Chapter 4. From Subprime Loans to Subprime Growth? Evidence for the Euro Area

Christopher Crowe, Simon Johnson, Jonathan Ostry, and Jeronimo Zettelmeyer
Published Date:
August 2010
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Martin Čihák and Petya Koeva Brooks1 

4.1. Introduction

The global financial crisis has underscored the importance of financial linkages among countries and the impact of financial conditions on real economic activity (e.g., Strauss-Kahn, 2008). This chapter empirically studies linkages between the financial and real sectors in the euro area, focusing on the credit channel. To assess the robustness of the main results, we use a battery of possible estimation approaches.

Since mid-2007, the subprime mortgage crisis in the United States has sparked a reassessment of risk across global markets. Risk premiums in money and credit markets have spiked, raising the cost of interbank and corporate financing, including in the euro area. The situation worsened substantially in September and October 2008, when key money market indicators—the three-month spreads over policy rates, use of central bank facilities, and measures of market segmentation on the basis of credit risk—all rose to unprecedented levels.

The impact of the global financial crisis on the euro area real sector is an important, and still open, question. It is still too early to observe the full effect of the deteriorated financial conditions on the euro area economy. Nonetheless, it is useful to examine the linkages between the financial and real sectors in the euro area, using a combination of past and recent data.

The tight financial conditions associated with the crisis affect euro area activity through four main channels:

  • First, the increase in bank funding costs (arising from higher money market premiums and rates) may be passed on to firms and consumers via higher lending rates. Indeed, retail lending rates have gone up somewhat since mid-2007, even though this has been largely a continuation of a previous trend (Figure 4.1).

  • Second, in response to their own deteriorated balance sheets and financial conditions, banks may limit the amount of credit available to borrowers for a given price. This could be in the form of stricter lending standards. Bank lending surveys indicate a considerable tightening in quantitative bank lending conditions since mid-2007, suggesting that the credit cycle has turned (Figure 4.2).

  • Third, the costs of corporate bond and equity financing may also be higher, limiting the scope for substitution from bank financing. The corporate bond and credit default spreads of all maturities and ratings have widened sharply, and the stock market has fallen since the start of the crisis (Figure 4.3).

  • Fourth, tighter financing conditions could create financial accelerator effects by depressing asset prices and reducing the value of collateral. Available data confirm that asset prices have declined precipitously (see Figure 4.3); this has an impact on collateral values.

Figure 4.1.Euro area: Money market and retail lending rates.

(Source: Deutsche Bundesbank, Datastream.)

Figure 4.2.Euro area: Changes in credit standards to enterprises and households, 2005–08.

(Source: European Central Bank.)

The data as of late 2008 show that monetary aggregates have decelerated, as has bank credit to the private sector. This is a combination of a continued slowdown of bank credit to households and an incipient slowdown in the (still relatively rapid) growth of corporate credit. Equity and bond issuance by nonfinancial firms has also decelerated (Figure 4.4).

Figure 4.3.Euro area: Corporate and equity market prices, 2007–08

(yields in percent, spreads in basis points).

(Source: European Central Bank.)

Figure 4.4.Euro area: Growth in bank loans and securities issuance, 2003–08


(Source: European Central Bank.)

To preview our findings, we find that the financial tightening affects euro area activity through several channels, including an increase in bank funding costs, more bank credit rationing, increased costs of corporate bond and equity financing, and depressed collateral values. Based on a set of closely linked empirical approaches, we find that (1) bank loan supply responds negatively to declines in banks’ soundness; (2) a cutback in bank loan supply is likely to weaken economic activity; (3) a positive shock to the corporate bond spread lowers industrial output; and (4) risk indicators for the banking sector, the nonbank corporate sectors, and the public sector show a steady improvement beginning in 2002–03, followed by a major deterioration since 2007. Combining the existing expert estimates of financial sector losses with our econometric estimates of the relationship between financial sector losses and aggregate output, we conclude that the currently estimated banking sector losses would translate into a negative 2 percentage point impact on real output in the euro area (with considerable uncertainty around this estimate).

The remainder of the chapter is structured as follows. Section 4.2 analyzes the empirical evidence on the financial-real sector linkages, using a battery of approaches. Section 4.3 puts the individual approaches together and quantifies the implications of the results. Section 4.4 concludes.

4.2. Empirical Evidence

We examine empirically the linkages between the financial and real sectors in several alternative but complementary ways. In the next four subsections, we focus on the following:

  • Linkages between bank characteristics and lending behavior. This analysis helps us understand how financing conditions for banks, which are a crucial part of the financial intermediation in Europe, translate into banks’ lending behavior, and thereby into financial conditions of banks’ clients.

  • Linkages between bank loan supply and aggregate output. This part of the analysis allows us to examine the relationship between the supply of bank credit and economic activity. Subsequently, we link the analysis in Section 4.2.2 with the analysis in Section 4.2.1 to examine the linkages among bank characteristics, bank lending, and aggregate output performance (the so-called bank lending channel).

  • Linkages between corporate sector financing conditions and economic activity, using data on corporate bond spreads and output. This part of the analysis allows us to gauge how a change in corporate sector financing conditions affects industrial output.

  • Risk transfers among banks, nonbank companies, and the public sector, using a combination of sectoral balance sheets and market-based data.

4.2.1. Linkages Between Bank Characteristics and Lending behavior

Is bank loan supply in the euro area adversely affected by deteriorating financing conditions? If so, it means that banks are not able to fully shield their loan portfolios from changes in financing costs.

Most of the literature on the bank lending channel deals with the U.S. economy (e.g., Bernanke and Blinder, 1988; and Bernanke and Gertler, 1995). It generally finds strong evidence that banks decrease their loan supply in response to tighter financing conditions (in particular for small, balance-sheet-constrained banks), although there is limited evidence that the cutback in bank loan supply reduces real activity (e.g., Driscoll, 2004).

