fx) to unhedged borrowers, even though banks keep foreign exchange open positions within the regulatory limit. We capture the risks associated with a credit boom-and-bust by including bank-by-bank credit growth (cg) and its square term (cgs) in the model. As a proxy for the riskiness of banks’ lending portfolio, we include loan-loss provisions in percent of net interest revenue (prov).19 We do not have any prior as to the sign on the coefficient of this variable. High provisioning may reflect high NPLs, and may be associated with a lower distance-to-default. Conversely, high provisioning could indicate prudence if a sound and profitable bank decides to boost precautionary reserves rather than distribute profits.20

Strong bank supervisory practices could mitigate some of the credit risk in so far as prudential guidelines encourage prudent risk management practices by banks. Assessment of these policies is made using the BCP (see Section I). We used some of these assessments to see to what extent the countries and regions in EU-10 + S-8 that have a high compliance with best practices are better able to withstand shocks (higher logz_rol). For this, we interact the two principles that assess the quality of credit and provisioning policies (CPs 7–8) with prov. We also interact credit growth (cg) with an aggregated index that combines the four principles (CPs7–10) that assess the overall quality of banks’ credit risk management practices (including policies on connected lending and large exposures).

Liquidity Risk

Liquidity risk is modeled by taking the ratio of liquid asset to deposits and short-term funding (liq). Although rising liq is a positive influence on stability at low levels of liquidity, excessive liquidity could be a structural problem for the bank, reducing the value of our stability indicator. Thus, a bank could be highly liquid by not lending enough and holding large quantities of government securities, often in the absence of liquid secondary markets in such securities.

A key to avoiding systemic liquidity problems is the smooth functioning of, and management of risk in, payments and settlement systems. We make use of CPSS 2–7 to judge the level of country compliance on these policies. For a bank-specific effect, we combine CPSS 2–7 with liq.

Bank Size and Foreign Ownership

We include total assets (ta) to capture bank size. A priori the sign on the coefficient of this variable is indeterminate, because the presence of very large banks could either be stabilizing or risky for the financial system, depending on the importance of economies of scale in each banking system (see De Nicoló, 2000).

Foreign bank ownership, which is very high in emerging Europe, introduces the risk that parent banks may fund credit expansion in the region in order to relieve tightening profit margins at home, generating rapid credit growth in the EU-10 + S-8 countries. As a result, the foreign branches and subsidiaries may have contributed to a disproportionately large portion of the bank group profits compared with their risk exposures. Moreover, as parent banks tend to own subsidiaries in more than one country in the region, the resulting cross-border networks of bank groups introduces the risk that problems in one bank belonging to the regional network may spread to others, and that macroeconomic deterioration may be transmitted across borders. We capture risks of foreign ownership by interacting ta with a dummy variable that takes the value of 1 if the bank is foreign-owned (fod).21

Country-Specific Market Risk

The standard deviation of monthly exchange rate changes is used as our proxy for market risk (sd_exchg). High exchange rate volatility is a source of potential vulnerability, but good risk management policies to monitor market risks could mitigate the balance sheet effects of such fluctuations. We capture the latter by interacting sd_exchg with (BCP) CPs 12–13—supervisors should be satisfied that banks have in place systems that accurately measure, monitor, control market and other risks, and (supervisors) have the power to impose prudential limits or capital charges against such risks.

Macroeconomic Environment

As country experience reported in the literature survey suggests, the macroeconomic environment could show some broad variations in stability trends across countries and country-clusters. We chose private sector credit to GDP (credgdp) as an indicator for overall financial development; trade openness (topen) to indicate susceptibility to real external shocks; and the inflation rate (infl) to indicate overall success of monetary policy.

III. Regional Variation in the Data

Before turning to our empirical results, we present key regional variations found in the data. For purposes of comparison, we created seven clusters—Total (the total pooled sample), EU3 (Spain, Portugal, Greece), Surroundings (also referred to as S-8—Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Macedonia, Moldova, Romania, and Serbia and Montenegro), High Credit Growth (Albania, Bulgaria, Estonia, Latvia, Lithuania, Moldova, and Romania) based on the classification in Hilbers and others (2005),22 Baltics (Estonia, Latvia, and Lithuania), New Member States (also referred to as EU-10—Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovak Republic, and Slovenia), and Foreign Owned Banks. The variables and their sources are described in Table 3. Differences across clusters are depicted in Figures 3–5, which show the pooled means of various macroeconomic variables, banking characteristics, and regulatory compliance.

