Bank Securitization and Systemic Risk

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1 Bank Securitization and Systemic Risk Zhizhen Chen a, Kose John b, Frank Hong Liu a,*, Mingming Zhou c This version: January 3rd, 2017 Abstract We hypothesize and document a positive impact of bank loan securitization on bank systemic risk, measured by SRISK% and CoVaR. Our results are robust for the instrumental variable approach and the difference-in-difference analysis. We also show that the positive impact of securitization on systemic risk is stronger for a larger, more diversified and more complex bank. We also show this effect is stronger for mortgage loan securitizations compared with non-mortgage loan securitizations. We finally show that banks with more active securitization activities in the pre-crisis period are more likely to fail, having subsidiaries to be taken over by other banks, and receiving TARP funds during the financial crisis. Our results have extensive implications for policy makers who consider the financial instability at whole market level. JEL Classification: G10, G20, G21 Keywords: Securitization; Systemic Risk; Interconnectedness; a Adam Smith Business School, University of Glasgow, U.K. b Stern School of Business, New York University, U.S.A. c College of Business, University of Colorado at Colorado Springs, U.S.A. * Corresponding author: Frank Hong Liu; University of Glasgow, Adam Smith Business School, Room 471, Gilbert Scott Building, Glasgow, G12 8QQ, Hong.Liu@glasgow.ac.uk. 1

2 1. Introduction The impact of bank securitization on financial fragility receives extensive attention from both academics and policy makers, especially after the global financial crisis of , which highlights the devastating impact of systemic risk on the entire financial system. It is natural to hypothesize that bank securitization activities lead to the increase of bank systemic risk. For example, Nijskens and Wagner (2011) show that credit risk transfer activities including credit default swaps (CDS) and collateralized loan obligations (CLOs) allows individual banks to be less risky, but may pose greater risks to the financial system due to an increase in banks correlations. Few empirical studies, however, have shown whether bank securitization activities cause the increase of systemic risk in the sense that systemic risk or the global financial crisis would counterfactually not have occurred had banks not engage in any securitization activities. An alternative hypothesis is that long-term bank characteristic, such as bank CEO compensation packages, risk taking incentives induced by the government deposit insurance policy, increase competition and/or increased bank interconnections through derivative trading and other non-traditional fee income activities, are the fundamental drivers of increase of systemic risk. It is also possible that banks with more connections with other banks (hence higher systemic risk) are more likely to seek additional opportunities to securitize. It is also natural to conjecture that there are potentially unobservable factors which might affect both bank securitization activities and its systemic risk level. For example, we would not be surprised to see a risk-seeking bank to have both higher level of securitization activities and systemic risk. We aim to fill this gap by examining the effect of bank loan securitization on systemic risk. We hypothesize that bank securitization increases bank systemic risk because bank s connections with the rest of the banking system is increased through securitization activities. For example, as argued in Gande and Saunders (2012), securitization allows banks to convert illiquid assets into liquid assets, which allow 2

3 them to share the risk of their loans with a wider group of investors, including mainly financial institutions. The increased connections with the whole financial system results in higher fragility of the bank if the whole financial system is in distress. To test this hypothesis, we first construct systemic risk measures of SRISK% and CoVaR following Brownlees and Engle (2016), and Adrian and Brunnermeier (2014), respectively. SRISK% is a bank s contribution to the shortfall of the financial system as a whole, while CoVaR is the difference between the CoVaR conditional on a bank being in distress and the CoVaR conditional on a bank performing on the median state. We then obtain quarterly accounting data of all public bank holding companies (BHC) operating in the U.S. during the period from 2001:Q3 to 2015:Q4 from the FR Y-9C reports provided by the FFIEC (Federal Financial Institutions Examination Council). We find that the involvement of securitization by BHCs increases their systemic risks. This finding holds after controlling for BHC and, time-fixed effects, and a wide array of time-varying BHC characteristics, including retained interest ratio, bank size, capital ratio%, liquidity ratio%, Log Z, ROA%, diversification ratio, stock return%, stock return volatility%, market to book ratio, and market leverage ratio. The economic impact of securitization on systemic risk is also significant. We find a one standard-deviation increase in total securitization ratio increases systemic risk defined as SRISK% and CoVaR by 16.88% and 4.61% of its standard deviations, respectively. We use two identification strategies to examine the causality of bank securitization on bank systemic risk. The first identification strategy uses the instrumental variable approach with three instruments for bank securitization, statelevel corporate tax rate, peer liquidity index and the interaction between the two instruments. The second identification strategy employs a difference-in-difference (DiD) approach by using the bankruptcy filing of Lehman Brothers in September 2008 as a source of exogenous variation (see Brunnermeier, Dong, and Palia, 2012 for similar practice). We find the impact of securitization on systemic risk decreases after 3

