Stock price effects of asset securitization: The case of liquidity facility providers

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Stock price effects of asset securitization: The case of liquidity facility providers Hilke Hollander a , Jörg Prokop a,b,∗ a Carl von Ossietzky University of Oldenburg, Department of Business Administration, Economics, and Law, Area Finance and Banking, D-26111 Oldenburg, Germany b ZenTra – Center for Transnational Studies, Carl von Ossietzky University of Oldenburg, D-26111 Oldenburg, Germany

a r t i c l e

i n f o

Article history: Received 20 November 2013 Received in revised form 28 September 2014 Accepted 2 November 2014 Available online xxx JEL classification: G14 G21 G32

a b s t r a c t We analyze the effects of asset securitization announcements on the market value of banks involved in the transaction as liquidity facility providers. Based on a unique sample of 97 European securitization transactions undertaken between 2002 and 2010, we find that abnormal returns occur around the announcement dates, and that they are positively related to the respective bank’s liquidity. Moreover, abnormal returns tend to be negative for transactions involving high-risk portfolios, and they have decreased significantly after the global financial crisis. Our results suggest that providing liquidity facilities in securitization transactions is considered value relevant information by equity investors, and that bank management may even be able to infer the likely sign of the market reaction based on the respective bank’s characteristics, and current business environment. © 2014 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved.

Keywords: Liquidity risk Event study Securitization

1. Introduction In August 2007, two mid-sized financial institutions called Deutsche Industriebank (IKB) and Sachsen LB were among the very first German banks to fall victim to the subprime crisis, and to be bailed out eventually. What makes these two cases interesting is that both banks’ core businesses were basically unrelated to the US subprime market, with IKB being specialized in lending to small and mid-sized companies, and Sachsen LB, a publicly owned German Landesbank, serving as the central institution to savings banks in the German federal state of Saxony. However, beyond that they also actively engaged in asset securitization by creating and sponsoring special purpose vehicles (SPVs), the latter investing mainly in international long-term/high yield asset-backed (real estate) securities, and funding their investments through the repeated issue of short-term/low yield asset-backed commercial papers (European Commission, 2009a, 2009b).

∗ Corresponding author at: Carl von Ossietzky University of Oldenburg, Department of Business Administration, Economics, and Law, Area Finance and Banking, D-26111 Oldenburg, Germany. Tel.: +49 4417984164. E-mail address: [email protected] (J. Prokop).

As long as the term structure of interest rates is normal, asset quality is sufficient and liquidity in the commercial paper market is high, this business model produces attractive returns. Moreover, if the sponsoring bank cannot be held liable for the SPV’s losses, the latter remains off balance sheet, and does not affect the bank’s regulatory capital requirement. However, in case of IKB and Sachsen LB, risk transfer to the SPVs was incomplete. Both banks retained significant exposure to their SPVs–and thus to the US mortgage market–by providing large unconditional liquidity facilities intended to cover the SPVs’ credit risk. In this vein, by 2007 IKB’s (Sachsen LB’s) exposure to SPVs and conduits had reached more than 20 per cent (30 per cent) of total assets, and about 500 per cent (1100 per cent) of equity capital (OECD, 2010, p. 88). Eventually, when the international commercial paper markets ran dry in 2007, the credit lines provided by IKB and Sachsen LB were drawn by the respective SPVs to restore liquidity, quickly bringing both banks to the brink of bankruptcy. However, regulators, rating agencies, auditors and the banks’ shareholders were seemingly not too concerned about these large exposures beforehand. Both companies maintained comparably high ratings in the run-up to the financial crisis, and received unqualified audit opinions on their annual financial statements issued right before the crisis began. Moreover, although

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Please cite this article in press as: Hollander, H., & Prokop, J. Stock price effects of asset securitization: The case of liquidity facility providers. The Quarterly Review of Economics and Finance (2014), http://dx.doi.org/10.1016/j.qref.2014.11.002

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information about IKB’s increasing liquidity risk exposure was made public in the respective SPVs’ pre-sale reports, and in the company’s financial reports since 2002, comparing the bank’s stock price behaviour to the development of a broad German stock market index (CDAX) does not reveal particularly unusual investor reactions at the respective dates. In this paper, we are mainly concerned with the latter phenomenon, that is, with the equity investors’ perception of risk arising from the unconditional provision of liquidity facilities in a securitization transaction. In case of IKB, the absence of a share price reaction suggests that investors either simply ignored (or at least underestimated) the respective liquidity risk, or that they considered the increases in risk to be compensated by proportional increases in expected returns. In this context, we analyze the effects of credit securitization announcements on the involved liquidity facility provider’s (LFP) share price. Thus, the underlying research questions are: Do equity investors consider the provision of liquidity as “good news”, “bad news”, or value irrelevant? Does their assessment of liquidity risk vary with firm characteristics, or general market conditions? Even after the global financial crisis the answers to these questions are not only of academic, but also of practical relevance, since worldwide securitization volume is still high, and liquidity provision remains of particular importance to originators trying to create marketable securitization structures. To the best of our knowledge, there is currently no other academic study addressing the effect of a securitization transaction on the LFP’s share price. This lack of research is somewhat surprising, since the volume of liquidity facilities provided in the above context is typically large.1 Most of the prior research on announcement effects of asset securitization concentrates on the originating bank’s abnormal returns. For instance, Lockwood, Rutherford, and Herrera (1996) analyze 294 ABS transactions conducted by banks as well as by non-banks between 1985 and 1992. While the overall announcement effect is negative for their sample, they find that the sign of the abnormal returns depends on the originators’ experience: while for experienced originators cumulative abnormal returns (CAR) are positive, CAR on inexperienced originators’ share prices tend to be negative (Lockwood et al., 1996, p. 153). Thomas (1999) analyses 236 US securitization transactions between 1991 and 1996. Contrary to Lockwood et al. (1996), he finds positive abnormal returns, with their amount depending on the respective bank’s creditworthiness, attributing this deviation from Lockwood et al. (1996) to the differing sample period (Thomas, 1999). In a follow-up study Thomas (2001) analyses the US securitization market for abnormal returns in several periods between 1983 and 1997 and identifies a time-dependent effect: in a tensed market environment, securitization announcements induce negative abnormal returns, while in good times abnormal returns are positive. Moreover, in line with Lockwood et al.’s (1996) results, he finds that abnormal returns are positive for large and experienced originators, while small and inexperienced originators experience negative abnormal returns (Thomas, 2001). Gasbarro, Stevenson, Schwebach, and Zumwalt (2005) analyze the impact of securitization announcements on share prices of US multibank holding companies. They find that abnormal returns depend on the individual financial structure of a bank. They argue that these characteristics can be regarded as proxies for information asymmetry, creditworthiness and comparative advantages. For the Spanish market and based on a sample of 44 transactions, Martinez-Solano, Yague-Guirao, and Lopez-Martinez (2009)