Figure 2a.
Figure 2a.

Mean of the z-Index and its Components

(In percent)

Citation: IMF Staff Papers 2010, 001; 10.5089/9781589069114.024.A002

Note: logz_rol is the rolling z-index or the logarithm of (return on assets (roaa) + equity on assets (ea))/3-yr standard deviation of roaa (roaa_sd).
Figure 2b.
Figure 2b.

Mean of the z-Index by Country: Selected Eastern European and the EU-3 Countries

Citation: IMF Staff Papers 2010, 001; 10.5089/9781589069114.024.A002

Note: Average log z_rol of banks in a country, weighted by total assets.
Figure 3.
Figure 3.

Mean of Macroeconomic Background

(In percent)

Citation: IMF Staff Papers 2010, 001; 10.5089/9781589069114.024.A002

Note: “Final assets” refers to gross financial assets, “Trade open” refers to trade openness (exports + imports), in percent of GDP, “Credit growth” refers to credit growth. All figures are averages over 1997–2004.
Figure 4.
Figure 4.

Mean of Banking Characteristics

(In percent, unless otherwise stated)

Citation: IMF Staff Papers 2010, 001; 10.5089/9781589069114.024.A002

Note: “Loan growth” refers to regional average of bank-by-bank loan growth 1997–2004, “tot loans/dsf” refers to total loans in percent of deposit and short-term funding, “liquidity” refers to liquid assets in percent of customer and short-term funding.
Figure 5.
Figure 5.

Mean of Regulatory Indices

Citation: IMF Staff Papers 2010, 001; 10.5089/9781589069114.024.A002

Note: bcp_all refers to the sum of squared assessment codes of all the 24 Basel Core Principles and cpss_2–7 refers to the sum of squared assessment codes on cpss. Refer to Table 3, footnote 1 for the codes, and section II for a discussion.

Figures 2a–b suggest a number of regional variations across country clusters in the overall stability indicator (z-index):

  • Compared with EU-3 banks, banks in the S-8 region are highly capitalized. In part owing to high interest margins, the banks in this region are also the most profitable, although they have the highest returns-volatility (measured by the standard deviation of returns on assets).

  • In spite of comparatively low capitalization and average profitability levels, EU-3 banks appear to enjoy a lower insolvency risk (a higher z-index) than other banks in the sample, primarily because they experience much lower returns-volatility.

  • Countries experiencing high credit growth do not appear to be more vulnerable than other banks in the region, because their equity levels are high. Also, even though their rates of return are modest, they do not vary greatly.

  • Country experiences have varied over time. Average z-indices by country over time (taking the average of the indices of banks, weighted by assets) are shown in Figure 2b. Overall, emerging European banks on average have become more stable. There was a general dip in stability around 2000–01 in selected countries, which could be related to the tech-bubble burst in the United States.

The differences in macroeconomic characteristics as shown in Figure 3 are:

  • The EU-3 countries have the highest ratio of financial assets to GDP but the lowest trade openness, whereas the reverse is true for the New Member States.

  • There seems to be a positive association between the inflation rate and bank insolvency risk (see Figures 2a and 3).

As far as liquidity and other bank characteristics are concerned:

  • The S8 banks exhibit ample liquidity and the highest average credit growth rate (see Figure 4).23 Despite higher profitability and capitalization, they are not more stable than the High Credit Growth group of banks (see Figure 2a), which could be due to their higher return-volatility.

  • In the EU-3 and S-8 countries the loans to deposit ratios are higher than for the Baltic countries and the High Credit Growth countries, which could reflect higher indebtedness.

Bank sizes differ considerably among groups—average EU-3 banks are nearly twice as large as the average for the entire pool, and new member country banks almost five times as big as the S-8 ones. However, there does not seem to be systematic association between size and the stability (logz_rol) (see Figures 2a,b and 4).

As in some other studies (Podpiera, 2004), we have converted qualitative indicators of supervisory standards to quantitative scores (see Table 3 for details). The computed scores are shown in Figure 5 and the standards are elaborated in Table A2:

  • The regulatory regime shows less variation across regions, except for the S-8 countries, which stand out with the lowest BCP scores.

  • Overall, countries seem to benefit from fairly strong payment systems infrastructure and oversight and there is not much difference in CPSS scores between regions.