4 the bankruptcy of Lehman Brothers, suggesting the sudden dried-up of liquidity in the securitization market weakens the interconnectedness between banks. Overall, our endogeneity analyses support our hypothesis. We further attempt to strengthen the interpretation of the impact of securitization on bank systemic risk by exploring the impact of banks heterogenous interconnectedness on this relationship. We thus expect impact of securitization on systemic risk is stronger for banks with larger size, higher level of complexity and higher level of diversification. Our tests confirm these predictions. We then show that the impact on systemic risk is more significant for mortgage securitization than non-mortgage securitization. This result reflects that mortgage loans are more attractive to security investors due to their better-quality collaterals and are also easier to securitize in the secondary market because of the stronger degree of commoditisation (e.g., mortgage loans enjoy a higher standardisation of credit assessment techniques) (Altunbas, Gambacorta, and Marques-Ibanez, 2009). The stronger degree of commoditization of mortgage loans increased the interconnectedness between banks, resulting in higher level of systemic risk. Finally, we show banks with securitization activities before the global financial crisis suffer more during this systemic crisis. Specifically, we use the fiveyear average securitization ratios before 2007 to identify active securitizers, and find that those banks acted more actively in securitization market before the systemic crisis are more likely to go bankrupt, become a target of other banks, or receive TARP funds during the crisis. Our paper provides several contributions to the existing literature. First, our paper directly contributes to the group of theoretical literature suggesting securitization increases the instability of the financial system. Literature suggests that potential risks can be spread and transferred among different financial institutions (Slijkerman, Schoenmaker, and de Vries, 2013; van Oordt, 2014). Gennaioli, Shleifer, and, Vishny (2013) propose a shadow banking model to explain that the increase of 4

5 vulnerability of the banking system is because banks become interconnected through markets. Piskorski, Seru, and Vig (2010) show that securitization impacts renegotiation decisions of loan servicers, which provides indirect evidence of securitization may increase instability of the system. This paper also contributes to our limited understanding of the determinants of bank systemic risk. Empirical studies find possible determinant factors of bank systemic risk may include banks exposures to common risk factors (Trapp and Wewel, 2013), higher level of interbank lending (Rochet and Tirole, 1996; Freixas, Parigi, and Rochet, 2000), non-interest income (Brunnermeier, Dong, and Palia, 2012), lack of competition (Anginer, Demirguc-Kunt, and Zhu, 2014; Weiß, Bostandzic, and Neumann, 2014a, 2014b) and regulation (Gauthier, Lehar, and Souissi, 2012). Our results suggest that the securitization activities among banks increase the interconnectedness of the banking system and thus increase the systemic risk of the whole system. Our paper also extends the literature documenting the relationship between securitization and bank risk. Classic theory suggests risk transfer is one of the motivations of banks to securitize (Pennacchi, 1988), but recent research shows regulatory arbitrage becomes more popular for securitizers by holding the highly rated tranches to decrease costs of capital (Erel, Nadauld, and, Stulz, 2014). For example, Acharya, Schnabl, and, Suarez (2013) report no risk transfer effect of securitization. Another strand of literature shows information asymmetry can increase bank risk due to a decreased credit standard and less incentive for banks to monitor loans (Keys et al., 2010). Several studies also document the fact that credit risk transfer activities could decrease bank risk but pose more risk to the system (Nijskens and Wagner, 2011). Our results add to the existing literature to show that, in addition to contributing to individual risk, securitization also leads to the increase of systemic risk. In Section 2 we provide the hypothesis development. Section 3 describes our dataset and empirical strategy, while Section 4 shows the main results. Section 5 5

6 presents additional analysis and Section 6 conclude. 2. Hypothesis development Securitization is arguably the most popular financial innovation in the last few decades, and it significantly increases the interconnectedness between banks. The traditional originate and hold banking model has been replaced by the originate and distribute model via securitization. The creation of new securities facilitated the large capital inflows (Loutskina and Strahan, 2009), or, better credit risk management (Cebenoyan and Strahan, 2004). Banks in turn become active loan sellers and buyers in the secondary market. For example, Loutskina and Strahan (2009) report that, on average, 60% of outstanding mortgages are securitized before the financial crisis. Gorton and Metrick (2012) also report that the asset-backed securitization market exceeded the issuance of all corporate debt in the U.S. from 2004 to By acting as buyers and sellers of the securitized assets at the same time, banks become more interconnected (Brunnermeier, 2009). This process is intensified by recent developments of structured finance products, such as commercial debt obligations (CDOs) and commercial loan obligations (CLOs). For example, Longstaff (2010) finds strong evidence of contagion effect within the banking network by studying the subprime asset-backed CDO market during the period. Since buyers of CDOs can also protect themselves by purchasing credit default swaps (CDS), the interconnectedness between banks is further increased through these structured products. Holding a fraction of each other s risky asset could lead to better risk management through diversification (Simons, 1993), but it also increases the commonality of asset holdings of different banks (Wagner, 2010). Studies of Cai, Saunders, and Steffen (2015) and Roukny, Battiston, and Stiglitz (2015) both highlight that interconnectedness is significantly related to the contagion effect. As banks have similar exposure to potential risk, the interconnectedness increases the likelihood of banks to respond to external shocks in similar patterns. When one bank 6

