Full text of Working Papers (Federal Reserve Bank of Chicago) : Are Covered Bonds a Substitute for Mortgage-Backed Securities?, Working Paper 2011-14

View original document

The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.

Federal Reserve Bank of Chicago

Are Covered Bonds a Substitute
for Mortgage-Backed Securities?
Santiago Carbó-Valverde, Richard J. Rosen,
and Francisco Rodríguez-Fernández

WP 2011-14

ARE COVERED BONDS A SUBSTITUTE
FOR MORTGAGE-BACKED SECURITIES?
Santiago Carbó-Valverde(a) (scarbo@ugr.es)
Richard J. Rosen(b) (rrosen@frbchi.org)
Francisco Rodríguez-Fernández(a) (franrod@ugr.es)
November 2011

Abstract: Covered bonds and mortgage-backed securities both allow mortgages to be
financed with duration-matched bonds. Given the problems in the MBS market during
the financial crisis, some suggest that covered bonds might be a substitute for MBS.
We examine the use of covered bonds and MBS in the U.S. and Europe, finding that the
two are used for different purposes. Covered bonds are used more to increase liquidity
than are MBS. MBS are more often used in ways consistent with exploiting some kinds
of agency problems.

The views expressed here are those of the authors and may not represent those of the
Federal Reserve Bank of Chicago or the Federal Reserve System. We wish to thank
seminar participants at the Federal Reserve Bank of Chicago and the 2011 Midwest
Finance Associations meetings for helpful comments.

(a)
(b)

University of Granada
Federal Reserve Bank of Chicago

Are Covered Bonds a Substitute for Mortgage-backed Securities?

1. Motivation and main goal
The recent financial crisis has a number of causes, but many lay much of the blame
on the movement of financing away from traditional bank lending to what is known as
the shadow banking system (see, e.g., Adrian and Shin, 2009; Brunnermeier, 2009;
Gorton and Metrick, 2009). The shadow banking system includes many things, but key
among them are the mechanisms by which loans (and loan-like debt instruments) are
financed by other than the originating bank. Securitization – the sale of bonds backed
by the payments on a group of loans – plays a major role in the shadow banking system.
The ability to easily securitize loans in the period before the recent crisis abetted the
rapid increase in the issuance of the loans that were used as collateral for securitizations.
However, the financial crisis exposed a lot of problems with the securitization process,
especially for residential mortgages, the largest asset class used to back securitizations,
leading to a rapid reduction in the issuance of new residential mortgage-backed
securities (hereafter MBS1; see Figure 1). In the aftermath, there has been a search for
alternatives to securitization (see Banking Supervision Committee of the European
Central Bank, 2011).
One alternative to securitization for residential mortgages is covered bonds (CB),
which have been used in some European countries for over a century. In the early
stages of the crisis, the critiques on the shortcomings and complexities of the
securitization process highlighted the robustness of traditional covered bond products
(such as German Pfandbriefe). In this paper, we compare MBS to CB and we examine
why banks issued each of these types of bonds. This allows us to address the question of
whether covered bonds can be a substitute for MBS.
At a very basic level, MBS and CB work similarly. A bank originates a group of
mortgages that are then put into a ‘ring-fenced’ pool.2 While the characteristics of the
ring fencing and the pool can differ across type of securities and across countries, the
common characteristics are that the mortgages serve as specific collateral for the bonds,
be they MBS or CB. This means that the mortgages are, in effect, financed by the
bondholders giving banks access to a broader set of investors than traditionally-financed
mortgages. The traditional model for mortgage financing is that the bank originating
the loan would keep it on its balance sheet until the mortgage was repaid. The loan
would be financed out of general liabilities which are primarily composed of bank

Securitizations backed by residential mortgages are sometimes abbreviated RMBS to differentiate them
from securitizations backed by commercial mortgages (CMBS).
2
As discussed below, the mortgages that go into a MBS or CB pool need not be originated by a bank, nor
do all the mortgages in a pool have to be originated by the company issuing the MBS or CB.

