The Impacts of Securitization on US Bank Holding Companies
By
Wenying Jiangli and Matt Pritsker1 March 2008
ABSTRACT
We use data from 2001-2007 to assess the impact of mortgage and other forms of asset securitization on the insolvency risk, profitability, and leverage ratios of US bank holding
companies. Using 3 different estimation techniques, we find that banks use mortgage securitization to reduce insolvency risk and increase leverage. We also find that securitization techniques
increase bank profitability. Our results suggest that securitization techniques have played a positive role. This suggests that the current turmoil in mortgage credit and securitization markets is related to recent excesses in those markets, and that securitization activity will resume after those excesses are cleared up.
Keywords: Banking, Securitization
JEL Classification: G21, G32
Wenying Jiangli is from Federal Deposit Insurance Corporation, 202 898 6537 and wjiangli@fdic.gov. Matt Pritsker is from the Board of Governors of the Federal Reserve System, 202 452 3534 and mpritsker@frb.gov. The views expressed in this paper are those of the authors but not necessarily those of the Federal Deposit Insurance Corporation, the Board of Governors of the Federal Reserve System. We are grateful for comments from Paul Kupiec, Bill Lang, Robert Merton, Dan Nuxoll, Paul Povel, Haluk Unal, Wolf Wagner, and members of the Basel Research Task Force on the Integration of Market and Credit Risk, as well as seminar participants at the FDIC, the European Finance Association, the Riksbank, and North Carolina State University. Philip Lee and Paul Reverdy provided excellent research assistance. All errors are our own. 1
1. Introduction
Asset securitizations have been an important and expanding part of banking practice since the early 1990s. By the end of 2006, the outstanding amount of asset backed securities stood at
$US 2.1 trillion, and global total issuance of CDOs (collateralized debt obligations) reached $US 550 billion (SIFMA, 2008). Many banks have reported record earnings from securitization until the second quarter of 2007. Since then, however, the ABS market has dried up due to the recent credit crisis triggered by sub-prime loan defaults. Many banks which used to tap the securitization market face near zero demand for some securitized assets, and banks have reported huge loan write downs, and many banks face increased insolvency risk that warrant regulatory concerns
(www.securitization.net).
Given the growth of the markets for securitization, and recent events in those markets, it is important to understand how securitization activities affect the health of the banking sector. In this paper, we help to address this question by using bank holding company data from 2001 to 2007 to empirically quantify the impact of securitization on banks’ insolvency risk, profitability, and leverage.
Prior to the advent of securitization (or other forms of credit risk transfer), a bank's
decision to extend a loan was a bundled decision to originate the loan, hold it on balance sheet, and fund it with the bank's debt and equity. Securitization un-bundles these activities, allowing the bank to separately choose which loans to originate, and which loans to hold and fund on balance sheet. For example, in a balance sheet CDO, the bank funds some of its loans that are held on
balance sheet, by selling them to an off-balance sheet special purpose vehicle (SPV). The SPV funds its loan purchases by issuing securities whose payments are backed by the performance of the loans. In many CDO securitizations, the securities issued by the SPV are tranched into risk classes, with the least subordinate (least risky) tranches receiving the highest credit rating, and the most risky tranche (often the equity tranche) receiving no rating at all. When a bank sells loans to an SPV, it often retains a tranche of the securities issued by the SPV, or provides other guarantees to the SPV structure in order to signal the quality of the loans, or to signal the bank’s intent to monitor the borrowers.
Securitization has the potential to significantly impact banks’ insolvency risk, leverage, and profitability. First, for insolvency risk, securitization can be understood as a credit-derivative transaction that transforms the risk profile of the asset side of the bank's balance sheet. Holding the liability side of the bank's balance sheet fixed, and focusing on the asset side alone,
securitization can in theory lower insolvency risk since it can serve as insurance against bank insolvency in some severe adverse states of the economy. This is sensible since in the standard securitization, the upper tranches are usually sold (through the SPV) to outside investors and the issuing bank usually holds the most subordinated or the equity tranche. The credit loss to the equity tranche is truncated by the level of subordination while losses in the most severe states, the tail loss, are absorbed by outside investors that own the upper tranches.2 On the other hand, securitization is a credit derivative that allows the bank to optimally choose its exposures to different aspects of the credit risk of an underlying pool of loans. Therefore, depending on the
2 Though the originating banks commonly provides credit enhancement which includes holding the firs-loss (equity) tranche that retains some of the credit risk, this is more likely to be the mean, not the tail loss.
rewards for different types of risk exposures, and the exact structure of the transaction, securitization can be used to increase or decrease the bank’s insolvency risk.
The above analysis on insolvency risk holds the liability side of the balance sheet fixed.
Because securitization changes the risk profile of the asset side of the bank’s balance sheet, in realistic settings with taxes and bankruptcy costs, the bank will also change its optimal on-balance sheet capital structure, which will in turn affect its insolvency risk, and on balance sheet leverage ratio [Leland, (2007), Jiangli, Pritsker and Raupach (2007)]. Securitization may also affect the bank's insolvency risk and leverage if the bank engages in regulatory capital arbitrage. This involves selling loans off its balance sheet to avoid regulatory capital charges, but retaining exposure to the credit risk by retaining an implicit agreement with the SPV that the bank will buy back its nonperforming loans. Regulatory capital arbitrage is anticipated to increase the bank's insolvency risk since the bank is anticipated to reduce its regulatory capital while still maintaining its exposure to the risk.
Securitization can affect the bank's profit in two ways. The direct effect of securitization is anticipated to increase bank profits. Simply put, securitization gives the bank more options for funding its activities, and managing its risk profile. All else equal, expanded opportunities should lead to greater expected profits. That said, securitization could lower the profits of some banks through many indirect channels. As one example, if securitization leads to more competition in originating securitizable loans, it may depress banks' spreads in originating those types of loans [Instefjord (2005)], and thus reduce banks' profits.