7 suffers a crisis, other banks which hold claims against the affected bank also have to account for the devaluation of these assets (Allen and Gale, 2000). The critical mispricing hence triggers information panic and fire sales (Caballero and Simsek, 2013; Alvarez and Barlevy, 2015), which may spread across seemingly unrelated assets and institutions. Thus, this cluster behavior may decrease the resilience of the banking system to extreme situations (Calmes and Theoret, 2014), and lead to a higher possibility of joint bank failure (de Vries, 2005; Wagner, 2010; Fiordelisi and Marqués-Ibañez, 2013). Hence we hypothesize that the involvement of securitization increases bank s systemic risk. 3. Data, variables and methodology 3.1 Data In order to estimate systemic risk measures, we first collect daily market equity data from the Center for Research in Security Prices (CRSP), financial statement data from Compustat and Federal Reserve form FR Y-9C filed by a bank with the Federal Reserve, T-bill and LIBOR rates from the Federal Reserve Bank of New York, and real estate market returns from the Federal Housing Finance Agency. 1 We focus on publicly traded bank holding companies (BHCs) in the U.S. with SIC codes 60 to 67 (financial institutions) and filing Federal Reserve FR Y-9C report in each quarter. By focusing on large BHCs, we drop BHCs with total assets under the $1 billion threshold following Bedendo and Bruno (2012). 2 Since June 2001, U.S. banks are required to provide detailed information on securitization activities. Our sample thus covers the time period from 2001:Q3 to 2015:Q4. We then combine the quarterly systemic risk measures with accounting data using the CRSP-FRB Link provided by Federal Reserve Bank of New York. Our final sample is an unbalanced 1 Detailed information refers to Adrian and Brunnermeier (2014). 2 Small BHCs are rare securitizers in the market due to the substantial upfront costs. We also use the full sample to redo all regressions, and results are consistent. 7

8 panel including 151 securitized BHCs and 635 non-securitized BHCs, accounting for total observations of 22, Variables The key independent variable is total securitization ratio, which is defined as the ratio of outstanding principal balance of assets securitized over total assets. We also use mortgage securitization ratio and non-mortgage securitization ratio in the additional analyses, which are defined as the ratio of outstanding principal balance of securitized mortgage and non-mortgage loans over total assets, respectively. We use two systemic risk measures in this research. The first systemic risk measure is SRISK%, which represents a bank s share of the entire banking system systemic risk. SRISK% is derived from the SRISK which proposed by Brownlees and Engle (2016). SRISK is defined as the expected U.S.-Dollar capital shortfall conditional on a systemic event: SRISK i,t = E[(k(D i,t + W i,t ) W i,t ) Crisis] (1) Where W i,t is the market value of equity, D i,t is the book value of debt, and k is the prudential capital fraction which is set to be 8% 3. This expectation can be simplified as: SRISK i,t = W i,t [k LVG i,t + (1 k)lrmes i,t 1] (2) Where LVG i,t denotes the quasi leverage ratio and LRMES i,t is the Long Run MES, the expectation of the firm equity multi-period arithmetic return conditional on the systemic event. We follow the latest calculation method from V-lab of the New York University to construct the Long Run MES which is specified as follows 4 : LRMES i,t = 1 exp (log(1 d) BETA i,t (3) Where d is the six-month crisis threshold for the market index decline with a 3 Although Admati and Hellwig (2013) argue that the prudential capital ratio should be much higher, Brownlees and Engle (2016) show that k = 8% is reasonable and results are substantially stable in a reasonable range of values of k. 4 Detailed information is on: 8

9 default value of 40%, and BETA i,t is the bank s CAPM beta. We estimate bank s quarterly CAPM beta from June 2001 to December SRISK is computed at the end of each quarter using all data available as of that date to avoid look ahead bias. SRISK thus combines both the bank s projected market value loss due to its sensitivity with market returns and its leverage. However, SRISK is naturally greater for larger banks. To make sure that the results are not driven solely by bank size, we use Systemic Risk Contribution, SRISK%, instead, which is constructed by dividing SRISK for one bank by the sum of SRISK across all banks at each point. The second systemic risk measure is CoVaR (Adrian and Brunnermeier, 2014), which captures the externality a bank causes on the system. Adrian and Brunnermeier (2014) develop CoVaR from the notation of value-at-risk (VaR) and define CoVaR system i q as the VaR of the entire financial system conditional upon bank i under distress, given a confidence level of q: Pr(R system CoVaR system i R i = VaR i q ) = q (4) Similarly, CoVaR system i,median q denotes the VaR of the entire financial system conditional upon bank i operating in median state, given a confidence level of q: Pr(R system CoVaR system i,median R i = median i ) = q (5) Therefore, individual bank i s contribution to systemic risk can be defined as the difference between CoVaR system i q and CoVaR system i,median q : CoVaR q i = CoVaR q system i CoVaR q system i,median (6) Following Adrian and Brunnermeier (2014), we run the following quantile regressions in the weekly data conditional on a vector of lagged state variables Z t 1 to i predict VaR 50% i (q=50%), VaR 99% and β system i 99% (q=99%): X i t = α i q + γ i i q Z t 1 + ε q,t (7) X system i t = α system i q + γ system i q Z t 1 + β system i q X i system i t + ε q,t (8) State variables include: (i) The change in the three-month yield from the 9

