Share Price Response to Credit Risk Securitization in European Banking

Christian Farruggio a, Tobias Michalak b and André Uhde c*

Abstract Using a unique cross-sectional dataset of 381 cash and synthetic securitizations issued by 53 banks from the EU-15 plus Switzerland between 1997 and 2007, this paper provides empirical evidence for a time-dependent negative share price response to securitization announcements in European banking markets. Baseline results hold when comparing estimated share price reactions with a control group of similar but non-securitizing banks for the relevant time period. Moreover, building several sub samples we find that the nexus between credit risk securitization, the issuing banks’ overall risk exposure and wealth effects is associated with a variety of transaction- and bank- specific factors.

JEL classification: G14; G21; G28; G32 Keywords: Credit Risk Transfer, Securitization, Wealth Effects, Event Study

a Christian Farruggio, University of Bochum, Department of Economics, 44780 Bochum, Germany. Fax: ++49 234 32 05308, email: [email protected]. b Tobias Michalak, University of Bochum, Department of Economics, 44780 Bochum, Germany. Fax: ++49 234 32 05345, email: [email protected]. c Dr. André Uhde *(corresponding author), University of Bochum, Department of Economics, 44780 Bochum, Germany. Fax: ++49 234 32 02278, email: [email protected].

1. Introduction

The complexity of some securitization instruments developed during the peak of the U.S. subprime market boom from mid-2007, as for instance collateralized debt obligations of asset backed securities (CDOs of ABS) or CDOs of CDOs (CDOs-squared), has exceeded the analytical and risk management capabilities of even some of the most sophisticated institutional market participants (such as banks themselves, insurance companies, mutual funds and pension funds).

Accordingly, most institutional investors have been dependent on rating agencies to assess the quality of underlying assets and potential risks created by certain transaction structures. Rating agencies, however, have not always proved to be effective in the face of complexness. It is even suggested that rating agencies have contributed to the subprime mortgage meltdown by failing to downgrade securities backed by subprime mortgages in time. Consequently, the market for credit risk transfer has experienced a significant decrease in institutional investors’ confidence. Investors have almost completely withdrawn from the securitization market (“flight to quality”) finally resulting in a drying-up of asset-backed security issuance from bank originators.

Taking into account that the economic benefits of credit risk securitization still exist, a number of policy and industry initiatives have been proposed that are designed to restart securitization markets under sounder conditions (BIS, 2009; IMF, 2009). Among others, one important prescription for revitalizing securitization markets includes improving disclosure and transparency standards (ASF,

2009; G30, 2009). Thus, in particular authorities have begun to introduce legislative standards that encourage or even force securitizing banks to disclose more detailed information on the underlying reference portfolios and the volume of risk retention. Obviously these disclosure standards aim at rebuilding confidence and winning back institutional investors for the securitization market since a broader and more stable investor base might support restarting credit risk transfer outside the banking sector.

Against this background, this paper empirically investigates share price reactions due to credit risk securitization in Europe using data on 381 cash and synthetic securitization transactions issued

2 by 53 banks from the EU-15 plus Switzerland over the period from 1997 to 2007. Employing event study methodology techniques, this paper complements and extends previous empirical studies

(Section 3) on the relationship between credit risk securitization and share price response by three specific aspects. First , utilizing a unique cross-sectional securitization sample we conduct the first comprehensive study on the nexus between credit risk securitization and changes in the overall risk exposures and shareholder values of issuing European banks. Second , estimating significant differences in share price reactions between our sample of issuing banks and a control group of similar, but non-securitizing banks for the relevant time period we rule out that wealth effects may result from unobservable factors affecting the whole European banking industry. Third , in contrast to previous related event studies we identify and address to autoregressive conditional heteroscedasticity ((G)ARCH) effects within our time series of bank stock prices in order to avoid overestimated test statistics and regression coefficients.

We initially provide empirical evidence that the announcement of a credit risk securitization transaction has a negative impact on the issuing bank’s shareholder value. In this context, evidence also reveals that wealth effects due to securitization activities in Europe are time-dependent suggesting that bank shareholders may have benefitted from learning curve-effects during the sample period (and in particular the last two years) resulting in a better understanding of structured finance instruments and the actual impact of credit risk securitization on the issuing banks’ overall risk exposures shortly before the subprime crisis has emerged in mid-2007.

Moreover, analyzing the nexus between credit risk securitization, the issuing bank’s overall risk exposure and shareholder value in greater detail, regressions from a variety of transaction- and bank-specific sub samples indicate that a negative share price response to securitization may result from European banks following a risky reinvestment strategy and retaining the more risky first loss position ex post. In this context evidence also suggests that shareholders may perceive securitization by banks exhibiting a low pre-event capital and liquidity ratio as signals of financial distress and financial slack.

The remainder of the paper is organized as follows. Section 2 introduces the theoretical background. Section 3 presents related empirical literature on the relationship between credit risk securitization and share price response. While Section 4.1 describes the dataset, Section 4.2 introduces our empirical model and strategy. Baseline results, robustness checks and sub samples are discussed in Section 5. Finally, Section 6 concludes.

2. Theoretical Background

Theoretical literature provides plenty of motives for financial intermediaries to engage in credit risk securitization. 1 These motives mainly include (a) reducing the bank’s economic and regulatory capital requirements (Merton, 1995), (b) serving the bank’s liquidity and funding management

(Greenbaum and Thakor, 1987; Rosenthal and Ocampo, 1988; Leland, 2007), and (c) reducing the bank’s overall risk exposure by credit portfolio diversification and specification (Carlstrom and

Samolyk, 1995; Gorton and Pennacchi, 1995). With regard to motives (a) and (b) it is assumed that bank shareholders may positively anticipate credit risk securitization (resulting in a positive share price reaction ex post) since these motives describe well-accepted benefits of this financial instrument.

With regard to motive (c), however, relevant literature provides contradictory predictions which might be due to the fact that the nexus between credit risk securitization and a likely change in the issuing bank’s overall risk exposure and shareholder value is not trivial and ambiguous. Thus, a positive share price reaction due to credit risk securitization is assumed if shareholders anticipate that the tail risk of senior tranches is transferred out of the bank’s balance sheet through securitization (Jiangli et al., 2007). Similarly, the bank’s shareholder value is likely to increase if it is expected that the issuing bank follows a conservative reinvestment strategy ex post resulting in a better diversification of its loan portfolio (Greenbaum and Thakor, 1987; Carlstrom and Samolyk,

1995; Gorton and Pennacchi, 1995) and in a decrease in its leverage ratio (Cebenoyan and Strahan,

1 Karaoglu (2005) as well as Bannier and Hänsel (2008) are excellent surveys on these motives.

2004). In contrast, a negative share price reaction due to securitization announcements is suggested if shareholders anticipate that the larger part of credit default risks of the first-loss position is retained by the issuing bank as a quality signaling advice towards external investors (Riddiough,

1997; DeMarzo, 2005; Instefjord, 2005) and as an instrument to realize regulatory capital arbitrage under former Basel I regulations (Merton, 1995; Allen and Gale, 2006). Similarly, the bank’s shareholder value is likely to decrease if shareholders expect a risky reinvestment strategy and a redistribution of the issuing bank’s capital structure ex post resulting in a higher bank leverage

(Rosenthal and Ocampo, 1988; Krahnen and Wilde, 2006; Leland, 2007).

3. Related empirical Literature

Empirical evidence on the relationship between credit risk securitization and shareholder wealth effects is ambiguous as well. While several studies of the U.S. banking market reveal both a significantly positive and negative impact of credit risk securitization on the issuing bank’s shareholder value, a single study for Europe does not provide any empirical evidence for a significant relationship between securitization and share price response.

To begin with, in an early cross-sectional study Lockwood et al. (1996) present data on 294 securitization announcement and issue dates (121 from banks) from 39 originators in the U.S. between 1985 and 1992. Controlling for overlapping and confounding events they provide evidence of significantly negative abnormal bank stock returns around the securitization’s announcement date. Their analysis reveals that wealth effects due to securitization activities depend on the pre- event financial soundness of the issuing bank, i.e. financial healthier banks experience a significant increase in shareholder value and vice versa.

Similarly, using a sample of 1,416 securitization transactions from 141 financial and non- financial originators in the U.S. for the period from 1983 to 1997 Thomas (2001) finds that credit risk securitization has a negative impact on the issuing bank’s shareholder value. In this context, the study reveals that securitization transactions are associated with a loss in shareholder value if and only if the capital market is under pressure.

In contrast, using data on 223 securitization transactions from 24 U.S. bank holding companies for the period from 1992 to 2000 Gasbarro et al. (2005) find that the announcement of a securitization transaction positively affects the issuing bank’s shareholder value. Evidence also suggests that positive wealth effects are significantly higher for banks exhibiting a high bond rating, a low non-interest expenses ratio and a high issue-frequency. Moreover, in contrast to Lockwood et al. (1996) their study reveals that financial healthier banks experience a significant decrease in shareholder value.