In sum, the net effect of securitization on banks' insolvency risk, leverage, and profits is theoretically ambiguous, and needs to be settled empirically. Our empirical analysis uses two methods. The first method analyzes the effects of securitization by studying how the risk profile, leverage, and profitability of securitizers would change if they had to take the assets that they securitize back onto their balance sheets. This method is prescient since in the recent turmoil some banks have had to purchase back securitized assets, or hold some loans on balance sheet that they had originally planned to securitize (www.securitization.net).
The second method takes into account that securitization is an endogenous decision of the bank. To control for endogeneity, we use a bank's size as an instrument for its securitization decision. To justify our approach we perform a set of analysis, including semi-parametric
regression to establish that bank size is a valid instrumental variable for securitization. We exploit size as an instrument in both univariate and multivariate analysis. In our univariate analysis, we compare the performance of banks that do securitize with the performance of slightly smaller banks that do not securitize. In our multivariate analysis, we study the performance among the same banks Instrumental Variable (IV) regression. Using the univariate and multivariate analysis, we find that securitization reduces banks’ insolvency risk, as measured by the relatively low spreads that securitizers pay for uninsured time deposits. Securitization is also positively related to banks' leverage ratios. For the relationship between securitization and profitability, our results were more mixed, using the univariate analysis we found that securitization increases bank profitability, but we failed to detective a statistically significant positive relationship using the multivariate analysis. Our results hold over a set of robustness checks including the propensity
score technique which creates a matching sample of non-securitizers whose properties are compared with those of securitizers.
The remainder of the paper contains five sections. Section 2 provides a brief literature review; Section 3 describes our data; section 4 analyzes the impacts of securitization on banks if banks have to retain their securitized assets on balance sheet. Section 5 contains our instrumental variable analysis. Section 6 describes our robustness checks. A final section 7 concludes.
2. Literature Review
Securitization is one of many forms of credit risk transfer, the most prominent being credit derivatives. There are three main strands of the related literature. The first strand examines how securitization issues, such as the tranches of a CDO should be priced. The second and third strands, which are more relevant for our analysis, pertain to how securitization affects banks' funding ability, and how it affects banks' risk management and risk profile.
The theoretical and empirical parts of the funding strand have slightly different focuses.
The theoretical part of the funding strand studies how securitization can be designed to overcome various information asymmetries that are associated with transferring credit risk [Gorton and Pennacchi (1995), Gorton and Souleles (2005), Duffie and DeMarzo (1999), DeMarzo (2005)], as well as how securitization can affect for better or for worse the quality of markets for sharing credit risk [Duffee and Zhou (2001), Parlour and Plantin (2007), Morrison (2005)].
The empirical literature on funding mostly studies whether securitization helps to facilitate credit extension by relaxing constraints on funding securitizable loans. Working with Bankscope data for 65 banks that issued Collateralized Loan Obligations (CLOs) between 1995 and 2004, Hirtle (2007), Cebenoyan and Strahan (2004), and Goderis (2007) et al. find that securitization helped to increase the supply of credit. Loutskina and Strahan (2006) find that it does so by reducing the influence of the bank's own financial condition on whether a loan is originated. For our purposes, relaxation of constraints is one of the channels through which securitization should increase bank profits, but the literature on funding constraints does not establish that securitization increases profits overall.
The risk profile strand of the literature studies how securitization alters banks’ overall risk profile. Jiangli, Pritsker and Raupach (2007) from a theoretical perspective show that optimally chosen securitization can sometimes reduce bank's risk of insolvency, and sometimes increase it, and that whether risk increases or decreases depends on the types of securitization opportunities that are available to the bank, as well as to other factors. Krahnen and Wilde (2006) theoretically show that securitizations can be structured to substantially increase bank's systemic risk; although they do not show that an optimizing bank would use securitization in this way. Franke and Krahnen (2005) perform event study analysis of 73 European CDO securitizations from 1999- 2002, and find these securitizations are associated with an increase in the average equity beta of securitizing banks. Using data on a small sample of Canadian banks, Dionne and Harchaoui (2003) measure banks’ insolvency risk based on the banks' ratios of regulatory capital to risk-weighted assets. They find that securitization is associated with a decrease in banks' capital ratios. This is interpreted as an increase in insolvency risk. Dionne and Harchoui’s methodology has two
shortcomings. First, regulatory capital ratios are a poor measure of insolvency risk because the risk-weighted assets in the denominator of such ratios do not properly account for the correlations among the returns of the assets in each bank’s portfolio. Second, if securitization truly transfers risks off of a bank's balance sheet, then lower capital ratios could still be consistent with lower insolvency risk of the bank.
A second part of the risk profile strand addresses why banks use securitization, and which banks would benefit from doing so. A consistent finding in the empirical literature is that securitization's most important role is for funding and liquidity management.3 Many papers also find that
securitization is not related to regulatorory capital arbitrage. Consistent with the liquidity and funding motive for securitization, and contrary to the regulatory capital arbitrage and risk-sharing motivations, using data on Spanish banks, Martin-Oliver and Saurina (2007) find that banks tend to use forms of securitization that do not shift credit-risk and do not provide regulatory capital relief. Using data on US Banks and Finance Companies, Minton et al (2004) also find that securitization is not for the purposes of regulatory capital arbitrage since unregulated finance companies and investment banks are even more likely to securitize than regulated banks.4
The results in the literature on whether high or low risk banks tend to securitize is more mixed. Minton et. al. find that when risk is measured based on capital ratios (book equity / book assets), banks with low risk are more likely to securitize. Using different risk measures (credit provisions / net interest income) and data on European banks, Bannier and Hansel (2007) find that
3 Also see Thomas and Wang (2004) for US Bank Holding Companies, Vickery (2007) for US banks, savings banks, and finance companies, Martin-Oliver and Saurina (2007) for Spanish Banks, and Bannier and Hansel (2007) for a broad sample of European banks.
high risk banks are more likely to securitize.5 A complication in determining how bank's risk profiles affect securitization is that the risk profile and securitization are both endogenous. It is therefore difficult to disentangle how one affects the other.