10 Federal Reserve Board s H.15. (ii) The change in the slope of the yield curve, measured by the yield spread between the long term bond composite and the threemonth bill rate obtained from the Federal Reserve Board s H.15 release. (iii) A short term TED spread, defined as the difference between the three-month Libor rate and the three-month secondary market bill rate. (iv) The change in the credit spread between Moody s Baa-rated bonds and the ten-year Treasury rate from the Federal Reserve Board s H.15 release. (v) The weekly market return computed from the S&P500. (vi) The weekly real estate sector return in excess of the market financial sector return (from the real estate companies with SIC code 65-66). (vii) Equity volatility, which is computed as the 22 day rolling standard deviation of the daily CRSP equity market return. i We then compute CoVaR q,t for each bank as: i CoVaR 99%,t i = CoVaR 99%,t i CoVaR 50%,t = β 99% system i i (VaR 99%,t i VaR 50%,t ) (9) We control for a set of bank-specific variable in the regression analysis. Retained interest ratio is defined as the total amount of retained interest divided by the total amount of securitized assets. Retained interests include the aggregate retained interests into credit enhancements, liquidity provisions, and seller s interest. Systemic risk may be decreased if securitizers provide credit enhancements to demonstrate their incentives for loan monitoring (Downing, Jaffee and Wallace, 2009; Fender and Michell, 2009). Bank size is defined as the natural logarithm of individual bank s total assets. We use capital ratio to control bank s capital ratio. We use natural logarithm of the Z-score (log Z), which equals to the return on assets plus the capital asset ratio divided by the standard deviation of asset returns, to represent individual risk. Bank liquidity ratio is calculated as liquid assets divided by total assets. Diversification ratio is represented by the ratio of non-interest income divided by total operating income. We also control bank performance using return on assets ratio (ROA). Market to book ratio is the ratio of market value to book value of equity. We use stock return and stock return volatility to control the performance of bank s stocks. Market leverage ratio is the market value of total assets divided by market 10

11 value of total equity. All variable definitions are reported in Appendix A. 3.3 Instrumental variable approach Our analysis aims to show whether bank securitization activities positively contribute to the systemic risk. We start with a baseline model using OLS regressions. However, the association identified by the baseline model could suffer from endogenous problems. On the one hand, the systemic risk increase effect may be driven by the long-term bank characteristics, such as bank CEO compensation packages, risk taking incentives induced by the government deposit insurance policy, increase competition and/or increased bank interconnections through derivative trading and other non-traditional fee income activities. On the other hand, it is possible that banks with more connections with other banks (hence higher systemic risk) are more likely to seek additional opportunities to securitize. It is also natural to conjecture that there are potentially unobservable factors which might affect both bank securitization activities and its systemic risk level. For example, we would not be surprised to see a risk-seeking bank to have both higher level of securitization activities and systemic risk. We hence use two identification strategies to examine the causality of bank securitization on bank systemic risk. First, we use an instrumental approach based on two-stage least squares (2SLS) regressions. We introduce three sets of exogenous instruments that empirical and theoretical literature suggest could shape bank securitization but that are not expected to have a first-order effect on bank systemic risk. The first instrument is the peer liquidity index, where bank loan portfolio liquidity index is proposed by Loutskina (2011) to effectively capture banks potential ability to securitize loans. Similar to Loutskina (2011), we break down a bank s loan portfolio into six groups: 1) 1-4 home mortgages, 2) home equity lines, 3) credit card receivables, 4) auto loans, 5) commercial and industrial (C&I) loans, and 6) other consumer loans plus all other loans. Bank loan portfolio liquidity index is defined as: Bank Loan Portfolio Liquidity Index it = 11 6 j=1 ( Economy Wide Securitization jt Economy Wide Total Loans jt )

12 (Loan Share j,it ) (10) In this equation, Economy Wide Securitization jt is the total securitized loans of type j at time t in the whole economy; Economy Wide Total Loans jt is the total loans outstanding of type j at time t in the whole economy; Loan Share j,it is the share of type j loans in bank i at time t in the whole economy. We then construct bank i s peer liquidity index by calculating the average of bank loan portfolio liquidity indexes of bank i s peers 5. Herd effect (Jain and Gupta, 1985; Chari and Kehoe, 2004) implies that individual bank s incentive to securitize loans can be stimulated by its industry peers, but it is unlikely that a bank s industry peers securitizing behavior can directly affect its systemic risk. Secondly, we exploit the state-time variations in corporate tax rates as an instrument for bank securitization. Higher corporate tax rate is found to increase bank s incentive to securitize assets due to the corporate tax exemption of securitized assets (Han, Park and Pennacchi, 2015), while local corporate tax rate should not have a direct impact on bank systemic risk, unless through the channel of securitization. Lastly, considering state-level corporate tax rate only provides information on the impact of securitization incentives of a state s average bank, we interact the statetime variation in the corporate tax rates with bank s peer liquidity index to develop a bank-specific instrumental variable of securitization Difference-in-difference approach In the second identification strategy, we employ a difference-in-difference (DiD) approach to examine the link between changes in securitization and changes in systemic risk. Following Brunnermeier, Dong, and Palia (2012), we use the bankruptcy filing of Lehman Brothers in September 2008 as a source of exogenous variation. The bankruptcy of Lehman Brothers caused a sudden erosion of liquidity in 5 Bank i itself is excluded. 6 See more recent empirical strategies using similar interaction terms as instruments on, e.g., Santos and Winton (2008), Leary (2009), Foos, Norden, and Weber (2010), Maskara (2010), Benmelech and Bergman (2011), He, Qian, and Stahan (2012), Callen and Fang (2013) among others. 12