The fact that banks still finance the bulk of investment in Europe constitutes a good reason for investigating the bank lending channel. However, the empirical evidence on the bank lending channel in Europe has been mixed. Several studies have tested for the existence of a bank lending channel across euro area countries (De Bondt, 1999; Favero, Giavazzi, and Flabbi, 1999; Altunbas, Fazylov, and Molyneux,2002; and Angeloni and Ehrmann, 2003), and a number of studies have examined the bank lending channel for individual countries (Angeloni, Kashyap, and Mojon [2003], for several countries; Kakes and Sturm [2002] for Germany; and Iacoviello and Minetti [2008] on four European housing markets).2 The results from these studies are inconclusive, suggesting that the bank lending channel may be operating significantly in Germany, Italy, and Greece, although it appears not to be important in some other euro area countries. Most of these studies focus on the first (necessary) condition for the existence of a bank lending channel (i.e., that bank loan supply is affected by higher financing costs), without examining whether the decline in credit supply has an adverse effect on the real economy.

Identifying the determinants of credit developments is complicated by the interplay of cyclical and long-term factors that influence both credit demand and credit supply. On the credit demand side, these include a combination of cyclical developments and structural shifts. On the credit supply side, the impact of the economic downturn on financial markets and the financial situation of the banks seem to have influenced their lending. Moreover, banks in the euro area have gone through important structural changes, including a move from relationship-based banking to more market-based banking, and a growing role for securitization (e.g., Gambacorta, Altunbas, and Ibañez, 2008).

A rough tool for distinguishing credit supply and demand factors is the bank lending surveys. These surveys, organized by the European System of Central Banks since 2003, summarize responses of senior loan officers regarding loan demand and changes in their banks’ lending policies in the previous quarter. The changes in demand conditions and credit standards in a preceding quarter are summarized by a difference between positive and negative responses, in percent of all responses (“net percentage”).3 When interpreting the results of the survey, one needs to take into account the qualitative, subjective nature of the survey data. In particular, experience from similar surveys suggests that bankers’ responses may be biased toward tightening, and therefore a zero net percentage may in fact mean a slight easing. With that in mind, the latest survey data indicate that bank credit standards have tightened considerably since mid-2007, both for households and for enterprises (see Figure 4.2). The three most important factors listed by banks when explaining changes in credit standards were those related to the perception of risk.

Empirically, there is some basic evidence that the bank lending surveys contain useful information about subsequent macroeconomic developments. For example, there is a positive correlation between the quarter-to-quarter growth of real GDP and lagged values of the net percentage balance of loan demand (interestingly, the correlation coefficient is the same, 0.41, for both household lending and enterprise lending); also, there is a negative correlation between the quarter-to-quarter growth of real GDP and the lagged net percentage balance reflecting credit standards (the correlation coefficient being –0.40 for household lending and –0.43 for enterprise lending). This suggests that both the loan demand and the lending standards are procyclical. The time series of lending surveys are too short to allow for a more elaborate analysis or to test for breaks in the correlations.

To analyze the bank lending channel in the euro area, we use a supply-demand disequilibrium model. Equilibrium approaches, such as vector error correction/ vector autoregression models or single-equation estimates, can provide only a limited answer to the causes of credit slowdown because they do not address the question of whether the demand or supply function determines the credit. Following the examples of Pazarbasioglu (1997) and Barajas and Steiner (2002), a credit demand- and a credit supply-function are estimated under the restriction that the minimum of the two determines the credit. This strategy avoids the identification problem of equilibrium models, and allows us to make a statement on the existence of a credit crunch.

The disequilibrium model is estimated with bank-by-bank panel data for the 50 largest euro area banks from 1997 to 2007.4 The specification of the demand side follows the Bundesbank (2002). The specification of the supply side is close to Pazarbasioglu (2002), but with the distance to default (DD) among the supply-side variables. The DD was used to approximate banking sector vulnerability as a possible source of credit supply strain (see Appendix I for details). The DD for this estimate was calculated for each individual bank. The advantage of using individual bank data is that it allows for testing whether weaker banks are more likely to restrain their credit. Nonetheless, we have also calculated the aggregate DD for a portfolio of euro area banks (using a methodology explained in Appendix I and also in De Nicol’d others [2005] and Cihák[2007]). To provide an illustration of the overall developments in the DD, Figure 4.5 shows the development of the portfolio DD for daily data since the early 1990s. The portfolio DD has generally been above two, except for a brief period in 2003 (which can be linked to weaknesses in German banks), and except for the most recent period: in late 2008, the portfolio DD reached zero, its lowest recorded value.

The estimated model provides a plausible explanation of the factors contributing to credit developments in the major euro area banks (Table 4.1). All the key coefficients have the expected signs and are significant. The model explains yearon- year real growth rates of customer loans as a function of a bank’s DD (with an expected positive sign, as higher DD is associated with greater soundness, making it easier for banks to expand lending), real GDP growth rate as a proxy for overall economic activity (positive sign), lending rate and net interest margin (expected negative signs, reflecting more expensive lending for borrowers), and bank size approximated by total value of loans (expected negative sign). The key variable of interest is the DD, which captures the effect of bank financial conditions on credit supply.

Figure 4.5.Euro area: Distance to default for banks, 1991–2008

(daily data; higher values mean higher soundness).

(Source: Authors’ calculations based on data from Datastream and Bankscope.)

Based on the estimated coefficients, the effect of bank soundness on loan supply is significant. The estimate implies that a one-standard-deviation drop in the DD is associated with a year-on-year real growth of credit that is 1.5 percentage points lower than otherwise.

Figure 4.6 illustrates the development of the excess demand for credit in the model. It is an aggregate number, calculated by aggregating the demand and supply estimates for all the individual banks. The figure suggests that 2000 was a period of excess supply of credit, whereas 2003 and 2004 were characterized by excess demand for credit. Since then, demand and supply have been relatively balanced.

Table 4.1Demand and Supply in the Disequilibrium Model, 1997–2007*(Dependent variable: year-on-year real growth rate of a bank’s total credit)
Explanatory VariablesParameterStandard ErrorParameterStandard Error
Real GDP growth1.180.09
Lending rate−0.030.01
Net interest margin−0.090.05
Distance to default0.030.01
Log (total loans)−0.020.01
Source: Authors’ calculations based on data from Datastream and Bankscope.

Maximum likelihood estimation. Log likelihood = 125.31

Source: Authors’ calculations based on data from Datastream and Bankscope.