Our paper makes three contributions to the empirical literature. First, we employ a new measure of insolvency risk, a bank's time deposit premium which is the interest rate spread between its uninsured and insured time deposits. The larger is the likelihood of bank insolvency, the larger is the spread that uninsured depositors will require to deposit money in the bank.
Because the time deposit premium is a forward looking market determined measure of ex-ante risk, we believe it improves on risk measures such as capital ratios that are based on accounting data. We also believe that interest-rate spreads are a better measure of insolvency risk than the beta measures that have been used in event studies because beta measures covariance, which is a second moment measure or risk, whereas insolvency risk is more related to higher order moments and tail events.6
Our second and third contributions are more methodological. We believe we are the first paper that has attempted to analyze how banks are affected if they have to place securitized assets back on their balance sheet. To the best of our knowledge, we are also the first paper to use size differences among relatively large banks as an instrument to control for the endogeneity of
4 Calomiris and Mason (2003) also find that securitization does not appear to be related to banks attempts to exploit underpriced deposit insurance.
5 An interesting work on the determinants of securitization, Karaoglu (2005) uses US BHC mortgage securitization data to examine whether securitization and loan sales decisions are made in order to manipulate accounting figures through the timing of profit realizations.
6 As an extreme example of the difference between beta and insolvency risk, if a bank could purchase full insurance against its own insolvency, its value will still fluctuate with the market, and it will thus have a non-zero beta even if its insolvency risk is zero.
securitization. This approach will help us to produce more concise estimates of how securitization affects bank insolvency, capital structure, and overall profitability.
3. Empirical Analysis and Data
We use two methods to empirically analyze the effects of securitization. The first method asks the counterfactual question: how would the financial well-being of securitizers be affected if they had to take the assets that they securitized back on to their balance sheets? This is a
particularly topical approach for analyzing securitization given the turmoil in credit markets, and in particular for how it has affected the balance sheets of securitizers. Our second method is a more conventional IV approach which corrects the endogeneity of securitization decision. The IV
approach is built on the observation that bank size is strongly related to whether banks’ engage in securitization, but is unrelated to banks’ performance once banks have grown beyond a moderate size7. Based on this observation we use differences in size among relatively large banks that do and do not securitize as an instrument for bank performance. Before describing these methods in more detail, we turn to a description of our data.
We use FR Y9-C US bank holding company (BHC) data from the second quarter of 2001 to the second quarter of 2007 to analyze the impact of asset securitization on banks. We study BHCs at a consolidated level because securitization within a BHC group may not be subject to the same informational and agency problems as securitizations outside the BHC. Our data start from
7 For example, Wheelock and Wilson (2001) estimated that banks experience increasing returns to scale up to $ 500 million of assets, and essentially constant returns to scale thereafter.
2001, when the Y9-C first began reporting securitization by asset type.8 The reported asset categories (from schedule HC-S) are 1-4 Family Residential Mortgage Loans (Mortgage), Home Equity Lines (HEL), Commercial and Industrial loans (C&I), Credit Card, Auto, and Other Consumer Loans (Other). Securitizations are recorded by their outstanding principal balance of assets sold and securitized with servicing retained or with recourse or other seller-provided credit enhancements. It should be noted that the data distinguish between outright loan sales, and sales for the purposes of securitization. We only focus on the latter9,10.
Although securitization activities have grown on global basis, based on FR Y9-C data, the number of U.S. bank holding companies (BHCs) that engage in these activities is small. For example, Table 1 shows that less than 7% of BHCs securitize 1-4 Family Residential Mortgage loans (Mortgage), which are the most commonly securitized asset class. The number of banks that securitize other type of assets are even smaller. Although the number of BHCs that are securitizers is small, the importance of these activities is better measured by the size of the banks that engage in these activities. By this measure securitizers are a significant part of the banking sector. The Table 2 columns “% Assets” show, for example, BHCs that are Mortgage securitizers account for 67% of all U.S BHC assets. The share of other type asset securitizers also account for more than 40% of all BHC. The columns “% Loans” show that the volume of securitized Mortgage and
8 We merger adjust and delete observations for which risk weighted capitals, leverage ratio, loan growth rate, return on assets are more than 100%, or loan to deposit ratio is more than 10.
9 For example, securitizing mortgage differs from sale of mortgage in that securitized mortgages are sold into a securitization, while sale of mortgage is sale, but not into a securitization. In the first case of selling into a
securitization the seller may retain the servicing rights for the mortgage. In both cases there may be some recourse or credit enhancement used to make the sale. Traditional accounting would not allow a seller to record the sale if there was any chance the seller would have to take the asset back. Now, banks are allowed to get sale treatment even though there may be some credit enhancement. The goal of tracking securitization and asset sales is to see if any sales leave the bank exposed to having to take the asset back (reversing the sale). The biggest difference here is that the first one is for a securitization while the second one is just a sale of a mortgage asset.
Credit Card loans are similar and sometimes exceed the amount retained on balance sheet, For other asset classes, such as HEL, C&I and Other, the amounts securitized are much smaller than the amounts held on balance sheet.
Before turning to the main analysis, we compare BHCs that securitize with those that do not securitize along a number of dimensions including their size, their tendency to specialize in originating particular classes of loans, credit risk, profitability and leverage (Table 3).
Most of the securitizers do not securitize in all quarters. This can happen when a
securitizer is accumulating loans to package for securitization, or if there are periods of time when securitization is less profitable. To compare the securitizers and non-securitizers, we calculate the time-series averages of the variables for each BHC and then compare the averages in cross- sectional tests.
