Credit Risk Securitization and Banking Stability Evidence from the Micro-Level for Europe

This draft: April 15, 2009

Tobias Michalaka

André Uhdeb*

Abstract: Using a unique sample of 743 cash and synthetic securitization transactions issued by 55 stock listed bank

holdings in Western Europe plus Switzerland over the period from 1997 to 2007 this paper provides empirical evidence

that credit risk securitization has a negative impact on the banks’ financial soundness as measured by the z-score

technique while controlling for macroeconomic, bank-specific, regulatory and institutional factors. Moreover, as a result

of further robustness checks we find a positive impact of credit risk securitization on the banks’ leverage and return

volatility as well as a negative relationship between securitization and the banks’ profitability.

Keywords: Credit risk securitization, Banking stability, European banking

JEL classification: G21; G28

a Tobias Michalak, University of Bochum, Department of Economics, 44780 Bochum, Germany. Fax: ++49 234 32

05345, email: tobias.michalak@rub.de. b Dr. André Uhde *(corresponding author), University of Bochum, Department of Economics, 44780 Bochum,

Germany. Fax: ++49 234 32 02278, email: andre.uhde@rub.de.

We thank Oliver Mueller and Carina Trimborn for thoughtful and helpful comments.

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1 Introduction

Worldwide and in particular in Europe the market for credit risk transfer has experienced a

remarkable growth in recent years. This refers not only to the volume of credit risk being

transferred by financial institutions but also to the total number of securitization transactions. On a

micro-level, the growing popularity of credit risk securitization can be put down to the fact that

banks typically use the instrument of securitization to diversify concentrated credit risk exposures

and to explore an alternative source of funding by realizing regulatory arbitrage and liquidity

improvements when selling securitization transactions. On a macro-level, credit risk securitization

is recommended since it is anticipated as a reduction of the overall concentration of credit risk in

the entire financial system if risks are transferred to less fragile (non-financial) institutions (BIS,

2005; ECB, 2004; IMF, 2002).

In response to the U.S. subprime mortgage crisis from mid-2007, however, a general

reassessment of risks inherent in structured finance instruments is observed across the whole

financial community. To date, the IMF values mark to market losses on structured finance

instruments at approximately 1,405 billion USD (IMF, 2008). Moreover, it is assumed that about

half of the amount of losses and write-downs on these instruments will explicitly affect the banking

industry (IMF, 2008), probably constituting a serious threat of systemic fragility. The latter is

supported by failures in valuating complex securitization instruments, a weak transparency in

structured finance markets as well as weak forces of market discipline, which in sum have exposed

the financial system to a serious funding and confidence crisis (BIS, 2008, 2008a; IMF, 2008,

2008a, 2007).

Referring to these findings, the Basel Committee has recently finalized its proposals for

enhancing the Basel II framework in the area of securitization (BCBS, 2009). The proposals aim at

strengthening the framework and responding to lessons learned from the financial crisis. In

particular, proposals mainly focus on (a) higher risk weights to securitization exposures and hence

higher minimum capital standards (Pillar 1), (b) addressing the bank’s on- and off-balance sheet

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securitizations within the frameworks of ICAAP and SREP as well as the bank’s MIS to enhance

the bank’s and supervisors’ sensitivity to securitization (Pillar 2), and (c) strengthening disclosure

requirements with regard to securitization activities and off-balance sheet vehicles to enhance

transparency (Pillar3).

Against this background this paper empirically investigates the impact of credit risk securitization

on banking stability using a unique sample of 743 cash and synthetic securitization transactions

issued by 55 stock listed bank holdings located in Western Europe plus Switzerland over the period

from 1997 to 2007. Our analysis complements and extends previous empirical studies on this issue

(Jiangli and Pritsker, 2008; Uzun and Webb, 2007; Dionne and Harchaoui, 2003) for several

specific aspects. First, to the best of our knowledge this is the first study that empirically

investigates the relationship between credit risk securitization and banking stability using a cross-

sectional time-series dataset for European banks. Second, while previous studies employ

(regulatory) capital ratios (Uzun and Webb, 2007; Dionne and Harchaoui, 2003) or time deposit

premiums (Jiangli and Pritsker, 2008) as respective proxies for the banks’ financial soundness, we

complement empirical work by utilizing the z-score ratio as a time-variant distance to default

measure. Third, by investigating the impact of credit risk securitization on single components of the

z-score ratio (ROAA, capital ratio, volatility of ROAA), we try to shed more light on the nexus

between credit risk securitization and banking stability. Finally, we extend previous empirical

studies by performing a large variety of sensitivity analyses controlling for banking and capital

market structure developments as well as the regulatory and institutional environment in Western

Europe.

The remainder of the paper is organized as follows. Section 2 presents related theoretical and

empirical literature on the relationship between credit risk securitization and banking stability.

Section 3 comprises our empirical analysis. While section 3.1 presents the data set, section 3.2

describes our empirical model. Empirical results are presented and discussed in section 3.3 and

illustrated within the statistical appendix. Finally, section 4 concludes.

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2 Related literature

Economic theory provides countervailing predictions of the relationship between credit risk

securitization and banking stability (Shin, 2009; Krahnen and Wilde, 2008; Jiangli et al., 2007).

This may be due to the fact that the relationship depends on both a direct and indirect impact. The

direct impact of securitization on banking stability depends on how much credit risk is actually

transferred to external investors. This relationship however is not distinct. While the “securitization-

stability” view points out that the bank’s overall risk exposure is likely to be reduced if the tail risk

of senior tranches being transferred to external investors exceeds the sum of default risks of the

first-loss position which is typically retained by the bank (Jiangli et al., 2007), the “securitization-

fragility” view replies that the major part of default risks typically remains within the bank’s first-

loss position acting as a quality signal towards external investors (DeMarzo, 2005; Instefjord, 2005;

Riddiough, 1997; Greenbaum and Thakor, 1987). In this context, it is additionally emphasized that

former Basel I regulations have provided an incentive to keep the larger part of risks within the

bank. Thus, as corporate and retail loans were not risk-adjusted but globally backed up with

regulatory capital under Basel I regulations, keeping the major part of default risks within the first-

loss piece typically provoked profits from regulatory arbitrage (Allen and Gale, 2006).

The indirect impact of credit risk securitization on financial stability is determined by the bank’s

strategy to utilize securitization as a source of additional funding to finance new assets with liquid

capital that has become available from selling securitization transactions. Thus, the indirect effect of

securitization is not obvious but rather depends on a wide range of investment policies and can

more probably be defined by the way the bank’s overall asset portfolio risk is restructured (Krahnen

and Wilde, 2008). In this context the “securitization-stability” view points out that reinvesting

liquid capital into new assets may provoke a better diversification of the bank’s asset portfolio if

remaining total assets are less correlated after securitization (Cebenoyan and Strahan, 2004;

Demsetz, 2000). In contrast, the “securitization-fragility” view suggests that the actual effect on a

bank’s financial soundness may depend on the risk-level of new assets being taken in, which again

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is determined by the current level of competition in the respective asset market (Instefjord, 2005).

Moreover, using liquid capital to extend the amount of total assets or to repurchase shares and pay

higher dividends to shareholders may additionally lead to an increase in the bank’s leverage (Shin,

2009; Leland, 2007).

Empirical evidence on the relationship between securitization and banking stability is ambiguous

as well. To begin with, applying event study methodology Uhde and Michalak (2009), Hänsel and

Krahnen (2007), Franke and Krahnen (2006) as well as Lockwood et al. (1996) provide empirical

evidence that credit risk securitization has a positive impact on the increase of a bank’s systematic

risk. This result holds even when controlling for the banks’ pre-event level of systematic risk, the

type of securitization transaction, the regulatory framework as well as the underlying reference

portfolio (Uhde and Michalak, 2009).

Turning to panel data analysis, using balance sheet data from commercial banks in Canada for the

period from 1988 to 1998, Dionne and Harchaoui (2003) find that credit risk transfer is inversely

related to a bank’s regulatory capital supporting the capital arbitrage theory. Moreover, they provide

empirical evidence that an increase in the volume of credit risk transfer has a negative impact on the

banks’ asset quality and hence financial soundness.