deposits, plus capital. MBS and CB both allow banks to access bond investors as well
as bank depositors to fund mortgages.
The similarities between MBS and CB suggest that the covered bond market might
serve as an alternative to the securitization market for financing mortgages. To see
whether banks issued CB for the same reasons that they issued MBS, we examine banks
in Europe and the U.S. There are a number of possible reasons why a bank uses
mortgages to back MBS or CB. One possibility that a number of studies have focused
on is the originate-to-distribute (OTD) model, where banks originate loans only to
collect the fee income from selling them (see, e.g., Rosen, 2011).3 Alternatively, a bank
may want to bring forward the profit from mortgages because it needs short-run
liquidity. Selling loans into an MBS pool or selling CB accomplishes this. Related to
this, a bank may also need to raise capital to satisfy regulatory (or market) requirements.
Finally, banks may use MBS or CB for risk management (as Packer, et. al, 2007,
suggest). We test whether banks systematically use MBS or CB for these reasons.
Bear in mind that banks might not view MBS and CB as substitutes since there are
some real and some regulatory differences between issuing MBS and issuing CB. As
we describe in the next section, the transfer of risk from banks to bondholders is more
complete with MBS than with CB. In addition, regulatory capital relief can also be
larger when loans are sold to a pool backing a MBS than when they are placed into a
pool backing CB. While these factors seem less important than the similarities between
MBS and CB, we find that banks use MBS and CB for different reasons and that these
reasons are related to differences between MBS and CB.
We find CB issuance, but not MBS issuance, to be consistent with bank liquidity
needs. Our results suggest that low liquidity banks are more likely to issue CB and that
CB issuance leads to increases in liquidity. As evidence of this, we find that a bank is
more likely to issue CB when it has relatively low return and a high loan-to-deposits
ratio. After the issuance of CB, return increases and the loan-to-deposit ratio adjusted
for CB issue decreases.
Our results indicate that MBS are more likely to be issued when banks are reducing
risk, but there is little evidence that they are issued for liquidity reasons. There is no
significant relationship between MBS issuance and changes in return. In addition, while
banks with high loan-to-deposit ratios are more likely to issue MBS, the issuance of
MBS does not predict lower loan-to-deposit ratios in the future. Also, MBS issuance
has no effect on loan growth or capital ratios. But, consistent with risk management,
banks are more likely to issue MBS when their loan provisions are high – indicating
high risk – and having issued MBS is associated with lower loan provisions in the

There is evidence that using the OTD model can affect the risk of loans a bank originates (see Keys, et
al., 2010; Purnandanam, 2011), something we do not explore here.

future. This is consistent with MBS, but not CB, allowing banks to transfer significant
risk to bondholders.
We also examine whether agency problems can explain why banks issue MBS and
CB, and find evidence that MBS issue is associated with these problems. For example,
there is evidence of herding behavior for MBS but not for CB. Faster growth in MBS
issuance in a country was positively associated future more MBS issuance by banks in
that country but faster CB growth in a country had no significant impact on future CB
issuance in that country.
During the runup to the recent financial crisis, some claim that banks took excessive
risks. We find that, even after controlling for size, issuing MBS during the final years
before the crisis (2006-2007) made a bank more likely to have been bailed out during
the crisis, something not true for banks that had issued CB during those years, which
suggest that banks involved in MBS were among those that took excessive risk.
The rest of the paper is as follows. Section 2 gives background on MBS and CB.
Then section 3 sets out the empirical model and describes the data we use. The main
analysis is in section 4. Section 5 examines whether banks that issued MBS or CB were
more vulnerable during the financial crisis. Concluding comments are in section 6.

2. A comparison of MBS and CB
This section reviews securitization and the covered bond process. After carefully
examining MBS and CB – which we refer to collectively as secondary mortgage
securities or SMS – we show ways in which they are similar and different. This allows
us to develop hypotheses about when they are used. As part of this, we present some
background data.

2.1 Data
To examine the decision to issue SMS, we use data from six countries – France,
Germany, Italy, Spain, the U.K., and the U.S. – over the period starting in 2003 and
ending in 2007. At least some banks in these countries issued either CB or MBS, but our
sample includes all banks with at least one billion dollars of total assets at the beginning
of our sample period. We get balance sheet and income statement data from
Bankscope and data on SMS issuance from Dealogic. House price indexes have been
obtained from Eurostat for the European countries and from the Federal Housing
Finance Agency in the U.S. To remove potential outliers, we trim our data at the 1st and
99th percentile of all variables used in the empirical analysis.4
4

All the empirical tests in this paper were re-run with winsorized data as opposed to the trimming of the
1st and 99th percentiles. The results do not suffer any significant changes.