A BHC is assigned as a mortgage securitizer if we observe its mortgage securitization activities in any quarter. BHCs assigned as other types of securitizers are defined in a similar way.
Those that ever securitize any type of assets are Ever securitizers, while those who never securitize any type of assets are Never-securitizers. There is a total of 2231 bank observations with 147 Mortgage, 23 HEL, 30 C&I, 35 Credit card and 46 Other Securitizers. 185 are Ever-securitizers and 2046 banks are Never-securitizers.
10 There is also one column that reports the "all other loans, all leases, and all other assets" which is omitted in our analysis because we cannot separate loans from leases. We combine Auto with Other because we compare the sold assets with on balance sheet assets which do not separate Auto from Other loans.
The most robust difference between securitizers and non-securitizers of any asset types is that securitizers are significantly larger than non-securitizers11 where size is measured as the natural logarithm of on balance sheet assets. The large size of securitizers may reflect economies of scale in underwriting and securitization, or it may reflect diseconomies of scale in funding through deposits. That is, the investable opportunities of large banks may outstrip their base of inexpensive deposits, causing them to turn to securitization as an alternative funding source.
Table 3 also shows that loans of securitizers as percentage of total asset value is slightly lower than for non-securitizers. This is probably because larger BHCs are more likely to engage in business lines other than loan origination.
In addition to size, securitizers also hold a much higher average percentage of the type of loan they securitize on their balance sheets than do non-securitizers. The percentage mean difference between securitizers' and non-securitizers' holdings are significant and vary from 12.6
% (Mortgage) to 183.6% (Credit Card). Consistent with financial intermediation theory, the data suggest that securitization allows banks to specialize in loan origination while allowing the banks to share the risks of their loan originations with others.
As noted earlier, securitization provides the bank with added flexibility to shape its risk profile. To contrast the risk profiles of securitizers and non-securitizers, we use three measures of risk. The first is the time deposit premium, which is the difference between the interest rates on small (< $100,000 ) insured time deposits and large (> $100,000) uninsured time deposits. We
11 Minton et al (2004) using US data also find that large commercial banks are more likely to securitize. Using CLO data from 17 European countries, Bannier and Hansel (2006) report that large banks are more likely to securitized CLOs. Martin-Oliver and Saurina (2007) use Spanish bank data also find size is positively related to asset securitization.
view the time deposit premium as a measure of the banks insolvency, or tail risk. As support for this interpretation, Gilbert, Meyer and Vaughan (2002) list a total of 12 papers with evidence pointing to risk pricing by large time deposit holders (their Table 2).12 Although we favor the use of time deposits as a measure of tail risk, there are some caveats associated with this measure. For example, the above US$100,000 time deposits may be insured if they are held in joint accounts, but US BHC data do not provide information on whether accounts are joint. Additionally, we do not have any information on the maturity and liquidity of the time deposits. Our second and third risk measures are the loan loss provision rate and banks charge-off ratio. We view both of these as measures of the expected loss rate of the bank’s portfolio.
A raw comparison of the risk measures suggests that securitizers and non-securitizers have different risk profiles. Securitizers have a lower time deposit premium than the non-securitizers, suggesting they have lower tail risk. The difference can be as high as 173% (HEL). However, the differences in the time-deposit premium between securitizers and non-securitizers for Mortgage, Credit Card, Other and Ever are statistically insignificant. We suspect this is because of high standard deviations of the time-deposit premiums among non-securitizers and the raw figures don't control for other sources of variation of the time deposit premia.
We recognize that there are other possible measures of tail risk. Perhaps the cleanest is the credit spread on the bank’s subordinated debt. The main problem with this measure is data availability since many non-securitizers do not issue subordinated debt. Other possible measures are based on capital ratios, but these ratios are backward looking and based on accounting data. Additionally, their quality erodes as banks approach insolvency since banks are hesitant to recognize losses on loans when they approach insolvency [(Dahl, O'Keefe, and Hanweck (1998), and Gunther and Moore (2000)]. Nevertheless, when a bank's condition weakens, its cost of deposit funding often rises, suggesting that measures of tail risk based on deposits may be a better measure of solvency risk.
For the measures of expected loss, our results are stronger. We find that both the provision and charge off ratios are statistically and economically significantly higher for securitizers than non-securitizers except the provision ratio of Mortgages and Ever. These results could, for example, imply that securitization enables the securitizers to extend loans that have higher expected losses. Alternatively, high observed provisions and charge-off may also reflect a size effect in that large banks are more likely to take positions that have higher expected losses than small banks13.
To study the profitability of securitizers and non-securitizers, we focus on return on equity (ROE). (We also examined return on assets, ROA and obtained the qualitatively same results). The ROE for securitizers ranges from around 10.4% (C&I) to 13.01% (Other) which is higher than that of the non-securitizers at around 10%. However, the large p-values indicate that the difference between securitizers and non-securitizers are statistically insignificant for HEL, C&I and Credit Card.
The mean leverage ratio of securitizers and non-securitizers are all above 89%. Leverage ratio of Mortgage, HEL and C&I securitizers are higher than non-securitizers but statistically insignificant, while a reverse pattern exists for Credit Card and Other. The Ever securitizers also have a low leverage ratio than the Never-securitizers, and the difference is significant. Our analysis shows that leverage and securitization decisions are inter-related. If there is a third factor (such as, perhaps, bank size) that is related to leverage and to the reason why some banks do not securitize, it may also help to explain our leverage results. In our instrumental variable analysis, we revisit the
13 Bannier and Hansel (2006) use credit risk provision over net interest income as a measure of credit risk also find that high credit risk banks are more likely to securitize CLOs.
importance of size when we compare securitizers and non-securitizers who are more similar in size than is the case with the raw comparisons presented here.