13 the capital market in terms of short-term financing (e.g., short-term repurchase agreements (repos)) (Carey, Correa, and Kotter, 2009; Covitz, Liang, and Suarez, 2009). 7 Since the repos served as the main funding source to keep packaging and reselling loans (Gorton and Metrick, 2012), securitized banks are more likely to suffer severe liquidities insufficiency after the bankruptcy of Lehman Brothers. We hence expect the liquidity-shock will have more significant impact on securitized banks than non-securitized banks. We adopt two DiD frameworks. First, we use a subsample including securitizers and their most similar counterpart non-securitizers to test this hypothesis. We start with assigning propensity scores to all individuals in the sample, based on the following bank specific characteristics: bank size, capital ratio%, liquidity ratio%, Log Z, ROA%, diversification ratio, stock return%, stock return volatility%, market to book ratio, and market leverage ratio. We then run the nearest-neighbor matching with replacement year by year to ensure every securitizer is matched with a nonsecuritizer in the same year, by imposing a 1% tolerance level on the maximum propensity score distance. In this matched subsample, we use securitizer dummy to identify the securitized banks. Post Lehman bankruptcy dummy is set to unity after the year of 2008, and zero before Empirical models are as follows: Systemic Risk i,t = β 0 + β 1 Securitizer Dummy i,t Post Lehman Bankruptcy i,t + β 2 X i,t + α i + τ t + φ i,t (11) Systemic Risk i,t is the systemic risk measure, X i,t is the vector of bank specific controls, α i is the intercept of for each bank, τ t is the intercept for each year, and φ i,t is the error term. The Post Lehman bankruptcy dummy and Securitizer Dummy do not appear on the right hand side of the regression because they would be perfectly collinear with the time and bank fixed effects, respectively. The coefficient of interest for assessing whether securitization has impact on bank systemic risk is β 1. A significant and negative coefficient of this interaction term 7 See Brunnermeier (2009) for a detailed introduction of the global financial crisis. 13

14 indicates that securitizers systemic risks are reduced in the post Lehman bankruptcy period more than non-securitizers. In the second DiD framework, we hypothesize that banks with higher incentive to securitize were more likely to be impacted by the Lehman Brother s bankruptcy than banks with less incentive to securitize. We identify banks incentives to securitize using bank loan portfolio liquidity index proposed by Loutskina (2011). Hence, our empirical strategy relies on the different sensitivity of banks with different bank loan portfolio liquidity index to the Lehman Brother s bankruptcy. We distinguish between banks with high and low bank loan portfolio liquidity index using the 90% distribution threshold 8 to define the most affected banks. We use bank loan portfolio liquidity index values of 2005 to define the size distribution of liquidity index, considering the year of 2005 is likely to be normal period (see Berger and Bouwman, 2013). We include only securitizers in this sub-sample analysis. Top 10% securitizers dummy is set to unity if a securitizer s bank loan portfolio liquidity index value is larger than 90% distribution of all securitizers, and zero otherwise. We replace securitizer dummy by Top 10% Securitizers dummy in Equation 10 and run the regression within securitizers. 4. Main results 4.1 Descriptive statistics Table 1 summarizes the statistics of both systemic risk measures, SRISK% and CoVaR, securitization ratios, and bank specific characteristics for securitizers and non-securitizers. We report sample means, medians and standard deviations (SDs). We use Student s t-test on means and Wilcoxon rank-sum test on medians of the differences between securitizers and non-securitizers. Letters "a" and "b" in the last column indicate a significant statistical difference of means and medians at 1% level, 8 In robustness tests we consider various other bank size thresholds (e.g., 95%, 98%). The results are qualitatively similar. 14

15 respectively. <Insert Table 1 Here> We first show that, on average, securitized assets account for 15.17% of securitizers total assets. The median of total securitization ratio is 2.60% and the standard deviation is 51.16, which is in the line with the argument that some banks are more frequent and massive securitizers (Nadauld and Weisbach, 2012). We find, on average, only 11.85% of the securitized loans are provided with credit enhancements. The low proportion of credit enhancements suggests that securitizers do not have the incentive to keep monitoring loans, which responds to the findings of Purnanandam (2011). Table 1 also shows significant differences between mortgage and nonmortgage securitizations in terms of proportions of total amount and retained interests. For example, the proportion of securitized mortgage loans (11.72%) is significantly higher than that of non-mortgage loans (3.51%), suggesting that mortgage securitization is more popular in the market than non-mortgage securitization. Retained interest ratio is higher for securitized non-mortgage loans, which is in the line with the fact that non-mortgage securitization requires securitizers to provide higher retention of risk exposures 9. We find that securitizers have higher systemic risks than non-securitizers. For example, the average share of systemic risk for securitizers, represented by SRISK%, is 1.33%, while this number for non-securitizers is only 0.31%. When measured by CoVaR, we find the average value is also higher for securitizers (3.80 vs of non-securitizers), suggesting that, when under distress, a securitizer increases the entire banking system s systemic risk by 3.80%, while this contribution for nonsecuritizers is 1.98%. We also find that securitizers are, on average, larger, safer, and more diversified than non-securitizers, which are consistent with previous studies (e.g., DeMarzo, 2005; Loutskina and Strahan, 2009; Lourtskina, 2011). We report the correlation matrix in Appendix B. The correlation between 9 It is found in International Monetary Fund (2009) that a minimum retention requirement of 5% could be binding for almost all types of asset-backed securities (ABS), but this retention ratio for mortgage-backed securities (MBS) is below 1%. 15