The primary sample includes 711 banks, of which 121 issue CB at least once and
107 issue MBS at least once. Table 1 presents summary statistics for the sample. Panel
A has data on the full sample, Panel B has data for banks that issue MBS, and Panel C
has data for banks that issue CB. In Panels B and C, the data are for the year before the
year in which the SMS was issued (a bank is in the data once for each year that it issues
CB or MBS).

2.2 MBS
MBS are bonds that are collateralized by a group of mortgages. The process that
produces MBS starts with the origination of mortgages. The typical path starts when a
bank or other entity originates (makes) a mortgage. The mortgage may then be sold,
eventually ending up with the firm that puts together the securitization (Figure 2). We
focus on banks that put together securitizations, but it is also done by governmentsponsored organizations (Fannie Mae and Freddie Mac in the U.S.). The securitizing
organization sells the mortgages to a shell corporation it sets up. The shell corporation
is known as a special purpose entity (SPE) or special purpose vehicle (see Figure 2).5
The SPE issues bonds and uses the revenues from selling the bonds to pay for the
mortgages it has purchased.6 The SPE uses the principal and interest paid on the
mortgages to repay the bondholders.7
There are several things about the securitization process that are relevant for this
paper. First, the originating bank may or may not share the same corporate parent as the
firm setting up the SPE (in Figure 2, compare the first example to the second example).
Most banks originate mortgages, but few banks securitize them (only 15.4% of the
banks in our sample ever do a mortgage securitization, and the banks in our sample are
much larger than the average bank).8 In part, this is because there are significant fixed
costs in setting up an SPE and underwriting the bonds issued by the SPE. But, whatever
the reason, it means that banks can sell loans as part of the securitization process
without ever putting together a securitization and that securitizations can contain
mortgages originated by banks other than the securitizing bank.9 While we use the
value of bonds sold by a bank as our measure of securitization, this both overstates and
understates the impact of securitization on the bank’s mortgage portfolio. To the extent
that securitization contains mortgages originated by other firms, it overstates the impact

The SPE gives bondholders legal protections if the issuing bank becomes insolvent.
The SPE also can get some initially equity funding.
7
Any funds left over after these payments (and expenses) go to the equity owner of the SPE, typically the
firm that sets it up.
8
Rosen (2011) finds that of banks in the U.S. with traded stock (most of which are among the top 10% of
U.S. banks in size), over 80% originate and sell mortgages as part of the securitization process, but less
than 3% actually put together securitizations.
9
This is true in Spain and the U.S.
6

while to the extent that the bank sells mortgages to other parties in addition to putting
together a securitization, it understates the impact.10
A second feature of securitization that may be important is the accounting treatment
of assets held in the SPE. The SPE is set up as a separate corporate entity to give its
bondholders legal protection if the issuing bank becomes insolvent. This legal
separateness may mean that regulatory accounting standards treat the mortgages as sold
and not owned by the bank. This means that regulatory capital requirements for the
bank are not applied to the mortgages in the SPE. In certain countries, such as the U.S.,
if the loans from a securitization were put in a SPE, the bank did not have to hold
capital against them unless it had an ownership position in the SPE (or purchased bonds
from it).11 In other countries, such as Spain, any assets in an SPE were required to be
consolidated on bank balance sheets. Thus, Spanish banks that securitized mortgages
were required to hold capital against the loans in the SPE.
Panel B of Table 1 presents some balance sheet and income statement information
for banks in our sample that issue MBS as of the year prior to the issue. On average,
banks that issue MBS are more levered, which may contribute to why they have lower
return on assets. MBS issuers also have lower ratios of loan provisions to total loans
than other banks. In general, these differences are economically small. Banks that issue
MBS also grow fast in the year prior to issue, something not surprising since they may
be gathering loans to put into the MBS pool.
During 2003-2007, banks in four of the six countries in our sample did at least one
mortgage securitization – in Germany, there was exactly one securitization (see Table
2). Securitization was most prevalent in the U.S., but was also not rare in Italy, Spain,
and the U.K. The average size of a MBS issue was much larger in the U.S. than in
European countries (average issue size was $1.7 billion in the US and $255 million in
Europe). This may reflect that the issuing banks in the U.S. were much larger (average
issuer size was $1.1 trillion in the US and $204 billion in Europe). The correlation
between issuer size and bank size in our sample is 0.55.
The sample period ends right as the financial crisis was starting. This is in part
because the crisis changed securitization markets. As Figure 1 shows, securitization
grew rapidly in the period leading up to the financial crisis, but then securitization – at