Similarly, Uzun and Webb (2007) examine the impact of credit risk securitization on banking

stability using data from a sample of 112 financial institutions in the U.S. for the period from 2001

to 2005. They find that securitization is negatively related to a bank’s capital environment.

Controlling for underlying assets they provide further empirical evidence that the decrease in

financial soundness is predominately associated with securitizations of credit card receivables

whereas securitizations of mortgage loans and home equity lines of credits have a positive impact

on banking stability.

Finally, Jiangli and Pritsker (2008) examine the effect of mortgage loan securitizations on bank

stability, profitability and leverage using data from U.S. bank holding companies for the period

from 2001 to 2007. In line with Uzun and Webb (2007) they find that mortgage securitizations tend

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to reduce a bank’s financial fragility. In contrast, however, they also provide empirical evidence for

a positive relationship between securitization and a bank’s leverage whereas profitability tends to

increase due to securitization.

3 Empirical analysis

3.1 Data and sources

Notes on variables and data sources are presented in Table 1. Table 2 reports descriptive statistics

for the entire set of included variables. Correlation matrices are provided in Tables 9-12.

Our empirical analysis focuses on consolidated balance sheet data from 55 stock listed bank

holdings across the EU-111 plus Switzerland for the period from 1997 to 2007 following the

beginning of credit risk securitizations in Europe in 1997. Banks’ consolidated balance sheet data

are retrieved from BankScope database provided by Bureau van Dijk. Table 3 reports the

geographical distribution of European banks in our sample.

A. Banking stability

We employ the banks’ distance to default as a proxy for financial soundness by including the

z-score as our dependent variable. This ratio has become a popular measure of bank soundness in

related empirical work on financial stability (Boyd and Runkle, 1993; De Nicoló et al., 2004; Uhde

and Heimeshoff, 2009) and is denoted as follows:

(1) σ

μ kz +≡

We construct the z-score per bank holding and time by aggregating the banks’ consolidated

balance sheet data and define µ as the return on average assets before taxes (ROAA), k as the equity

capital in percent of total assets and σ as the standard deviation (volatility) of the ROAA. Thus, the

z-score combines the banks’ profitability (µ), capital ratio (k) and return volatility (σ) in one single

1 The EU-11 comprises Belgium, Denmark, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, Spain

and the United Kingdom.

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indicator. Obviously, the indicator will increase with the banks’ capital ratio and profitability, and

decrease with increasing return volatility. Hence, the z-score measures the probability of a bank

becoming insolvent when the value of assets falls under the value of debt. Hence, a higher (lower)

z-score implies a lower (higher) probability of insolvency risk. Table 2 reports z-score ratios for

European banks in our sample across countries and over time.

B. Credit risk securitization

Our initial and unique sample of 776 cash and synthetic credit risk securitizations issued by 60

stock listed bank holdings across the EU-15 plus Switzerland from 1997 to 2007 is obtained from

offering circulars and presale reports provided by Moody’s, Standard & Poor’s and FitchRatings.

These reports provide detailed information on securitizations including the type and structure of the

transaction as well as the underlying reference portfolio. We first of all omit those countries

(Austria, Finland, Luxembourg and Sweden) exhibiting less than three securitizations over the

entire sample period. Subsequently, we exclude data on transactions by banks whose shares are not

traded on European Stock Exchanges. Summarized, this finally reduces the sample to 743 credit

risk securitizations between 1997 and 2007 issued by 55 European bank holdings located in the

EU-11 plus Switzerland. We include these securitization transactions as the log of the total extent of

credit volumes being transferred each year (Table 2).

As Table 4 reports, the cumulated volume of credit risk transfer amounts to € 1,410,423 million.

Moreover, our sample is mainly represented by the risk transfer of residential mortgage loans

(€ 751,227 million) and corporate loans (€ 488,565 million). Figures 1 and 2 more precisely

illustrate the distribution of credit risk securitizations over the sample period. Hence, in Europe a

notable transfer of credit risks through securitization did not begin until 1997. Furthermore, with the

exception of the year 2004 the number of credit risk transfer transactions has continuously increased

over the sample period peaking in 2006 and decreasing afterwards probably as a result of the U.S.

subprime crisis in mid-2007. Similarly, with the exception of the years 2001 and 2004 the volume

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of credit risk transfer in Europe continuously increased over the sample period but did not decrease

in 2007.

C. Further explanatory variables

When examining the relationship between securitization and stability it is imperative to control

for macroeconomic, bank-specific, regulatory and institutional factors that are likely to affect bank

soundness, securitization, or both, and hence help mitigate omitted variable biases. We lagged some

of the variables to avoid simultaneity.

Macroeconomic control variables are retrieved from the World Development Indicator (WDI)

database provided by the World Bank. We include the log of real GDP, the rate of real GDP

growth, the annual change of inflation and real short term interest rates to cover macroeconomic

developments that are likely to affect the quality of bank assets and hence may influence credit risk

securitization. The log of real GDP is included to control for a country’s overall level of economic

environment. We assume banks operating under a higher level of economic environment to be more

stable. The rate of growth of real GDP is a control variable since the banks’ investment

opportunities may be correlated with business cycles (Laeven and Majoni, 2003). Hence, we expect

a positive sign of the coefficient of real GDP growth if investment opportunities rise under

economic booms. In addition, borrowers’ solvency should be higher under an increasing economic

performance which raises the bank’s asset quality and may reduce credit risk securitization

activities. The latter is indirectly confirmed by Stanton (1998) who provides empirical evidence that

the number of securitization transactions increase in periods of cyclical downturns. However, as

Estrella (2002) finds that securitization of mortgage loans tends to decline during economic

recessions the evidence is not conclusive. We further include the one-period lagged changes in

inflation rates. The effect of changes in inflation rates depends on whether banks anticipated

inflation or not and whether inflation coincides with general economic fragility. Since interest rates

tend to rise in the presence of inflation, inflation is probably associated with a higher realization of

net interest margins and profitability. However, as the banks’ funding costs may also increase under

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inflation (Hortlund, 2005) the effect on profitability and bank soundness depends on the net effect

from increasing net interest margins and costs. Similarly, changes in real short term interest rates

are likely to implicitly influence asset quality. Again, we expect an ambiguous effect. While a

passing through of increasing short term interest rates to deposit rates will raise the banks’ funding

costs, a handing down to lending rates should raise profitability but might let loan repayment be

more difficult for borrowers which may result in higher loan default rates and decreasing asset

quality. Obviously, the actual effect depends on the differences in the average maturity of assets and

liabilities or the bank’s capability to reprice assets and liabilities. In this context, however,

securitization may not only provoke a raise in asset quality by transferring credit risk to external

investors but it may also provide an alternative funding source which in sum let the bank be more

independent from interest rate changes (Goswami et al., 2009).

Due to the fact that characteristics of securitizing banks in our sample vary across the EU-11 plus

Switzerland we employ further bank-specific variables. We include the delta of log of total assets to

control for changes in the bank’s size since Bannier and Hänsel (2008) as well as Martín-Oliver and

Saurina (2007) provide empirical evidence that the financial institution’s size is a strong

determinant of the frequency of securitization activities. We further employ the bank’s net interest

margin to control for profitability, the one-period lagged non-performing loans to total assets as a

key measure for credit risk and loan-portfolio quality, the cost-income ratio to control for efficiency

and the one-period lagged liquid assets to total assets as a proxy for the bank’s liquidity. We expect

a positive sign of the coefficients of net interest margin and liquid assets to total assets and a

negative sign of the coefficients of non-performing loans to total assets and cost-income ratio.

Moreover, referring to securitization, Bannier and Hänsel (2008) provide empirical evidence that

the number of securitization transactions tend to increase under the framework of lower asset

quality, efficiency and liquidity.