identify a positive announcement effect on the part of the originator for the period ranging from 1993 to 2004. They also find that the stock price reaction is more pronounced if banks are less profitable, have a lower debt to equity ratio, and are more experienced in undertaking securitization transactions. Finally, Farruggio, Michalak, and Uhde (2012) study European securitizations between 1997 and 2007 and find a negative announcement effect for the period from 2003 to 2007. In particular, they conclude that the market considers securitization transactions by unprofitable banks as signals indicating liquidity shortages, which leads to negative abnormal returns on these banks’ shares. To answer our research questions, we use a unique proprietary data set of LFPs involved in 97 European securitization transactions that occurred between 2002 and 2010. Controlling for the LFPs’ financial characteristics, as well as the market environment at the time of the respective transactions, we study the stock price performance of LFPs involved in these transactions, and find that abnormal returns do occur around the announcement date. Expected returns are determined using the market model as well as a Fama and French (1992)-type model, and potential event-induced heteroscedasticity is taken into account using a GARCH(1,1) approach. We identify the LFP’s liquidity situation as the most relevant explanatory fundamental factor. While liquid LFPs earn significantly positive CAR, less liquid LFPs tend to show negative CAR. Moreover, stock market reactions vary with the type of the underlying asset: While transactions involving asset-backed securities (ABS) and prime residential mortgage-backed securities (RMBS) seem to have no effect on abnormal returns, providing liquidity for commercial mortgage backed securities (CMBS) and subprime RMBS transactions tends to affect CAR negatively. Furthermore, we find evidence that abnormal returns following securitization announcements are significantly lower in the post-global financial crisis period than before 2007. Our results are robust to variations of the underlying asset pricing model, and to different market index specifications. In contrast, our findings regarding the role of market liquidity are more ambiguous. While we hypothesize that in times of high market liquidity (indicated by low market interest rates, such as the 3-month EURIBOR),2 providing liquidity for a credit securitization may be considered less risky than in times of lower market liquidity, this notion is only weakly supported by the results of our multivariate analysis. In addition, neither the general economic environment (proxied by real GDP growth and the inflation rate), nor the (absolute or relative) size of the securitization transaction seem to play a particular role in explaining abnormal returns. In sum, the evidence suggests that providing liquidity in a securitization transaction has an impact on the market value of the LFP even if this company is not the originator of the securitization. Our results shed light on the factors driving stock market investors’ reactions, and they are relevant to financial institutions in assessing the pros and cons of acting as LFPs. While our findings are restricted to the latter, the underlying approach could also be applied to scenarios involving other typical parties to a securitization transaction, such as transaction account providers, or interest rate or currency swap providers. The remainder of the paper is structured as follows. Section two develops our hypotheses regarding the factors driving LFP’s abnormal returns as well as the signs of the respective regression

1 For instance, in case of DECO 12 – UK 4 plc., a typical transaction within our sample with a principal amount of £672,883,707, the liquidity facility provided by one bank amounts to £41,000,000, that is, about six per cent of the total volume.

2 The 3-month EURIBOR is the average interest rate at which interbank loans denominated in EUR with a maturity of 3 months are provided by a selection of European banks.

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coefficients. Section three outlines the underlying data set, and section four contains the methodology used. Section five summarizes the results of the univariate analysis of factors influencing abnormal returns as well as the results of the multivariate regression model. Section six concludes.

2. Potential drivers of abnormal LFP returns The primary role of the LFP is to close liquidity gaps occurring due to differences in interest payment dates between the involved special purpose vehicle’s (credit) assets and its liabilities (Beekwilder and Mos, 2006, p. 261). In addition, the LFP may also be contracted to serve as a credit enhancer, for instance by providing capital in case of unexpected liquidity shortages due to debtor defaults. In this study, we focus on cases in which LFPs also fulfil the latter function. When taking risk as a liquidity facility provider the latter charges a commitment fee for the undrawn liquidity facility as well as an interest rate on any amount drawn. Typically, the interest rate to be paid on the amount drawn is higher than the undrawn commitment fee, and the cash flow from the underlying assets should be able to absorb these additional liquidity costs. Liquidity facility contracts in Europe are usually negotiated on a one-year basis and need to be rolled over annually. In case that the liquidity facility provider does not extend the facility and no replacement can be found, the liquidity facility will be fully drawn and credited to a standby account. If liquidity is needed, it can be debited against this account (see Beekwilder and Mos, 2006). Providing liquidity facilities bears substantial risks, as it may impact the respective LFP’s short-term liquidity. Typically, an initial liquidity facility for a securitization amounts to EUR 100,000,000 or more, and a liquidity provider often supports multiple transactions. Banks with ample sources of short-term liquidity should be more resilient to shocks from unexpected liquidity calls by SPVs than banks facing potential liquidity shortages. Thus, the announcement of a liquidity provision to an SPV is expected to affect stock returns of small banks or banks with current liquidity shortages negatively. Moreover, in economic downturns, liquidity shortages are persistent also for large banks. Due to increased refinancing costs, providing liquidity is more risky during a financial turmoil than in a healthy market environment. According to Collin-Dufresne, Goldstein, and Martin (2001), interest rates should be a good indicator of the current economic condition. Therefore, we expect the abnormal returns of liquidity providers to be negative when interest rates increase. In addition to market environment, size and liquidity, the credit rating of a bank is likely to have an impact on abnormal returns of liquidity provisions for securitizations. Rating agencies typically derive their ratings from fundamental data indicating the credit risk of a firm. Thus, investment grade banks are characterized by high equity ratios, sufficient liquidity ratios, high profits, and low non-performing loan levels. Banks with higher ratings are more likely to be able to access short-term liquidity also during more difficult market conditions than poorly rated banks. Consequently, announcements of liquidity provisions for securitizations of highly rated banks are expected to result in positive abnormal returns, whereas poorly rated banks’ abnormal returns are predicted to be negative. Recently, credit ratings came under increased criticism due to deficiencies in accuracy, and timeliness of rating adjustments due to changes in credit risk, and several studies suggest using credit default swap (CDS) spreads instead as an alternative proxy for credit risk (e.g., Alexopoulou et al., 2009, p. 23). Since the CDS spread reflects the price of insuring a CDS buyer against the default

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of a reference entity, a high spread indicates a high default probability of the reference firm (Mengle, 2007). Moreover, unlike ratings, CDS spread quotes are typically provided on a daily basis, and they are based on real investor behaviour instead of analyst opinion. Thus, CDS spreads are likely to be a more timely and more value relevant proxy of a bank’s level of creditworthiness than credit ratings. Since CDS markets are most liquid for contracts with a maturity of five years (Mengle, 2007), we use the latter as alternative proxies for credit risk. We expect negative abnormal returns for banks with high 5yr CDS spreads during the event window. Another variable which may affect abnormal returns is the credit risk associated with the assets underlying the transaction. Since investors may view the liquidity provision as a transfer of credit risk, the latter may affect the quality of the liquidity providing bank’s portfolio negatively. Thus, abnormal returns related to liquidity provisions for securitizations are predicted to be negative for banks with high-risk credit portfolios. In this context, we differentiate between two types of securitizations: Mortgage-backed securities (MBS) and asset-backed securities (ABS). While for ABS, virtually any type of loan may serve as an underlying, MBS transactions are either residential mortgage-backed securities (RMBS) or commercial mortgagebacked securities (CMBS). The latter can be based on less than 10 commercial loans, whereas RMBS are typically based on several hundreds of private residential mortgage loans (Bhattacharya and Fabozzi, 1996). Thus, CMBS and RMBS differ with respect to the granularity of risks. As a consequence, CMBS transactions are more likely to be affected by correlation risk than RMBS transactions and, therefore, we expect abnormal returns related to liquidity provisions for CMBS transactions to be more negative than those related to RMBS transactions. Furthermore, RMBS transactions can be classified as PrimeRMBS or Subprime-RMBS, depending on whether they are based on low-risk loans, or high-risk loans (Keys, Mukherjee, Seru, and Vig, 2009). In the US, loans are supposed to be subprime when the respective borrowers’ FICO score is less than 620 points (Keys et al., 2009). We expect abnormal returns related to liquidity provisions for Prime-RMBS to be positive, whereas abnormal returns of liquidity provisions for Subprime-RMBS are predicted to be negative. 3. Data Regarding announcement effects of securitization transactions, prior research mainly focuses on the originator’s share price. Our study contributes to the literature in that we analyze the announcement’s impact on liquidity providers who are not the originators of the securitized assets – an issue that has to the best of our knowledge not been investigated so far. As a basis for our analysis, we use the full set of European securitization transactions between 2000 and 2010 which were supported by a liquidity facility provider, and which were rated by Moody’s. In a first step, this sample is corrected for non-listed LFPs. Then, since our event study requires the existence of an event date for each observation, we select only those transactions for which the release date of a pre-sale report by either Moody’s or Standard & Poor’s is available and, thus, an event date exists. Moreover, as the aim of our study is to measure the stock price effect of being liquidity provider in a securitization transaction, we eliminate all transactions in which the originator and the LFP are identical to avoid measurement errors due to the potential price effect of being an originator. In addition, we eliminate transactions with multiple liquidity facility providers, because in these cases potential price effects of providing liquidity cannot be attributed to a single company.