An example of the latter would be if the bank sold its high quality (prime) mortgages to others (such as
Fannie Mae and Freddie Mac for U.S. banks) and put together securitizations with its subprime
mortgages.
11
This has changed for some types of securitizations in the U.S. because the U.S. Financial Accounting
Standards Board approved Financial Accounting Standards (FAS) 166 and 167, which took effect in late
2009. FAS 166 and FAS 167 meant that some types of securitizations, but not necessarily MBS, would
have to be consolidated on a firm’s balance sheet. The FDIC said that this would apply to regulatory
capital, but delayed the implementation of the requirement.

least issuance by private firms – essentially stopped (privately-issued MBS issuance in
the U.S. fell by 95% between 2006 and 2008).12

2.3 CB
Covered bonds have been around a lot longer than securitized bonds. The first
mortgage securitization is thought to be in 1970 when banks and other lenders put
together pools of home mortgages that were then guaranteed by the government agency
known as Government National Mortgage Association (now also known as Ginnie
Mae). The first covered bond, on the other hand, was issued in the 1700s to finance
public works projects in Prussia. CB are still commonly used to finance public
obligations in Europe. They are also used to finance residential mortgages, the focus of
this paper.
Like MBS, CB are debt securities that are backed by a pool of mortgages. Except in
the U.K. (see below), the pool of mortgages remains on the issuing bank’s balance
sheet. In its simplest form, a bank originates a mortgage, designates the mortgage as
part of a pool (known as ring-fencing), then issues bonds collateralized by the pool (see
Figure 4). The face value of mortgages in the pool is required to be at least as large as
the face value of the CB, although the value of mortgages almost always exceeds the
value of the bonds (overcollateralization). Thus, while the interest and principal on a
covered bond may be paid out of the issuing bank’s general funds, the ring-fenced pool
is there to repay the bondholders if the issuer becomes insolvent.13 One other important
feature of CB is that if a mortgage in the CB pool defaults or is repaid early, the bank
replaces the loan with a new mortgage. This keeps the size of the pool predictable.14
As with MBS, there are some differences in the structure and regulation of
covered bonds across countries. As one example of this, in the United Kingdom, banks
issue what are known as structured covered bonds. The key difference between
structured CB and their more traditional cousins is that the issuer of structured CB is a
limited liability partnership (analogous to an SPE). The partnership purchases the
mortgages from the issuer and guarantees the bonds. This serves as a different way of
ring-fencing the mortgages.
While 121 banks issued covered bond issues during our sample period, usage
was not uniform across banks in different countries. CB were common in three of the
six countries in our sample during 2003-2007 although there was at least one CB issue
in all six countries (see Table 2). As noted above, covered bonds originated in Prussia,
and they are still most common in Germany. Spain and the U.K. also have active CB
markets, which is interesting because banks in those countries are the most active
12

Source: Inside Mortgage Finance.
If the pool is not sufficient to repay bondholders, the bondholders become general creditors of the bank.
14
Since the mortgages are naturally amortizing, the size of the pool can fall over time.
13

securitizers after the U.S. Some of the difference in CB markets may be driven by
regulation. For example, in the U.S., the FDIC has not assured bondholders that CB
have priority over the FDIC in case of bankruptcy. This means that the mortgages that
are intended as collateral for CB may be claimed by the FDIC when a bank fails,
increasing the risk for covered bondholders.
The average size of a covered bond issue is $576 million, about half as large as the
average MBS issue. In Spain and the U.K., which have both CB and MBS, CB issues
tend to be larger than MBS issues.
The volume of covered bonds issued was roughly flat during our sample period (see
Figure 3).