IV. Empirical Results

The estimates of pooled regressions are shown in Table 4. Columns 1–4 present the results with all the risks discussed in the previous section. Columns 5–8 focus on credit risk, as this has been consistently outlined as the main stability risk for banks. For each specification, we ran the regression controlling for banks in EU-3 countries (columns 1 and 5), for all banks (columns 2 and 6), banks in EU-10 + S-8 or emerging Europe (columns 3 and 7), and all banks with country fixed effects (columns 4 and 8). Table 5 provides estimates for the same model using the three different components of logz_rol as dependent variables: profitability (columns 1–3), equity-to-assets (columns 4–6), and returns-volatility (columns 7–9). We then run the credit risk part of the model on subsections of regional banks (Table 6). Overall, we find broadly robust results across specifications.

Table 4.

Risks and Stability

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Note: Pooled with standard errors clustered by country. Significance levels: ** 1 percent; * 5 percent; + 10 percent. Absolute values of t-statistics in parentheses. The dependent variable is logz_rol = log of (return on assets + equity on assets)/standard deviation of return on assets.

Credit Risk

In our model, several variables capture various aspects of credit risk and empirical tests yield the following results for each of them.

Rapid Credit Growth

  • Higher bank-by-bank credit growth is associated with greater stability (positive sign on cg in Table 4). Regressions on the components of logz_rol suggest that this result is driven by the association of faster credit growth with higher profits, higher equity, and lower volatility (although not a significant influence), all of which raise the stability indicator logz_rol (Table 5).

Table 5.

Components of Stability

(logz_rol)

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Note: Pooled with standard errors clustered by country. Significance levels: ** 1 percent, * 5 percent; + 10 percent. Absolute values of t-statistics in parentheses. The dependent variable is logz_rol = log of (return on assets + equity on assets)/standard deviation of return on assets).

  • However, banks become more vulnerable as credit growth accelerates (the quadratic effect of credit growth, lcgs, is strongly negative in Table 4). Returns become more volatile as credit growth accelerates (Table 4), particularly in the case of the EU-10 + S-8 group of banks.

Two results on the credit policy regime are puzzling. First, banks operating under a stricter credit policy regime (including limits on large exposures and connected lending, CP 7–10), have lower stability indicators when credit growth is higher (there is a negative coefficient of lcg_cp710s in Table 4). Second, the returns to a bank with higher credit growth are lower where the supervisory regime is stronger (there is a negative coefficient of lcg_cp710s in Table 5, columns 1–3). In future work it would be useful to explore these results further with a dynamic model. One hypothesis is that in the short run, tougher supervisory standards may adversely affect banks’ profitability (through higher provisioning) and therefore reduce their apparent stability. Over time, however, the higher costs associated with a stricter regulatory framework should translate into a greater ability to withstand shocks and therefore a lower returns-volatility and a higher value of the stability indicator. Our current model specification does not allow us to test this hypothesis.

Loan-Loss Provisioning

  • Higher provisioning for loan-losses is associated with a lower stability. Evidence from the components of logz_rol indicates that banks with higher provisioning tend to be less profitable (columns 1–3 of Table 5) and exhibit higher returns-volatility (see columns 7–9 of Table 5). Procyclical provisioning policies could result in such returns volatility.

  • There is mild evidence that a higher score on the BCP that address credit and provisioning policies (CP 7–8) mitigates the negative effect of provisioning on stability. The coefficient of lprov_cp78s is positive but not always statistically significant in Table 4. But the components regressions (Table 5) show that the mitigating effects from higher compliance with CP 7–8 arises from higher profitability and lower returns-volatility (columns 1–3 and 7–9 of Table 5).

Liquidity Risk

Liquidity risks (lliq) have mixed effects on stability.

  • Overall, there appears to be a negative association between liquidity and stability, but an insignificant one for emerging European banks (Table 4). Individual component estimations indicate that highly liquid banks in emerging Europe tend to exhibit significantly higher returns-volatility that could offset the good effects of higher capitalization (columns 5 and 8 of Table 5).

  • However, emerging European banks operating in countries with good payment systems infrastructure and oversight (CPSS 2–7) experience lower returns-volatility but lower capitalization as well, with an insignificant effect on insolvency risk.

Market Risk

Country-wide exchange rate volatility has a somewhat counterintuitive effect on stability.