Finally, using 73 securitization announcements by 27 European banks between 1999 and 2002

Franke and Krahnen (2006) do not provide any empirical evidence on the relationship between securitization announcements and wealth effects although their analysis indicates that issuing banks face a higher post-event market risk.

4. Empirical Methodology

4.1 DATA AND SOURCES

Our initial sample of 618 securitization transactions issued by 54 banks located in the EU-15 plus

Switzerland between 1997 and 2007 is obtained from offering circulars and presale reports provided by Moody’s , Standard & Poor’s and FitchRatings . These reports provide detailed information on securitization issue dates including the type and structure of the transaction as well as the underlying reference portfolio. We retrieved respective announcement dates from the online archive of the Euroweek newsletter . Announcement dates are defined as the first notification of a future securitization transaction or the first trading day following the announcement in case that the announcement or the closing of a securitization transaction is made on a non-trading day.

Our analysis is based on the identification of the ultimate originator of a credit risk securitization transaction. However, due to mergers and acquisitions (M&A) within the European banking industry some banks in our sample (1997-2007) no longer existed when data were collected in early

2008. We address this problem by omitting those securitization transactions from banks that were announced or issued during the time period between the announcement of an M&A and the final

6 closing of the legal M&A transaction. From this point in time, we identify the acquirer or combined company as the ultimate originator of the securitization transaction. Table I reports the name and nationality of each originating bank in the sample.

The history of the banks’ stock prices is retrieved from the Datastream Database provided by

Thomson Financial Service s. We omit data on transactions from banks whose stocks are not traded on European Stock Exchanges as well as transactions from banks that exhibit missing stock returns over the entire event period of 221 trading days. Furthermore, we identify and exclude confounding events 2 during the event window in order to avoid biased estimation results (McWilliams and

Siegel, 1997; McWilliams and McWilliams, 2000). As Figure 1 (announcement day) and Figure 2

(issue day) illustrate, 211 and 212 confounding events are identified for the entire sample period respectively. Omitting transactions exhibiting confounding events finally reduces the sample to 381

(380) securitization transactions by 53 banks of the EU-15 plus Switzerland.

As shown in Table II, the final sample consists of 266 traditional (cash) and 115 synthetic securitizations. The cumulated volume of credit risk securitized amounts to € 651,522 million and thus, covers nearly 30 percent of the entire cumulated volume of credit risk being transferred through securitization by the financial and non-financial sectors of the EU-15 between 1997 and

2007 as reported by the European Securitization Forum .3 Our sample is mainly represented by the securitization of risks from corporate loans (€ 199,896 million) and residential mortgage loans

(€ 339,450 million). The mean, median volume and standard deviation are higher for synthetic securitizations compared to cash securitizations. Figures 3 and 4 more precisely illustrate the distribution of cash and synthetic securitizations over the sample period. As shown, a notable transfer of credit risks through securitization in Europe did not begin until 1997. From this point in time, both the number of transactions and the volume of credit risk being transferred by banks

2 We retrieved relevant confounding events from the online archive of Financial Times Europe .

3 Unfortunately, the cumulated volume of credit risk securitization by banks only is not separately available for the

EU-15.

7 initially increase over the sample period reaching a peak in 2006 and then decrease as a result of the subprime mortgage crisis in mid-2007.

Figures 5 and 6 illustrate the quarterly distribution of the entire sample of securitization transactions. As reported, both the number of securitization transactions and the cumulated volume of credit risk transferred by banks in the second and fourth quarter are more than twice as high as compared to the first and third quarter. This may indicate that banks in our sample utilize (upfront) accounting profits and losses from securitization transactions in order to influence financial statements which is in line with the “(discretionary) earnings management hypothesis” suggested by

Dechow et al. (2005) and Niu and Richardson (2006).

Finally, Figures 7 and 8 illustrate the number and volume of cash and synthetic transactions by the ten most frequent issuers in our sample. While Figure 7 refers to cash securitizations, Figure 8 illustrates the distribution of synthetic securitizations respectively. As shown, credit risk securitization activities are highly concentrated among a rather limited number of large banks which enter the group of securitizing banks repeatedly. The number and the cumulated volume of cash securitizations by the ten most frequent issuers comprise 50.4 percent of the total number of cash securitizations and 54.0 percent of the total cumulated volume of credit risk being transferred by cash securitizations. In contrast, synthetic securitizations are not only less frequent in absolute numbers but also more concentrated among the issuing banks in our sample. As Figure 8 reports, the ten most frequent issuers are responsible for 65.2 percent of the total number and 75.0 percent of the total volume of synthetic securitizations.

4.2 EMPIRICAL STRATEGY AND REGRESSION MODEL

Standard Brown and Warner (1985) event study methodology is used to analyze the impact of securitization transactions on the issuing bank’s shareholder value. The objective of this strategy is to measure any share price reaction to securitization announcements and closings by computing abnormal stock returns at and around the event date of a securitization agreement. Accordingly,

abnormal stock returns ( AR ,ti ) are defined as the difference between the realized stock return ( R ,ti )

8 and a benchmark return, which is the expected return resulting from the assumption that there has been no further price sensitive event (e.g. in our case the announcement or a closing of a further securitization transaction). Consistent with related previous empirical studies we adopt the standard market model (Capital Asset Pricing Model, CAPM) to predict the benchmark returns (expected returns). The CAPM parameters are estimated as follows:

= β + β + ε R ,ti i 0, i 1, R ,tm ,ti , (1)

where R ,ti and R ,tm are the daily log returns on a bank’s stocks i and the market portfolio m at

ε β β trading day t . ,ti is the error term and i are the parameters to be estimated. i are estimated

over a period of 200 trading days running from 211 to 11 days prior the event day t0

(announcement and issue date) with an event window period of 10 trading days being

symmetrically set around t0 .

As relevant empirical literature on daily stock return volatility states its inclination to cluster

ε (Mandelbrot, 1963; Fama, 1965, 1970), we assume that the variance of the standard error ,ti is not constant but more probably conditionally heteroscedastic and thus, predictable by including

(G)ARCH structures (Bollerslev et al., 1992; Bera and Higgins, 1992). Table III.A indicates that time series of daily log returns within our sample are stationary and from a leptokurtic distribution.

In addition, performing Engle’s Lagrange Multiplier test (Engle, 1982, 1983) to control for autoregressive conditional heteroscedasticity (ARCH), we find empirical evidence of first order

ARCH effects for the majority of time series. 4 In answer to this, we employ a more general

4 Engle’s LM test was applied to the entire 380 and 381 securitization events respectively, separated by issue and

announcement dates, different indices and event window lenght, however, without obtaining significant differences

in results. We do not report the huge amount of empirical results in this paper but provide them on request.

GARCH (1,1) model to account for volatility clustering. Allowing the conditional variance of the

ε error term ,ti to follow a GARCH (1,1) process, we generalize

σ 2 = α +α ε 2 +α σ 2 ,ti i 0, tii −1,1, i ti −1,2, , (2)

σ2 = ε where i,tVar( i,tI t-1 ) with I t-1 denoting the information set at time t-1.

We estimate the mean and conditional variance (Equations (1) and (2)) jointly by maximum likelihood while assuming a normal standard distribution. As regressions (1) and (2) in Table III.B

α report, the GARCH (1,1) structure identifies 222 and 205 significant 1 coefficients as well as 221

α and 222 significant 2 coefficients for the entire sample period including announcement and issue dates respectively. Thus, for more than half of the total of 381 (380) time series the assumption of a constant variance of the error term is rejected in favour of ARCH effects. To the extent that this result holds for related previous event studies on credit risk securitization and wealth effects, which have not accounted for conditional variance at all (Lockwood et al., 1996; Thomas, 2001; Gasbarro et al., 2005; Franke and Krahnen, 2006), CAARs as well as parametric and non-parametric test statistics in these studies may be biased and conclusions incorrect. In contrast, if as is likely, the variance increases within the event period, test statistics will be systematically overestimated due to a systematically understated variance from the pre-event period in these studies.

CAARs for N securitization events over event window periods moving around day t0 (from day

T1 to day T2 ) are calculated as follows:

N NT2 N T 2 =1 = 1 = 1 −+β* β * (3) CAARst∑ CARs it, ∑∑ ARs it , ∑∑ R itiimt ,,0,1,( R ). = == == Ni1 N itT 11 N itT 1 1

As weekly editions of the Euroweek newsletter are published on Fridays and cover securitization activities from Monday to Friday of the respective week, we analyze four different event windows

of [-4; 0], [-4;+1], [-4;+4] and [-6;+2] trading days around the announcement and issue date t0 to control for possible information leakages ex ante and delayed market reactions ex post.