4. What if Banks have to Place Securitized Assets Back on Securitizers Balance Sheets-What if Banks cannot Securitize Assets?
The univariate comparisons of securitizers and non-securitizers in Table 3 does not control for other variables that could contribute to their differences. In this section, we rely on a
methodology which analyses the impact of securitization by comparing the averages of the
observed securitizers’ insolvency risk, profitability and leverage ratio with hypothetical values that are calculated by assuming the securitized loans were put back on balance sheets. This method can help assess the consequences to banks of not being able to tap the securitization market, and of having to purchase large amounts of loans from conduits and move them on their balance sheets.
As one illustration of our approach, we assume that there is a relationship between a bank’s insolvency risk and the composition of its balance sheet, including the share of each type of loans held on the balance sheet (loan shares)14. We use the non-securitizers data to estimate this
relationship. We then use this estimated relationship to compute the bank's insolvency risk if its securitized loans were placed back on the balance sheet. This method allows us to predict the bank's insolvency risk if the securitizer did not securitize its loans. We then compare the predicted values with the average of the observed securitizers' insolvency risk. The average difference
14 The loan share variables do not sum to 1 since we only include the five types of loans with available securitization information.
between the observed and predicted values is one measure of the expected quantitative impact of securitization. We apply the same exercise to bank's profitability, and leverage ratio.
To estimate the relationship between balance sheet composition and bank performance, following the literature, we assume for each bank holding company i, our measures of bank performance, Y, are a linear function of loan shares; other controls, Z; and bank size, as measured by Ln (Assets)15:
(1)Y 0 1 Mort 2 HEL 3 C&I 4CreditCard 5Other 6 7 ( )
Loans Loans Loans Loans Loans Z Ln Assets
β β β β β β β β ε
= + + + + + + + + .
In equation (1), Y is insolvency risk, profitability, or a bank's leverage ratio; and the other control variables are the share of loans to assets, and costs of funding as measured by the average deposit rate, and interest rates on small (insured) and large (uninsured) time deposits. The dependent and explanatory variables for each BHC in our sample are again the time-series averages. We thus report the between estimator, which exploits the full panel data set but estimates the regression using the time-series averages.
This model specification is based on the empirical models of Wheelock and Wilson (2000), and Estrella, Park and Peristiani (2000) for bank insolvency risk16, Cebenoyan and Strahan (2004)
15 For simplicity, unless they are needed we have chosen to suppress subscripts i for individual BHCs.
16 These papers also include the equity to asset ratio, an important variable in explaining bank failure, ROA and problem loans. We do not include equity to asset ratio and ROA since we do not know how these two ratios change if the securitized loans were put back on balance sheet.
for ROE17, and Flannery and Rangan (2004), and Gropp and Heider (2007) for the leverage ratio18. We estimated five model specifications of equation (1) for the non-securitizers using OLS.
The results are reported in Tables 4A through 4C. Model (1) includes the shares of Mortgage, HEL, C&I, Credit Card and Other to loans, and uses size as a control. Model (2) adds loans to assets to Model (1); Model (3) adds the deposit rate to Model (2); and Models (4) and (5) replace the deposit rate with insured and uninsured deposit rates respectively.
It is important to emphasize that each coefficient of the loan share variable in the regressions for the time deposit premium, ROE and leverage ratio cannot be interpreted in isolation since if the share of one type of loan is increased, the others share must necessarily decrease.
Across the five models, Table 4 A shows that the share of Mortgage, HEL and Other loans, the deposit rate, and the uninsured deposit rate are positively related to bank's time deposit premium, while size, and the share of C&I, Credit Card loans, the insured deposit rate, and the share of loans over assets are negatively related to the time deposit premium. To control for the effect of problem loans on insolvency risk, we also added the share loans that are past due 30-89 days (3m-pastdue/Loans), 90 days plus (3m+pastdue/Loans), and we controlled for nonaccrual and charge off loan ratios (NoAcc+Chargeoff/Loans). The 3m+pastdue/Loans and
NoAcc+Chargeoff/Loans rates are positively related to the time-deposit premium, while 3m- pastdue/Loans are negatively related. The F-statistics reject that the three coefficients are jointly
17 The additional variables are capital asset ratio and dummies variables which indicate whether or not a bank is a loan seller, buyer or both, whether or not a bank belong to a multi-BHC or multi-state BHC.
insignificant across four models except for Model (5). The adjusted R-squares vary from 21.2% to 91.2%. Adding the insured and uninsured deposit rates significantly increases the model's
explanatory power.
ROE is negatively related to the share of Mortgage, HEL, C&I and Other loans, deposit rate, and insured deposit rate, while positively related to size, share of Credit Card loans, share of loans to assets, and the uninsured deposit rate. The adjusted R-squares vary from 6.8% to 9.8%.
The leverage ratio are negatively related to the share of Mortgage, C&I, Credit Card, Other loans, deposit rate and uninsured deposit rate, and positively rated to size, share of HEL, C&I loans, loans to assets, insured deposit rate. The adjusted R-squares vary from 3.7% to 11.3%.
Next, we apply the estimated relationship between time deposit premium, ROE and leverage ratio to securitizers assuming that the securitizers put the securitized loans back on
balance sheet. Adding back these securitized loans will change total assets, total loans, the share of all type of assets and the share of problem loans. We recalculated the time series average of these new variables labeled with “A-” for augmentation. Table 5 reports the mean, and standard
deviation of securitizers’ time series average. The columns labeled with “%Δ” are the percentage change in observed values when securitized assets are added back on balance sheet.