Maximum likelihood estimation. Log likelihood = 125.31

Figure 4.6.Euro area: Excess demand for loans, 1997–2008.

(Source: Authors’ calculations based on data from Datastream, Bankscope, and national authorities.)

As a robustness test, we ran the same model, replacing the DD by the probability of distress (PD) calculated in Poghosyan and Cihák(2009). The PD has the expected (negative) sign in the loan supply (higher PD, that is, lower soundness, implies lower supply of loans), and the coefficients of the other variables are largely unaffected.

As another robustness check, we have also performed this analysis at the level of countries rather than the level of individual banks. Specifically, this means that instead of individual bank DDs, we have used the aggregated DDs for portfolios of banks in the individual euro area countries (see Appendix I). This reduces the number of available observations, but it allows for an easier link to the subsequent analysis (of linkages to aggregate output), which is also carried out at the level of countries rather than banks.

In addition to the disequilibrium model presented in Table 4.1, a series of pairwise Granger causality tests were run to assess the relationships among real credit growth, real output growth, and banking sector vulnerability (approximated again by the DD). The results of the exercise suggest that banking sector vulnerability, measured by the DD, is influenced by real GDP and real credit on a horizon of two to four quarters. The DD influences real credit, but not GDP, with a lag of six quarters (detailed results available upon request).

4.2.2. Linkages Between Bank Loan Supply and Aggregate Output

Our next step is to examine the relationship between the supply of bank credit and economic activity. Declining loan supply may suppress economic activity if firms and households cannot replace completely the “missing” loans with other funding. For this to take place, a substantial group of borrowers (firms or households) must be unable to insulate their spending from the reduction in bank credit.

As the data illustrate, bank credit to the private sector and output do move together (Figure 4.7). But this does not necessarily mean that the supply of bank loans has a significant effect on output. An alternative (and equally plausible) possibility is that as economic activity slows, the demand for bank loans declines, creating a positive relationship between the two series. Disentangling the demand and supply effects (i.e., solving the identification problem) is very hard, because these effects tend to occur at the same time but only the equilibrium outcome is observed.

The identification problem can be addressed by using an instrumental variables technique to isolate the loan supply effect on real output. We use shocks to country-specific money demand as an instrument for shocks to the loan supply, as first proposed by Driscoll (2004) in addressing a similar question for the United States. The logic behind this approach is based on the premise that country-specific shocks to money demand should lead to country-specific changes in the supply of loans, and therefore changes in output. This would allow us to isolate the effect of loan supply on real activity.

The identification scheme involves the following three steps, with all variables used in the regressions constructed as deviations from their cross-sectional mean values, as implied by the identification scheme (see Appendix II for details).

  • The overall effect of bank credit on output is investigated by regressing output growth on the growth rate of bank loans (and its lagged value), as well as on its own lagged values. The resulting coefficient will reflect both the supply and demand effects of bank credit on real activity.

  • The shocks to money demand are recovered after estimating money demand functions for each euro area country in the sample. Then the growth rate of bank loans is regressed on its lagged values and the estimated money demand shocks, in order to establish whether the latter are a good instrument for shocks to the loan supply.

  • The effect of bank credit on output (see first bullet) is reestimated using the country-specific shocks to money demand as instruments. The resulting coefficient of bank loans is indicative of the supply effect, as the demand effect has been stripped out.5

Figure 4.7.Euro area: Growth in real output and bank loans, 2000–08.

Source: Authors’ calculations based on data from European Central Bank and Eurostat.

The estimations are done using country-level data from the first quarter of 1999 to the third quarter of 2008. The sample includes 11 euro area countries (Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, and Spain).6 The key variables used in the analysis are: real GDP, money supply (M3), deposit rates, and bank loans to nonfinancial corporations. For each country, the M3 and bank loan variables are deflated by the corresponding GDP deflator. Except for deposit rates, all other variables are in logarithmic form.

One issue when doing any analysis for euro area banks involving interest rate statistics is that harmonized data on interest rates are available from the European Central Bank only from January 2003. For the earlier period, we have to rely on nonharmonized country-level data, available since January 1999. To address this issue, we carry out a robustness check by performing the same analysis only for the subperiod for which harmonized deposit rate data are available, that is for the period since the first quarter of 2003.

The estimation results from the first step confirm the positive relationship between bank credit and economic activity. As shown in Table 4.2, real bank credit has a significant and positive effect on output. The size of the coefficient suggests that an increase in bank credit (in real terms) of 10 percentage points is associated with an increase in real GDP of about 1.5 percentage points.

Table 4.2Ordinary Least Squares Regression of Output on Loans *
RegressorsCoefficientStandard Error
Number of observations232
Source: Authors’ calculations, based on data from the European Central Bank and Eurostat.

All variables are demeaned by their cross-sectional averages. Critical values for 1 percent are denoted by ***.

Source: Authors’ calculations, based on data from the European Central Bank and Eurostat.

All variables are demeaned by their cross-sectional averages. Critical values for 1 percent are denoted by ***.

Turning to the second step, we find that positive money demand shocks are associated with higher growth in bank loans. The shocks to money demand are constructed using estimates of country-specific money demand functions (see Appendix II). Their impact on bank loans is illustrated by the positive and significant coefficient of the (country-specific) residuals from the estimated money demand functions on the growth of bank loans, even after controlling for lagged values of output (Table 4.3). Therefore, the money demand shocks can be used as an instrument for loan demand in the next step.

Table 4.3First Stage IV Regression: Loans on Money Demand Shocks * Dependent Variable ΔĨit
RegressorsCoefficientStandard Error
Number of observations232
Source: Authors’ calculations, based on data from the European Central Bank and Eurostat.

All variables are demeaned by their cross-sectional averages. Critical values for 1 percent are denoted by ***.

Output shocks.

Money demand shocks.

Source: Authors’ calculations, based on data from the European Central Bank and Eurostat.

All variables are demeaned by their cross-sectional averages. Critical values for 1 percent are denoted by ***.

Output shocks.

Money demand shocks.