To substantiate our hypothesis that the calculated CAARs are different from zero, we compute the standardized cross-sectional parametric test statistic suggested by Boehmer et al. (1991), which captures possible cross-sectional heteroscedasticity and changes of the variance of abnormal stock returns during the event window. We further adopt a non-parametric test statistic provided by

Corrado and Zivney (1992) as well as Maynes and Rumsey (1993) which is more effective since it abstracts from any distributional assumptions concerning abnormal stock returns and hence, provides additional information on the robustness of the parametric test statistic. In addition, this non-parametric test statistic corrects for possible cross-sectional dependence of abnormal stock returns during the event window period.

5. Empirical Results

Baseline results are presented in Tables IV.A and IV.B as well as Tables V.A and V.B. Results of our robustness checks are reported in Table VI, whilst sensitivity analyses from building several sub samples are presented in Tables VII-IX.

5.1 MAIN FINDINGS

As Tables IV.A and V.A report, estimated CAARs follow alternative lengths of specified event windows. Highlighted baseline results refer to an event window period of 8 [ −4;+4] trading days

symmetrically set around each securitization’s announcement and issue date t0 . The baseline specification includes the Dow Jones Eurostoxx 50 index as a reference blue chip index for Europe

(representing the largest and most liquid European companies) to measure the return of the market portfolio. Changes of the issuing banks’ shareholder values are estimated over the full sample of

381 (announcement day) and 380 (issue day) cash and synthetic transactions between 1997 and

2007. 5 Moreover, taking the specific growth of the volume of credit risk securitization in Europe into account (Figure 3), we additionally split the entire sample into two different samples with one sub sample covering the period from 1997 to 2002 (the onset stage) and another sub sample covering the period from 2003 to 2007 (the later stage).

5.1.1 Announcement Dates

As Table IV.A reports, the mean of estimated cumulated abnormal returns ( CARs ) is significantly negative at -0.37 percent for the entire sample period indicating that credit risk securitization announcements have a negative impact on issuing European banks’ shareholder values. This result is in line with previous empirical findings provided by Lockwood et al. (1996) and Thomas (2001) for the U.S. banking market. In contrast, our results do not support previous empirical findings provided by Franke and Krahnen (2006) for the European banking market suggesting that the announcement of a credit risk securitization neither causes significantly positive nor significantly negative share price reactions.

Introducing the onset and later stage sub samples, Table IV.A reports that the mean of estimated

CARs is negative at -0.32 percent for transactions during the onset stage, however, without exhibiting any statistical significance. In contrast, the average value of estimated CARs is significantly negative at -0.39 percent for securitizations during the later stage indicating a negative share price reaction during this time period. Considering these results, we provide empirical evidence that wealth effects due to securitization activities in Europe are time-dependent, which is in line with previous empirical findings provided by Thomas (2001) for the U.S. banking market.

In order to analyze the time-dependency of wealth effects more precisely we further build eleven different sub samples of securitization transactions with regard to each single year of the entire sample period (1997-2007). As Table IV.B reports, the mean of estimated CARs is significantly negative at -0.53 and -1.25 percent for securitizations transacted in 2006 and 2007 indicating that a

5 Note that the difference in the number of transactions results from the fact that we identify different confounding

events for securitizations based on announcement days and issue days respectively (Figures 1 and 2).

12 significant negative share price response to securitization announcements is observed for the first time two years before the subprime mortgage crisis has started. Empirical results may initially be explained by the fact that both the number and the volume of credit risk transactions in Europe have rapidly increased especially between 2005 and mid-2007 (Figures 3 and 4). Thus, bank shareholders may have benefitted from learning curve-effects during the sample period (and in particular the last two years) resulting in a better understanding of structured finance instruments and their underlying risks. If this is true, a negative share price response to securitization shortly before the subprime crisis has started in mid-2007 may be the consequence of an upcoming uncertainty among bank shareholders concerning the actual impact of credit risk securitization on the issuing banks’ overall risk exposures. We will investigate this nexus between securitization, the overall risk exposure and wealth effects in greater detail in Section 5.3 by building a variety of transaction- and bank-specific sub samples.

5.1.2 Issue Dates

Announcement dates are substituted in favor of issue dates in Tables V.A and V.B. As expected, the mean of estimated CARs becomes insignificant independent from analyzing different time periods of securitization activities. Since the announcement of an intended securitization transaction in our sample is given 20 days on average before the securitization transaction is actually closed, we suggest that relevant information on the amount of credit risk to be transferred has already become public and hence, the successful closing of a securitization transaction might not be a shareholder value relevant event.

5.1.3 Alternative Event Window Periods

The exogenous choice of a symmetric event window period of 8 trading days [ −4;+4] is arbitrary since it is assumed that the response of share prices to securitization announcements fully reflects the whole economic impact of a transaction on the issuing bank’s shareholder value in an unbiased manner exactly within these 9 trading days. Obviously, however, one cannot completely rule out that the event window period of 9 trading days accurately captures all of the information that is

13 revealed during a securitization transaction. Thus, as market responses to securitization announcements may be incomplete or biased we control for the robustness of our baseline results by estimating CAARs for further three event window periods of 4 [ −4;0], 5 [−4;+1] and 8 [−6;+2] trading days being set around the announcement dates and issue dates respectively. 6 As Tables IV.A and V.A report, results from the baseline regressions are reconfirmed even when setting alternative event window periods.

5.2 ROBUSTNESS CHECKS

Several tests are performed to control for the robustness of our baseline results. As reported in

Table VI, all specifications of CAARs are based on announcement dates and follow alternative lengths of event windows with baseline specifications being highlighted.7 Again, securitization transactions are analyzed during the entire sample period as well as the onset and later stage.

(1) To begin with, the market model is estimated assuming a more leptokurtic Student’s t distribution and Generalized Error Distribution (GED). 8 As Table VI indicates, main results from baseline regressions are generally reconfirmed even when assuming different distributions of the error term. As expected, average values of estimated CARs become notably smaller in value when employing a more leptokurtic Student’s t or GED distribution.

(2) Addressing to an alternative specification of the market model as used in related literature the market model is estimated based on excess returns, defined as log return minus log (risk-free) one

6 We additionally estimate the share price response to securitization announcements and closing for an event window

period of ten trading days symmetrically set around the announcement and issue days respectively, however,

without obtaining different results. We will provide results on request.

7 Moreover, we perform all robustness checks for securitization transactions based on issue dates without obtaining

different results. Results are provided on request.

8 In addition, we perform Student’s t and GED regressions for the following robustness checks (2) – (5) without

obtaining different results. We will provide results on request.

14 month interbank lending rate LIBOR. As Table VI reports, baseline results are reiterated even when modifying the market model.

(3) Furthermore, different stock price indices are employed to measure the market portfolio. First of all, the reference index Dow Jones Eurostoxx 50 is exchanged in favor of the Dow Jones

Eurostoxx 600 index . In contrast to the reference index, this measure represents small, medium and large capitalized companies and hence, allows controlling for “size effects”. As shown in Table VI, means of estimated CARs exhibit lower values across all event window periods when employing the Dow Jones Eurostoxx 600 index . However, as our baseline results are generally reconfirmed, we do not provide empirical evidence that our main findings are significantly affected by a probable

“size effect”.

(4) Similarly, the Dow Jones Eurostoxx 50 reference index is substituted by local market indices

(national blue chip indices). Table VI reports that baselines results are reiterated when employing a different market portfolio, however, with means of estimated CARs decreasing in value. The decrease might be explained by the fact that our sample of banks also comprises smaller financial institutions, which should be more affected by a local rather than a pan-European index.

(5) Finally and most important, compared to M&A for example, credit risk securitization may be a “weak” event (in particular for large banks in our sample). Thus, one cannot completely rule out that the negative share price response may result from unobservable factors affecting the whole

European banking industry. In order to control for this aspect we examine the differences in share price reactions between our sample of issuing banks and a control group of similar, but non- securitizing banks for the relevant time period.

We build two different control groups of non-securitizing (listed) banks located in the EU-15 plus

Switzerland. Following Barber and Lyon (1997) a modified iterative matching procedure is employed to select appropriate banks for the first control group . Thus, in a first step total assets of the control bank must be in a range between -25 and +25 percent of total assets of the sample bank.