To draw accurate statistical inference concerning the relationship between securitization and

banking stability we perform a large variety of sensitivity analyses. Thus, we control for cross-

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country differences regarding the banking and capital market structure as well as the regulatory and

institutional environment to provide information on possible linkages between these sensitivity

measures and banking stability.

We include two measures of banking market concentration. To begin with, 5-bank concentration

is constructed as the fraction of assets of the total banking system’s assets held by the five largest

domestic and foreign banks per country (Uhde and Heimeshoff, 2009). The Herfindahl-Hirschman

Index (HHI) is computed as the sum of the squared market shares of a country’s domestic and

foreign banks. Calculating concentration ratios in this way addresses the fact that the banking

industry is further globalizing and that banks compete not only within national boundaries but also

cross-border. As both theoretical and empirical studies are not conclusive about the impact of

banking market concentration on financial stability (Uhde and Heimeshoff, 2009; Beck et al., 2006;

Schaeck et al., 2006; De Nicoló et al., 2004), we expect an ambiguous effect of our concentration

measures on financial stability.

The competitiveness of a country’s banking market is proxied by the H-Statistic proposed by

Panzar and Ross (1987). We estimate the H-Statistic on an aggregated, consolidated bank balance-

sheet basis cross-sectionally for each country in our sample for the period from 1997 to 2007. In

contrast to related literature the H-Statistic includes interest bearing revenues only to make sure that

the competition measure is more related to our sample of exclusively interest-bearing asset

securitizations.2 Moreover, since Schaeck et al. (2006) provide evidence that the effect of

competition is reduced under a more sophisticated economic environment, the measure interacts

2 Hence, following Claessens and Laeven (2004) and Schaeck et al. (2006) the H-Statistic is based on revenue

equations and measures the degree of market competitiveness by means of the bank’s elasticity of interest bearing

revenues with respect to its input factor prices while controlling for a long-run market equilibrium. Thus, an

increase in factor prices (a) will be mirrored by an equal-proportional increase in the interest bearing revenue under

perfect competition (H = 1), (b) will be mirrored by an under-proportional increase in the interest bearing revenue

under monopolistic competition (0 < H < 1) and (c) will not at all be reflected by an increase in the bank’s interest

bearing revenue in the monopoly case (H ≤ 0).

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with a country’s GDP to control for country-level heterogeneity. Similar to the nexus between

concentration and stability, even the relationship between competition and stability is not

conclusive (e.g., Beck 2008). Hence, we expect an ambiguous effect of this measure on banking

stability. Next to cross-country differences concerning the banking market structure we additionally

control for differences in a country’s stock market development. We again expect an ambiguous

effect of this measure. On the one hand, a well-developed stock market may provoke

disintermediation tendencies and spur competition for retaining bank customers which results in

lower financial soundness. On the other hand, however, we assume that the level of stock market

development is an appropriate proxy for the number and quality of potential external investors

engaging in securitization transactions on capital markets. If this is true, higher developed stock

markets are anticipated to support credit risk securitization activities and, as a possible result, the

securitizing bank’s financial soundness.

Turning to cross-country differences in the regulatory environment, we employ three time-

variant measures of banking regulation and supervision proposed by Barth et al. (2004).3 The

capital regulatory index is built by means of principal component analysis and describes a summary

measure of initial capital stringency and overall capital requirements. To the extent that greater

capital stringency encourages prudent behavior of bank managers and equity capital is an

appropriate measure of a bank’s solvency, we expect better capitalized banks to be more stable.

Activity restrictions is a key determinant for the scope of a bank’s business by aggregating measures

of whether and how far a bank is allowed to engage in securities, insurance and real estate markets.

To the extent that activity restrictions keep banks from operating in too risky lines of business,

banking systems with greater restrictions are assumed to be more stable (Beck et al., 2006; Barth et.

al., 2004). In contrast, if a high level of activity restrictions prevents banks from diversifying asset

risks outside traditional business, banking systems with greater restrictions may become more

3 We combine data from three World Bank Surveys on Bank Regulation and Supervision conducted in 1997, 2001

and 2005 to construct time-variant data series.

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fragile. We further include the private monitoring index as a measure of the degree to which

regulations empower, facilitate and encourage the private sector to monitor banks. As a higher

degree of market discipline is anticipated to support state banking supervision, we expect a positive

sign of the coefficient of the private monitoring index. Finally, we employ Basel I as a dummy

variable to control for structural breaks in our time series of securitization transactions.4 Structural

breaks over the sample period may arise due to modified banking regulations concerning credit risk

securitization when changing from Basel I to Basel II regulations. Though both regulatory

frameworks obligate banks to raise equity capital as a buffer against potential default losses of

securitized assets, former Basel I regulations provided an incentive to realize regulatory arbitrage if

the major part of default risks was retained within the bank’s first-loss position.5 Taking this into

account, we expect a negative sign of the coefficient of Basel I.

Apart from regulatory aspects we finally control for the institutional environment. To begin with,

we include the privatization index provided by Abiad et al. (2008) to control for cross-country

differences in the ownership structure of banks. We expect banking markets exhibiting a lower

fraction of government-owned banks to be more efficient and stable since government-owned banks

are assumed to suffer from moral hazard problems and X-inefficiency (Berger et al., 2004). This is

due to the fact that these banks may anticipate to be bailed out in case of a financial distress

encouraging bank managers to be less committed to prudent risk-taking behavior and efficiency

aspects. Furthermore, the soundness of a bank is likely to depend on the influence exerted by

shareholders since bank managers being closely monitored by shareholders are expected to avoid

excessive risk-taking behavior. With regard to securitization, these bank managers are assumed to

4 We assume that new regulations on securitizations by Basel II have at the latest been anticipated by banks when the

New Basel Accord has been published in June 2004.

5 Addressing this concern, Basel II now follows a substance over form principle whilst determining the required

regulatory capital for all retained tranches of a securitization. As a consequence, Basel II provides stronger

incentives to transfer subordinated tranches and in particular the first loss position of a securitization to external

investors.

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transfer higher amounts of credit risk to external investors and use liquid capital to take in less risky

assets or to release own liabilities ex post. We account for these effects of shareholder influence by

including the shareholder rights index provided by La Porta et al. (1998) and expect a positive

relationship between bank stability and shareholder influence. Finally, we employ the rule of law as

a proxy for the strength and quality of a country’s institutional environment. The index is obtained

from the Worldwide Governance Indicators provided by the World Bank. We expect that greater

strength and quality of institutions are key factors for a well-developed and stable financial system.

3.2 Empirical model

To test the hypothesis that credit risk securitization affects banking stability, we utilize a bank-

specific random-effects model and set time dummies to control for time-specific effects.6 Since

some of the banks in our sample continuously securitize credit risk over the entire sample period

while others do not, we additionally address to heterogeneous securitization frequencies by

clustering standard errors on a bank-level following the generalized method based on Huber (1967)

and White (1980).

We estimate the financial soundness of bank i at time t as follows:

1 ,it it it k it k ity c xα β β (2) ε= + + +∑

where ity represents the z-score ratio as our measure of banking stability and is the measure of

credit risk securitization. The vector

itc

,it kx includes control variables described above. itε is an error

term and α and the ' sβ denote the parameters to be estimated.

6 As Table 2 reports, the number of observations in our panel varies. Thus, in addition to random effects, we apply

the consistent estimator for the variance components by Baltagi and Chang (1994) as a robustness check to avoid

possible biases resulting from our unbalanced panel. However, as results did not differ significantly from the

ordinary random effects estimations, we do not comment them in this paper.

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The Hausman test clearly rejects a fixed-effects specification in favor of the panel estimation with

bank-specific random effects. Hence, assuming that itα can be composed into a bank-specific time-

invariant component α and a component itν capturing the remaining disturbance that is assumed to

be uncorrelated over time so that the equation itit ναα += holds, the equation can be estimated

with the random effects model.

With regard to our analysis, the random effects model is a consequent strategy, as most variations

should be observed over time. Moreover, random effects allow for the inclusion of time-invariant

variables among regressors which, in particular holds for the major part of our sensitivity measures.