16 SRISK% and CoVaR is 0.10, suggesting the two measures capture different aspects of systemic risk. We find no significant correlation between the various independent variables. <Insert Appendix B Here> 4.2 The impact of loan securitization on systemic risk Table 2 reports the baseline results of the impact of total loan securitization on systemic risk measures including SRISK% and CoVaR. All independent variables are lagged with one quarter, and time and BHC fixed-effects are also controlled for. T- statistics are based on standard errors clustered on the BHC level. <Insert Table 2 Here> We find coefficients of securitization ratio are positive and statistically significant at 1% level for both SRISK% and CoVaR, suggesting that securitization contributes to the increase of systemic risk. This positive association is in the line with the theoretical prediction that the profit-maximizing behaviours of banks which participate in securitization markets could create systemic risk (Shleifer and Vishny, 2010). We report a one-standard-deviation increase in total securitization ratio increases, an average, 0.61% standard deviation of SIRISK% (column 1), and 4.61% standard deviation of CoVaR (column 2). The diversification of securitization allows an efficient risk sharing in the financial system (DeMarzo, 2005), and thus increases the interconnectedness between banks, and even with other financial institutions, e.g., hedge funds and equity investors (Duffie, 2007). The increased interconnectedness may amplify the contagion effect and increase the fragility of the financial system (de Vries, 2005; Fiordelisi and Marqués-Ibañez, 2013; Calmes and Theoret, 2014) because the diversification increases the similarity between different institutions (Wagner, 2011). Systemic risk hence is highly possible because the large losses incurred in some risky institutions (e.g., hedge funds) can be quickly transmitted to other interconnected institutions (e.g., BHCs) and experienced by portfolios highly overlapped to the portfolio that initiated the unwind (Khandani, Lo, and Merton, 16

17 2013). Results on the control variables are largely in the line with our expectation and previous literature. We find higher retained interest ratio is related to lower systemic risk, supporting the theory that credit enhancements increase originators incentives to carefully screen loans (Demiroglu and James, 2012). Since declined screening standards is deemed to be the main trigger of the financial crisis, the credit enhancement level should be negatively related to systemic risk. We find banks with larger size, lower probability of default, higher diversification and leverage contribute positively to systemic risk. 4.3 Endogeneity To identify the causal impact of securitization on bank systemic risk, we adopt two identification strategies. We first use an instrumental variable analysis. We introduce three exogenous instruments, state-level corporate tax rate, peer liquidity index, and state-level corporate tax rate peer liquidity index. The second-step results are reported in Table 3, while the first-step results are reported in Appendix C. <Insert Table 3 Here> In the first step, we find all instruments are positive and statistically significant at 1% level. Consistent with the baseline framework, we find a positive relationship between securitization ratio and both systemic risk measures in the second step. The statistical significances are at 1% levels across all specifications. These findings confirm our main results by suggesting a positive impact of securitization on systemic risk. We conduct an F-test of the excluded exogenous variables in the first-stage regressions. We reject the null hypothesis that the instruments do not explain crosssectional differences in the involvement of bank securitization at the 1% level in all model specifications. Overall, we hold that the instruments are reasonably exogenous and have decent explanatory power in explaining the level of bank securitization. In the second identification strategy, we use a Difference-in-Difference framework to examine the link between changes in securitization and changes in 17

18 systemic risk after the bankruptcy of Lehman Brothers. The results are reported in Table 4. <Insert Table 4 Here> We first examine the changes in the impact on systemic risk between securitizers and non-securitizers after the bankruptcy of Lehman Brothers, using a 1:1 matched sample. We show that, in Panel A, the coefficients of Securitizer Dummy Post Lehman bankruptcy are all negative and statistically significant at 1% level (column 1 and 2 for SRISK%, and column 3 and 4 for CoVaR), suggesting the impacts of securitized banks on systemic risk statistically decreased more significantly than non-securitized banks when the liquidity of the market dried up after the bankruptcy of Lehman Brothers. This finding is in the line with our hypothesis that the sudden dried-up of short-term financing in the market impacts more significantly on securitized banks, which in turn supports our main findings. In Panel B, we find the coefficients of Top 10% Securitizers Post Lehman bankruptcy are also negative and statistically significant at the 1% level in all regressions, suggesting that the impact of top 10% securitizers decreases systemic risk more than the rest of securitizers. Overall, the DiD framework supports our main results of the positive impact of securitization on systemic risk. 4.4 The impact of heterogeneity of bank-specific characteristics on the relationship between securitization and systemic risk Analyses so far suggest a positive association between securitization and systemic risk. We argue that the impact of securitization on bank systemic risk is through banks interconnectedness with the rest of the financial system. Thus, we would expect the impact of securitization on bank systemic risk would be stronger for banks with higher level of interconnectedness with other banks. First, large borrowers require the participants of large banks, but regulatory restrictions such as counterparty exposure limits discourage banks to retain larger exposures to same borrower, limiting their relationships with larger firms. With 18