This matching criterion is based on empirical evidence that the bank’s size is negatively correlated

15 with its overall risk exposure (Diamond, 1984; Boyd and Prescott, 1986) and that size is a strong determinant of the frequency of securitization activities (Martín-Oliver and Saurina, 2007; Bannier and Hänsel, 2008). In a second step, the control bank must not enter into securitization business during the relevant event window of 21 trading days. Finally, in a third step those banks with the closest market-to-book ratios to that of the sample banks are selected in favor of the first control group. Differences in the market-to-book ratio between our sample banks and banks from the first control group are on average below 10 percent. The purpose of employing this matching criterion is to incorporate a long-term performance characteristic and to proxy the bank’s franchise value since higher franchise values usually deter excessive risk-taking behavior by the bank’s management

(Keeley, 1990; Demsetz et al., 1996). With regard to the second control group , we perform the first and second step in exactly the same way. However, during the third step each sample bank is matched with a portfolio of three control banks exhibiting the closest market-to-book ratios in order to smooth the impact of probable bank-specific confounding events (e.g. earning announcements, announcement of share repurchases).

As reported in Table VI, average values of estimated CARs become notably smaller in value, without exhibiting statistical significance for the first and second control group independent from setting different event window periods and analyzing different time periods of securitization activities in Europe. The difference in means of estimated coefficients for the entire sample period is statistically significant at the ten-percent level (first control group) and five-percent level (second control group) applying the difference-in-means t-Test. In addition, the difference in means of estimated coefficients for the later stage is statistically significant at the ten-percent level (first control group) and one-percent level (second control group) applying the difference-in-means t-Test. Thus, as results from control group regressions clearly reiterate our baseline findings, we rule out that the negative share price reaction results from unobservable factors affecting the whole

European banking industry but is rather associated with credit risk securitization.

5.3 SUB SAMPLES

Theoretical reflections on the nexus between credit risk securitization, the issuing bank’s overall risk exposure and its change in shareholder value as discussed in Section 2 are at best indirect evidence. In order to shed a brighter light on this nexus, we build three transaction-specific sub samples to control for (1) the type of securitization, (2) the underlying reference portfolio, and (3) the issue frequency (Table VII). In addition, we generate five bank-specific sub samples to control for the securitizing bank’s (1) size, (2) capitalization, (3) liquidity, (4) profitability, and (5) loan portfolio quality (Table IX). 9

Bank-specific sub samples are constructed by classifying (ranking) our sample of banks with financial characteristic variables and dividing the sample at the median based on each characteristic

(Gasbarro et al., 2005). Hence, the first sub sample explicitly comprises banks exhibiting financial characteristics above the median of the cumulated characteristics of all banks in our sample whereas the second sub sample consists of banks exhibiting financial characteristics below the median of the cumulated characteristics. Table VIII presents descriptive statistics of the bank-specific variables for the entire sample of securitizing banks as well as the above-median and below-median groups.

As shown, the discriminatory power of the partitioning of financial variables is adequate. The mean of selected variables of the above-median group is nearly twice the value of the below-median group. Moreover, the mean value for each financial characteristic variable of the above-median group is significantly different from the mean value of the financial characteristic variable of the below-median group at the one-percent level applying the difference-in-means t-Test.

In order to ensure comparability with regard to our baseline findings from Section 5.1, all sub samples are based on a symmetrical event window period of 8 [−4;+4] trading days around each

securitization’s announcement date t0 for the entire sample period from 1997 to 2007 as well as the onset (1997-2002) and later stage (2003-2007).

9 We retrieved respective bank balance sheet data from BankScope database provided by FitchRatings . 17

5.3.1 Controlling for Cash vs. Synthetic Transactions

As Table VII reports, the average value of estimated CARs is significantly negative at -0.28 percent (entire period) and -0.37 percent (later stage) for the sub sample including cash transactions.

The difference in means of estimated CARs is significant at the five-percent level respectively applying the difference-in-means t-Test. Thus, empirical results reveal a negative share price reaction due to cash transactions suggesting that banks in our sample may use liquid capital that has become available from selling true sale securitizations as an additional founding source to take in new but risky assets ex post.

5.3.2 Controlling for the Underlying Reference Portfolio

Table VII indicates that the average value of estimated CARs is significantly negative for the sub samples of Collateralized Debt Obligations (CDOs) from large enterprises and Residential

Mortgage Backed Securities (RMBS) concerning the entire sample period. Moreover, the mean of estimated CARs is significantly negative for the sub sample of CDOs from large enterprises as regards the onset stage and for the sub sample of RMBS during the later stage. We suggest that the impact of the respective underlying is driven by the difference in its risk characteristics (e.g. the degree of diversification, the asset granularity and the counterparty risk) and the amount of credit risk within the residual loan portfolio. Thus, since RMBS and CDOs represent highly granular reference loan portfolios (RMBS) and less diversified but high-rated reference loan portfolios

(CDOs), a negative share price response to securitization indicates that banks in our sample may transfer less risky tranches out of their balance sheets whereas the more risky parts of the loan portfolio are retained.

5.3.3 Controlling for the Issue Frequency

As shown in Table VII, the mean of estimated CARs is significantly negative at -0.64 percent

(entire time period) and at -0.80 percent (later stage) for the sub sample comprising securitization transactions from the ten high-frequency issuing banks in our sample (Figures 5 and 6). The

18 difference in means of estimated CARs is significant at the five-percent and ten-percent level respectively applying the difference-in-means t-Test. Thus, our results do not support theoretical assumptions that higher securitization frequencies may lead to an increase in the issuing bank’s reputation. In contrast, empirical results are in line with the “overcollateralization hypothesis” and

“asset deterioration hypothesis” (Instefjord, 2005; Greenbaum and Thakor, 1987) proposing that high-frequency issuers tend to securitize less risky tranches but retain the more risky first-loss position. Moreover, high-frequency securitization is assumed to increase the complexity of the issuing bank’s balance sheet structure, which in turn may exacerbate a correct assessment of the bank’s overall credit risk exposure by bank shareholders.

5.3.4 Controlling for the Securitizing Banks’ Size

Table IX indicates that the mean of estimated CARs is significantly negative for the sub sample of large banks concerning the entire period and later stage. As the difference in means of estimated

CARs is significant at the ten-percent level respectively applying the difference-in-means t-Test, we suggest that the announcement of a securitization transaction may be a significant event even for large financial institutions in our sample. Furthermore, a negative share price response to securitization may also be due to the fact that particularly and only large sample-banks act as high frequency issuers (Section 5.3.3).

5.3.5 Controlling for the Securitizing Banks’ Capitalization

As Table IX reports, the mean of estimated CARs is statistically negative at -0.71 and -0.78 percent for the sub sample of high-leveraged banks with regard to the entire sample period and the later stage. The net effect of the difference in means of estimated CARs is significant at the ten- percent and five-percent level respectively applying the difference-in-means t-Test. As it is suggested that high-leveraged banks are more prone to a risky reinvestment strategy ex post resulting in a deterioration of asset quality, a negative share price response indicates that bank

19 shareholders may perceive securitization transactions by undercapitalized banks as a signal of financial distress.

5.3.6 Controlling for the Securitizing Banks’ Liquidity

Table IX illustrates that the average value of estimated CARs is significantly negative at -0.43 and -0.69 percent for the sub sample of banks with a low pre-event liquidity ratio during the entire sample period and the later stage. The difference in means of estimated CARs is significant at the ten-percent level respectively applying the difference-in-means t-Test. These results are in line with our findings from Section 5.3.1 indicating that securitization may be used as an alternative funding source for new but risky assets. Hence, a negative share price response to securitization from banks exhibiting a high level of pre-event liquidity risk indicates that bank shareholders may interpret securitization activities as a signal of a distinct need for cash (financial slack).

5.3.7 Controlling for the Securitizing Banks’ Profitability

As shown in Table IX, the average value of estimated CARs is significantly negative at -0.71 and

-0.83 percent for the sub sample of less profitable banks during the entire sample period and the later stage. The difference in means of estimated CARs is statistically significant at the ten-percent and one-percent level respectively applying the difference-in-means t-Test. Thus, empirical results reveal a negative share price reaction due to securitization by banks exhibiting a low pre-event level of profitability. Corresponding with our findings from Section 5.3.1, evidence suggests that shareholders may perceive securitizations by this banking group as a signal of a risky reinvestment strategy ex post (gambling for resurrection).

5.3.8 Controlling for the Securitizing Banks’ Portfolio Quality

Finally, Table IX reports that the mean of estimated CARs is significantly negative at -0.70 percent (entire sample period), at -1.35 percent (onset stage) and at -0.69 percent (later stage) for the sub sample of banks exhibiting a high pre-event loan portfolio quality. The net effect of the difference in means of estimated CARs is significant at the five-percent, one-percent and ten-

20 percent level respectively applying the difference-in-means t-Test. A negative relationship between securitization and wealth effects for banks exhibiting a high asset quality is surprising at first sight

However, if it is true that comparative advantages in risk management techniques may provide an incentive to securitize less risky tranches and instead, retain and manage the major part of credit default risks within the bank shareholders may not favor this strategy.