Moreover, considering banking regulation, all Western European countries in our sample follow the

European Capital Requirement Directive (transformation of Basel II) and the European Banking

Directive respectively. In this context, regulatory policies and national supervisory institutions have

remained almost unchanged over the sample period. The absence of time variation in regulatory and

supervisory control variables as well as the existence of a considerable time lag between regulatory

changes and their effect on bank performance are commonly accepted in related literature and

pointed out by Barth et al. (2004). Hence, from this point of view, financial markets in Western

Europe form a homogenous entity. As a consequence, variation within the cross-section between

regulatory and institutional explanatory variables is low and applying the random effects techniques

is appropriate.

3.3 Empirical results

We present main empirical results in Table 5. Regression (1) reports our baseline regression

results assessing the impact of credit risk securitization on banking stability as measured by the

z-score-technique. While regression specifications (2)-(3) omit macroeconomic and bank-specific

variables, regressions (4)-(5) are additional robustness checks to control for endogeneity and

possible reverse causality between bank soundness and credit risk securitization using instrumental

variable regressions. Results from the first-stages of instrumental variable regressions are presented

in Table 6. Table 7 reports further empirical results from regressing credit risk securitization on

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single components of the z-score, whereas Table 8 presents empirical results from a large variety of

sensitivity analyses.

3.3.1 Main findings from z-score regressions

As Table 5 reports, securitization enters regression specification (1) significantly negative at the

five-percent level indicating that an increase in credit risk securitization has a negative impact on

Western European banks’ financial soundness. In line with the “securitization-fragility” view from

related theoretical literature, our empirical results indicate that Western European banks may

predominantly employ securitization as a source of capital relief by retaining the major part of their

credit risk exposures within the first-loss position (direct effect of securitization on stability).

Moreover, our empirical findings suggest that banks may further utilize securitization as an

additional funding source to take on new but risky assets using liquid capital from selling

securitization transactions (indirect effect of securitization on stability). These hypotheses are in

line with empirical evidence provided by previous event studies (Uhde and Michalak, 2009; Hänsel

and Krahnen, 2007; Franke and Krahnen, 2006; Lockwood et al., 1996) applying nearly the same

sample of banks as well as from panel data analysis for the U.S. and Canadian banking market

(Uzun and Webb, 2007; Dionne and Harchaoui, 2003). In contrast, our findings do not support

theoretical arguments and earlier empirical findings provided by Jiangli and Pritsker (2008)

promoting the “securitization-stability” view.

Among the control variables, the log of GDP enters the regression significantly positive at the

five-percent level indicating that securitizing banks operating under a more sophisticated economic

environment are less prone to financial fragility. As expected, one-period lagged non-performing

loans to total assets and cost-income ratio enter the regression significantly negative at the five- and

one-percent level respectively, suggesting that higher asset quality and operational efficiency have a

positive impact on financial soundness. Introducing net interest margin, this variable enters the

regression significantly positive at the one-percent level indicating that securitizing banks

exhibiting a higher level of profitability are more stable.

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3.3.2 Robustness checks

By means of regressions (2)-(5) we investigate the robustness of our main results. To begin with,

as Table 9 indicates, both the log of GDP as well as the rate of growth of real GDP are highly

correlated with several control variables which is especially true for our bank-specific measures.

Due to this, we omit both macroeconomic measures in regression specification (2). However, as

specification (2) reiterates the negative relationship between credit risk securitization and banking

stability while significances from other coefficients remain robust, it is not sensitive to include GDP

variables in our regressions.

Moreover, the negative relationship between securitization and banking stability is likely to

suffer from endogeneity with regard to our baseline regression specification (1). Hence, we first of

all address this statistical problem by eliminating the bank-specific control variables in regression

(3) to control for bank-specific endogeneity. As shown, even though bank-specific variables are

excluded, our main finding of a negative relationship between securitization and bank soundness is

reconfirmed. Hence, we rule out that results are driven by bank-specific endogeneity. Among the

control variables, the log of GDP, the rate of growth of real GDP and the delta of log of total assets

enter the regression significantly positive indicating that banks issuing credit risk securitizations

under a more sophisticated economic environment being attended with economic boom phases are

less prone to financial fragility.

We further address possible endogeneity problems by applying 2SLS instrumental variable

techniques in regression specification (4). We employ the log of a bank’s net loans as an

instrumental variable since the accounting value of a bank’s total loans is considered to be the main

source for credit risk securitizations in our sample.7 Concerning the validity of our instrumental

variable Table 10 reports that the banks’ net loans are highly correlated with the securitization

variable whereas its correlation with the z-score ratio is suffieciently low. In addition, the validity is

7 With the exception of Collateral Bond Obligations (0.5 per cent of the total volume of transactions in our sample)

all underlyings are classified as “net loans”.

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confirmed by results from the first stage of the 2SLS regression. (Table 6). As shown by regression

specification (4), results from instrumental variable regressions entirely reiterate our main findings

from the standard random effects model. Hence, we rule out that our baseline estimation results

may be driven by endogeneity.

Finally, the causality running from securitization to banking stability is not clear since it is not

obvious if the frequency of securitization activities itself depends on the bank’s financial soundness.

Hence, reverse causality may arise as a particular case of endogeneity, for example, if a bank

exhibiting a high risk exposure or a shortage of liquidity tends to increase credit risk securitization

activities to “gamble for resurrection”. Thus, to address likely reverse causality, we again apply

instrumental variable techniques using a 2SLS panel estimator in regression (5) and employ two-

period lagged securitization as an instrumental variable (Tables 10 and 6 indicate the validity of our

instrument). As reported by regression specification (5), the instrumental variable regression

reconfirms our baseline results from the standard random effects model suggesting that the negative

impact of credit risk securitization on bank stability is not biased by reverse causality.

3.3.3 Main findings from z-score components regressions

By means of regressions (1)-(3) in Table 7 we try validate our hypotheses from our baseline

regression suggesting that securitization is predominantly utilized as a source of capital relief and

additional funding and that both strategies may provoke a decrease in the banks’ financial

soundness. We scrutinize our hypotheses by regressing the securitization variable on single

components of the z-score ratio. We use the same set of control variables being employed in the

baseline regression but have to omit single bank-specific variables that are highly correlated with

each of the new dependent variables to avoid biased estimation results (Table 11).

To begin with, we include the banks’ ROAA as the dependent variable in specification (1).

Securitization enters the regression significantly negative at the five-percent level indicating that

increasing securitization activities may reduce bank profitability. This result supports our

hypotheses from the baseline regression as it is assumed that retaining the major part of credit risk

18

exposure within the bank’s first-loss position and taking on new risky assets may reduce bank

profitability and soundness due to a higher probability of future loan losses. Moreover, Altunbas et

al. (2007) and Goderis et al. (2006) provide empirical evidence that securitization has a positive

impact on the bank’s target loan levels. Hence, applying traditional industrial organization theory to

banking, an explanation for the negative relationship between securitization and profitability may

be the increase in competition in loan markets triggered by credit risk securitization which should

result in a decrease in the banks’ interest and profit margins. Among the control variables log of

GDP enters the regression significantly negative confirming the trend of declining bank margins,

which especially holds for well-developed Western European banking markets for the last decade.

As expected, the coefficient of rate of growth of real GDP exhibits a significantly positive sign

suggesting that the banks’ investment opportunities and borrowers’ solvency may rise under

economic booms. Finally, real short term interest rate enters the regression significantly positive

indicating that banks may predominantly hand down increasing interest rates to borrowers rather

than to depositors.

By means of regression (2) we assess the relationship between securitization and the banks’

capital ratio as the second component of the z-score’s numerator. Securitization enters the

regression significantly negative at the five-percent level indicating a positive relationship between

credit risk securitization and the bank’s leverage. Again, this finding supports our hypotheses from

the baseline regression as it is assumed that the bank’s leverage may increase if the bank realizes

regulatory capital arbitrage by means of credit risk securitization. This causality is also proposed by

related theoretical literature (Leland, 2007; Cebenoyan and Strahan, 2004) and has been confirmed

by previous empirical studies (Jiangli and Pritsker, 2008). Among the control variables inflation

enters the regression significantly negative supporting theoretical arguments and empirical findings

that the banks’ funding costs may increase under inflation (Schaeck et al., 2006; Hortlund, 2005).