Once demand effects have been taken into account in the second step, the loan supply effect on output is positive and statistically significant. As the results show (Table 4.4), the coefficient of the bank loan variable becomes somewhat smaller than in the first step (0.10 instead of 0.15) when the instrumental variables estimation is implemented. Nonetheless, it is still significantly positive. This is an interesting difference from Driscoll (2004), who found that for the United States, the impact of loan growth on GDP growth is not significantly different from zero. This difference in results may be because of the different financial structures of the two economies: whereas the U.S. financial structure is relatively more marketbased, the euro area’s is more bank-based.

As another robustness check, we have tried an alternative approach in which we have introduced the difference between unsecured and government-backed deposit rates as an additional instrument for credit risk. This is motivated by the approach of Greenlaw and others (2008), who used the Treasury-Eurodollar (TED) spread as an instrument for credit supply in the United States. A weakness of the TED spread is that it may be influenced by “flight to quality” flows that move Treasury bill yields, as well as the funding pressures that drive London interbank offered rates (LIBOR). Nonetheless, as the difference between unsecured and government-backed deposit rates, the TED spread could potentially provide a measure of credit risk, which is likely to be correlated with credit supply. Using a similar line of argumentation, we have introduced in our regressions the spread between the three-month euro LIBOR and the German government bund rate to instrument for credit risk. This robustness check has not affected the quantitative predictions from the main regression.

Table 4.4Second Stage IV Regression of Output on Loans Dependent Variable Δy˜it
RegressorsCoefficientStandard Error
Number of observations232
Source: Authors’ calculations, based on data from the European Central Bank and Eurostat.*All variables are demeaned by their cross-sectional averages.

Critical values for 5 percent are denoted by Country-level money demand shocks are used as instruments.

Source: Authors’ calculations, based on data from the European Central Bank and Eurostat.*All variables are demeaned by their cross-sectional averages.

Critical values for 5 percent are denoted by Country-level money demand shocks are used as instruments.

Overall, the estimation results suggest that an increase in the supply of bank loans of 10 percentage points is likely to lead to an increase in real GDP of about 1 percentage point. Therefore, our analysis implies that a cutback in bank loan supply is likely to have a negative impact on economic activity.

4.2.3. Linkages Between Corporate Financing Conditions and Economic Activity

Turning to the question of how corporate sector financing conditions affect activity, we analyze the relationship between the corporate bond spread and the euro area output. The corporate bond spread is defined as the difference between the yield on a corporate bond (risky asset) of a given maturity and quality and the yield on a government bond (riskless asset) of the same maturity. The corporate bond risk premium has been shown to be a good predictor of real activity in the United States (Chan-Lau and Ivaschenko, 2002; and Mody and Taylor, 2004), and there were some early results suggesting a similar relationship for the euro area (De Bondt, 2002).

There are a number of reasons why the corporate bond spread can be a good predictor of real activity. First, financial instruments, such as corporate bonds, ultimately represent claims on the real economy. Financial information is readily available at high frequencies and transmitted more rapidly than economic information, such as that on output. Therefore, financial prices, such as corporate bond spreads, could provide useful leading information on economic activity. Second, corporate-sovereign bond spreads are a key measure of the credit terms. Their role in predicting output is consistent with the presence of a financial accelerator in the economy (i.e., with the presence of a mechanism linking the condition of borrower balance sheets to the terms of credit) and hence to the demand for capital. Third, the bond market has become a relevant source of corporate financing in the euro area. Since 1999, the euro area market for corporate debt securities has grown tremendously (Figure 4.8). Fourth, as corporate bond spreads tend to move together with the tightness of bank lending standards (for evidence in the United States, see Duca, 1999; and Gertler and Lown, 2000), they can also be treated as a proxy for corporate sector financing conditions.

At the euro area level, aggregate data on corporate bond yields are available for securities of different maturities and quality. The spreads for AAA, AA, A, and BBB seven-year corporate bonds in the euro area (in relation to a seven-year government bond) are shown in Figure 4.3 Given the high frequencies of these data, we use monthly industrial production (rather than real GDP) as an indicator for economic activity.

Figure 4.8.Euro area: Corporate debt issuance, 1990–2008.

Source: Datastream.

The analysis is conducted using vector autoregression (VAR) estimates run over the period from January 1999 to January 2009. The key variables in the regressions are the corporate bond spread, the annual growth in industrial production, and the annual change in the real effective exchange rate. Our baseline specification of the VAR includes three lags; as a robustness check, we also experiment with increasing the number of lags in the VAR. The corporate bond spread is defined as the BBB yield minus government bond yield in the benchmark regressions; as a sensitivity analysis, we also conduct the same analysis for AA- and A-rated bonds.

The estimation results show that a positive shock to the corporate bond spread leads to a significant negative response of industrial output. The impulse responses of the baseline regressions are shown in Figure 4.9. 7 The results illustrate that a one-standard-deviation shock to the corporate bond yield (about 60 basis points) has an adverse effect on the growth rate of industrial output, which peaks at about 0.25 percent in 8–20 months. This effect is statistically significant, as shown by the 95 percent confidence bands.

A limitation of these estimates is that simultaneity might be an issue in the basic VAR estimation (we are not using a structural VAR). Nonetheless, these results are fairly robust across alternative specifications.

4.2.4. Risk Transfers Between Banks and Other Sectors: Contingent Claims Analysis

How are risks transmitted among the corporate sector, the financial sector, and the public sector in the euro area? One way of addressing this question is to employ a contingent claims analysis (CCA). CCA is an improved version of the balance sheet approach, which incorporates not only accounting data, but also information contained in market prices. It starts with information on the size and structure of assets and liabilities of key economic sectors, with the aim of assessing the extent of currency and maturity mismatches, or imbalances in the debt and equity structure. Given that economy-wide balance sheet data do not provide a full picture of all the risks (because of the contingent nature of many risks), and given that they are usually valued at book value, such balance sheet data do not capture changes in the likelihood of default related to recent market developments. To provide a more complete picture of the risks inherent in a balance sheet, CCA values assets using marked-to-market prices and incorporates contingent liabilities.

Figure 4.9.Euro area: Response of annual growth in industrial production to one-standarddeviation innovation in corporate bond spread

(based on a generalized impulse response)

Source: Authors’ calculations based on data from Datastream.