Please cite this article in press as: Hollander, H., & Prokop, J. Stock price effects of asset securitization: The case of liquidity facility providers. The Quarterly Review of Economics and Finance (2014), http://dx.doi.org/10.1016/j.qref.2014.11.002

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Fig. 1. Global securitization issuance 2000–2010. Source: SIFMA, 2011, p. 2.

Finally, to rule out measurement errors due to other valuerelevant events that occur close to the event date, we eliminate all confounding events within an eleven-day window centred on the event date. The elimination of confounding events is well-established in event-study literature (see Campbell, Lo, and MacKinlay, 1997; McWilliams and Siegel, 1997). Table 1 shows the types and numbers of confounding events accounted for. The final sample contains 97 transactions that took place between 2002 and 2010, with 12 different European banks involved as LFPs. We obtain daily stock price data from Datastream, and bank-level financial data from BankScope. Table 2 summarizes the banks involved, their total assets, and the relative sizes of their loan portfolios. CDS spreads were available for these banks for the period from 2004 to 2010. Since in terms of coverage Moody’s is one of the two main rating agencies in the European securitization market, we deem our sample unlikely to be strongly affected by sample selection bias. However, since transactions rated by Moody’s are typically large, our sample might be skewed towards the price effect of large transactions. Fig. 1 shows the development of the international securitization market between 2000 and 2010. While most of the transactions occurred in the United States of America, the volume of the European market increased considerably during this period, starting at about 85 billion USD in 2000, and reaching about 800 billion USD in 2007.

Table 2 Liquidity facility providing banks. Bank

Country

#

Total assets (million EUR)

Danske Bank Group Barclays Royal Bank of Scotland Lloyds Banking Group Credit Agricole HSBC BNP Deutsche Bank HBOS Dresdner Bank Natixis Mediobanca

DK GB GB GB FR GB FR DE GB DE FR IT

24 21 17 15 8 4 2 2 1 1 1 1

342,556 1,128,111 1,246,263 504,111 1,231,482 1,189,213 1,354,881 1,341,791 580,301 482,163 326,921 48,565

Total

97

Median

580,301

During the investigation period, two major stock market crises occurred: the first one between 2002 and 2003, and the second one between 2007 and 2009. Since the latter was closely connected to problems with structured products, the public securitization market nearly came to a standstill after 2007, as Table 3 illustrates.3 Moreover, the table shows that our final sample mainly consists of CMBS and RMBS transactions. The ABS transactions included credit card receivables, student loans, and car loans. 4. Methodology

Table 1 Confounding events.

4.1. Return generating model

Confounding Event

#

%

Earnings Announcement M&A Capital Increases Investments/Disinvestments Public Offerings Rating Actions Restructurings Total

28 17 3 17 2 3 3 73

38.4 23.3 4.1 23.3 2.7 4.1 4.1 100.0

We perform an event study following the general methodology laid out, for instance, by Campbell et al. (1997). If acting as an LFP in a securitization transaction is considered to be value relevant

3 While the total volume of securitizations in 2008 was even higher than in 2007, most of these transactions were non-public, with banks securitizing assets only to sell the resulting claims to central banks.

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Table 3 Distribution of transaction types. Year

2002

2003

2004

2005

2006

2007

2008

2009

2010

Total

ABS CMBS RMBS-Prime RMBS-Subprime CDO Total

0 1 3 2 0 6

1 0 2 4 0 7

2 2 6 7 0 17

1 14 2 9 0 26

3 8 0 2 2 15

1 9 2 8 0 20

2 0 2 1 0 5

0 0 0 0 0 0

1 0 0 0 0 1

11 34 17 33 2 97

information by market participants, the public release of this information in a sufficiently efficient market should affect stock prices and thus lead to positive or negative abnormal returns. The latter are defined as the difference between actual and expected returns during the event window: ARit = Rit − E(Rit )

(1)

where ARit is the abnormal return of stock i on day t and Rit denotes the continuously compounded return of stock i on day t, derived from day-end prices and corrected for stock splits and dividends. E(Rit ) denotes the expected return of stock i on day t. Securitization transactions usually involve a bookbuilding process to determine the prices of the securities to be issued. Therefore, it is likely that information about a planned transaction reaches the market before the respective presale report is released (Farruggio et al., 2012). Moreover, prior studies indicate that information about a securitization transaction is completely reflected in stock prices only a few days after the release date (McWilliams and Siegel, 1997; Farruggio et al., 2012; Martinez-Solano et al., 2009). As a consequence, we calculate cumulative abnormal returns (CAR) for event windows of different lengths using the following formula: CARi(K,L) =

L 

ARit

(2)

E(Rit ) = ˛i + si SMBt + hi HMLt + ˇi Rmt + εit

(4)

where SBMt denotes the size factor, HMLt is the value factor and si ,hi ,ˇi are the respective factor sensitivities for stock i. For this model, we choose STOXX EUROPE TMI as a market index. The respective Fama and French factors are derived from style indices (Faff, 2003), using STOXX EUROPE TMI Small, STOXX EUROPE TMI Large, STOXX EUROPE TMI Growth and STOXX EUROPE TMI Value as a basis: SMBt = (ln(Psmallt ) − ln(Psmallt−1 )) − (ln(Plarget ) − ln(Plarget−1 )) (5) where Psmallt denotes the closing price of the STOXX EUROPE TMI Small on day t. Plarget is the closing price of the STOXX EUROPE TMI Large on day t. We estimate the HML factor using the following equation: HMLt = (ln(Pvaluet ) − ln(Pvaluet−1 )) − (ln(Pgrowtht ) − ln(Pgrowtht−1 ))

t=K

with K,L denoting the days relative to the event date and CARi(K,L) being the cumulative abnormal return of stock i during the period from K to L. In the following, CAAR denotes cumulative average abnormal returns, which are average CARs across all banks. In particular, we focus on the (−5,+5)-window, but we also take the following event windows into account to validate our results: (−5,+4), (−5,+3), (−5,+2), (−5,+1), (−5,0), (−3,+3), (−3,0), (−1,+1) and (−1,0). We refrain from employing longer event windows since the latter are increasingly diluted by confounding events. Expected returns are calculated using the market model, as well as a Fama and French (1992)-based multi-factor model. The market model is defined as follows (Campbell et al., 1997): E(Rit ) = ˛i + ˇi Rmt + εit

To validate our results, we also employ a multi-factor model based on the Fama and French (1992) approach as an alternative return generating model. In addition to the market portfolio’s return, the latter takes into account a size factor based on market capitalization, and a value factor based on the book-to-market equity ratio to determine a stock’s expected return:

(3)

where ˛i denotes the component of stock i’s return that is independent of the market’s return (Rmt ) on day t, ˇi is a stock-specific sensitivity measure and εit is a random error term. The market portfolio is proxied by STOXX EUROPE 600.4 The betas for the CARs are estimated using a 261 trading days period which starts 6 days before the event date of a transaction. The estimation period covers one year of prior to the event date. The use of long estimation periods of approximately one year is recommended in literature in order to receive reliable parameters (see Goerke, 2008).