6. Conclusion

Using a unique cross-sectional dataset of 381 cash and synthetic securitizations issued by 53 banks from the EU-15 plus Switzerland over the period from 1997 to 2007, this paper provides empirical evidence that credit risk securitization announcements have a negative impact on the issuing banks’ shareholder values. Our results correspond to previous empirical findings provided by Lockwood et al. (1996) and Thomas (2001) for the U.S. banking market but do not support an earlier study by Franke and Krahnen (2006) for the European banking market promoting that the announcement of credit risk securitization transaction neither causes significantly positive nor negative average abnormal bank stock returns.

Moreover, our analysis reveals that wealth effects due to securitization activities in Europe are time-dependent suggesting that bank shareholders may have benefitted from learning curve-effects during the sample period (and in particular the last two years) and have gained a better understanding of structured finance instruments and the actual impact of credit risk securitization on the issuing banks’ overall risk exposures shortly before the subprime crisis has emerged in mid-

2007. Finally, analyzing the nexus between credit risk securitization, the issuing banks’ overall risk exposures and shareholder values in greater detail, regressions from a variety of transaction- and bank-specific sub samples indicate that a negative share price response to securitization may result from European banks following a risky reinvestment strategy and retaining the more risky first loss position ex post. In addition, evidence suggests that shareholders may perceive securitization by banks exhibiting a low pre-event capital and liquidity ratio as signals of financial distress and financial slack.

Against the background of our empirical results we point out that the number of policy and industry initiatives that are designed to restart securitization markets under sounder conditions are a step in the right direction. Empirical findings at hand reveal an increasing uncertainty among investors with regard to the impact of credit risk securitization on the issuing bank’s overall risk exposure. However, since we are not able to evaluate if this uncertainty is based on fundamental data or merely noisy signals we emphasize the necessity to establish further standards that specify a higher level of transparency for structured finance instruments. The need for transparency standards is clearly underlined by the subprime mortgage crisis from mid-2007, which has disclosed some important insights regarding the actual effect of securitizations on financial stability. Furthermore, as we provide evidence that the change of bank shareholder value might be explained by the fact that bank shareholders and potential capital market investors anticipate a possible asset deterioration effect and an increase in the level of complexity of the financial system arising from securitization transactions in recent years, strengthening the disclosure of more detailed information on the underlying reference portfolios and the volume of risk retention is necessary.

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Statistical appendix

Tables and figures

Table I. Geographical distribution of banks in the sample

Country Banks Country Banks Austria Erste Group Bank Netherlands ABN Amro Holding Fortis Bank Belgium KBC Groupe Portugal Banco BPI Denmark Danske Bank Banco Espr. Santo Sydbank BCP

France BNP Paribas Spain BBVA Crédit Agricole Banco De Sabadell Natixis Banco De Valencia Banco Pastor Germany Bay. Hypo- u. Vereinsbank Banco Popular Espanol Commerzbank Banco Santander Deutsche Bank Bankinter Deutsche Postbank IKB Dt. Industriebank United Kingdom Abbey National Alliance & Leicester Greece EFG Eurobank Ergasias Barclays Bank Bank of Piraeus Bradford & Bingley HBOS Ireland Anglo Irish Banks HSBC Holdings Bank of Ireland Lloyds TSB Group Depfa Bank Northern Rock Royal Bank of Scotland Italy Banca Carige Standard Chartered Banca Lombarda Banca Monte Dei Paschi Sweden Nordea Bank Banca Naz. Lavoro SEB Banca Popolare Milano Swedbank Banca Popolare Italiana Capitalia Switzerland Credit Suisse Intesa Sanpaolo UBS Unicredito Italiano

Capital Increase & Restructuring 12 Stock Repurchse 7

Mergers & Acquisitions 40 Annual & Interim Results 56

Issue of Securities 11

Investments4 Earnings Outlook 7 Disinvestment 8 Subprime Strategy 6 Crisis 55 Rating Actions 5

Figure 1. Confounding events (211) in the sample (announcement day)

Capital Increase & Restructuring 16 Stock Repurchse 7

Mergers & Annual & Acquisitions 51 Interim Results 46

Issue of Investments 4 Securities 4 Earnings Outlook 9 Disinvestment 10 Subprime Strategy 5 Crisis 55 Rating Actions 5

Figure 2. Confounding events (212) in the sample (issue day)

Table II. Descriptive statistics of securitizations in the sample between 1997 to 2007 (in million €)

n Total volume Mean Median Standard deviation Minimum Maximum Types of Transaction Cash Transactions 266 436,405 1,641 918 2,016 25 13,935 Synthetic Transactions 115 215,117 1,871 1,008 2,740 28 22,000 Total Transactions 381 651,522 1,710 1,000 2,257 25 22,000 Underlyings Collateralized Debt 129 199,896 3,075 1,000 1,842 25 12,500 Obligations from small and medium 61 81,492 2,629 1,000 1,183 25 5,000 enterprises from large enterprises 68 118,404 3,432 1,004 2,269 28 12,500 Resid ential Mortgage Backed 159 339,450 4,243 1,277 2,832 56 22,000 Securities Commercial Mortgage 32 45,252 2,743 728 1,806 108 8,483 Backed Securities Credit Cards Receivables 10 9,735 1,770 665 957 143 3,360 Consumer Loans 23 22,790 1,899 560 1,014 40 4,158 Others 28 34,3992,372 755 1,287 219 5,500

Cash Securitizations Synthetic Securitizations Cash & Synthetic Securitizations 175,000 150,000 125,000 100,000 75,000 50,000 25,000

Volume Volume of Credit Risk Transfer 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Years

Figure 3. Volume of credit risk securitization transactions issued by 53 banks during the period from 1997 to 2007 (in

million €)

Cash Securitizations Synthetic Securitizations Cash & Synthetic Securitizations 90 80 70 60 50 40 30 20

Number Number ofTransactions 10 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Years

Figure 4. Number of credit risk securitization transactions issued by 53 banks during the period from 1997 to 2007

Cash Securitizations Synthetic Securitizations Cash & Synthetic Securitizations 250,000

200,000

150,000

100,000

50,000

0 Volume Volume Creditof Risk Transfer Q1 Q2 Q3 Q4 Quarters

Figure 5. Quarterly distribution of the volume of credit risk securitization transactions during the sample period (in

million €)

Cash Securitizations Synthetic Securitizations Cash & Synthetic Securitizations 140 120 100 80 60 40

Number Number Transactions of 20 0 Q1 Q2 Q3 Q4 Quarters

Figure 6. Quarterly distribution of the number of credit risk securitization transactions during the sample period

Banco Santander 31

Northern Rock 18 BNP Paribas 2% Others 46% Credit Suisse 2% Deutsche Bank 3% Banco Pastor 1% Barclays Bank 16 Bankinter 2%

Others 132 Banca Monte HBOS 12% dei Paschi 13

HBOS 11 Banca Monte Bankinter 10 dei Paschi 2% Barclays Bank 3% Banco Santander 10% Banco Pastor 10 Deutsche Bank 9 Northern Rock 17% BNP Paribas 8 Credit Suisse 8

Figure 7. Frequent securitizers of cash transactions (n=266)

Deutsche Bank 11 Deutsche Bank 9% IKB Dt. Others 25% IKB Dt. Industriebank 10 Industriebank 5%

KBC Groupe 6%

KBC Groupe 9 Crédit Agricole 1% Others 40 Natixis 2%

BNP Paribas 9 Royal Bank BNP Paribas 16% of Scotland 1%

Crédit Agricole 5 Bay.Hypo-u. Vereinsbank 9 ABN Amro Holding 25% Bay. Hypo-u. Natixis 5 UBS 6 Vereinsbank 7% UBS 3% Royal Bank of Scotland 5 ABN Amro Holding 6

Figure 8. Frequent securitizers of synthetic transactions (n=115)

Table III.A Descriptive statistics of bank stock returns

Table III.A reports the descriptive statistics of time series of bank stock returns from 53 banks during the sample period from January, 1997 to June, 2 2007. PP denotes the Phillips-Perron (1988) test for unit root (7 lags), Z(t) test statistic. p1(rt), p1(r t), p1(| rt|) is the Box-Pierce (1970) statistic for first- order autocorrelation of log, squared and absolute stock returns respectively. S is the Skewness and K is the Kurtosis. JB denotes the Jarque-Bera (1980) normality test. ***, **, * indicates significance at the 1 %, 5 % and 10 % level respectively.