We finally include the volatility of the ROAA as the z-score’s denominator in regression

specification (3). If it is true that credit risk is the main source of a bank’s overall risk exposure, the

19

return volatility is a measure of a bank’s loan portfolio quality. Securitization enters the regression

significantly positive but weak at the ten-percent level indicating that securitization tends to

increase the volatility of bank asset returns which results in a decrease in the bank’s loan portfolio

quality. Hence, in line with results from regressions on the ROAA empirical findings support our

hypotheses from the baseline regression as it is assumed that retaining the more risky first-loss

position and taking on new risky assets may increase the bank’s return volatility due to a higher

probability of future loan losses. As expected, among the control variables liquid assets to total

assets enters the regression significantly negative.

To sum up, taking the single results from regressions on z-score components into account, main

rationales for the negative impact of credit risk securitization on Western European banks’ financial

soundness may be derived from the positive relationship between credit risk securitization and the

banks’ leverage and return volatility as well as from a negative relationship between securitization

and bank profitability. In this way, each single result from the z-score component’s regressions

validates our hypotheses suggesting that securitization is predominantly utilized as a source of

capital relief and additional funding and that both the direct and indirect effect of securitization may

provoke a decrease in the banks’ financial soundness.

3.3.4 Sensitivity analyses

We perform a large variety of sensitivity analyses. As a general result, our main finding of a

negative relationship between securitization and banking stability holds even when controlling for

cross-country differences concerning the banking market structure, the capital market development

as well as the regulatory and institutional environment. Due to high correlation between these

control variables (Table 12), we include them in turn in separate regressions (Table 8).

With regard to cross-country differences in the banking market structure, we first of all include

the 5-bank concentration ratio in regression specification (1) and the HHI in regression

specification (2). As Table 8 reports, both concentration measures enter the regressions significantly

positive indicating that securitizing banks in more concentrated banking markets are less prone to

20

financial fragility. Corresponding with theoretical arguments and empirical findings provided by

related literature on this issue (e.g., Uhde and Heimeshoff, 2009) we suggest that larger banks may

engage in “credit rationing” more heavily since fewer high quality credit investments will increase

the return of the singular investment and hence foster financial soundness. Moreover, larger banks

may exhibit comparative advantages in providing credit monitoring services and may be able to

diversify loan portfolio risks more efficiently due to higher economies of scale and scope.

Introducing H-Statistic, this variable enters regression (3) significantly negative at the one-percent

level indicating that securitizing banks operating under increasing market competition are more

prone to financial fragility. This result is in line with theoretical models and empirical findings

predicting that in a more competitive environment with higher pressures on profits, banks have

higher incentives to take more excessive risks, resulting in higher fragility. In addition, banks are

anticipated to earn fewer informational rents from their relationship with borrowers in competitive

markets, which may reduce their incentives to properly screen borrowers, again increasing the risk

of fragility (e.g. Beck, 2008). We finally control for cross-country differences concerning the

capital market development. Stock market capitalization enters regression specification (4)

significantly positive. This result does not correspond with theoretical arguments and empirical

evidence stressing that a well-developed capital market may support financial disintermediation and

spur competition for retaining bank customers which results in lower financial soundness (Schaeck

et al., 2006, Bikker, 2004). In contrast, evidence suggests that the level of stock market

development may be an appropriate proxy for the number and quality of potential external investors

engaging in securitization transactions on capital markets. Hence, higher developed stock markets

are anticipated to support credit risk securitization. However, as our main findings reveal, the effect

of a higher developed stock market on banking stability depends on the amount of credit risks that

are actually transferred by banks.

Turning to the regulatory environment, we initially include the capital regulatory index. This

variable enters regression specification (5) significantly positive at the five-percent level suggesting

21

that better capitalized securitizing banks are more stable and that greater capital stringency may

encourage prudent behavior of bank managers. We further employ activity restrictions to control for

governmental restrictions on banking business. As regression (6) reports, the coefficient of activity

restrictions exhibits a significantly negative sign suggesting that securitizing banks operating under

higher restrictions are more prone to financial fragility. Our findings correspond with theoretical

predictions and empirical findings stressing that restricting the banks’ business activities is likely to

reduce the banking system’s efficiency and stability (Barth et al., 2004) since it detains financial

institutions from reaping benefits of diversification opportunities. As expected, the private

monitoring index enters regression specification (7) significantly positive at the weak ten-percent

level indicating that forces of higher market discipline may support state bank supervision and

hence, help promoting financial stability. Finally, Basel I enters regression (8) significantly

negative. Hence, evidence suggests that securitizing credit risk under former Basel I regulations

affects financial stability negatively since Basel I regulations provided an incentive to realize

regulatory arbitrage from securitization if the major part of credit default risks were retained within

the first loss position.

We finally control for cross-country differences concerning the institutional environment.

Introducing the privatization index, this variable enters regression (9) significantly positive at the

one-percent level indicating that banking markets with a lower amount of government-owned banks

are less prone to financial fragility. This result was expected since government-owned banks are

anticipated to strongly suffer from moral hazard problems and X-inefficiency. Employing the

shareholder rights index, this measure enters regression specification (10) significantly positive

confirming theoretical assumptions and empirical findings that managers of securitizing banks

being closely monitored by shareholders tend to avoid excessive risk-taking behavior and may

transfer the major part of credit risks to external investors (Park and Peristiani, 2007). Finally, the

coefficient of rule of law exhibits a significantly positive sign in regression specification (11)

22

suggesting that securitizing banks operating under a stronger institutional environment of higher

quality are less prone to financial fragility.

4 Conclusion

Using a unique sample of 743 cash and synthetic securitization transactions issued by 55 stock

listed bank holdings located in the EU-11 plus Switzerland over the period from 1997 to 2007, this

paper provides empirical evidence that credit risk securitization has a negative impact on Western

European banks’ financial soundness as measured by the z-score technique while controlling for

macroeconomic, bank-specific, regulatory and institutional factors. Empirical results from panel

estimations hold when applying instrumental variable techniques to address probable endogeneity

and performing a variety of further sensitivity analyses. Our empirical findings support theoretical

assumptions provided by the “securitization-fragility” view and confirm empirical evidence from

previous studies by Uzun and Webb (2007) and Dionne and Harchaoui (2003).

Investigating single z-score components we additionally find a positive impact of credit risk

securitization on the banks’ leverage and return volatility as well as a negative relationship between

securitization and the banks’ profitability. Hence, in line with baseline regressions on the z-score

ratio we suggest that securitization is predominantly utilized as a source of capital relief and

additional funding and that both the direct and indirect effect of securitization may provoke a

decrease in Western European banks’ financial soundness.

Moreover, evidence from sensitivity analyses reveals that securitizing banks operating in more

concentrated banking markets and higher developed capital markets are less prone to financial

fragility whereas a higher level of banking market competitiveness negatively affects the banks’

financial soundness. While capital regulations support financial stability, high restrictions on

banking activities do not. Finally, stronger forces of market discipline and a higher shareholder

influence tend to support state banking regulation and supervision resulting in greater banking

stability.

23

Against the background of our empirical results we point out that recent proposals by the Basel

Committee to enhance the Basel II framework in the field of securitization are a step in the right

direction. First of all, we expressly emphasize the necessity for further focusing on improved

minimum capital standards concerning a bank’s securitization exposures in Pillar 1 since our

empirical findings reveal that Western European banks tend to utilize securitization as a source of

capital relief. Furthermore, strengthening disclosure requirements in the area of securitization is

assumed to be a useful instrument, however, as banks do not yet reveal whether the first-loss

position is actually transferred out of the balance sheet, disclosure requirements should be further

reinforced in this regard. Finally, addressing securitization within the frameworks of ICAAP, SREP

and MIS in order to enhance the banks’ and supervisors’ sensitivity to securitization was expected

to be a remedy for lessons learnt from the recent financial crisis. However, one has to reconsider if

modifying the Basel Accord in this way could really help to prevent future financial turmoil as long

as state supervisors lack relevant knowledge to fully understand and monitor high complex

structured finance instruments and, as long as external investors knowingly invest in risky

instruments to make fast money at the expense of a safeguard.