4 As a robustness check, we also use STOXX EUROPE TMI and STOXX EUROPE 600 BANKS as alternative market portfolio specifications. The respective findings (not reported) are consistent with the results for STOXX EUROPE 600.

(6) with Pvaluet denoting the STOXX EUROPE TMI Value’s closing price on day t and Pgrowtht being the closing price of the STOXX EUROPE TMI Growth on day t. Since liquidity is high in the stock market segment underlying our analysis, we refrain from using lead or lag structures in our regression model to account for potential thin trading (e.g., Dimson, 1979; Scholes and Williams, 1977). However, the return time series in our sample may still be subject to volatility clustering (Mandelbrot, 1963). If the variance of the random error term εit exhibits autoregressive conditional heteroscedasticity, or ARCH effects (Engle, 1982), this might affect the results of our regression models negatively. Therefore, we test for heteroscedasticity using the Lagrange Multiplier test proposed by Engle (1982): LM = T ∗ R2 ∼2

(7)

where T denotes the number of observations and R2 is the coefficient of determination of the following regression: εˆ 2t = ˛0 + ˛1 εˆ 2t−1 + ˛2 εˆ 2t−2 + · · · + ˛m εˆ 2t−m

(8)

with εˆ 2t denoting residuals of the regression Rit = E(Rit ). LM follows a 2 -distribution with m degrees of freedom. Table 4 shows that significant ARCH effects occur in 45% of the respective time series. Therefore, similar to Martinez-Solano et al. (2009, p. 124), we take these effects into account by adjusting our basic regression equations using a Bollerslev (1986)-type GARCH(1,1) model (cf. Corhay & Tourani, 1996, p. 532). This leads to the following regression models:

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with i = step i during the maximisation of vector

> 0 = step length

Table 4 ARCH effects in sample data. Level of significance

ARCH effects No ARCH effects

n

1%

5%

10%

22

18

4

%

∂Lt = first order gradient of the log − likelihood function 44 53

45.36 54.64

I. Market model with GARCH(1,1) extension (MMG) E(Rit ) = ˛i + ˇi Rmt + εit

(9)

with (εit |it−1 )∼D(0, it2 ) 2 it2 = 1i + 2i ε2it−1 + 3i it−1 II. Fama and French model with GARCH(1,1) extension (FFG) E(Rit ) = ˛i + si SMBt + hi HMLt + ˇi Rmt + εit

(10)

with (εit |it−1 )∼D(0, it2 ) 2 it2 = 1i + 2i ε2it−1 + 3i it−1 For both model (I) and model (II)  it−1 denotes the firm ispecific information set available at time t − 1, it2 is the conditional variance of εit and  1i is a constant, specific to i.  2i ,  3i are sensitivities of εit ’s variance to the prior day’s error term or εit ’s conditional variance of εit ’s, respectively. Since returns in our sample tend to be leptokurtic, we assume that the conditional error terms follow a t-distribution, as suggested by Corhay and Tourani (1996). As a robustness test, we also replicate our results using a GED-distribution instead to better account for fat tails (Lee, Lee, and Liu, 2009). Following Martinez-Solano et al. (2009), regression parameters are estimated simultaneously using the maximum likelihood method based on Berndt, Hall, Hall, and Hausman (1974). Since the GARCH(1,1) model’s error terms are not independent, the maximum of the loglikelihood function is determined using an iterative approach. For a GARCH (p,q) model and t-distributed conditional error terms, the density function used for the calculation of the maximum likelihood function is given by following equation (see Herwartz, 2004):

vv/2  ((v + 1)/2) f (εit |v) = √   (v/2) (v − 2)it2 /v

 v+

v ∗ ε2it (v − 2)it2

−((v+1)/2) (11)

where v = degree of freedom and



 (.) =

∞

t x−1 e−t dt = Gamma function

0

Furthermore, Herwartz (2004) shows that the log-likelihood function of a t-distribution for the starting values  it and ε2it is given by the following equation: L( |εi1 , . . ., εiT ) =

T     v+1

ln 

2

  v

− ln 

2

t=1

−



1 1 ln(v − 2)it − (v + 1) ln(1 + ε2it it−1 ) 2 2

Following Berndt et al. (1974), the vector maximizing the parameters of the ML function is determined iteratively: ˆ i = ˆ i−1 +

 T  ∂Lt ∂Lt t=1

∂ ∂ 

−1 | = ˆ i−1

T   ∂Lt t=1

and

∂ 

T = observations √ Typically, converges with speed T (Davidson, 2000). The estimation window comprises the 261 trading days prior to the beginning of the longest event window (i.e., going backwards from t = −6). Thus, the estimation period covers an entire trading year, which allows us to account for potential seasonal effects (see Goerke, 2008). 4.2. Univariate model As Thomas (2001), Gasbarro et al. (2005), and Farruggio et al. (2012) show for originating banks, abnormal returns may depend on the respective banks’ fundamentals. We investigate the role of the fundamental variables size, liquidity, capitalization, portfolio quality, and profitability on the LFP’s abnormal returns by using the following procedure: In the first step, we divide our sample into two portfolios, using the banks’ latest total assets as a threshold. The first portfolio contains all banks with average total assets above or equal to the median, the second portfolio contains all remaining banks.5 Then, we repeat the procedure for the remaining fundamental variables, each time using the median of the respective variable as a threshold to allocate banks either to the first or to the second portfolio. We use the ratio “1 − (net loans to total assets)” as a proxy for liquidity. Capitalization is measured using the equity to total assets ratio. Portfolio quality is proxied by “1 − (impaired loans to gross loans)”. Profitability is defined as the respective bank’s net interest margin, that is, the difference between interest received and interest paid divided by the average interest earning assets. In addition, Thomas (2001) shows that the amount of abnormal returns ascribable to a securitization transaction may vary with the general market environment. While decreasing stock prices suggest a tense market situation, increasing or high interbank rates indicate low market liquidity, rendering bank refinancing more difficult. By contrast, increasing stock prices are associated with a more optimistic view on the economy, and decreasing or low market rates imply cheaper access to liquidity for refinancing purposes. To account for such differences in the general market environment, we divide our sample into the following four subsamples: 2002–2003 (period I), 2004–2005 (period II), 2006–2007 (period III), and 2008–2010 (period IV). Period I and IV are characterized by decreasing stock prices, and low or falling interbank interest rates indicating increasing market liquidity. In period II, stock prices increased, while interest rates were low. In period III, stock prices as well as interest rates increased strongly indicating decreasing liquidity. Fig. 2 illustrates this development using STOXX EUROPE 600 and the 3M EURIBOR. Finally, we investigate whether the type of the transaction affects CAAR, as suggested by Farruggio et al. (2012). We divide our sample into the following subsamples: ABS, CMBS, RMBS, and CDO (with the latter not being analyzed due to the small sample size). The RMBS transactions are further divided into Prime-RMBS and Subprime-RMBS, where the latter category contains all transactions classified as either subprime, nonconforming, or MBS-other by Moody’s or Standard & Poor’s, respectively.

 | = ˆ i−1 5 Amounts stated in DDK or GBP are converted into EUR using the respective exchange rates as of March 30, 2011 (DKK/EUR = 0.13409 and GBP/EUR = 1.132).

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Fig. 2. European stock and bond market development 2002–2010.