2 Banks PP p1(rt) p1(r t) p1(| rt|) S K JB

Abbey National (UK) -40.253*** 0.0252 0.0953*** 0.1502*** 0.0426 6.1078 810.7*** ABN Amro Holding (NL) -43.469*** 0.0129 0.2484*** 0.2829*** -0.0362 7.6556 2428.0*** Alliance & Leicester (UK) -44.558*** -0.0241 0.1264*** 0.1883*** 0.3164 6.4704 1355.0*** Anglo Irish Banks (IE) -43.157*** 0.0376* 0.2578*** 0.2020*** 0.0756 6.9539 1753.0*** Banca Carige (IT) -39.683*** -0.0586*** 0.2552*** 0.3313*** 0.1500 12.8503 11000.0*** Banca Lombarda (IT) -41.420*** -0.0666*** 0.2046*** 0.2648*** 0.5548 8.3816 3302.0*** Banca Monte Dei Paschi (IT) -39.126*** 0.0141 0.1539*** 0.2098*** 0.0614 6.0432 794.7*** Banca Naz. Lavoro (IT) -36.612*** -0.0277 0.1740*** 0.2969*** -0.0647 9.0299 3048.0*** Banca Popolare Milano (IT) -42.506*** 0.0164 0.1054*** 0.2348*** -0.6327 15.7791 18000.0*** Banca Popolare Italiana (IT) -39.830*** 0.0859*** 0.1698*** 0.3010*** 0.0578 20.5259 34000.0*** Banco BPI (PT) -41.927*** 0.1017*** 0.1120*** 0.2503*** 1.1643 21.6058 39000.0*** Banco Santander (ES) -43.249*** -0.0019 0.2529*** 0.2765*** -0.1444 8.5437 3451.0*** BCP (PT) -40.348*** 0.0970*** 0.1783*** 0.2962*** -0.3147 13.3668 12000.0*** Banco De Valencia (ES) -41.195*** -0.0625*** 0.1884*** 0.2715*** 0.9344 9.7506 5495.0*** Banco Espr. Santo (PT) -39.403*** 0.1637*** 0.2147*** 0.3236*** 0.1540 12.9978 11000.0*** Banco Pastor (ES) -42.646*** 0.0911*** 0.2042*** 0.2354*** 0.3389 8.9556 4024.0*** Banco Popular Espanol (ES) -44.281*** 0.0179 0.1883*** 0.2301*** 0.1159 7.0704 1862.0*** Banco De Sabadell (ES) -37.422*** 0.0040 0.0434* 0.1588*** 0.5769 23.6935 29000.0*** Bankinter (ES) -42.574*** 0.0840*** 0.3129*** 0.2520*** 0.2934 9.0400 4125.0*** Bank of Ireland (IE) -44.372*** 0.0600*** 0.2218*** 0.2090*** -0.0468 5.9384 968.0*** Bank of Piraeus (GR) -39.616*** 0.1536*** 0.3234*** 0.3207*** 0.4015 5.9364 1038.0*** Barclays Bank (UK) -43.703*** 0.0838*** 0.2163*** 0.2490*** 0.1297 5.2937 596.8*** Bay. Hypo- u. Vereinsbank (DE) -43.917*** 0.0214 0.2048*** 0.2590*** 0.0077 6.7625 1586.0*** BBVA (ES) -43.148*** 0.0611*** 0.2847*** 0.2843*** 0.0295 8.4664 3347.0*** BNP Paribas (FR) -44.245*** 0.0476** 0.2053*** 0.2430*** 0.0548 7.2333 2008.0*** Bradford & Bingley (UK) -37.391*** -0.1205*** 0.1155*** 0.1266*** 0.0341 5.1151 314.4*** Capitalia (IT) -41.693*** 0.0071 0.3312*** 0.2867*** 0.4251 10.1284 5772.0*** Commerzbank (DE) -43.767*** 0.0523*** 0.2013*** 0.2441*** 0.1177 7.5559 2331.0*** Crédit Agricole (FR) -33.398*** -0.0685*** 0.1357*** 0.2112*** -0.1478 7.0871 994.2*** Credit Suisse (CH) -42.221*** 0.0013 0.2458*** 0.2974*** -0.3169 8.4903 3421.0*** Danske Bank (DK) -44.180*** -0.0350* 0.0521*** 0.2095*** 0.6197 17.1606 23000.0*** Depfa Bank (IE) -44.018*** -0.0253 0.1458*** 0.2030*** 0.0809 7.5251 2296.0*** Deutsche Bank (DE) -44.101*** 0.0232 0.1821*** 0.2428*** -0.0503 7.0031 1796.0*** Deutsche Postbank (DE) -35.555*** 0.0223 0.1440*** 0.0636*** -1.5545 19.074 1423.0*** EFG Eurobank Ergasias (GR) -37.123*** 0.1556*** 0.4456*** 0.3951*** 0.2139 6.0104 1035.0*** Erste Group Bank (AT) -43.114*** 0.0263 0.1616*** 0.1697*** -0.1316 5.9826 915.2***

Table III.A (continued) Descriptive statistics of bank stock returns

2 Banks PP p1(rt) p1(r t) p1(| rt|) S K JB

Fortis Bank (NL) -41.972*** 0.0606*** 0.2952*** 0.3102*** 0.4309 11.3879 7963.0*** HBOS (UK) -40.235*** 0.0186 0.3483*** 0.3004*** 0.4137 9.3772 4452.0*** HSBC Holdings (UK) -40.907*** -0.0469** 0.4556*** 0.3166*** -0.0477 20.9692 36000.0*** IKB Dt. Industriebank (GE) -45.249*** -0.0398** 0.0893*** 0.1771*** -0.1849 9.2460 4385.0*** Intesa Sanpaolo (IT) -42.639*** -0.0073 0.2449*** 0.2738*** 0.3318 6.7169 1597.0*** KBC Groupe (BE) -42.562*** 0.0490** 0.2477*** 0.2481*** 0.3454 8.1017 2968.0*** Lloyds TSB Group (UK) -43.299*** 0.0420** 0.1878*** 0.2580*** 0.2379 5.8041 906.0*** Natixis (FR) -44.745*** 0.0293 0.0895*** 0.1952*** 0.4779 10.5989 6570.0*** Nordea Bank (SE) -42.609*** -0.0508** 0.1925*** 0.2153*** -0.0058 6.3513 1146.0*** Northern Rock (UK) -45.191*** 0.0008 0.3755*** 0.1389*** 0.3585 8.1703 2835.0*** Royal Bank of Scotland (UK) -42.620*** 0.0413** 0.2160*** 0.2784*** -0.0117 6.9427 1741.0*** SEB (SE) -43.776*** 0.0243 0.2120*** 0.2394*** 0.1414 6.1425 1115.0*** Standard Chartered (UK) -43.387*** 0.0467** 0.1551*** 0.2455*** 0.0075 7.4368 2205.0*** Swedbank (SE) -45.704*** 0.0081 0.2124*** 0.1806*** 0.2445 6.6928 1554.0*** Sydbank (DK) -46.070*** 0.0299 0.0850*** 0.1705*** 0.0562 10.7052 6651.0*** UBS (CH) -42.433*** 0.0702*** 0.2347*** 0.2853*** -0.2421 8.7655 3749.0*** Unicredito Italiano (IT) -40.581*** 0.0314 0.2373*** 0.3405*** 0.5010 8.0886 3013.0***

Table III.B GARCH effects =β + β + ε σ2= α + αε 2 + ασ 2 The model estimated is Rit, i ,0 imt ,1, R it , with it, i ,0 iit ,1,1− iit ,2,1 − . Coefficients are obtained from jointly maximum likelihood regressions assuming a standard normal distribution while standard errors are received from a two-sided t-Test and Wilcoxon signed rank test applied to means of estimated coefficients. α0, α1 and α2 are estimated coefficients of the conditional variance of the error term εi,t which is assumed to follow an GARCH (1,1) process. Values in parentheses indicate the total number of each coefficient being significantly different from zero at the five-percent level. Constant term and β1 estimated but not reported. ***, **, * indicates significance of the two-sided t-Test with a probability of error of 1 %, 5 % and 10 %. a, b, c indicates significance of the two-sided Wilcoxon signed- rank test with a probability of error of 1 %, 5 % and 10 %. Regression specification (1) is based on announcement dates; specification (2) is based on issue dates. Both specifications include the Dow Jones Eurostoxx 50 index as a reference index for Europe.