19 securitization, large banks can securitize a greater fraction their risk exposure to syndicated loan market and continue accommodating the financing needs of large borrowers. In this case, large banks are more likely to significantly interconnect with different financial institutions through the securitization market. Thus, we expect the impact of securitization on systemic risk is stronger for larger banks. Second, a more diversified bank is likely to have established a larger connection network to share the potential risk. Since securitization increases the connections between securitizers and other risky financial institutions such as hedge funds, the potential risk caused by securitization can be more easily to pass through a more diversified bank to other banks. We would thus expect the systemic risk increase effect of securitization to be stronger for a more diversified bank. Last, a higher level of complexity of a bank suggests a higher likelihood to participant in different financial markets and work with different financial institutions (Billio et al., 2012). Considering that securitized-banking activities are also central to the operations of investment banks (e.g., Bear Stearns, Lehman Brothers, Morgan Stanley, and Merrill Lynch), a more complex securitizer is expected to have higher connections with riskier financial institutions. We expect the impact of securitization on systemic risk to be stronger for more complex banks. We use the following empirical model to test these hypotheses: Systemic Risk i,t = β 0 + β 1 Securitization Ratio i,t + β 2 Bank Characteristics i,t + β 3 Securitization Ratio i,t Bank Characteristics i,t + β 4 X i,t + α i + δ t + φ i,t (12) Where Securitization Ratio i,t the vector of securitization ratio, Bank Characteristics i,t is the vector of bank specific characteristics, including bank size, complexity, and diversification. We measure bank size using the natural logarithm of total assets, diversification using non-interest income divided to total operating income ratio, and bank complexity using the total number of subsidiaries (Lee, 1992). X i,t is the vector of bank specific controls, α i is the intercept of for each bank, δ t is the intercept not related to bank characteristics, and φ i,t is the error term. The interaction term beta coefficient ( β 3 ) can be interpreted as the additional 19

20 responsiveness to the change of bank systemic risk given different bank characteristics. Table 5 reports the results. We find coefficients of interest (β 3 ) are all positive and statistically significant at 10% and above, supporting our hypotheses the impact of securitization on bank systemic risk would be stronger for banks with higher level of interconnectedness with other banks. <Insert Table 5 Here> 4.5 The impact of mortgage and non-mortgage securitization on systemic risk In this subsection we explore the different impact between mortgage and nonmortgage securitizations on systemic risk. Mortgage securitization is more likely to increase the interconnectedness between different institutions. On the one hand, mortgage loans are considered by the market as with lower risk because the underlying assets (e.g., real estate properties) are not easily depreciated (Campbell and Cocco, 2015). Mortgage securitization is in turn more attractive to investors. For example, before the financial crisis, around 60% of the mortgage loans were securitized (Loutskina and Strahan, 2009). On the other hand, mortgage loans can be easily securitized due to the higher quality and stronger degree of commoditisation (e.g., mortgage loans enjoy a higher standardisation of credit assessment techniques) (Altunbas, Gambacorta, and Marques-Ibanez, 2009). The rapid development of the secondary market makes it even more convenient for banks to securitize mortgage loans (Frame and White, 2005). Non-mortgage securitization, however, requires securitizers to provide higher retention of risk exposures during the process in order to signal the quality of the underlying assets (Guo and Wu, 2014), which forces nonmortgage securitizers to keep monitoring loans (Kiff and Kisser, 2010). Thus, we expect the impact of mortgage securitization on systemic risk to be stronger than nonmortgage securitization. We replace total securitization ratio in the baseline and 2SLS regressions by mortgage securitization ratio and non-mortgage securitization ratio, respectively. Main results are reported in Table 6, while the first-step results on instruments of 20

21 2SLS are reported in Appendix C. <Insert Table 5 Here> We find mortgage and non-mortgage securitization ratios are both positively associated with systemic risk measures. This finding supports our main argument that the involvement of securitization is positively related to the systemic risk increase. We find, however, the impact on systemic risk is statistically stronger for mortgage securitization, as the positive relationship identified by both OLS and 2SLS regressions is at 1% and 10% significance levels for mortgage and non-mortgage securitization ratios, respectively. We also find the economic impact on systemic risk of mortgage securitization ratios is higher. For example, a one-standard deviation increase in mortgage securitization ratio leads to an average 0.40% (column 1 to 3) and 5.51% (column 7 to 9) increase of its standard deviation for SRISK% and CoVaR, respectively, while both economic impacts for non-mortgage securitization are significantly lower (0.13% (column 4 to 6) and 1.07% (column 10 to 12), respectively). 5. Do active securitization activities lead to real problems for banks during the financial crisis? In the previous sections we document that banks involvement of securitization activities will lead to higher systemic risks. While our systemic risk measures are ex ante in nature, we attempt to investigate whether securitization leads to ex post bank risk during the financial crisis period in this section. In particular, we examine whether a more active securitizer in the pre-crisis period will have higher probability of failure, more likely to have subsidiaries to be the targets by other banks, and more likely to receive government TARP funds during the global financial crisis period. First, Wagner (2010) argues that a joint failure is more likely under an extreme event. Securitized banks that established solid connections with other institutions in the pre-crisis period may in turn be more significantly hammered by the joint failure 21