To the best of our knowledge, this is the first time in the literature that CCA has been used to identify vulnerabilities in the corporate, banking, and public sectors in the euro area (and to estimate the associated value of risk transfer across the balance sheets). Appendix III provides more details on the CCA methodology employed in this study.

Default indicators for both the banking system and nonbank corporate sector show an improvement from the lows of 2002–03, followed by a major deterioration in the second half of 2007 and 2008 (Figure 4.10). The distribution of default risk by assets confirms the general improvement in both banking system and nonbank corporate sector indicators, with the riskiest banks (those with the highest default probability) accounting for a smaller percentage of total assets over time. Expected losses for the banking system have been declining steadily since 2002–03; the indicator, however, deteriorated in 2007–08. The generally positive trend of the last five years reflected rising equity valuations and declining volatilities, as balance sheet structures improved and nonperforming assets declined (the measure of expected losses for the largest banks moves closely with the overall nonperforming loan ratio and tends to lead changes in the nonperforming loan ratio by one to two quarters).

Figure 4.10.Euro area: Estimated default probability (banks and nonbanks), 1991–2008.

Source: Authors’ calculations based on data from Datastream and Bankscope.

For the public sector balance sheets, Figure 4.11 shows an improvement in the soundness since 2002–03. It shows the estimated default probability when 100 percent of expected losses of the banks are assumed to be guaranteed by the sovereign (solid line) and when expected losses are excluded (dashed line). The figure suggests a gradual decline in default probabilities beginning in 2002–03. For the later period for which daily information is available, however, the default probabilities spike up, reflecting the sharp increase in spreads on government debt quoted by the market.

The estimated probability of default for the public sector is substantially lower than those reported in previous CCA studies. This reflects the fact that previous applications of CCA (surveyed in Appendix III) have covered emerging market economies (e.g., Brazil, Turkey, Thailand, Indonesia), whereas this analysis focuses on an advanced economy (or, more specifically, the set of advanced economies that form the euro area). For example, Gray and Jones (2006) examined the one-year sovereign default probability in Indonesia in 1999–2006, and found that, for most of the period, it was in the range of 2–6 percent (with a spike to 11 percent in 2001). The other studies surveyed in Appendix III found numbers in a similar range. The numbers reported for the euro area in Figure 4.11 are lower by an order of magnitude, being generally below 0.2 percent. This is consistent with the high sovereign ratings of euro area countries.

Figure 4.11.Euro area: Estimated default probability (public sector), 1997–2008.

Source: Authors’ calculations based on data from Datastream and Bankscope.

The global financial turbulence, which started in mid-2007 and intensified in September 2008, has increased volatility in a variety of risk indicators. Figure 4.10 shows the development in the estimated probabilities of default for the large banks and nonbank corporations in the euro area. The two move broadly in line, but banks have so far been affected much more by the recent financial turmoil. The global market turmoil experienced since mid-2007 has substantially worsened risk indicators for the banking system. A combination of reduced market capitalization and an increase in its volatility has decreased implied assets and increased their volatility, leading to a decline in DD measures and increases in expected losses. These developments reflect the increased market volatility, as well as declining capitalization and lower earnings.

Figure 4.11 illustrates the impact of financial sector instability on the estimated public sector default probabilities. The public sector probability of default is low, but if the public sector were to guarantee the large banks, the impact on public sector default probabilities (indicated by the line “with guarantees”) would be considerable.

4.3. Quantitative Implications

What do the calculations imply quantitatively for euro area developments? Based on the estimates presented in the preceding section, we can calculate the potential impact of banking sector losses on future economic growth in the euro area. Specifically, the current estimates of losses in the banking sector would imply a loss of 2 percentage points in euro area GDP. Here is how this estimate is derived:

  • A natural starting point is estimated losses in euro area commercial banks. These losses have been somewhat of a moving target, as the crisis evolved from the subprime crisis in the United States into a global crisis. The estimated subprime-related losses in euro area banks as of March 2008 were “only” US$45 billion, as reported in IMF’s April 2008 Global Financial Stability Report (IMF, 2008a); the latest estimates of the total exceptional losses in euro area global banks (which combines the subprime-related losses with the exceptional part of losses generated on European assets) may be as large as ten times that amount. The estimated losses for the whole of Europe were even larger, but substantial chunks of these losses were in global banks based in the United Kingdom and Switzerland. 8

  • These estimated losses correspond to about 14 percent of the euro area banks’ capital and reserves. If nothing else happened, the ratio of capital to total (unweighted) assets in euro area banks, currently at 5.6 percent (Figure 4.12), would decline to 4.8 percent, and the banks’ leverage would increase correspondingly.

  • One way to translate these losses into the potential impact on asset growth is to ask how much assets would have to shrink to prevent the leverage ratio from declining. Keeping the leverage ratio at 5.6 percent would at the new (decreased) level of capital require that assets go down by 14 percent.

  • The impact of bank losses on lending (and thereby on output) can be larger if banks (or their regulators) aim to deleverage, that is decrease their leverage target, which is quite likely given the overall increase in risk aversion (see, e.g., IMF, 2008b), and if they get hit by additional shocks, such as stock price declines.9 To increase the leverage ratio to 5.9 percent (the sample maximum in Figure 4.12), assets would have to go down by 19 percent. To illustrate the sensitivity of this result, increasing the leverage ratio to 7 percent (which, although beyond the recent historical experience, is not implausible) would in this situation imply a decline in assets of 31 percent.

  • From the estimate in Section 4.2.2, a decline in the supply of bank loans of 10 percentage points is likely to lead to a decline in real GDP of about 1 percentage point. Following up on the calculations from the previous bullet point, a loan decline of 14 percent therefore corresponds to a loss of 1.4 percentage points in real GDP; a loan decline of 19 percent corresponds to a loss of 1.9 percentage points in real GDP; and a loan decline of 31 percent corresponds to a loss of 3.1 percentage points in real GDP.

Figure 4.12.Capitalization in euro area banks, 1997–2008

(capital and reserves as percentage of total assets)

Source: Authors’ calculations based on data from Datastream and Bankscope.