4.3. Regression model We perform a multivariate regression choosing CAR derived from the FFG approach for the (−5,+5) event window as dependent variable. The CAR in this study are based on abnormal returns related to 12 European banks. Thus, firm clusters may affect our data due to residual correlation. According to Petersen (2009), these effects can be corrected by estimating robust standard errors. To account for firm clusters, we run a panel regression with robust standard errors using the clustered standard errors approach. We further account for country and time fixed effects by including dummy variables for the country in which the transaction took place and a dummy variable to control for whether the transaction took place before or after the start of the global financial crisis. The underlying regression model (I) is specified as follows: (I)

The variables EURIBOR and GDP serve as proxies for the market situation at the time of the transaction. We use the 3M EURIBOR to reflect market liquidity, and real growth in European gross domestic product during the quarter in which a transaction takes place as a measure of economic strength. Consistent with the above results, we expect negative regression coefficients for both variables. We add the inflation rate (INF) measured for the LFP’s home country during the quarter in which the transaction takes place as a control variable. Since there is usually a positive relationship between inflation and liquidity, we expect inflation to be associated positively with CAR. Finally, we include PREGFC as a dummy variable to capture time-dependent effects (1 in case the transaction took place before 2007, 0 otherwise). Since the recent financial crisis began in 2007,

CAR(−5,+5)fit = c + ˛LNSIZEfit + ˇLIQUIDITYfit + EQUITYfit + ıPROFITfit

+εQUALITYfit + SUBPRIMEfit + CMBSfit + oEURIBORfit + GDPfit + INFfit + PREGFCfit where f, i and t denote firm, transaction and time, respectively. The variables LNSIZE (the log of a firm’s total assets), as well as LIQUIDITY, EQUITY, PROFIT and QUALITY (as defined in Section 4.2) serve as proxies for bank structure. In each case we employ the latest bank characteristics. Our prior results shown in Tables 5 and 6 indicate a positive relationship between a company’s CAR and its size and liquidity. However, the relationships between CAR and EQUITY, PROFIT, and QUALITY are less predictable. In addition, we control for the type of the transaction using the dummy variables SUBPRIME (1 in case of a subprime transaction, (II)

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we expect higher abnormal returns for transactions that took place before 2007 which implies a positive relation between PREGFC and CAR. To capture the explanatory power that is given only by the variable LIQUIDITY we ran a second model which includes only LIQUIDITY as a proxy for bank structure. The second regression model is given by:

CAR(−5,+5)fit = c + ˇLIQUIDITYfit + SUBPRIMEfit + CMBSfit + oEURIBORfit

+GDPfit + INFfit + PREGFCfit

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In a third regression model, we use the respective LFP’s average credit default swap spread (CDS) as an alternative proxy for bank structure: 0 otherwise) and CMBS (1 for a CMBS transaction, 0 otherwise). Based on our prior findings, we expect the signs of the respective regression coefficients to be negative.

(III)

CAR(−5,+5)fit = c + CDSfit + SUBPRIMEfit + CMBSfit + oEURIBORfit

+GDPfit + INFfit + PREGFCfit

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8 Table 5 Portfolio CAARs (MMG). CAAR

BW

BMP

CORRA

n

Mean diff.

SIZE Above median Below median

1.10% −1.45%

2.89*** −2.77**

2.48*** −3.01***

1.69** −3.11***

54 43

4.03***

LIQUIDITY Above median Below median

1.14% −0.55%

2.63** −1.22

2.69** −2.04**

2.15** −2.09**

30 67

2.37**

EQUITY Above median Below median

0.72% −0.40%

1.47 −0.86

1.06 −1.40

0.65 −1.17

32 65

1.57*

QUALITY Above median Below median

−0.47% 1.18%

−1.13 2.20**

−2.02** 2.07**

−1.61 3.92

71 26

−2.19**

PROFIT Above median Below median

0.43% −0.46%

0.84 −0.58

0.42 −1.48

−0.07 −1.01

47 50

1.31*

TIME 2002–2003 2004–2005 2006–2007 2008–2010

1.95% 0.24% −1.58% 2.84%

3.95*** 0.58 −3.11*** n.a

4.31*** 0.39 −2.93*** n.a

1.38* 0.26 −2.37*** n.a

13 43 35 6

TRANSACTION ABS CMBS RMBS SUBRMBS PRIMERMBS

2.92% −0.91% −0.11% −0.70% 1.04%

3.08*** −1.56* −0.24* −1.30 1.75**

4.39*** −1.66* −0.75 −1.75** 1.83**

2.07** −1.74** −0.59 −1.25 0.66

11 34 50 33 17

Entire Sample

−0.03%

−0.09

−1.02

−0.75

97

Notes: Table 5 reports cumulative average abnormal returns for portfolios based on bank fundamentals, time, and transaction type, respectively. SIZE is the respective banks’ total assets. LIQUIDITY is measured as “1 − (net loans/total assets)”. EQUITY is given by the equity to total assets ratio. QUALITY is proxied by “1 − (impaired loans to gross loans)”. PROFIT is defined as the respective bank’s net interest margin. TIME groups firms conditional on the time periods indicated, and TRANSACTION indicates the type of securitization. The underlying return generating model is the market model with a GARCH(1,1) extension. The conditional error terms are assumed to follow a Student-t distribution. The first column of each portfolio table refers to the cumulative abnormal returns over a (−5;+5) window calculated using the approach laid out by Brown and Warner (1985). The following columns show the Brown and Warner (1985) t-values (BW), the Boehmer, Musumeci and Poulsen (1991) t-values (BMP), and the Corrado and Zivney (1992) z-values (CORRA). The column “mean diff.” shows the t-values for the difference in CAAR between the respective “above median” and “below median” portfolios. *** ,** , and * indicate significance at the 1%, 5%, and 10% level, respectively

As CDS spreads were available only for the period 2004–2010, our sample size is reduced to n = 84 for this regression model specification. We expect a negative relationship between CDS and CAR, since the CDS spread reflects the market’s assessment of a bank’s default risk, and our above results show that less liquid LFPs tend to earn negative CAR. Furthermore, we test whether the explanatory power of the regression changes if we exclude bank structure variables, considering only the type of the transaction and the market situation as independent variables: (IV)

in particular between LNSIZE and LIQ, and between EURIBOR and PREGFC. To account for potential adverse effects, we specify further regression models (I.a to V.c) in which some of these variables are left out. Furthermore, regression coefficients in our study may be biased due to small sample size. In addition to calculating t-scores from the sample directly, we assess their reliability using the pairs bootstrap method, which is also employed by Martinez-Solano et al. (2009). As Flachaire (2005) shows, this approach provides robust

CAR(−5,+5)fit = c + SUBPRIMEfit + CMBSfit + oEURIBORfit + GDPfit + INFfit + PREGFCfit

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Finally, we take into account that CAR may be influenced by the size of the transaction, or by the amount of liquidity provided by the LFP, and define the following regression model: (V)

CAR(−5,+5)fit = c + ˛LNSIZEfit + ˇLIQUIDITYfit + EQUITYfit + ıPROFITfit

+εQUALITYfit + SUBPRIMEfit + CMBSfit + oEURIBORfit + GDPfit + INFfit + LNVOLTfit + LNVOLLfit + ωLIQCSHfit + PREGFCfit LNVOLT reflects the log of a transaction’s size in million EUR, LNVOLL is the log of the amount of liquidity provided. LIQCSH is LNVOLL divided by the company’s log amount of cash. In each case, we expect a negative sign of the regression coefficient. Since information on the amount of liquidity provided was not available in all cases, our sample size is n = 69 for this model specification. To avoid model misspecification due to multicollinearity (Heij et al., 2004, pp. 158–159), we analyze the correlation of the above independent variables. Table 7 shows that correlations are high

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results in heteroskedastic regression models, and it is frequently used in empirical studies based on cross-sectional data (MartinezSolano et al., 2009; Johnston and Dinardo, 1997). The idea is to replace the unknown distribution of t-scores with an empirical distribution derived from our sample of 97 securitization transactions. For each regression model, we draw n random pairs of the dependent and of the independent variable from the regression matrix with replacement, and run the regression to determine the

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Table 6 Portfolio CAARs (FFG). CAAR

BW

BMP

CORRA

n

Mean diff.