22 of other interconnected institutions during crisis. We hence expect the likelihood of bank failure during crisis is higher for more active securitizers in the pre-crisis period. Second, securitization not only increases bank s connections with other banks through diversification, but also with other non-bank financial institutions (e.g., hedge funds). Thus, securitized banks are more likely to face higher potential risk during crisis caused by the distress of such connected risky institutions. Classic theory of mergers and acquisitions (M&A) suggests a risk decrease and performance increase effect (Lewellen, 1971). Therefore, the subsidiaries of those securitizers are more likely to be acquired by other banks during crisis in order to help the BHCs to overcome the unfavourable risk or performance. We would expect the likelihood of being acquired is higher during crisis for the subsidiaries of an active securitizer before the financial crisis. Third, securitizers are more likely to suffer from liquidity shortage due to the sudden dried-up of repos. To stabilize banks under trouble to raise capital in the public market, the Troubled Asset Relief Program (TARP) was introduce by the U.S. government. Since active securitizers before crisis are more likely to suffer by the liquidity shock, we expect the likelihood of participating in TARP programs to be higher for those more active securitizers. To test those hypotheses, we use the five-year average 10 securitization ratios before 2007 as the indicator of an active securitizer. We use the bank failure, M&A, and TARP dummies to represent whether a bank to go bankruptcy, be acquired by other banks, or participate in the TARP program during the financial crisis. We use a logit regression and report the marginal effects instead of coefficients in Table 6. We find in column (1) and (2) that more active and massive securitization activities before 2007 can significantly (at the 1% level) increase the likelihood of bank failure during the crisis by 10.1%. We report a positive relationship between active and massive securitization activities and the likelihood of M&A in column (3) 10 We also test three- and four-year average securitization ratios, and results are consistent. 22

23 and (4), suggesting the subsidiaries of active securitizers are more likely to be acquired during crisis. We also provide evidence in column (5) and (6) that the likelihood of receiving government funds is higher (by 14.0%) for those active securitizers, which is consistent with the argument that banks that posed systemic risk and faced high financial distress costs are more likely to be provided with equity infusions (Bayazitova and Shivdasani, 2012). Overall, our results support the findings that quality of loans deteriorated significantly before the crisis caused by active and massive securitization activities (Demyanyk and van Hemert, 2011), which may eventually lead to real problems. 6. Conclusion We study how securitization affects systemic risk in the banking system in this paper. To address the endogeneity problem, we employ three identification strategies. The first identification strategy uses three instruments for bank securitization, statelevel corporate tax rate, peer liquidity index and state-level corporate tax rate peer liquidity index. The second identification strategy employs a propensity score matching (PSM) method and PSM weighted least squares regressions to address the possible self-selection problem. In the third identification strategy we employ a difference-in-difference (DID) approach by using the bankruptcy filing of Lehman Brothers in September 2008 as a source of exogenous variation. We find that the involvement of securitization by BHCs increases their systemic risks. This positive association is statistically significant at 1% level. This finding is consistent in all specification. We argue the systemic risk increase effect is interconnectedness driven. To strengthen this argument, we explore cross-variations in the securitization ratio and other bank characteristics. We find larger, more complex and diversified securitizers contribute more significantly to the systemic risk in the banking system. We also find the systemic risk increase effect is stronger for mortgage securitization, compared with non-mortgage securitization. Last, we find 23

24 active and massive securitization activities before the crisis could lead to real problems (e.g., failure, M&A, or TARP) when the crisis comes. Our paper contributes to the group of theoretical literature suggesting securitization increases the instability of the financial system, which in the line with Slijkerman, Schoenmaker, and de Vries, 2013; van Oordt, This study also contributes to the limited understanding of the determinants of bank systemic risk, suggesting an interconnectedness driven mechanism. Last, this paper also extends the literature documenting the relationship between securitization and bank risk. 24

25 Reference Acharya, V.V., Schnabl, P., Suarez, G., Securitization without risk transfer. Journal of Financial Economics 107, Admati, A., Hellwig, M., The Bankers New Clothes. Princeton University Press, Princeton, NJ. Adrian, T., Brunnermeier, M.K., CoVaR. Working Paper. Allen, F., Gale, D., Financial Contagion. The Journal of Political Economy 108, Altunbas, Y., Gambacorta, L., Marques-Ibanez, D., Securitisation and the bank lending channel. European Economic Review 53, Alvarez, F., Barlevy, G., Mandatory disclosure and financial contagion (No. w21328). National Bureau of Economic Research. Anginer, D., Demirguc-Kunt, A., Zhu, M., How does competition affect bank systemic risk? Journal of Financial Intermediation 23, Bayazitova, D., Shivdasani, A., Assessing TARP. Review of Financial Studies 25, Bedendo, M., Bruno, B., Credit risk transfer in U.S. commercial banks: What changed during the crisis? Journal of Banking and Finance 36, Benmelech, E., Bergman, N.K., Bankruptcy and the Collateral Channel. The Journal of Finance 66, Berger, A.N., Bouwman, C.H.S., How does capital affect bank performance during financial crises?? Journal of Financial Economics 109, Billio, M., Getmansky, M., Lo, A.W., Pelizzon, L., Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics 104, Brownlees, C., Engle, R., SRISK: A Conditional Capital Shortfall Index for Systemic Risk Measurement. Work. Pap. doi: /ssrn Brunnermeier, M.K., Deciphering the liquidity and credit crunch of Journal of Economic Perspectives 23, Brunnermeier, M.K., Dong, G.N., Palia, D., Banks non-interest income and systemic risk. In AFA 2012 Chicago Meetings Paper. Caballero, R.J., Simsek, A., Fire Sales in a Model of Complexity. The Journal of Finance 68, Cai, J., Saunders, A., Steffen, S., Syndication, Interconnectedness, and Systemic Risk. Working Paper. Available at: Callen, J.L., Fang, X., Institutional investor stability and crash risk: Monitoring versus short-termism? Journal of Banking and Finance 37, Calmes, C., Theoret, R., Bank systemic risk and macroeconomic shocks: Canadian and U.S. evidence. Journal of Banking and Finance 40, Campbell, J.Y., Cocco, J.F., A Model of Mortgage Default. The Journal of Finance 70,

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