Mathematically, adding back any type of securitized asset will increase total assets, loans, and the share of loan to assets. In particular, adding back securitized mortgages will increase the
18 Flannery and Rangan (2004) , Gropp and Heider (2007) both regress leverage ratio on market to book asset ratio, profitability, risk, and size. Most of our no-securitizers are not publicly traded BHCs. Thus, we cannot construct the
share of mortgage loans and decrease shares of other type of loans. However, whether share of problem loans increases when securitized assets are retained on the balance sheet is less clear cut because it depends on whether higher quality assets are securitized or retained on balance sheet19. When Mortgage, HEL and C&I are moved on balance sheet, average loan quality in each category increases by some measure but decreases by other measures. For example, Mortgage securitizers have an increased 3m-pastdue/Loans, 3m+pastdue/Loans ratio but a decreased
NoAcc+Chargeoff/Loans ratio. However, for C&I and Other, all these three measures of problem loan consistently show that the average quality of securitized C&I and Other loans are lower than the quality of the loans retained on balance sheets20.
Our method of calculating the average difference between the predicted and observed value of insolvency risk, ROE and leverage ratio involves three steps. First, we create a quarterly
predicted time-series sample for each bank by applying its quarterly augmented variables to the predicted relationships from equation (1). This produces estimates of quarterly predicted
insolvency risk, ROE and leverage ratios for each bank when its assets are moved back on balance sheet. In the second and third steps we average these predicted values over time, and then across banks. The resulting predicted averages are then compared to securitizers' performance when averaged across time and across securitizing banks.
market to book ratio. However, our other variables can approximate the profitability and risk.
19 The share of added back problem loans is calculated, for example, for mortgage as the ratio of the sum of on balance sheet problem loans plus the securitized problem mortgages divided by the sum of on balance sheet loans and
securitized mortgages.
20 However, we realize that to compare the credit quality of the securitized versus the on balance sheets assets poses a number of data challenges. First, with aggregate level data, it is impossible to separate the truly kept on book loans from the temporarily kept on book loans which are intend to be put into a securitization pool. Second, the vintage of the securitized loans can be quite different from the on balance sheet loans. For example, unseasoned credit cards have a larger credit risk than the seasoned ones. Without adjusting seasonality, the comparison can be problematic. Third, there maybe significant heterogeneity of both securitized and on balance sheet assets. For now, we just report what we observe in the data and leave these difficulties for the future research.
Table 6 presents our results on how securitizers are affected when securitized assets are moved back on balance sheet. In the table, sample averages of securitizers performance as measured by insolvency risk, ROE and leverage ratios are presented in bold face. Estimates of how performance would change if assets are moved back on balance sheet are presented for five models. For example, the first cell in the first column indicates that using model (1), the predicted time deposit premium if securitized mortgages are moved back on balance sheet is 2.75%, while securitizers observed average time deposit premium is 2.26%. This suggests that putting the securitized mortgage back on balance would cost the securitizers an additional 49 basis points when issuing large time deposits.
Overall, our results on insolvency risk, as measured by the time deposit premium, suggest that securitization of Mortgages, Home Equity Loans (HEL), and C&I loans lower banks time deposit premium about 50 bps in most model specifications. These numbers are within the range reported by Elghanayan (2006) who documented that the rated banks can save 20-80 bps in funding costs by issuing securitization rather than debt, while the saving for unrated banks can be well above 100 bps. The predicted values for Credit Card and Other indicate that securitization of those types of assets may increase banks’ insolvency risk. Our findings that different types of securitization have different effects on tail risk is consistent with Chen et. al. (2007), who note that risk retention by the securitization sponsor varies by type of securitization, and is relatively low in the case of Mortgages, while relatively high for revolving loans such as credit cards.21
21 The net effect on tail risk also depends on how securitization affects the bank's capital structure. Lang et. al. (2005) note that credit card banks tend to hold more economic capital against their off-balance sheet positions. They do not measure the net effect of credit card securitization on tail risk. For more on credit card securitization and implicit recourse see also Gorton and Souleles (2005).
Our result on profitability based on ROE suggest that securitization of Mortgages and the Other category of assets increases banks' profitability by a range of 34 to 150 bps depending on the model. By contrast, we find that HEL, C&I and Credit Card securitizations lower banks’ profits.
These results should be interpreted with caution because they have multiple interpretations. For example, our findings on credit card securitization are consistent with the possibility that
securitization reduces profitability by increasing competition. If this is the case, it does not imply that an individual credit card bank would be more profitable if it did not use securitization.22
In the case of bank's on balance sheet leverage ratios, we found that securitization increases banks’ on-balance sheet leverage ratio from 43 to 153 bps except for securitizations of the Other category of assets where we found that securitization is associated with reduced leverage.
We also experimented by putting all of the securitized assets back on the balance sheet at the same time. The results are reported in the last column of Table 6 (labeled "All"). Our results are very mixed. Three out of five models indicate that securitization reduces time deposit premiums, three out of five models shows that securitization increases ROE and four out of five models shows that securitization leads to increased leverage ratios. One reason for the mixed results on tail risk is that securitization of different asset types has different implications for tail risk. Therefore, the sign of the change in tail risk when moving several asset types back on balance sheet together is ambiguous. We suspect this may also help to explain why some of our other results on moving all assets back on balance sheet are mixed. Another reason for the mixed
22 Similarly, if mortgage securitization has high fixed costs, then banks with low amounts of mortgage lending activity that do not securitize may be less profitable if they did securitize.
results is that our estimates of how changes in balance sheet composition affects bank performance (Table 4) should be understood as local linear approximations that will be less accurate when used to study the effects of large changes in balance sheet composition. The last three columns in Table 5 shows that the impact of putting back all securitized assets creates relatively large percentage changes in some balance sheet components, suggesting that our approximations may perform less well in these cases.