An alternative approach to analyzing the recent developments is to start from the recent changes in the DD and their estimated impact on loan supply. As previously discussed, DD is a market-based indicator that incorporates market participants’ views on banks’ situations and outlooks. It can therefore provide an alternative assessment of the likely impact of the shocks that hit the banks.

To calculate the impact on banks’ lending, one can use the results of the DD calculations in Section 4.2.1. The average DD was 0.0 in late 2008 (see Figure 4.5), compared with 8.0 in mid-2007. Using the estimates in Section 4.2.1, this translates into a decline in real credit of 19 percentage points. Using the estimates from Section 4.2.2, this in turn translates into a real GDP decline of some 1.9 percentage points. This method therefore yields an estimate of the likely GDP impact that is broadly similar to that from the method based on projected capital losses. The difference between the two approaches reflects a variety of factors, such as the extent to which the banks will (or will not) be recapitalized. The extent of recapitalization is not trivial to estimate, making the market’s guess a useful alternative input.

The above calculations illustrate that there are linkages between the financial sector’s soundness and real economic developments. They also illustrate the challenges of quantifying the exact relationship and the uncertainties surrounding the estimates. We find that, based on current information, the likely impact of the recent and projected banking losses on output to be about 2 percentage points (with substantial uncertainty relating to the impact of the recapitalization and, more generally, to the impact on market confidence).

4.4. Conclusion

This chapter examines the impact of financing conditions on real economic activity in the euro area, exploring some key linkages between the financial and real sectors. To explore the evidence, it applies a broad range of empirical approaches and estimation methods to bank-level, country-level, and aggregate data.

We find that a deterioration in the financial health of banks could translate into a lower bank loan supply; this effect is statistically significant. A cutback in bank loan supply is likely to have a negative impact on economic activity in the euro area; again, this effect is statistically significant. These findings are not dissimilar to the literature on the bank lending channel in the United States, which generally finds strong evidence that banks decrease their loan supply in response to tighter financing conditions, but little evidence that the cutback in bank loan supply lowers real activity.

Turning from bank lending to corporate bond financing (which could also reflect broader financial conditions in the economy), we find that higher costs of bond financing tend to weaken industrial production. Finally, risk indicators for the banking, corporate, and public sectors in the euro area show a steady improvement in balance sheets beginning in 2002–03, followed by a major deterioration in 2007 and especially 2008, reflecting a combination of the increased market volatility and lower capitalization. Conditions as of late 2008 were the worst in the whole sample (since the early 1990s).

The estimates of this chapter can be used to calculate the potential impact of the banking sector losses on future economic growth in the euro area. They suggest that current estimates of losses in the banking sector would mean a loss of 2 percentage points in euro area GDP, but with substantial uncertainty around this estimate.


The DD measure is based on the structural valuation model of Black and Scholes (1973) and Merton (1974). The authors first drew attention to the concept that corporate securities are contingent claims on the asset value of the issuing firm. 10 This insight is clearly illustrated in the simple case of a firm issuing one unit of equity and one unit of a zero-coupon bond with face value D and maturity T. At expiration, the value of debt, BT, and equity, ET, are given by

where VT is the asset value of the firm at expiration. The interpretation of equations (A1) and (A2) is straightforward. Bondholders get paid fully only if the firm’s assets exceed the face value of debt; otherwise, the firm is liquidated and assets are used to partially compensate bondholders. Equity holders thus are residual claimants in the firm because they get paid only after bondholders.

Note that equations (A1) and (A2) correspond to the payoff of standard European options. The first equation states that the bond value is equivalent to a long position on a risk-free bond and a short position on a put option, with the strike price equal to the face value of debt. The second equation states that equity value is equivalent to a long position on a call option with strike price equal to the face value of debt. Given the standard assumptions underlying the derivation of the Black-Scholes option pricing formula, the default probability in period t for a horizon of T years is given by the following formula.

where N is the cumulative normal distribution, VT is the value of assets in period t,r is the risk-free rate, and σA is the asset volatility.

The numerator in equation (A3) is referred to as DD. An examination of equation (A3) indicates that estimating default probabilities requires knowing both the asset value and asset volatility of the firm. The required values, however, correspond to the economic values rather than the accounting figures. It is thus not appropriate to use balance sheet data for estimating these two parameters. Instead, the asset value and volatility can be estimated. It is possible to solve the following equations (A4) and (A5) for the asset value and volatility.

if ET, the value of equity; σE, the equity price return volatility; and D, the face value of liabilities, are known; and d1 and d2 are given by

The parameters can be calibrated from market data:

  • The time horizon, T, is usually fixed at one year.

  • The value of equity, Et, corresponds to the market value of the firm. The data are obtained from Bloomberg by multiplying the number of shares outstanding for a firm by the closing share price on a particular day.

  • The equity volatility, σE, corresponds either to historical equity volatility or implied volatility from equity options. This is derived by calculating the standard deviation of daily share price returns over a one-year period (around 260 days).

  • The face value of liabilities, D, is usually assumed equal to the face value of short-term liabilities plus half of the face value of long-term liabilities. 11 This number represents the so-called default barrier. The liability data are obtained from Bankscope. The item, “Deposits and Short-Term Funding,” is used to represent short-term liabilities, whereas the long-term liabilities are derived by deducting the short-term liabilities from the “Total Liabilities” item. To obtain daily liability data from annual balance sheets, the data are intrapolated between two year-end balances.

  • The risk-free rate, r, is the one-year government bond yield, in the same currency as those of the market and balance sheet data.

Once the asset value and volatility are estimated, the default probability of the firm can be derived from equation (A3).