  • Exchange rate volatility (sd_exchg) is associated with higher stability (see columns 1–4 of Table 4), mostly through reduced returns-volatility in EU-10 + S-8 banks (column 7 of Table 5). This is plausible if banks anticipate the impact of possible exchange rate fluctuations on their balance sheets and allow for higher lending margins to absorb exchange rate shocks.

  • However, the positive effect of exchange rate volatility on bank stability is somewhat mitigated when bank supervisors enforce strict market risk management practices (CP 12–13). This suggests that a strict regulatory framework may induce banks to better match their capitalization levels with the underlying risks, leaving less need for extra buffers (sde_cp1213s). This evidence is not strong for emerging European banks.

Macroeconomic Performance and Structure

  • Banks in countries with greater financial depth—a higher private sector credit in percent of GDP, lcredgdp—are more stable, supporting earlier evidence that banks operating in countries with more developed financial markets exhibit lower insolvency risk (De Nicoló, 2000).

  • Other macroeconomic indicators, such as trade openness (ltopen) and inflation (linfl) do not have significant influence on bank stability. Profitability of banks is higher in countries with higher inflation—inflation could be an indicator of overheating in an economy that could lead to higher volume of banking business, making it more profitable for banks.

Bank Structure and Ownership

  • Larger foreign-owned banks are less stable (negative coefficient of fodlta), mainly due to lower profitability and lower capitalization (Table 4, Table 5). But the association is not significantly strong for emerging European banks (Table 4, column 3). This result supports evidence in De Nicolo and Loukoianova (2007) about foreign banks being riskier than domestic private banks. Because foreign banks typically have access to a very large pool of equity funds abroad, they may operate with much lower levels of capitalization of local operations, than would be the case for local banks.

  • Profitability increases with size (except for EU-10 + S-8 banks), whereas both capitalization and returns-volatility decrease in size (Table 5). Larger emerging European banks have lower returns volatility but size does not have a significant impact on stability. The effects appear to cancel each other, and the size variable is not significant for the overall stability of banks included in our sample. Still, the coefficient on size is negative, corroborating earlier evidence on the negative relationship between bank size and stability (De Nicoló, 2000; De Nicoló, Hayward, and Bhatia, 2004).

Regional Credit Risk

Table 6 examines credit risk variation among the different groups defined earlier, with a simplified model containing only credit risk. Table 6 shows that the basic conclusions of the fuller model remain intact, especially for the new member states or EU-10.

  • For most regions, except for the Baltics and the Surroundings, credit acceleration is associated with greater vulnerability.

Table 6.

Regional Credit Risk

article image
Note: Pooled with standard errors clustered by country. Significance levels: ** 1 percent, * 5 percent, “+” 10 percent. Absolute. Values of t-statistics in parentheses. The dependent variable is logz_rol = log of (return on assets + equity on assets)/standard deviation of return on assets. EU-3 represents Spain, Portugal, and Greece. Baltics represent Estonia, Latvia, and Lithuania. Surrounding represents Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Macedonia, Moldova, Romania, and Serbia and Montenegro. EU-10 represents the 10 new member states that joined the European Union in 2004. High credit growth represents countries with high credit growth in 2004: Albania, Bulgaria, Estonia, Latvia, Lithuania, Moldova, and Romania, from Hilbers and others (2005). Also see Table 3 for details on variables.

  • Greater financial depth (credgdp) enhances stability significantly in the EU-10.

  • Higher provisioning is associated with lower bank stability in the EU-10. According to the regressions on the individual logz_rol components, this result is driven by the negative effect of higher provisioning on profitability (not shown). Good provisioning policies help to mildly mitigate such negative effects.

  • The result that good credit policies adversely affect stability is repeated here for EU-10 banks. For banks operating in the EU-10, a high score in the quality of supervision of credit policies (CPs 7–10) is associated with higher insolvency risk.

  • Higher inflation adversely affects bank stability in the EU-10.

V. Concluding Remarks

The results in this paper indicate that while a focus on credit quality is justified, it is the acceleration of credit, rather than its rate of growth, that warrants extra vigilance. The observed rates of growth of credit are associated with greater bank stability for our sample, and it is only when credit growth speeds up that banks appear more vulnerable. When credit growth accelerates it is important to ensure sound supervisory practices, in order to minimize risk exposure.