24

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28

Statistical appendix2 Table 1 Notes on variables and data sources

Variable Description Data Sources

Z-score

Ratio of the sum of equity capital to total assets and the return on average assets before taxes (ROAA) to standard deviation of ROAA (sdROAA).

BankScope, authors’ calc.

ROAA

Accounting value of a bank’s return on average assets before taxes (ROAA).

BankScope CN 4006

Capital ratio

Accounting value of a bank’s ratio of equity capital to total assets.

BankScope CN 2095

sdROAA

Standard deviation of a bank’s ROAA.

BankScope CN 4006, authors’ calc.

Securitization

Log of the total extent of cash and synthetic credit risk securitizations issued by 55 banks across the EU-11 plus Switzerland from 1997 to 2007.

Moody’s, Standard & Poor’s and FitchRatings

Securitization (t-2)

Lag (2) of the total extent of cash and synthetic credit risk securitizations issued by 55 banks across the EU-11 plus Switzerland from 1997 to 2007.

Moody’s, Standard & Poor’s and FitchRatings

GDP

Log of real GDP.

World Development Indicators (WDI)

GDP growth

Rate of real GDP growth at constant 2000 prices (annual percentage change).

World Development Indicators (WDI)

Inflation (t-1)

Lag (1) of annual change of GDP deflator.

World Development Indicators (WDI)

Interest rate

Annual change of real short term interest rate, adjusted for inflation (GDP deflator).

World Development Indicators (WDI)

Total assets

Proxy for the bank’s size. Delta of log of the accounting value of a bank’s total assets.

BankScope CN 2025

Net interest margin

Proxy for the bank’s profitability. Log of accounting value of a bank's net interest revenue as a share of its interest-bearing (total earning) assets.

BankScope CN 2035

Non-performing loans (t-1)

Proxy for the bank’s asset quality. Lag (1) of the accounting value of a bank’s non-performing loans as a share of its total assets.

BankScope CN 2170

Cost-income ratio

Proxy for the bank’s efficiency. Accounting value of the ratio of a bank’s overhead costs to its total revenue.

BankScope CN 4029

Liquid assets (t-1)

Proxy for the bank’s liquidity. Lag (1) of the accounting value of a bank’s liquid assets to its total assets. Variable constructed as 1 − net loans to total assets.

BankScope CN 4032

Net loans

Log of accounting value of a bank’s net loans.

BankScope CN 5190

5-bank concentration

EU-11 plus Switzerland concentration ratios: Fraction of assets of a country’s total banking system's assets held by the largest 5 domestic and foreign banks.

ECB statistics, national central banks

HHI

Herfindahl-Hirschman Index computed as the sum of the squared market shares of a country’s domestic and foreign banks.

ECB statistics, national central banks

29

Table 1 (continued) Notes on variables and data sources

Variable Description Data Sources

H-Statistic

H-Statistic estimated on an aggregated consolidated bank balance-sheet basis cross-sectionally for each country for the period from 1997 to 2007. H-Statistic comprises bank’s interest bearing revenues. Variable is interacted with a country’s GDP.

BankScope, authors’ calc.

Stock market capitalization

Proxy for the development of the capital market. Proportion of the banking sector assets to stock market capitalization.

Beck et al. (2000)

Capital regulatory index

Index that measures the overall capital stringency. Index is built by first principal component analysis of initial capital stringency and overall capital stringency. Higher index values indicate greater capital stringency.

Barth et al. (2004), Authors’ calc.

Activity restrictions

Index aggregates measures that indicate whether bank activities in the securities, insurance and real estate markets, ownership and control of non-financial firms are unrestricted, permitted, restricted, or prohibited. Index ranges between (0) and (10), with higher values indicating greater activity restrictions arising from legal requirements.

Barth et al. (2004)

Private monitoring index

Index aggregates measures that indicate the degree to which regulations empower, facilitate, and encourage the private sector to monitor banks. Index ranges between (0) and (9), with higher values indicating higher market discipline.

Barth et al. (2004)

Basel I

Dummy variable that takes on the value of 1 if credit risk securitizations were issued under Basel I regulations (1997-2003), and zero otherwise.

Authors’ calc.

Privatization index

Aggregate index of banking system privatization. Index ranges from 0 to 4, with higher indices indicating higher privatization.

Abiad et al. (2008)

Shareholder rights index

Aggregated index for the emphasis on shareholder rights, with higher values indicating more shareholder rights.

La Porta et al. (1998)

Rule of law

Indicator that measures individual’s degree of confidence in rules of society and the likelihood of crime and violence. Scores range between –2.5 and 2.5, with higher scores corresponding with better outcomes.

Worldwide Governance Indicators (WGI)

30

Table 2 Descriptive statistics

Variable N Mean SD Min Max Z-score 568 0.6934 1.2620 0 14.1327 ROAA 568 0.0067 0.0046 -0.0174 0.0222 Capital ratio 568 0.0531 0.0234 0.0079 0.1606 sdROAA 568 0.0422 0.0585 0.0001 0.5349 Securitization 303 7.6825 1.2834 3.2189 10.5308 Securitization (t-2) 224 7.4856 1.2420 3.2189 10.3315 Net loans 578 11.0773 1.2526 6.8628 13.9462 GDP 605 27.3182 0.9442 25.0997 28.4388 GDP growth 605 0.026 0.0173 -0.0074 0.1168 Inflation (t-1) 550 0.001 0.0286 -0.2005 0.2928 Interest rate 568 0.0568 0.5961 -2.3579 5.2874 Total assets 567 0.1304 0.2079 -0.66 2.66 Net interest margin 579 -4.0571 0.6008 -6.5023 -2.7031 Non-performing loans (t-1) 497 0.0349 0.0367 0 0.2958 Cost-income ratio 579 0.6182 0.1401 0.2035 1.1683 Liquid assets (t-1) 531 0.4491 0.1629 0.11 0.88 5-bank concentration 597 0.4118 0.1868 0.17 0.86 HHI 597 567.89 494.57 114 2108 H-Statistic 594 0.7885 0.1485 0.5057 0.9779 Stock market capitalization 605 0.8928 0.7033 0.1191 4.2785 Capital regulatory index 605 0.9172 0.5207 -0.6393 1.4352 Activity restrictions 605 7.1091 2.0878 3 10 Private monitoring index 605 6.0182 1.2293 4 8 Basel I 605 0.6364 0.4814 0 1 Privatization index 605 2.3736 0.8194 0 3 Shareholder rights index 605 2.6 1.6373 0 5 Rule of law 605 1.3651 0.4335 0.36 2.08

31

Table 3 Geographical distribution of banks in the sample

Country Banks Country Banks Belgium Dexia Netherlands ABN Amro Holding KBC Groupe Fortis Bank SNS Reaal Denmark Danske Bank Sydbank Portugal Banco BPI Banco Espr. Santo France BNP Paribas BCP Crédit Agricole Natixis Spain BBVA Société Générale Banco De Sabadell Banco De Valencia Germany Bayr. Hypo- u. Vereinsbank Banco Pastor Commerzbank Banco Popular Espanol Deutsche Bank Banco Santander Deutsche Postbank Bankinter Hypo Real Estate Holding IKB Dt. Industriebank United Kingdom Abbey National Alliance & Leicester Greece EFG Eurobank Ergasias Barclays Bank Bradford & Bingley Ireland Anglo Irish Banks HBOS Bank of Ireland HSBC Holdings DePfa Bank Lloyds TSB Group Northern Rock Italy Banca Antonveneta Royal Bank of Scotland Banca Carige Standard Chartered Banca Lombarda Banca Monte Dei Paschi Switzerland Credit Suisse Banca Naz. Lavoro UBS Banca Popolare Milano Banca Popolare Italiana Capitalia Intesa Sanpaolo Mediobanca San Paolo IMI Unicredito Italiano