In addition to the individual bank DDs, we also calculate the so-called portfolio DD. Following, for example, De Nicol’d others (2005), the portfolio DD is defined as

where AP= ΣiAti and LP= ΣiAti are the total values of assets and liabilities, respectively, for all financial institutions in the portfolio. The mean and variance of the portfolio are given respectively by μP=Σiwtiμi and σP2iΣjwtiwtjσij, where wti=AtiiAti and σij is the asset return covariance of financial institutions i and j. Thus, the portfolio DD to some extent embeds the structure of risk interdependencies among the financial institutions. “Default” at date t + 1 occurs if APt<: LPt. Thus, the DD indicates how many standard deviations ln (APt/LPb) has to deviate from its mean for default to occur. Because APt= LPb+EbP, declines in APt/LPb are equivalent to declines in capitalization, EtP/LPb. The portfolio DD can be viewed as a risk profile measure tracking the evolution of the joint risks of failure of the firms composing a portfolio. Lower (higher) levels of the DD imply a higher (lower) probability of firms’ joint failure. Because variations in the individual firms’ DD are allowed to offset each other, the DD of a portfolio is always higher than the (weighted) sum of the DDs of the individual firms. As a result, the probability of default associated with the portfolio DD is always lower than that associated with the actual probability of joint failures of sets of firms in the portfolio. Thus, the portfolio DD tracks the lower bound to the joint probabilities of failure (for an in-depth discussion of the pros and cons of the portfolio DD, see, for example, Cihák 2007).

Appendix II. Identifying the Linkage Between Bank Loan Supply and Aggregate Output

The theoretical framework used to derive the empirical specification of the model is a version of the investment saving/liquidity preference money supply model that adds a credit channel of monetary transmission to the traditional interest rate channel (Bernanke and Blinder, 1988). A possible solution to the problem of identifying loan supply effects within this framework is offered by Driscoll (2004) in investigating the analogous question for the U.S. economy. As noted by Driscoll, “the approach could also be applied to regions in other countries, or other collections of small open economies under fixed exchange rates, such as the European Union” (2004, p. 469).

The basic model consists of four equations for each country i in the euro area. There are three markets: a loan market, a money market, and a goods market.

On the loan market, banks face the following loan demand litd from households and firms.

where yit denotes output, ρit is the interest rate on loans, rt is the interest rate on bonds (i.e., the price of financing expenditures from an alternative source), and vit is a demand shock. The loan rate is allowed to vary across euro area countries, but the bond rate is assumed to be the same for all countries. This is consistent with the evidence on a well-integrated bond market and segmented loan markets.

The loan supply function is specified by the following equation

where (mit-pit) denotes money supply, and wit is the shock to loan supply. The supply of loans depends on deposits as a way to generate loans and the interest rates on loans (ρit) and bonds (rt). The underlying assumption is that loans and bonds are imperfect substitutes.

The money market equilibrium for each country is given by

where ritdis the country-specific rate on deposits, and εit is a country-specific money demand shock. Note that the money supply mit is determined by the European Central Bank. (This is a reasonable simplification even though in practice the European Central Bank controls money supply indirectly by setting the money market rate.)

Finally, aggregate output is specified as a function of the interest rate on bond (rt), the interest rate on loans (ρit), and a country-specific shock (zit):

Then the model is solved for output and loans, producing the following relationships:

These two equations illustrate the problem of identifying demand-and-supply effects in bank lending (i.e., separating the bank lending and interest rate channels), as bank loans and output are endogenous (jointly determined) as described above.

To solve the identification problem, Driscoll (2004) proposes to demean each variable with its cross-sectional mean. This effectively “shuts down” the interest rate channel, as illustrated below.

After transforming each variable xit into a deviation from its cross-sectional mean,x˜it=1Ni=1Nxit

the model can be rewritten as follows.

The corresponding expressions for the (demeaned) country-specific output and loan variables are

The last two relationships indicate that the money demand shock εit is correlated with it but does not affect it independently of its effect on it. That is, it is uncorrelated with the disturbance terms in equation (A12’). This makes money demand shocks a good candidate for an instrumental variable.

The shocks εit are obtained by estimating a money demand function for each euro area country. In the first stage, an instrumental variable estimation, we estimate if money demand shocks have a significant effect on aggregate lending in a pooled panel ordinary least squares regression using the demeaned values of all variables. In the second stage, the money demand shocks are used as an instrument in a regression of loans on output, which helps isolate the supply effect of bank lending on real activity.

Appendix III. Contingent Claims Analysis: A Primer

In the main text, risk transfers among banks, the nonbank corporate sector, and the public sector in the euro area are examined using CCA. This appendix provides more details on the method.

CCA is a type of the balance sheet approach (Allen and others, 2002). It starts by collecting information on the size and structure of assets and liabilities of key sectors of an economy in order to assess the extent of currency and maturity mismatches, or imbalances in the debt and equity structure. However, balance sheet data do not provide a full picture of all the risks facing a country because of the contingent nature of many risks. Balance sheets at the economy-wide level are typically valued at book value, without adjusting for fluctuations in market prices or changes in the likelihood of default. CCA attempts to provide a more complete picture of the risks in a balance sheet by using marked-to-market prices and incorporating contingent claims.

Approaches similar to CCA have been used for some time by risk managers and investors for analyzing individual institutions;12 these approaches have recently been extended to the systemic level (Gray, Merton, and Bodie, 2007). CCA has been performed on emerging markets, such as Brazil and Thailand (Gapen and others, 2004), Indonesia (Gray and Jones, 2006), and Turkey (Keller, Kunzel, and Souto, 2007). This may be the first time, as far as we know, that CCA-style analysis is attempted for advanced Europe. The application of CCA at the sovereign or industry level is for illustrative purposes only, because there are numerous challenges involved in calibrating the methodology without extensive cross-sectional or historical databases, such as those available for models of the corporate sector.

The basic idea of CCA is that changes in observed variables (e.g., the value of securities in the capital structure) can be used to infer changes in unobserved variables (e.g., the value of the firm). The basic tool of CCA is the risk-adjusted balance sheet, which shows the sensitivity of the enterprise’s assets and liabilities to external shocks. At the national level, the sectors of an economy are viewed as interconnected portfolios of assets, liabilities, and guarantees. Traditional approaches have difficulty analyzing how risks can accumulate gradually and then suddenly erupt in a full-blown crisis. CCA is well-suited to capturing such nonlinearities and to quantifying the effects of asset-liability mismatches within and across institutions. Risk-adjusted CCA balance sheets facilitate simulations and stress testing to evaluate the potential impact of policies to manage systemic risk.

CCA can be used to derive a set of risk indicators that can serve as barometers of risk and financial sector vulnerability. Specifically, two useful credit risk indicators that arise from the implementation of CCA are the DD and PD.