Regr. (1) Entire Sample Period 1997-2007 Onset Stage 1997-2002 Later Stage 2003-2007

Announcement Mean Estimate (n=381) Mean Estimate (n=133) Mean Estimate (n=248) Date ***a ***a ***a α0 0.0001070 (190) 0.0001703 (72) 0.0000721 (118) ***a ***a ***a α1 0.1729024 (222) 0.1947254 (83) 0.1611990 (139) ***a ***a ***a α2 0.2454180 (221) 0.2956663 (85) 0.2184704 (136)

Regr. (2) Entire Sample Period 1997-2007 Onset Stage 1997-2002 Later Stage 2003-2007 Issue Date Mean Estimate (n=380) Mean Estimate (n=136) Mean Estimate (n=244)

***a ***a ***a α0 0.0001081 (182) 0.0001477 (59) 0.0000860 (123) ***a ***a ***a α1 0.1678341 (205) 0.1805574 (71) 0.1607424 (134) ***a ***a ***a α2 0.2565425 (222) 0.3801651 (92) 0.1876381 (130)

Table IV.A Wealth effects around the announcement day

N N T2 N T2 = 1 = 1 = 1 − β * + β * The model estimated is CAARs t ∑ CARs , ti ∑∑ ARs ,ti ∑∑ R ,ti ( 0, 1, R ,tmii ). = = = = = N i 1N i 1 Tt 1 N i 1 Tt 1

Daily average abnormal stock returns (AR t) and cumulative average abnormal stock returns (CAAR t) are calculated for single event days and different event window periods around the securitization’s announcement date t 0. Coefficients of the market model are obtained from jointly maximum likelihood GARCH (1,1) regressions assuming a standard normal distribution. Standard errors are received from the two-sided parametric test proposed by Boehmer et al. (1991) and the two-sided non-parametric test proposed by Corrado and Zivney (1992) applied to AR t and CAAR t. The regression specification of the market model includes the Dow Jones Eurostoxx 50 index as a reference index for Europe. All sub samples are based on the entire sample period (1997 to 2007) as well as the onset stage (1997 to 2002) and later stage (2003 to 2007) of securitization activities in Europe. ***, **, * indicates the significance of the two-sided parametric test proposed by Boehmer et al. (1991) with a probability of error of 1 %, 5 % and 10 %. a, b, c indicates the significance of the two-sided non-parametric test proposed by Corrado and Zivney (1992) with a probability of error of 1 %, 5 % and 10 %.

Event Window Entire Sample Period 1997-2007 Onset Stage 1997-2002 Later Stage 2003-2007 CAARs (n=381) CAARs (n=133) CAARs (n=248) -6 -0.16 -0.19 -0.08 -5 0.06 0.06 0.06

-4 -0.10 * -0.10 -0.10 c

-3 -0.12 **a -0.09 -0.13 **b

-2 0.06 c 0.12 0.03

-1 -0.10 *c -0.11 -0.09 c

0 0.06 0.05 0.06

1 -0.15 -0.34 *c -0.05

2 -0.05 0.05 -0.10 **c

3 -0.03 0.06 -0,08 4 0.06 0.03 0,08 [-4;0] -0.20 **c -0.13 -0.23 **b

[-4;+1] -0.35 ***b -0.47 -0.28 **b

[-4;+4] -0.37 ***c -0.32 -0.39 ***a

[-6;+2] -0.46 ***b -0.55 -0.41 **a

Table IV.B Wealth effects around the announcement day (by year)

The empirical model and test statistics are defined in Table IV.A. All estimates are based on a symmetrical event window period of 8 days [-4;+4] around each securitization’s announcement date t 0.

Event Window

[-4;+4] CAARs n CAARs n 1997 n.a. (2) 2003 -0.25 (39) 1998 0.13 (10) 2004 0.39 (36) CAARs 1999 -0.37 (15) 2005 -0.26 (55) by Year 2000 0.73 (29) 2006 -0.53 *b (81) 2001 -0.76 (41) 2007 -1.25 ***c (37) 2002 0.21 (36)

Table V.A Wealth effects around the issue day

The empirical model and test statistics are defined in Table IV.A. All estimations are based on issue dates t 0.

Event Window Entire Sample Period 1997-2007 Onset Stage 1997-2002 Later Stage 2003-2007 CAARs (n=380) CAARs (n=136) CAARs (n=244) -6 -0.02 -0.16 0.05 -5 -0.06 -0.11 -0.03 -4 -0.04 -0.06 -0.03 -3 0.00 0.00 0.00 -2 -0.03 -0.12 0.02 -1 -0.03 -0.08 -0.01 0 0.00 0.02 -0.01 1 0.07 0.24 -0.02 2 -0.01 0.02 -0.02 3 -0.07 -0.17 -0.02 4 0,01 0.05 -0.01 [-4;0] -0.11 -0.24 -0.03 [-4;+1] -0.03 0.00 -0.05 [-4;+4] -0.10 -0.09 -0.10 [-6;+2] -0.12 -0.24 -0.05

Table V.B Wealth effects around the issue day (by year)

The empirical model and test statistics are defined in Table IV.A. All estimates are based on a symmetrical event window period of 8 days [-4;+4] around each securitization’s issue date t 0.

Event Window

[-4;+4] CAARs n CAARs n 1997 n.a. (2) 2003 0.87 (40) 1998 n.a. (7) 2004 -0.24 (36) CAARs 1999 -0.45 (11) 2005 -0.14 (52) by Year 2000 0.80 (29) 2006 -0.27 (74) 2001 -0.14 (42) 2007 -0.42 (42) 2002 -0.79 (45)

Table VI. Robustness checks

The empirical model and test statistics are defined in Table IV.A. All estimations in Table VI are based on announcement dates t 0. Specification (1) and (2) assume a Student’s t distribution and a Generalized Error Distribution (GED). Specification (3) is based on excess returns defined as log return minus log (risk-free) one month interbank lending rate LIBOR. Specification (4) employs the Dow Jones Eurostoxx 600 index which contains small, medium and large capitalized companies. Specification (5) includes national blue chips indices. Specification (6) refers to the first control group while specification (7) refers to the second control group of non-securitizing banks respectively.

Entire Sample Period

Event Window (1) (2) (3) (4) (5) (6) (7) Student’s t Distribution GED Distribution Interest Rate (Libor) DJ Stoxx 600 National Blue Chips First Control Group Second Control Group

(DF=5.90) (Shape=1.29) CAARs (n=381) CAARs (n=381) CAARs (n=381) CAARs (n=381) CAARs (n=381) CAARs (n=381) CAARs (n=1143) -6 -0.10 -0.09 -0.11 -0.12 -0.07 -0,01 -0.06 *

-5 0.08 0.08 0.07 -0.01 0.07 -0,03 -0.02

-4 -0.09 * -0.08 ** -0.10 * -0.07 -0.09 ** -0,03 -0.04

-3 -0.11 **b -0.13 **b -0.12 **b -0.07 -0.09 **b -0,04 -0.04

-2 0.08 0.10 c 0.07 0.07 0.05 0,06 0.07

-1 -0.08 c -0.08 c -0.10 *c -0.10 -0.06 0,04 0.01

0 0.06 0.06 0.05 0.03 0.05 0,07 0.06 *

1 -0.14 -0.13 -0.15 -0.14 -0.15 * -0,06 -0.03 ** 2 -0.04 -0.08 -0.06 -0.02 -0.06 -0,06 -0.02

3 -0.01 -0.02 -0.02 0,00 0.00 0,07 0.07 **

4 0.07 0.07 0.06 0,07 0.06 0,07 0.01 [-4;0] -0.13 *b -0.12 b -0.20 **b -0.14 *c -0.15 **c 0.10 0.06

[-4;+1] -0.27 **b -0.25 **b -0.35 ***a -0.28 **b -0.30 **b 0.03 0.03

[-4;+4] -0.25 **b -0.28 **b -0.37 ***b -0.23 *c -0.30 **b 0.12 0.10

[-6;+2] -0.32 **a -0.35 **b -0.45 ***a -0.42 ***b -0.36 **b -0.06 -0.07

Table VI. (continued) Robustness checks

Onset Stage

Event Window (1) (2) (3) (4) (5) (6) (7) Student’s t Distribution GED Distribution Interest Rate (Libor) DJ Stoxx 600 National Blue Chips First Control Group Second Control Group

(DF=5.86) (Shape=1.26) CAARs (n=133) CAARs (n=133) CAARs (n=133) CAARs (n=133) CAARs (n=133) CAARs (n=133) CAARs (n=399) -6 -0.16 -0.16 -0.18 -0.18 -0.05 -0,14 -0.16 ** -5 0.10 0.10 0.08 -0.04 0.08 -0,28 *b -0.15 c

-4 -0.08 -0.08 -0.09 -0.05 -0.07 -0,06 -0.12 *

-3 -0.07 -0.07 -0.08 0.01 -0.01 -0,20 -0.11

-2 0.16 0.15 0.13 0.17 0.07 0,11 0.13

-1 -0.10 -0.10 -0.12 -0.10 0.01 * 0,04 0.04

0 0.05 0.04 0.03 0.01 0.01 0,26 0.14

1 -0.33 -0.33 -0.33 *c -0.31 -0.34 * -0,16 -0.06

2 0.05 0.04 0.03 0.05 0.02 0,04 -0.06

3 0.10 0.10 0.07 0.10 0.16 0,05 0.06

4 0.03 0.04 0.03 0.00 0.03 0,20 0.07 [-4;0] -0.04 -0.06 -0.13 0.03 0.02 0.15 0.08