Higher loan-loss provisioning is associated with lower stability, mainly through lower profitability and higher returns volatility. Procyclical provisioning practices—that is, provisioning more when returns are low—could increase profit volatility. However, improved supervisory policies on provisioning help to sustain profits, reduce volatility, and lessen the adverse effect of provisioning on stability.

The results show that too much liquidity may not be a good thing. Banks flushed with liquidity might be pursuing nonlending activities that could yield volatile returns. Such volatility could offset the good effects of relatively higher capitalization of more liquid banks.

In addition, the paper finds support for earlier evidence that greater financial depth in countries improves bank stability (De Nicoló, 2000); that foreign owned banks are riskier (De Nicoló and Loukoianova, 2007); and, that bank size negatively but insignificantly affect stability (De Nicoló, 2000; De Nicoló, Hayward, and Bhatia, 2004). Foreign banks tend to have a higher risk profile than domestic banks because of their relatively lower profitability and capitalization, which is a reflection of their ability to rely on extra funding from their parent institutions when needed. But the foreign banks are not significantly less stable in emerging Europe. There is mild evidence that larger banks are more risky although the returns-volatility of larger banks tends to be lower, suggesting a positive diversification effect for emerging European banks.

Two results on the credit policy regime may need to be further explored, using a dynamic model. First, the returns to a bank with higher credit growth fall with the strength of the supervisory regime. Second, banks experiencing rapid credit expansion in a context of stricter credit policy regime exhibit lower stability. These phenomena may result from the adjustments that banks were required to make in response to supervisory tightening (that is, higher provisioning), but our model prevents us from investigating that possibility. Long lags could be present for good credit policies to improve stability. In the short run, there could be an adverse effect of good provisioning policies on profitability, but the long run returns could be high.

This paper is the first attempt at identifying the role of selected risk factors in affecting banking stability and how they may be mitigated by a strong prudential and regulatory framework. Over time, with the availability of a larger data set, the research may be extended to a wider sample of countries, a broader range of exchange rate regimes and macroeconomically diverse profiles. Longer time series will permit the investigation of dynamic effects such as the impact of costly risk mitigation regulations on (future) financial stability benefits. Access to a currency breakdown of banks’ balance sheet information and financial income statements will permit exploration of banks’ exposure to credit risk induced by potential exchange rate volatility. There is a need to refine the bank stability indicator, to ensure that it more faithfully reflects market perceptions of bank risk exposure. Finally, much work remains to be done on refining the computation of financial regulation indices, from the impact of using different weighting and scoring systems to documenting changes over time.

Appendix

See Tables A1 and A2.

Table A1.

Sources of Risk and Risk-Mitigation Practices

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Table A2.

Standards and Codes

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*

Andrea M. Maechler is a senior economist in the IMF Western Hemisphere Department; Srobona Mitra is an economist in the IMF European Department; and Delisle Worrell is the director of the Caribbean Centre for Money and Finance at the University of West Indies. The authors were in the IMF Monetary and Capital Markets Department when the paper was written. The authors especially would like to thank Pamela Madrid for helping to inspire and conceptualize the study, and for many useful references and comments, during its gestation; Kiran Sastry and Nada Oulidi, for their invaluable data assistance; Jochen Andritzky, Martin Cihak, Vidhi Chhaochharia, Gianni De Nicolo, Alain Ize, Inutu Lukonga, Kathleen McDill, Franziska Ohnsorge, David Parker, Mark Swinburne, Jan-Willem van der Vossen, and Francesco Vasquez; and participants at the Second Annual DG ECFIN Research Conference, for helpful suggestions. They are also indebted to Richard Podpiera for providing material for the computation of the BCP indices.

1

The EU-10 comprise Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovak Republic, and Slovenia. The S-8 are Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Macedonia, Moldova, Romania, and Serbia and Montenegro.

2

Adopting the terminology introduced by Schadler and others (2004), the three “noncore” countries refer to Greece, Portugal, and Spain, as these countries joined the EU much later than the rest of their western European counterparts.

3

A higher z—and higher log(z)—implies a lower upper bound of insolvency risk and hence a lower probability of bank insolvency. Ideally, the z-index should be computed based on the market values of shareholders’ equity and assets rather than the book value of banks’ balance sheets, as is done here to overcome the lack of data on market capitalization of most of the banks in our sample. Because book values are invariably lower than market values, our measure of z gives a more conservative (but less volatile) measure of risk than would in fact exist.