32

Table 4 Descriptive statistics of securitization transactions in the sample (in million €)

N Total Volume Mean Median Standard

Deviation Minimum Maximum

Total

Total Transactions 743 1,410,423 1,898 1,076 2,192 25 22,000

Single Underlyings Collateralized Debt Obligations 233 488,565 2,097 1,335 2,396 60 16,863

from small and medium enterprises 107 181,712 1,698 1,250 1,640 60 7,728

from large enterprises 126 306,853 2,435 1,500 2,850 196 16,863 Residential Mortgage Backed Securities 312 751,227 2,408 1,490 2,415 87 22,000 Commercial Mortgage Backed Securities 84 74,999 893 661 907 199 7,092 Credit Cards Receivables 20 16,340 817 855 404 56 1,658 Consumer Loans 44 33,066 752 490 654 25 3,000 Others 50 46,226 925 704 736 28 3,100

33

Figure 1 Development of the volume of credit risk transfer through securitizations during the sample period (in million €)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Years

Volume of Credit Risk Transfer

Figure 2 Development of the number of securitization transactions during the sample period

34

Table 5 Z-score and securitization

(1) Z-score (2) Z-score (3) Z-score (4) Z-score (5) Z-score Securitization −0.1638 −0.1447 −0.2249 −0.2227 −0.3532 (0.016)** (0.023)** (0.007)*** (0.059)* (0.034)** GDP 0.1833 0.1393 0.2107 0.2562 (0.042)** (0.098)* (0.067)* (0.111) GDP growth 2.9212 21.7043 2.7683 12.5745 (0.698) (0.005)*** (0.755) (0.349) Inflation (t-1) 0.6310 0.4095 1.0313 0.8320 0.6357 (0.634) (0.739) (0.277) (0.818) (0.871) Interest rate 0.0388 0.0320 0.0363 0.0488 −0.0285 (0.625) (0.694) (0.517) (0.721) (0.856) Total assets 0.3810 0.4439 0.9649 0.3953 0.8361 (0.390) (0.307) (0.031)** (0.464) (0.338) Net interest margin 0.7751 0.6846 0.7674 0.5672 (0.004)*** (0.003)*** (0.001)*** (0.123) Non-performing loans (t-1) −6.3266 −5.2742 −6.7152 −8.1254 (0.022)** (0.011)** (0.009)*** (0.035)** Cost-income ratio −2.2221 −2.4431 −2.0141 −2.3225 (0.000)*** (0.000)*** (0.023)** (0.042)** Liquid assets (t-1) 0.6755 0.5175 0.6246 0.1353 (0.269) (0.371) (0.368) (0.884) Time Dummies yes yes yes yes yes No. of Obs. 260 260 291 259 155 No. of Groups 52 52 55 52 44 Wald χ2 146.77*** 119.99*** 28.80** 72.30*** 84.20*** Adj. R2 0.24 0.23 0.11 0.23 0.40 The panel model estimated is Z-score (i=bank, j=time) = α + β1 Securitizationi,t + β2 GDPi,t + β3 GDP growthi,t + β4 Inflationi,t-1 + β5 Interest ratei,t + β6 Total assetsi,t + β7 Net interest margini,t + β8 Non-performing loansi,t-1 + β9 Cost-income ratioi,t + β10 Liquid assetsi,t-1 +εi,t. GDP and GDP growth are omitted in specification (2) and bank-specific variables are omitted in specification (3). Securitization is instrumented using net loans in specification (4) and the two-period lagged securitization variable in specification (5). Regressions (4) and (5) are estimated by means of a 2SLS instrumental variable regression. Constant term included but not reported. Heteroscedasticity consistent P-values are in parenthesis. ***, **, *: statistically significant at the 1, 5 and 10% level.

35

Table 6 First stage regressions (instruments)

(1) (2) Net loans 0.7083 (0.000)*** Securitization (t-2) 0.5892 (0.000)*** GDP −0.0023 0.065 (0.978) (0.571) GDP growth −6.7188 0.6379 (0.306) (0.948) Inflation (t-1) 0.0374 −1.1212 (0.989) (0.695) Interest rate 0.0660 0.0055 (0.512) (0.962) Total assets 0.0492 1.7222 (0.902) (0.006)*** Net interest margin −0.6341 −0.5458 (0.000)*** (0.031)** Non-performing loans (t-1) −1.6609 −3.0856 (0.379) (0.261) Cost-income ratio 0.2138 0.1264 (0.744) (0.879) Liquid assets (t-1) −2.1702 −1.0013 (0.000)*** (0.141) No. of Obs. 259 155 Wald χ2 238.00*** 125.00*** Securitization is instrumented by net loans in specification (1). It is instrumented by the two-period lagged securitization variable in specification (2). P-values are in parenthesis. ***, **, *: statistically significant at the 1, 5 and 10% level.

36

Table 7 Z-score components and securitization (1) ROAA (2) Capital Ratio (3) sdROAA Securitization −0.0004 −0.0017 0.051 (0.036)** (0.029)** (0.064)* GDP −0.0012 −0.0020 0.022 (0.007)*** (0.452) (0.597) GDP growth 0.0597 0.0262 −0.3150 (0.073)* (0.796) (0.424) Inflation (t-1) 0.0019 −0.0284 −0.0751 (0.606) (0.016)** (0.479) Interest rate 0.0004 −0.0001 0.0029 (0.005)*** (0.778) (0.174) Total assets −1.59e-07 0.0006 −0.0313 (1.000) (0.871) (0.289) Non-performing loans (t-1) −0.0016 0.1346 (0.976) (0.403) Liquid assets (t-1) −0.0018 −0.0121 (0.429) (0.647) Time Dummies yes yes yes No. of Obs. 291 260 260 No. of Groups 55 52 52 Wald χ2 87.27*** 88.82*** 31.45** Adj. R2 0.30 0.20 0.10 The panel model estimated is Z-score (i=bank, j=time) = α + β1 Securitizationi,t + β2 GDPi,t + β3 GDP growthi,t + β4 Inflationi,t-1 + β5 Interest ratei,t + β6 Total assetsi,t + β7 Net interest margini,t + β8 Non-performing loansi,t-1 + β9 Cost-income ratioi,t + β10 Liquid assetsi,t-1 +εi,t. Z-score is substituted by its single components ROAA, capital ratio and standard deviation of ROAA in specifications (1)-(3). Net interest margin, non-performing loans (t-1) and cost-income ratio are omitted in specification (1). Net interest margin, cost-income ratio and liquid assets (t-1) are omitted in specification (2). Net interest margin and cost-income ratio are omitted in specification (3). Constant term included but not reported. Heteroscedasticity consistent P-values are in parenthesis. ***, **, *: statistically significant at the 1, 5 and 10% level.

Table 8 Sensitivity analyses: Market structures, regulatory and institutional environment Z-score (1) (2) (3) (4) (5)

Securitization −0.1981 −0.1902 −0.2080 −0.1900 −0.1612 (0.007)*** (0.009)*** (0.004)*** (0.008)*** (0.019)** 5-bank concentration 1.7753 (0.046)** HHI 0.0004 (0.050)* H-Statistic −2.02e-12 (0.006)*** Stock market capitalization 0.2740 (0.020)** Capital regulatory index 0.5021 (0.044)**

Time Dummies yes yes yes yes yes No. of Obs. 258 258 259 260 260 No. of Groups 52 52 51 52 52 Wald χ2 148.42*** 142.85*** 151.72*** 136.32*** 180.11*** Adj. R2 0.25 0.24 0.26 0.25 0.25 The panel model estimated is Z-score (i=bank, j=time) = α + β1 Securitizationi,t + β2 GDPi,t + β3 GDP growthi,t + β4 Inflationi,t-1 + β5 Interest ratei,t + β6 Total assetsi,t + β7 Net interest margini,t + β8 Non-performing loansi,t-1 + β9 Cost-income ratioi,t + β10 Liquid assetsi,t-1 +εi,t. Constant term included but not reported. Heteroscedasticity consistent P-values are in parenthesis. ***, **, *: statistically significant at the 1, 5 and 10% level.