[-4;+1] -0.37 -0.38 -0.46 -0.28 -0.32 -0.01 0.02

[-4;+4] -0.18 -0.20 -0.34 -0.12 -0.11 0.29 0.10

[-6;+2] -0.38 -0.40 -0.53 -0.45 -0.27 -0.39 -0.34

Table VI. (continued) Robustness checks

Later Stage

Event Window (1) (2) (3) (4) (5) (6) (7) Student’s t Distribution GED Distribution Interest Rate (Libor) DJ Stoxx 600 National Blue Chips First Control Group Second Control Group (DF=5.91) (Shape=1.30) CAARs (n=248) CAARs (n=248) CAARs (n=248) CAARs (n=248) CAARs (n=248) CAARs (n=248) CAARs (n=744) -6 -0.06 -0.06 -0.08 -0.07 -0.07 0.06 -0.01

-5 0.08 0.07 0.06 0.00 0.05 0.10 0.05 *

-4 -0.09 c -0.08 *c -0.10 -0.08 -0.12 *c -0.01 0.00

-3 -0.13 **b -0.16 **b -0.14 ** -0.11 * -0.08 0.05 0.00

-2 0.05 0.08 0.03 0.02 0.00 0.04 0.04

-1 -0.08 c -0.07 c -0.09 -0.09 -0.08 0.03 0.00

0 0.07 0.08 0.07 0.03 0.00 -0.04 0.02

1 -0.04 -0.03 -0.05 -0.06 0.01 -0.01 -0.01 2 -0.09 -0.15 **b -0.11 * -0.07 -0.05 -0.11 **c -0.01

3 -0.07 -0.08 -0.08 -0.04 -0.04 0.08 0.08

4 0.09 0.09 0.08 0.10 0.08 0.00 -0.02 [-4;0] -0.17 *b -0.16 b -0.23 **b -0.23 **b -0.27 **b 0.07 0.05

[-4;+1] -0.21 *b -0.18 b -0.28 **b -0.29 **b -0.26 **b 0.06 0.04

[-4;+4] -0.29 **a -0.32 **b -0.39 **a -0.29 *b -0.27 *c 0.03 0.10

[-6;+2] -0.30 *a -0.32 **a -0.41 **a -0.43 **a -0.33 **b 0.11 0.07

Table VII. Sub sample: Transaction-specific factors

The empirical model and test statistics are defined in Table IV.A. All sub samples are based on a symmetrical event window period of 8 days [-4;+4] around each securitization’s announcement date t 0. The sub samples are built to control for the (a) type of transaction, (b) underlying reference portfolio and (c) issue frequency of securitization transactions.

Event Window Entire Sample Period Onset Stage Later Stage [-4;+4] CAARs n CAARs n CAARs n Type of Transaction **b Cash Transactions -0.28 **b (266) -0.11 (92) -0.37 (174)

Synthetic Transactions -0.57 (115) -0.80 (41) -0.44 (74) Underlyings

Credit Cards Receivables 1.85 (10) n.a. (3) n.a. (7)

Consumer Loans 0.03 (23) 0.11 (14) n.a. (9)

Collateralized Debt Obligations -0.51 (129) -1.28 ** (46) -0.09 (83) from small and medium enterprises 0.25 (61) 0.02 (14) 0.31 (47)

from large enterprises -1.19 ** (68) -1.85 ***c (32) -0.61 (36) Commercial Mortgage Backed Securities -0.33 (32) n.a. (3) -0.49 (29) Residential Mortgage Backed Securities -0.49 **b (159) -0.15 (53) -0.65 ***a (106)

Others -0.20 (28) -0.57 (14) 0.16 (14) Issue Frequency High Frequency -0.64 ***a (158) -0.31 (50) -0.80 ***a (108) Low Frequency -0.17 (223) -0.33 (83) -0.08 (140)

Table VIII. Descriptive statistics of the partitioning of financial variables of securitizing banks

Table VIII reports descriptive statistics of the partitioning of financial variables of all securitizing banks, the above-median group of banks and the below-median group of banks. The partitions are based on the median value of the bank’s size (total assets), bank’s capitalization (equity-to-total assets), bank’s liquidity (liquidity-to-total assets), bank’s profitability (net interest margin) and bank’s portfolio quality (non-performing loans-to-total loans).

Size Capitalization Liquidity Profitability Portfolio Quality 1997-2007 1997-2002 2003-2007 1997-2007 1997-2002 2003-2007 1997-2007 1997-2002 2003-2007 1997-2007 1997-2002 2003-2007 1997-2007 1997-2002 2003-2007

Entire Sample of Banks Mean 328,236 282,711 357,478 0.0488 0.0482 0.0492 0.2208 0.2806 0.1871 0.0187 0.0196 0.0181 0.0332 0.0404 0.0282 Median 211,736 213,182 190,418 0.0467 0.0449 0.0476 0.1990 0.2532 0.1433 0.0179 0.0189 0.0169 0.0220 0.0292 0.0180 Standard Deviation 338,823 257,502 380,086 0.0173 0.0151 0.0185 0.1655 0.1363 0.1710 0.0089 0.0090 0.0088 0.0390 0.0475 0.0309 Min 7,593 7,883 7,593 0.0079 0.0270 0.0079 0.0073 0.0785 0.0073 0.0015 0.0015 0.0018 0.0019 0.0034 0.0019 Max 1,571,768 927,918 1,571,768 0.1010 0.1010 0.0876 0.6682 0.6682 0.6275 0.0524 0.0524 0.0418 0.1866 0.1110 0.1866

Banks Above the Median Mean 590,277 490,305 663,132 0.0630 0.0598 0.0648 0.3461 0.3801 0.3108 0.0259 0.0268 0.0254 0.0545 0.0653 0.0470 Median 536,535 452,756 608,480 0.0605 0.0575 0.0640 0.3024 0.3365 0.2700 0.0247 0.0252 0.0243 0.0375 0.0423 0.0355 Standard Deviation 301,387 205,665 344,068 0.0118 0.0129 0.0108 0.1429 0.1231 0.1631 0.0061 0.0066 0.0057 0.0447 0.0581 0.0340 Banks Below the Median Mean 68,514 75,116 64,472 0.0347 0.0367 0.0336 0.0967 0.1819 0.0652 0.0115 0.0124 0.0110 0.0112 0.0167 0.0091 Median 50,222 45,996 50,291 0.0360 0.0369 0.0357 0.0909 0.1918 0.0641 0.0120 0.0127 0.0120 0.0110 0.0180 0.0088 Standard Deviation 53,442 60,521 48,892 0.0076 0.0048 0.0088 0.0576 0.0528 0.0430 0.0041 0.0039 0.0042 0.0061 0.0084 0.0050

Table IX. Sub Samples: Bank-specific factors

The empirical model and test statistics are defined in Table IV.A. All sub samples are based on a symmetrical event window period of 8 days [-4;+4] around each securitization’s announcement date t 0. Note that the first sub sample comprises securitization transactions issued by banks exhibiting financial characteristics above the median of the cumulated characteristics of all banks in our sample whereas the second sub sample consists of transactions by banks exhibiting financial characteristics below the median of the cumulated characteristics. The sub samples are based on the median value of the bank’s size (total assets), bank’s capitalization (equity-to-total assets), bank’s liquidity (liquidity-to-total assets), bank’s profitability (net interest margin) and bank’s portfolio quality (non-performing loans-to-total loans). Note that differences in the number of securitization transactions in parentheses are due to missing data on financial variables in the Bankscope database.

Size Capitalization Liquidity Profitability Portfolio Quality Event Window [-4;+4] CAARs n CAARs n CAARs n CAARs n CAARs n

Entire Sample Period Above Median -0.63 ***a (218) -0.27 (176) -0.28 (169) -0.02 (162) -0.23 (164) Below Median -0.01 (163) -0.71 ***a (193) -0.43 ***b (197) -0.71***a (209) -0.70 ***a (193)

Onset Stage Above Median -0.90 (67) -0.77 (66) 0.00 (59) -0.34 (67) -0.05 (63)

Below Median 0.26 (66) -0.57 (59) -0.62 (62) -0.47 (60) -1.35 **c (60)

Later Stage

Above Median -0.50 **b (151) 0.03 (109) -0.07 (114) 0.24 (94) -0.01 (107) ***a ***a ***a ***a Below Median -0.22 (97) -0.78 (135) -0.69 (131) -0.83 (150) -0.69 (127)