SIZE Above median Below median

0.93% −1.15%

2.22** −2.13**

2.10** −2.55***

0.93 −2.59***

54 43

3.24***

LIQUIDITY Above median Below median

1.01% −0.44%

2.28** −0.90

2.27** −1.60

1.12 −1.74**

30 67

2.02**

EQUITY Above median Below median

0.29% −0.13%

0.53 −0.30

0.58 −0.76

−0.02 −1.16

32 65

0.59

QUALITY Above median Below median

−0.37% 1.10%

−0.92 1.60

−1.43 1.82*

−1.65** 3.12***

71 26

−1.89**

PROFIT Above median Below median

0.50% −0.46%

0.78 −0.57

0.73 −1.31

−0.20 −1.33

47 50

1.43*

TIME 2002–2003 2004–2005 2006–2007 2008–2010

2.67% 0.14% −1.29% 0.85%

2.93*** 0.35 −2.97*** n.a.

3.06*** 0.43 −2.71*** n.a.

0.99 0.03 −2.29** n.a.

13 43 35 6

TRANSACTION ABS CMBS RMBS SUBRMBS PRIMERMBS

2.41% −0.77% −0.01% −0.26% 0.47%

2.19** −1.48* −0.02 −0.45 0.66

2.43** −1.52* −0.21 −0.76 0.87

1.85** −1.65* −0.94 −1.20 0.01

11 34 50 33 17

Entire Sample

0.01%

0.02

−1.02

−0.58

97

Notes: Table 6 reports cumulative average abnormal returns for portfolios based on bank fundamentals, time, and transaction type, respectively. SIZE is the respective banks’ total assets. LIQUIDITY is measured as “1 − (net loans/total assets)”. EQUITY is given by the equity to total assets ratio. QUALITY is proxied by “1 − (impaired loans to gross loans)”. PROFIT is defined as the respective bank’s net interest margin. TIME groups firms conditional on the time periods indicated, and TRANSACTION indicates the type of securitization. The underlying return generating model is the Fama and French model with a GARCH(1,1) extension. The conditional error terms are assumed to follow the Student t-distribution. The first column of each portfolio table refers to the cumulative abnormal returns over a (−5;+5) window calculated using the approach laid out by Brown and Warner (1985). The following columns show the Brown and Warner (1985) t-values (BW), the Boehmer, Musumeci and Poulsen (1991) t-values (BMP), and the Corrado and Zivney (1992) z-values (CORRA). The column “mean diff.” shows the t-values for the difference in CAAR between the respective “above median” and “below median” portfolios. *** ,** , and * indicate significance at the 1%, 5%, and 10% level, respectively.

t-values of the regression coefficients. We perform this procedure 10,000 times and obtain the empirical distribution of t-values, from which we can calculate the bootstrap error terms to determine the bootstrap t-values (Flachaire, 2005). Finally, we test the time series in our sample for stationarity, to ensure that the regression results are not biased due to spurious correlations. We use the modified Dickey-Fuller test developed by Elliott, Rothenberg, and Stock (1996), which is an Augmented Dickey–Fuller test on GLS detrended time series. The optimal number of lags in the regression model is determined using the Ng and Perron (1995) criterion. We find that most of the time series are stationary for all variables except CDS spreads, which are stationary in only about 36% of the cases. 5. Results 5.1. Univariate analysis The aim of the univariate analysis is to explore the relationship between the CAAR and each independent variable separately. The findings of the univariate analysis are used to predict the signs of the independent variables in the multivariate regression analysis. Tables 5 and 6 contain CAAR for the (−5;+5) event window as well as the t-/z-scores for the significance tests according to Brown and Warner (1985) (BW), Boehmer, Musumeci and Poulsen (1991) (BMP), and Corrado and Zivney (1992) (CORRA). *** , ** , and * indicate significance at the 1%, 5%, and 10% level, respectively. In Table 5,

the underlying return generating model is the market model with a GARCH (1,1) extension (MMG). CAAR in Table 6 are based on the Fama and French model with a GARCH (1,1) extension (FFG). When grouping conditional on size, we find that while banks with above-average total assets earn significant positive abnormal returns, small banks incur significant negative CAAR. We conclude that investors deem the provision of liquidity facilities to be riskier for smaller banks than for large LFPs, thus considering the respective announcement to be bad news. We observe a similar CAAR behaviour with respect to liquidity: while more liquid LFPs benefit from participating in a securitization transaction, banks with low liquidity earn significant negative CAAR. In this regard, our results are consistent with the findings of Farruggio et al. (2012) for originating banks. With respect to capitalization, both Martinez-Solano et al. (2009) and Farruggio et al. (2012) identify a positive relationship between equity financing and abnormal returns of originating banks. However, our results do not support this finding with respect to the LFPs. Although the amounts of abnormal returns measured indicate that banks with a stronger relative equity position earn positive abnormal returns while CAAR for banks with higher debt are negative, most of our test statistics are insignificant. It is conceivable that potential negative effects from assuming liquidity risk and potential positive effects from additional income due to the provision of liquidity cancel out each other in our sample. Similarly, prior research reports a negative relationship between portfolio quality and abnormal originator returns

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ARTICLE IN PRESS 1.00 1.00 0.08 1.00 0.57 −0.19 1.00 0.53 0.23 −0.30 1.00 0.19 0.20 0.08 −0.41 1.00 −0.16 −0.28 −0.27 −0.09 0.32 1.00 −0.07 0.40 0.37 0.14 0.09 −0.64 1.00 −0.03 0.16 −0.04 −0.07 0.12 −0.09 0.13 1.00 −0.51 −0.17 0.03 −0.14 −0.07 0.06 −0.01 0.14 1.00 −0.01 −0.33 −0.14 −0.47 −0.06 0.05 0.02 0.26 0.01

1.00 −0.21 0.20 0.05 0.26 0.30 −0.33 −0.04 −0.12 −0.09 0.26 1.00 −0.11 −0.01 −0.06 0.29 −0.30 0.05 −0.11 −0.10 0.13 0.20 0.41 1.00 −0.28 −0.40 −0.43 0.11 −0.06 −0.10 −0.22 −0.38 0.22 −0.06 −0.15 −0.14 −0.05

1.00 0.66 −0.19 −0.20 −0.34 0.33 −0.11 0.06 0.19 0.05 0.06 −0.15 0.06

PREGFC LIQCSH LNVOLL LNVOLT INF GDP EURIBOR CMBS SUBRMBS CDS QUALITY PROFIT

1.00 0.73 0.19 −0.25 −0.45 0.25 −0.32 −0.03 0.07 −0.52 0.45 0.13 −0.06 −0.22 −0.35

EQUITY LIQUIDITY LNSIZE

LNSIZE LIQUIDITY EQUITY PROFIT QUALITY CDS SUBRMBS CMBS EURIBOR GDP INF LNVOLT LNVOLL LIQCSH PREGFC

Table 7 Correlation matrix.

Notes: LNSIZE is the log of total assets. LIQUIDITY is measured as “1 − (net loans/total assets)”. LNSIZE is calculated as the log of the banks’s latest total assets. EQUITY is given by the equity to total assets ratio. PROFIT is defined as the respective bank’s net interest margin. QUALITY is proxied by “1 − (impaired loans/gross loans)”. CDS is given by the LFP’s average credit default swap spread. The transaction type is captured by the dummies SUBPRIME (1 in case of a subprime transaction, 0 otherwise) and CMBS (1 for a CMBS transaction, 0 otherwise). EURIBOR denotes the 3 month EURIBOR, and GDP is the real growth in gross domestic product. INF is the inflation rate. LNVOLT reflects the log of a transaction’s size in million EUR, LNVOLL is the log of the amount of liquidity provided. LIQCSH is LNVOLL divided by the company’s log amount of cash. PREGFC is a dummy which is set to 1 for years before 2007, and 0 otherwise.