37

Table 8 (continued) Sensitivity analyses: Market Structures, regulatory and institutional environment Z-score (6) (7) (8) (9) (10) (11)

Securitization −0.1843 −0.1866 −0.1638 −0.1844 −0.1812 −0.1792 (0.010)** (0.010)** (0.016)** (0.007)*** (0.010)** (0.012)** Activity restrictions −0.1069 (0.057)* Private monitoring index 0.1303 (0.098)* Basel I −0.5402 (0.044)** Privatization index 0.2149 (0.003)*** Shareholder rights index 0.1259 (0.027)** Rule of law 0.4274 (0.073)*

Time Dummies yes yes yes yes yes yes No. of Obs. 260 260 260 260 260 260 No. of Groups 52 52 52 52 52 52 Wald χ2 152.27*** 123.56*** 146.77*** 139.38*** 149.11*** 142.67*** Adj. R2 0.25 0.24 0.24 0.25 0.25 0.24 The panel model estimated is Z-score (i=bank, j=time) = α + β1 Securitizationi,t + β2 GDPi,t + β3 GDP growthi,t + β4 Inflationi,t-1 + β5 Interest ratei,t + β6 Total assetsi,t + β7 Net interest margini,t + β8 Non-performing loansi,t-1 + β9 Cost-income ratioi,t + β10 Liquid assetsi,t-1 +εi,t. Constant term included but not reported. Heteroscedasticity consistent P-values are in parenthesis. ***, **, *: statistically significant at the 1, 5 and 10% level.

Table 9 Correlation matrix (z-score regressions)

Sec

uriti

zatio

n

GD

P

GD

P gr

owth

Inf

latio

n (t-

1)

Int

eres

t Rat

e

Tot

al a

sset

s

Net

inte

rest

mar

gin

Non

-per

form

ing

loan

s (t-1

)

Cos

t-inc

ome

ratio

Liq

uid

Ass

ets (

t-1)

Securitization 1.00

GDP 0.27*** 1.00

GDP growth -0.15*** -0.41*** 1.00

Inflation (t-1) -0.01 0.03 -0.03 1.00

Interest rate 0.11* 0.02 0.09** -0.01 1.00

Total assets 0.06 -0.03 0.16*** -0.01 0.08* 1.00

Net interest margin -0.29*** -0.11*** 0.13*** -0.07 -0.07 0.04 1.00

Non-performing loans (t-1) -0.07 0.34*** -0.29*** 0.04 -0.09* -0.08* 0.10** 1.00

Cost-income ratio 0.05 0.01 -0.25*** -0.02 -0.01 -0.11*** -0.06 0.27*** 1.00

Liquid assets (t-1) 0.14** 0.01 -0.12*** 0.05 -0.05 -0.03 -0.54*** 0.09* 0.37*** 1.00

Table 10 Correlation matrix (instruments)

Z-score Securitization Net loans Securitization (t-2)

Z-score 1.00

Securitization -0.21*** 1.00

Net loans -0.16*** 0.62*** 1.00

Securitization (t-2) -0.26*** 0.61*** 0.55*** 1.00

38

Table 11 Correlation matrix (z-score components regressions)

Sec

uriti

zatio

n

RO

AA

Cap

ital R

atio

sdR

OA

A

GD

P

GD

P gr

owth

Inf

latio

n (t-

1)

Int

eres

t Rat

e

Tot

al a

sset

s

Net

inte

rest

mar

gin

Non

-per

form

ing

loan

s (t-1

)

Cos

t-inc

ome

ratio

Liq

uid

Ass

ets (

t-1)

Securitization 1.00

ROAA -0.21*** 1.00

Capital Ratio -0.40*** 0.47*** 1.00

sdROAA 0.10* -0.38*** -0.16*** 1.00

GDP 0.27*** -0.28*** -0.04 0.05 1.00

GDP growth -0.15*** 0.43*** 0.03 -0.11*** -0.41*** 1.00

Inflation (t-1) -0.01 -0.01 -0.02 -0.09** 0.03 -0.03 1.00

Interest rate 0.11* 0.07 -0.08* 0.03 0.02 0.09** -0.01 1.00

Total assets 0.06 0.19*** 0.04 -0.13*** -0.03 0.16*** -0.01 0.08* 1.00

Net interest margin -0.29*** 0.45*** 0.61*** -0.17*** -0.11*** 0.13*** -0.07 -0.07 0.04 1.00

Non-performing loans (t-1) -0.07 -0.31*** 0.03 0.15*** 0.34*** -0.29*** 0.04 -0.09* -0.08* 0.10** 1.00

Cost-income ratio 0.05 -0.48*** -0.26*** 0.26*** 0.01 -0.25*** -0.02 -0.01 -0.11*** -0.06 0.27*** 1.00

Liquid assets (t-1) 0.14** -0.24*** -0.28*** 0.09** 0.01 -0.12*** 0.05 -0.05 -0.03 -0.54*** 0.09* 0.37*** 1.00

39

Table 12 Correlation matrix (country level variables)

S

ecur

itiza

tion

GD

P

GD

P gr

owth

Inf

latio

n (t-

1)

Int

eres

t rat

e

5-b

ank

conc

entra

tion

HH

I

H-S

tatis

tic

Sto

ck m

arke

t cap

italiz

atio

n

Cap

ital r

egul

ator

y in

dex

Act

ivity

rest

rictio

ns

Priv

ate

mon

itorin

g in

dex

Bas

el I

Priv

atiz

atio

n in

dex

Sha

reho

lder

righ

ts in

dex

Rul

e of

law

Securitization 1.00

GDP 0.27*** 1.00

GDP growth -0.15*** -0.41*** 1.00

Inflation (t-1) -0.01 0.03 -0.03 1.00

Interest rate 0.11* 0.02 0.09** -0.01 1.00

5-bank concentration 0.02 -0.31*** 0.09** 0.02 0.07 1.00

HHI 0.06 -0.24*** 0.02 0.01 0.08* 0.97*** 1.00

H-Statistic 0.21*** 0.26*** -0.41*** 0.03 -0.02 -0.79*** -0.73*** 1.00

Stock market capitalization 0.28*** 0.20*** 0.09** -0.14*** 0.12*** 0.09** 0.05 0.03 1.00

Capital regulatory index 0.11* 0.04 0.12*** 0.06 0.08* 0.16*** 0.12*** -0.22*** 0.37*** 1.00

Activity restrictions -0.32*** -0.26*** -0.09** 0.04 -0.11** -0.01 -0.03 -0.06 -0.53*** -0.67*** 1.00

Private monitoring index 0.08 0.01 0.30*** 0.01 0.13*** -0.04 -0.10** -0.14*** 0.34*** 0.51*** -0.31*** 1.00

Basel I -0.23*** -0.06 0.08** 0.11** -0.26*** -0.14*** -0.11*** -0.07* -0.31*** 0.01 0.01 0.01 1.00

Privatization index 0.06 0.02 0.30*** 0.12*** 0.09** 0.17*** 0.11*** -0.21*** 0.34*** 0.43*** -0.15*** 0.43*** -0.08* 1.00

Shareholder rights index 0.04 0.01 0.45*** 0.04 0.09** -0.08** -0.18*** -0.13*** 0.46*** 0.56*** -0.46*** 0.85*** 0.01 0.51*** 1.00

Rule of law 0.21*** -0.08** 0.24*** -0.05 0.09** 0.21*** 0.25*** -0.28*** 0.31*** 0.64*** -0.83*** 0.33*** 0.12*** 0.17*** 0.41*** 1.00

40