To understand changes in the overall level of risk facing a balance sheet, an estimate of the value of total assets and their volatility is needed, because they are typically not observable directly. Because many of the assets on the balance sheet are not traded and are observed only at infrequent intervals, it is difficult to derive marked-to-market balance sheets. In contrast, many liabilities are traded, and thus can be valued more readily by methods from finance theory to impute the value and volatility of assets, using the liability side of the balance sheet. Merton’s (1974) key insight in option pricing theory was that liabilities are contingent claims on total assets, with each liability having a different priority and maturity structure. The most junior liability on the balance sheet can be valued as an implicit call option on total assets. When the value of assets declines relative to the face value of debt, the value of the junior claims declines. Because the liability structure is observed, and many of the liabilities are traded, market prices of different liabilities can be used to derive information on the evolution of total assets. The framework can be applied to individual firms, or at a more aggregated level for an industry or for the sovereign.

The following figure provides an illustration of CCA for the sovereign. To estimate the risks to the euro area public sector balance sheets, we follow the approach of Gray, Merton, and Bodie (2007) and Gapen and others (2004), dealing with the added complications of working in a multicountry rather than a single sovereign context. The main elements on the asset side of the public sector balance sheet include international reserves, the net present value of primary surpluses, and the public sector’s monopoly on the issuance of money. These assets are net of any guarantees the public sector may implicitly or explicitly provide to the private sector. The main elements on the liability side of the public sector balance sheet are domestic currency liabilities (domestic currency debt and base money) and foreign currency debt.

Sovereign Balance Sheet

Source: Gray and Jones, 2006.

Estimating the observed value and volatility of sovereign assets directly is difficult because only international reserves are observable on the asset side of the public sector balance sheet. In contrast, each entry on the liability side of the balance sheet is directly observable on a high-frequency basis. CCA uses observed liabilities, together with well-known option pricing techniques, to derive implied estimates for sovereign asset value and asset volatility.

Domestic currency liabilities of the sovereign can be modeled as junior claims, whereby holders of these liabilities have a residual claim on sovereign assets above what is necessary to service foreign currency debt. If sovereign assets fall to a level where foreign currency debt payments cannot be made, then default is the result. This level is referred to as the “distress barrier” and is equivalent to the default-free value of debt. Therefore, the value of domestic currency liabilities can be viewed as a call option on sovereign assets with a strike price equal to the level of the distress barrier. Holders of such liabilities receive the maximum of either sovereign assets minus the distress barrier, or nothing in default. The Black-Scholes option-pricing formulas can be used to estimate sovereign asset value and volatility with only a few select variables: the value and volatility of domestic currency liabilities, the distress barrier, the risk-free interest rate, and time. Once the implied asset values and volatilities have been calculated, a range of risk indicators can be derived, including the distance to distress (the number of standard deviations away from the distress barrier), the probability of default, and the credit spread on sovereign assets.

The process of estimating total assets and their volatility for the banking system is similar to that for the sovereign, but instead of focusing on the value of domestic currency liabilities, the market value of equity and its volatility, together with the distress barrier, are used to calculate implied assets and their volatility. We include data from Moody’s KMV for the 50 largest euro area banks. The daily market capitalization based on traded stock prices is used to calculate the volatility of bank equity for all banks. The book values of short- and long-term obligations are used to calculate the distress barrier for the bank. The distress barrier, market capitalization, and volatility of market capitalization can be used to calculate the implied asset value and implied asset volatility. These are then used to calculate the distance to distress and the probability of default, as well as the expected losses of the individual banks. Aggregated figures for all banks are then derived by summing the respective balance sheets and calculating the risk indicators for the banks.


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This chapter is based on IMF Working Paper 09/69 and reflects data up to late 2008.

We thank, without implicating, JöDecressin and Luc Everaert for guidance; Chanpheng Dara for research assistance; Thomas Walter for editorial help; and Marie Donnay, Heiko Hesse, Luc Laeven, Kevin Ross, James Vickery, Lorenzo Cappiello, Boris Hofmann, and participants in the IMF’s Annual Research Conference and seminars at the European Central Bank, the European Commission, and the IMF for useful comments on earlier versions of the paper. All remaining errors are our own.

In addition, other studies cover banking intermediation in the euro area, but focus on other issues than the bank lending channel. For instance, Bruggeman and Donnay (2003) estimate a monthly monetary model with banking intermediation for the euro area in 1981–2001, but, instead of the bank lending channel, focus on the relationships among short-term market interest rates, retail interest rates, and inflation.

A positive net balance on the demand side means more demand, whereas on the supply side, it means less supply. See Berg and others (2005) for an overview of the methodology of the surveys.

Data are from the Bankscope database by Bureau van Dijk for 1997–2007. To explain the factors contributing to credit developments, the following variables are used: total bank assets, total loans, shareholders’ equity, short-term liabilities, long-term liabilities, liquid holdings (cash, European Central Bank and other financial institutions’ securities, government securities), equity price data (“last price,” daily), and equity shares outstanding (daily).

Assuming that shocks to loan demand and supply are positively correlated, we would expect the instrumented coefficient of bank loans to be smaller than those from the regression without instruments.

Cyprus, Malta, Luxembourg, and Slovenia are not included because of data limitations.

These are obtained using a generalized impulse response function (Pesaran and Shin, 1998).

The calculations underlying the October 2008 Global Financial Stability Report (IMF, 2008b) suggest total exceptional losses in large banks in continental Europe of close to US$500 billion.

On the other hand, these effects can be mitigated to some extent if banks increase their capital-to-asset ratios (decrease leverage) through capital injections rather than (or in addition to) adjustments in assets.

0 Models built on Black and Scholes (1973) and Merton (1974) are usually called structural models.

This is based on work done by Moody’s KMV (see Crosbie and Bohn, 2003).

An important example of private sector application of CCA has come from Moody’s KMV (see, e.g., Crosbie and Bohn, 2003). Using 30 years of extensive data on corporate defaults, Moody’s KMV uses firm asset value, asset volatility, and the default barrier to derive firm-specific probabilities of default.

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