H. Hollander, J. Prokop / The Quarterly Review of Economics and Finance xxx (2014) xxx–xxx

10

(Farruggio et al., 2012), and a positive relationship between originator profitability and abnormal originator returns (Martinez-Solano et al., 2009; Farruggio et al., 2012). Both findings are consistent with the amounts of CAAR measured in our study for LFPs. However, our results are statistically significant only for the quality criterion. Table 6 shows that size effects are still significant when employing FFG instead of MMG as a reference model. This is interesting since the Fama and French approach explicitly accounts for differences in size, implying that the effect of the securitization announcement on LFPs returns is more pronounced than what can be explained by the respective banks’ “normal” size factor. The other conclusions drawn from the MMG are supported as well, although the amount of CAAR measured tends to be lower under the FFG approach, probably due to the latter’s higher explanatory power. Table 5 shows that based on MMG as a return generating model, LFPs earned significantly positive CAAR in the more tensed periods I and IV. However, evidence for period IV is statistically weaker than for period I, presumably due to lower sample size. In period II, which is characterized by a more relaxed market situation with increasing stock prices, but constant interest rates, we do not find significant CAAR. Period III, with stock prices and interest rates increasing strongly at the same time indicating decreasing market liquidity, shows significantly negative CAAR. Thus, we conclude that LFPs earn negative abnormal returns during tensed liquidity markets and positive abnormal returns during more relaxed times. These findings are essentially in line with Thomas’ (2001) results, who states that originators earn positive abnormal returns if the market situation is more relaxed, but negative abnormal returns in a tensed market. However, it is important to note that the focus of our study is on the LFPs instead of the originators, and that average interest rates are way lower in our investigation period than during the eighties and the nineties analyzed by Thomas (2001). Focussing on originating banks, Farruggio et al. (2012) find significant negative CAAR for the period from 1997 to 2007, while Martinez-Solano et al. (2009) identify a positive effect for Spanish banks between 1993 and 2004. In our LFP-based study, positive and negative effects cancel out each other, leading to statistically insignificant CAAR close to zero for the total period 2002–2010. Table 5 further shows that CAAR are significantly positive for ABS and Prime-RMBS transactions, while abnormal returns for CMBS and Subprime-RMBS transactions tend to be negative. In case of CMBS transactions, this is consistent with expectations since granularity of the underlying portfolio is usually markedly lower than in case of ABS transactions, implying higher chance that the liquidity facility is drawn by the special purpose entity. Likewise, negative abnormal returns on subprime transactions also reflect higher liquidity risk due to a higher probability of default than in case of Prime-RMBS transactions (Keys et al., 2009). Apparently, investors do not consider this additional risk to be adequately compensated by the additional income expected from providing liquidity. Table 6 shows the respective results for the FFG approach. Again, the main results derived from MMG are supported. However, the difference between Subprime-RMBS and Prime-RMBS transactions is less pronounced, since the CAAR measured – while having the expected sign – are statistically insignificant. As another robustness check, we repeat the univariate analysis using the STOXX EUROPE 600 BANKS as a market portfolio. The respective results support our previous findings and are therefore not reported here in detail. 5.2. Multivariate analysis The above analysis shows that bank structure, the market situation at the time of the transaction, and the type of the

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Table 8 Regression results. Coef.

I

II

LIQUIDITY

0.0015 3.41*** 3.24*** −0.0056 −0.49 −0.64 −0.0003 −0.05 0.10 0.0321 3.74*** 2.02** −0.0050 −5.00*** −0.85

0.0004 1.86* 2.28**

LNSIZE

EQUITY

PROFIT

QUALITY

CMBS

EURIBOR

GDP

INF

IV

−0.0157 −2.48** −1.99** −0.0150 −3.35*** −2.01** 0.0032 0.47 0.97 −0.8748 −0.58 −0.30 0.0026 0.28 0.07

−0.0095 −1.56 −1.30 −0.0105 −3.31*** −1.68* 0.0009 0.13 0.41 −1.5764 −1.09 −0.72 0.0051 0.49 0.35

0.01483 1.43 1.52 0.3938 2.28** 0.66 Yes 97 30.2%

0.02019 2.83*** 2.31** −0.0650 −1.35 −1.86* Yes 97 22.1%

0.01065 1.07 1.349996 −0.0358 −0.93 −0.84 Yes 84 23.7%

−0.0097 −1.57 −1.36 −0.0114 −4.03*** −1.86* −0.0011 −0.23 −0.12 −1.7808 −1.19 −0.91 0.0055 0.54 0.34

0.01798 2.49** 1.95* −0.0348 −1.06 −1.20 Yes 97 20.9%

LNVOLT

LNVOLL

LIQCSH

PREGFC

Intercept

Country Obs. Adj. R2

V 0.0016 2.74*** 3.05*** −0.0082 −0.57 −0.76 0.0095 1.31 1.37 0.0034 0.30 0.00 −0.0073 −2.19** −0.52

0.0000 0.10 −0.02 −0.0152 −3.16*** −2.30** −0.0105 −1.80* −1.45 −0.0063 −1.01 −1.01 −1.2976 −0.76 −0.61 0.0134 1.14 1.04

CDS

SUBRMBS

III

−0.0064 −0.61 −0.23 −0.0063 −0.81 −0.20 0.0061 1.68 1.21 0.2879 0.14 0.18 −0.0068 −0.87 −0.70 −0.0046 −0.95 −0.59 0.0025 0.35 0.28 −0.0001 −0.20 −0.27 0.01710 0.75 0.58 0.6422 1.66 0.92 Yes 69 19.7%

Notes: Dependent variable is the cumulative abnormal return for the (−5,+5) event window. LNSIZE is the log of total assets. LIQUIDITY is measured as “1 − (net loans/total assets)”. LNSIZE is calculated as the log of the banks’s latest total assets. EQUITY is given by the equity to total assets ratio. PROFIT is defined as the respective bank’s net interest margin. QUALITY is proxied by “1 − (impaired loans/gross loans)”. CDS is given by the LFP’s average credit default swap spread. The transaction type is captured by the dummies SUBPRIME (1 in case of a subprime transaction, 0 otherwise) and CMBS (1 for a CMBS transaction, 0 otherwise). EURIBOR denotes the 3 month EURIBOR, and GDP is the real growth in gross domestic product. INF is the inflation rate. LNVOLT reflects the log of a transaction’s size in million EUR, LNVOLL is the log of the amount of liquidity provided. LIQCSH is LNVOLL divided by the company’s log amount of cash. PREGFC is a dummy which is set to 1 for years before 2007, and 0 otherwise. The underlying return generating model is the Fama and French model with a GARCH(1,1) extension. The conditional error terms are assumed to follow the Student t-distribution. For each independent variable, regression coefficients are given in the first row, followed by t-values in the second row, and bootstrap t-values in the third row. *** ,** , and * indicate significance at the 1%, 5%, and 10% level, respectively.

transaction affect the amount of abnormal returns observed around the announcement date. Finally, our multivariate regression models are to shed light on the interaction of these factors. Table 8 summarizes the respective results. For each independent variable, regression coefficients are reported in the first row, followed by t-values in the second row, and bootstrap t-values in the third row. *** , ** , and * indicate significance at the 1%, 5%, and 10% level, respectively. The regression analysis shows that LIQUIDITY has high explanatory power for LFPs’ abnormal returns. Regression coefficients are consistently positive, and they are statistically significant

in all model specifications. Thus, we conclude that CARs depend on the LFP’s liquidity at the time the transaction takes place. EURIBOR shows the expected negative sign but is statistically significant in only one of the model specifications without the PREGFC dummy. Moreover, coefficients for the variables SUBPRIME and CMBS have the expected (negative) signs, and are significant in most versions of the regression model. The variables PROFIT and QUALITY show the expected positive (negative) regression coefficients. However, the explanatory power of these variables turns out to be low when omitting these variables. The same applies to the variables LNSIZE