5. Instrumental Variables Estimation
Our second approach for studying the impact of securitization on banks by using regression analysis with instrumental variables (IV). Our IV approach is motivated by the empirical
observation that securitizers tend to be large banks, presumably because those are the banks that can overcome the fixed costs of setting up a securitization program. A few studies have
documented the factors that determine bank securitizations [Karaoglu (2005), Bannier and Hansel (2006), and Martin-Oliver and Saurina (2007)]. All these studies find that large banks are more likely to securitize assets. The following section formally examines whether or not size is a valid instrumental variable for securitization. Currently, our analysis forces on mortgage securitizers and non-securitizers only. For other types of securitization, our sample of securitizers is too low for statistical inference. In addition, when banks securitize multiple types of assets, as many do, our IV methodology cannot identify which type of securitization generates our results. Therefore, for the IV analysis, we chose to focus on the form of securitization which we believe is the most important and in the most widespread use, which is mortgage securitization.
5.1 Size as a valid instrument
To establish the bank size is a valid instrument we need to show that size is related to bank's decision to securitize, but is not related to our performance variables. To first study the size and the securitization decision we sorted the BHCs in the sample into seven size-buckets based on the quantiles of Ln(Assets), with bucket seven containing the 22 largest bank holding companies, and each succeeding size-bucket containing increasingly smaller BHCs. The groupings are provided in Table 7. The most important feature of the table is an upward jump in the fraction of securitizers as the size of BHCs increases. For example, among the smallest BHCs, only 1% are securitizers. This fraction grows to 10% in bucket 5, and then jumps to 41% in bucket 6, and 100%
in bucket 7. Based on this data, we treat the banks in buckets 6 and 7 as likely to securitize based on size, while banks in smaller buckets are less likely to securitize.
Although securitization is related to BHC size, as noted earlier, we claim that size is only weakly related to performance, and principally among smaller banks. To verify this claim , for banks that do not use securitization we estimated both linear and semi-parametric variants of the regressions in equation (1) in which all variables other than bank size enter linearly, while bank size is allowed to enter either linearly or semi-parametrically via the unspecified function G[Ln(Assets)]:
(2)
&
0 1 Mort 2 HEL 3 C I 4CreditCard 5 Other 6 [ ( )]
Y G
Loans Loans Loans Loans Loans Z Ln Assets
β β β β β β β ε
= + + + + + + + +
This equation can be more compactly written as:
i i i i
Y = X + G[Ln(Assets )] + , i = 1, .... Nβ ε
Table 8 reports the results from regressing measures of bank performance on linearly specified bank size and other controls, following the specifications from equation (2), but using a narrowing window of size buckets.23 To save space, after the regressions for the first window of size
buckets, we only report the coefficients on Ln(Assets). The analysis shows that once the smallest 1000 BHCs are omitted from the analysis (and these are all very small banks), size fails to explain differences in performance among banks that do not securitize.
To verify that our linear regression results are not due to the choice of linear functional form, we next approximated the function G[.] semi-parametrically using a cubic spline. Under this specification, the parameter vectorβ and the function G[.] are chosen to minimize the objective function:
{ }
( )
2 2
1 ( )
{ [ ( )]} ''[ ( )] [ ( )]
Ln Assets N
i i i i
i Ln Assets
w Y X β G Ln Assets λ G Ln Assets d Ln Assets
=
− − +
∑ ∫
where λ> the penalty for roughness of G[.] is chosen using least squares cross-validation, the 0, weights w are chosen using a GLS to procedure to account for the heteroskedasticity of i εi conditional on bank size, and where Ln Assets and ( ) Ln Assets are the upper and lower bounds ( ) of the bank-size variable in-sample. The cubic spline estimate of G[.] is asymptotically equivalent to a kernel regression of Y X− β on Ln(Assets) with a variable bandwidth [Silverman (1984)].
Using this principle, we report asymptotic 95% confidence intervals for G[.] following the
approach that is outlined in Silverman (1985). In the analysis, for each of three Y variables, we estimated four variants of equation (2): one with a constant and Ln(Assets), a second with a constant, balance sheet composition variables, and Ln(Assets), a third with a constant, portfolio performance variables and Ln(Assets), and a fourth with a constant, portfolio composition variables, portfolio performance variables, and Ln(Assets).24 In the interest of brevity, we only report results for the fourth specification.
Our principal results from the semi-parametric estimates of the effect of bank size on performance verify our results with parametric analysis. More specifically, beyond the smallest banks, for non-securitizers, increases in size alone do not appreciably alter bank performance.
This is shown by the flatness of the G[.] function for non-securitizing banks with Ln (Assets) >
14.58, which correspond to size buckets 5 and 6 for the non-securitizers.25 In the case of banks' leverage (Figure 2), and ROE (Figure 3), size has little or no affect on performance along the entire range of Ln(Assets). In the case of insolvency risk, a larger bank size is clearly associated with a smaller time-deposit premium up to a point, but it is clear that for relatively large banks (Ln (Assets) > 14.58), the relationship of size to performance appears statistically indistinguishable from a flat line (Figure 1).
Our parametric and semi-parametric analysis suggests that size is a valid instrument for identifying the effects of securitization on bank performance, especially when studying banks in
23 We exclude the top size bucket from this analysis because all banks within this bucket securitize.
24 The balance sheet composition variables are Mortgages/Loans, HEL/Loans, C&I/Loans, Other Loans/Loans, and Loans/Assets. The portfolio performance variables are 1 - 3 month past due loans / Loans, 3 months or more past due loans / Loans, and Non-Accruing and Charged-off Loans / Loans.