4

In countries with short time series, booms are extremely difficult to identify. Hence, a quadratic term is used as a proxy for measuring rapid credit growth.

5

Studies of rapid credit growth include IMF (2004a, 2004b, and 2005); Schadler and others (2004); Coricelli and Masten (2004); Cottarelli, Dell’Ariccia, and Vladkova-Hollar (2003); and Borio and Lowe (2002). Recent studies include Dell’Ariccia and Marquez (2006) showing how banks loosen their lending standards and increase credit with the decrease of information asymmetries across banks; Barajas, Dell’Ariccia, and Levchenko (2008) exploring how credit booms are related to banking crises; and Dell’Ariccia, Igan, and Laeven (2008) presenting evidence that fast credit growth can be linked to decline in lending standards and problems in loan performance.

6

A few studies, however, have found limited evidence of credit boom-induced banking crises (IMF, 2004a; and Tornell and Westermann, 2002).

7

See the Bank for International Settlements (2005) for an analysis of the experiences of Asia, Central and Eastern Europe, and Latin America.

8

A discussion on the role of foreign banks as a risk transmission mechanism in emerging Europe can be found in Sorsa and others (2007). The risk implications of the centralization of operational functions in cross-border bank groups are discussed in IMF (2007).

9

See, for example, Schinasi (2006). For an in-depth discussion on the impact of rising vulnerabilities on the macroeconomic and financial sector stability of emerging southeastern European countries, see Sorsa and others (2007).

12

There are other sources of risk which we were unable to explore for lack of data, including issues of financial integration among European countries (Manna, 2004; European Commission, 2004; and Corker and others, 2005); capital flows, including spillovers and sudden large-scale reversals (IMF, 2005; Kobor and Szekely, 2004; Vincze, 2001; and Portes and Rey, 1999); and direct and indirect euroization risks.

13

Podpiera (2004); and Das and others (2005) are two exceptions.

14

They are surveyed in Worrell (2004).

15

All variables are taken as natural logarithms, except for the dummy variables. For variables that can take 0 or negative values, we have used a transformation when taking logs as follows: ln(1 + x), for small x (expressed as fraction).

16

The presence of time-invariant and country-specific supervisory variables (the CPs) makes it difficult to use bank level fixed-effects panel method, which would drop a number of relevant bank-specific variables. A pooled OLS, however, enables us to exploit both variations within and between banks as well as regional variations.

17

We interact the supervisory scores with a bank-specific variable to avoid losing too many degrees of freedom.

18

See http://www.imf.org/external/np/fsap/fsap.asp for published FSSAs by country.

19

NPLs would have been a good indicator, but using this would have led to a sharp decline in our sample size due to missing observations on most banks.

20

As FitchRatings (2005) notes, prudential behavior of banks could be a risk factor if banks’ risk behavior is procyclical—excessively optimistic or pessimistic prudential behavior could amplify the business cycle and result in higher risk of bank failure.

21

The information on foreign ownership is from Bankscope that does not provide a time series on ownership of individual banks. The dummy variable, fod, is 1 if the most recent observation on a bank shows foreign ownership. Thus, the variable does not capture changes in ownership that might have happened during the sample period.

22

Countries with real credit growth exceeding 16.8 percent (year over year) on an average between 2000 and 2004. However, the banks included in the High Credit Growth countries are not necessarily the ones with the highest average bank-by-bank nominal loan growth because of differences in their inflation rates.

23

Because of inflation, some countries in High Credit Growth and Surroundings overlap.

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Cover IMF Staff Papers
IMF Staff Papers, Volume 57, No. 1
Author:
International Monetary Fund. Research Dept.
Volume/Issue:
Volume 2010: Issue 001
Publisher:
International Monetary Fund
ISBN:
9781589069114
ISSN:
1020-7635
Pages:
287
DOI:
https://doi.org/10.5089/9781589069114.024
Figure 1.

Credit Growth in Europe, end-2006 1

(In percent)
Figure 2a.

Mean of the z-Index and its Components

(In percent)
Figure 2b.

Mean of the z-Index by Country: Selected Eastern European and the EU-3 Countries
Figure 3.

Mean of Macroeconomic Background

(In percent)
Figure 4.

Mean of Banking Characteristics

(In percent, unless otherwise stated)
Figure 5.

Mean of Regulatory Indices