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and EQUITY. The log of the amount of liquidity provided divided by the company’s log normal amount of cash (LIQCSH) has the expected negative coefficient, but its impact on the LFP’s abnormal returns is statistically insignificant. The coefficient for PREGFC turns out to be positive and highly significant indicating lower abnormal returns for transactions in the post-GFC period. Finally, adjusted R2 range between 18.9 and 30.2 per cent, indicating that the regression models considered possess explanatory power. 6. Conclusions Using a unique data set of 97 European securitization transactions, we investigate whether investors consider the provision of liquidity in such a transaction as “good news”, “bad news”, or value irrelevant with respect to the liquidity facility provider (LFP). Moreover, we try to answer the question which risk factors – bank-specific, the market environment, or both – drive value creation. Our results suggest that providing liquidity in a securitization transaction does affect the LFP’s market value. In particular, cumulative abnormal returns on the LFP’s stock are positively related to the LFP’s liquidity, and they tend to be negative for CMBS or subprime RMBS portfolios, and they are significantly lower after the global financial crisis. The effects are particularly strong for our longest event window (−5, +5), indicating that prices do not adjust instantaneously to new information. Our results have implications for investors as well as for value-based bank management. They indicate that while stock market investors basically do assess whether providing liquidity is potentially good or harmful to the LFP, this assessment depends on several factors, and takes some time. From a bank management perspective, this enables decision makers to assess the likely effects of providing liquidity on their company’s stock, and may in this respect serve as a rationality check on the decision to participate in a securitization transaction. Acknowledgements Part of this work was done while Hollander was a visiting researcher at the Collaborative Research Centre 649 “Economic Risk”, Humboldt-University Berlin. The authors wish to thank participants in the Annual Meeting of the German Academic Association for Business Research, participants in the GdRE International Symposium on Money, Banking and Finance, and two anonymous referees for helpful comments. Financial support by Stiftung Bremer Wertpapierbörse and VolkswagenStiftung is gratefully acknowledged. Appendix A. A.1. Test statistics To determine the statistical significance of abnormal returns measured, we follow Martinez-Solano et al. (2009) and employ the portfolio time-series method proposed by Brown and Warner (1985) (BW), the cross-sectional test suggested by Boehmer et al. (1991) (BMP), and the nonparametric rank test developed by Corrado (1989) (Corra). Each test has different strengths when applied in an event study context: The first one takes into account potential correlation of returns due to event clustering, the second one captures heteroscedasticity of abnormal returns. Both BW and BMP are parametric tests, and assume that abnormal returns follow a normal distribution, resulting in the test statistics being Student t-distributed. The nonparametric Corrado test does not assume a particular distribution of the underlying abnormal returns. It is robust to increased volatility within the event window (Corrado

and Zivney, 1992), and serves as a robustness check for the results derived from the two parametric tests (Campbell et al., 1997). Its test statistic is normally distributed. The test statistic suggested by Brown and Warner (1985) is given by the following equation: BWAARt =

1/N



N i=1

N

1/(N(N − 1))

i=1

ARit

(ARit −

N i=1

ARit /N)

2

with AARt = 1/N

N 

ARit = average abnormal return on day t

i=1

N = number of transactions Consequently, the test statistic BWCAAR[K,] for cumulative average abnormal returns (CAAR) from day K to L is given by: BWCAAR[K,L] =

1/N





N

1/(N(N − 1))

N

i=1

i=1

CARi[K,L]

CARi[K,L] −

2

N

CARi[K,L] /N i=1

where CARi[K,L] = cumulative abnormal return of asset i from day K to L CAAR[K,L] = 1/N

N 

CARi[K,L] = cumulative average abnormal return from day K to L

i=1

The test statistics TAARt and TCAAR[K,] are Student t-distributed with n − 1 degrees of freedom (see Brown and Warner, 1985). Brown and Warner (1985) proxy the standard deviation of the abnormal returns by the standard deviation of the abnormal returns during the event window. In contrast, Boehmer et al. (1991) use a combination of the tests suggested by Brown and Warner (1985), and Patell (1976). The Patell-test is characterized by the use of standardized abnormal returns. Typically, abnormal returns are divided by the standard deviation of the abnormal returns during the estimation period. The test statistic BMPASARt of the test suggested by Boehmer et al. (1991) is given by the following equation: BMPASARt =

1/N



N

N

1/(N(N − 1))

i=1

i=1

SARit

(SARit −

N i=1

SARit /N)

2

where SARit = standardized abnormal return of transaction i on day t SARit =

s j

ARit



2

T

1 + (1/Tj ) + ((Rmt − R¯ m ) /

j=1

2

(Rmj − R¯ m ) )

sj = standard deviation of abnormal returns during estimation period Tj  s j =

(1/ (N − 1)

Tj j=1

(ARj − ARj ))

R¯ m = average market return during estimation period Tj ARj = average abnormal return during estiamtion period Tj Tj = number of days of the estimation period

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The test statistic BMPCASAR[K,] for cumulative average abnormal returns is given by: 1/N

BMPCASAR[K,L] =  1/ (N(N − 1))

N

i=1

N i=1

CSARi[K,L]

(CSARi[K,L] −

N i=1

2

CSARi[K,L] /N)

where CSARi[K,L] = cumulative standardized abnormal return of transaction i from day K to L

CSARi[K,L] =

L 

SARit

i=K

CASAR[K,L] = cumulative average standardized abnormal return from day K to L

1 CSARi[K,L] N N

CASAR[K,L] =

i=1

In this study, we set Tj = 261 and N = 97. BMPASARt and BMPCASAR[K,] are also Student t-distributed with n-1 degrees of freedom. Out of the non-parametric tests, we select Corrado’s (1989) rank-test because this test exhibits only a small error in presence of event induced variance (Corrado and Zivney, 1992, p. 477). Furthermore, Corrado’s test does not assume a specific type of distribution, and is applicable to small samples. Usually, parametric test assume a normal distribution, which can only be approximated by large samples. However, returns are typically not normally distributed, but exhibit e.g. fat tails. In case of no missing returns, Corrado’s (1989) test statistic is given by6 : CorradoAKt =

1/N

 1/Dj

N

i=1

+5 t=−261

(Kit − ((Dj /2) + (1/2)))

(1/N

N

i=1

(Kit − ((Dj /2) + (1/2))))

2

with Kit = rank (ARit ), t = −261, . . .., +5 Dj = number of days in the event and estimation period AKt = average rank on day t The test statistic CorradoCAK[K,] for cumulative average abnormal returns from day K to L can be calculated by the following equation (Serra, 2002, p. 9):

L

CorradoCAK[K,L] =



l=K

(1/Dj

(1/N

+5 t=−261

N

i=1

(1/N

(Kit − ((Dj /2) + (1/2))))

N i=1

l

2 √ (Kit − ((Dj /2) + (1/2)))) ) B

where CAK[K,L] = cumulative average rank from day K to L B = number of days in the event window In this study, we use Dj = 272 days. CorradoAKt and CorradoCAK[K,] follow a standard normal distribution. In order to analyze negative z-values, we use the symmetry property of the normal distribution: P(Z ≥ −z) = 1 − ˚(−z) = 1 − (1 − ˚(z)) = ˚(z)

6 In case of no missing returns, the test statistic of Corrado (1989) equals Corrado and Zivney (1992), see Corrado and Zivney (1992), p. 467.

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