25 Because an intercept is included in the regresssion, the average level of the G[.] function is not identified. We normalized the G[.] function so that its average level, evaluated at the data observations, is 0. Only changes in the G[.] function as Ln(Assets) vary can identify whether Ln(Assets) affects bank performance.
groups 5, 6, and 7. We use size in two ways. First, we perform univariate analysis in which we compare banks that are similar in size (within the same size bucket), but one set of banks securitizes while the other does not (Table 9, panel A). In panel B of Table 9 we compare securitizers in one group of size buckets with non-securitizers in other size-buckets. The within bucket results are mostly insignificant. When comparing across size buckets, the results are more frequently significant. The ideal comparison is the close size buckets in the neighborhood of sizes 6 and 7 because the fraction of securitizers jumps there, but hopefully other bank characteristics that may be correlated with size do not. Thus, the ideal comparison should be between buckets 6 and 7 with bucket 5. The ideal comparison shows that securitizers have lower insolvency risk, and a higher ROE and leverage ratio than non-securitizers. Comparing the securitizers in groups 6 and 7 with the other size groupings produces similar results on the effects of securitization.
5.3 Instrumental variables results
Our second approach for using size involves multivariate IV regressions in which size is used as an instrument for securitization in a first stage OLS and then second-stage regressions are estimated using predicted values for securitization. The results from the first stage regression are reported in Table 10. The second stage regressions for insolvency risk, ROE and leverage ratio are presented in Tables 11 A, 11 B and 11 C. In all regressions, we only use data from size groups 5, 6 and 7, or 6 and 7. Our securitization variable is mortsec, which is an indicator for whether the BHC is a mortgage securitizer. As Table 10 shows, across all model specifications and sample sizes Ln(Assets) is positive and significantly correlated with mortsec. Tables 11-A, 11-B, and 11- C present our results for the time deposit premium, ROE, and Leverage. Panel A of each of the
tables presents the two-stage OLS results with mortsec being the instrumented variable based on size buckets 5, 6 and 7. Panel B repeats this exercise using only buckets 6 and 7. For comparison with the IV approach, Panels C and D of the Tables report the simple OLS regression results based on size buckets 5, 6 and 7, and 6 and 7 respectively. To save space, for panels B, C, and D of each table, we only report the estimates for the instrumented variable, mortsec, sample size, and R- squared.
In the case of insolvency risk, the IV and OLS regressions produce significantly negative coefficients on mortsec, suggesting that mortgage securitization is associated with a reduction in BHC insolvency risk. Our point estimates suggest that mortgage securitization can reduce the time deposit premium by 50bps to 380bps [Table 11, panel B, model (5); and Table 11, panel A, model (1)]. These reductions are economically substantial and statistically significant.
The IV results on bank profitability fail to detect a statistically significant relationship between mortgage securitization and bank profitability as measured by ROE. We plan to analyze these results in more detail in future revisions. The results for mortsec on leverage ratio, reported in Table 11 C, are positive and statistically different from zero under most model specifications.
Such results indicate that securitizing mortgage allows banks to increase leverage ratio by 1.5% to 4.6%.
6. Robustness Checks
We performed a series of robustness checks to verify our results sections 4 and 5. In section 4 equation (1), we dropped the three measures of loan quality, and replaced insured (uninsured) deposit rates by total interest expense on insured (uninsured) deposits divided by total insured (uninsured) deposits; our results still hold. In section 5, we performed the same exercise in our OLS and IV regressions and produce the same results. We also used a propensity score technique to compare mortgage securitizers and non-securitizes in section 5, and our results still hold (not reported but available upon request)26.
One question is whether our finding that securitization reduces time deposit premia is due to securitization or due to (other) scale economies in risk-taking that are difficult to detect without accounting for the endogeneity of risk-taking [Hughes, Mester and Moon (2001)]. Disentangling these possibilities will be difficult since securitization is itself an important source of scale
economies in risk-taking. Nevertheless, we believe that our analysis extends in controlling for these scale effects since much of our work relies on comparisons of securitizers and non-
securitizers that are similar in size. By making these comparisons, we automatically control for other sources of endogenous risk-taking that are related to size, and we have potential to add other controls such as measures of portfolio diversification in our future analysis. Additionally, it is
26 The method is to compare the outcome of two samples. One sample receives treatment
(mortsec=1) and the other sample does not receive treatment (mortsec=0). We first run the logit or probit to get the predicted value of securitization. We create a set of matching sample criteria that choose untreated (mortsec=0) banks which have a high potential to be a treated banks (mortsec=1).
This sample of untreated banks is our matching banks to the treated banks. This method relies on the assumption that conditional on other controls in the logit or probit regression, the treatment is random. It is essentially a conditional comparison of the outcomes of two samples and it corrects the bias of the selection of the treatment. In our case, we compare insolvency risk, profitability and leverage ratio of the mortsec=1 banks with that of the mortsec=0 banks by conditional on a set of other control variables.
important to emphasize that most researchers find that scale economies disappear once bank asset sizes reach about $10 billion (see the survey by Amel et al (2004)), which is about the size of banks in group 5 in our sample This suggests that scale economies from non-securitization activities are essentially exhausted for groups 5, 6, and 7, which are the banks where most of our analysis is focused.
7. Conclusion
Using bank holding company data from 2001-2007, we have conducted an empirical analysis of the effect that securitization has on BHCs. The bulk of our analysis focused on mortgage securitizations, and analyzed them using three methodologies. The first is an
unconventional but very timely approach that assesses what would happen if mortgage securitizers had to take their securitized mortgages back on balance sheet. The second approach compares the average performance of large banks that securitize with banks of comparable size that do not. Our final approach uses instrumental variable regression in which we use bank size as an instrument for securitization since beyond very small banks, bank size has little effect on our performance measures, but has a significant effect on the likelihood that banks securitize. Our results across the three methodologies present a fairly consistent picture in the case of mortgage securitizers. Using our second and third approaches we find that mortgage securitization reduces bank insolvency risk (5 percent significance level based on second and third approaches), increases bank leverage (5 percent significance using the second approach, 10 percent significance with the third approach), and increase bank's profitability (5 percent significance second approach, not significant third