2.4 Comparing CB and MBS
CB and MBS are similar in many of their basic economic functions, yet they have
some potentially important differences. They both offer many possible benefits for loan
originators.15 They can increase liquidity for banks by allowing them to access a
broader class of investors. As part of this, CB and MBS can make it easier for some
lenders to specialize in particular types of lending such as mortgage loans. These can
lead to more efficient loan provision. As described above, they can also allow
regulatory arbitrage.16
The transfer of mortgages to an SPE in a MBS issue means that the issuing bank no
longer bears the risk of the loans. This is in contrast to CB where, because the mortgage
pool is constantly adjusted to maintain the pool size, the issuing bank bears the credit
risk of the mortgages.17 Possibly because of this, more information about the contents
of mortgage pools is available for MBS investors than for CB investors.18
Since the pool of mortgages backing a MBS issue is static, this allows issuers to
create a broader set of bonds that are backed by the pool. Specifically, the bonds in a
MBS issue are often tranched. The tranching allows bonds to differ in the timing and
security of repayment.
MBS and CB also differ in the degree to which moral hazard can be a problem. One
potential issue for both kinds of bondholders is that the issuing bank may know more
15

For a more extensive discussion of why assets such as mortgages are securitized, see Elul (2005) and
Kothari (2006).
16
Securitization can also allow tax arbitrage (Kohler, 1998).
17
The holders of covered bonds bear the residual risk that the issuing bank fails and the mortgage pool is
not sufficient to repay the bonds. It is important to note that no covered bond failed, at least within the
sample and the period that we have considered in this study.
18
Rating agencies monitor asset quality for both types of pools. Also, there is no evidence that most
MBS investors carefully analyzed detailed pool information before the financial crisis (see, for example,
http://stonestreetadvisors.com/2011/02/15/john-paulsons-interview-with-the-financial-crisis-inquirycommission-the-signs-were-there/).

about the credit risk of mortgage borrowers than investors do. During our sample
period, it would have been difficult and expensive for investors to examine the credit
risk of each mortgage in a pool. This gave banks an incentive to have the mortgages in
a pool be riskier than investors thought. Many claim that this is what happened with
subprime MBS in the U.S. during our sample period (e.g., Keys, et. al, 2010). Going
forward, although not in our sample period, there is likely to be more attention paid to
structuring MBS to reduce moral hazard.19 As an alternative, banks can choose to issue
CB where this moral hazard is limited because if a mortgage defaults, the bank must
transfer a replacement loan from its general portfolio to the mortgage pool, thus
restricting the potential gains from fooling investors.

3. Empirical model
As discussed in the last section, the major economic benefits for CB and MBS are
similar, however there are some legal, regulatory, and structural features that may lead a
bank to prefer one type of SMS over the other. The decision to issue a SMS also can be
influenced by how it fits into a bank’s overall strategy and situation. For some banks,
SMS are part of a line of business. A bank may originate mortgages with the sole intent
of financing those mortgages using a SMS. There is an expanding literature on the use
of the originate-to-distribute (OTD) model as part of the securitization process (e.g.,
Purnanandam, 2009; Rosen, 2010a), but the same model can also be used when the end
product is a covered bond. Alternatively, banks may use SMS for occasional balance
sheet management. For example, a bank with sudden liquidity needs may issue a SMS
to bring forward future profits on loans it owns. Still another possibility is that there
may be agency reasons for issuing SMS. An example of this would be if banks were
influenced by herd behavior (Scharfstein and Stein, 1999). The idea here is that a bank
is more likely to issue SMS when other banks have recently done the same. The driving
forces could be related to agency issues at the bank or among the purchasers of the SMS
bonds. We explore which of these possibilities are consistent with the data on SMS
issuance.
To investigate the reasons behind a SMS issue, we need to examine the factors that
lead a bank to issue SMS and, then, how issuance affects the bank. The basic model for
predicting issuance by bank i in year t is:
SMS issuei,t = f(bank characteristicsi,t-1, other controls)

(1)

where the SMS issue can be either CB or MBS. Because banks in some countries can
issue either type of bond, we use a multinomial logit framework to test (1). This
explicitly assumes that banks are choosing among issuing CB, issuing MBS, or not

For example, the recent Dodd-Frank financial reform law in the U.S. will require securitizers to retain
5% of the credit risk in a MBS issue. This reduces the gain from putting bad mortgages into a pool.

issuing. Our results are robust to examining CB and MBS separately. To control for
differences across countries, we include country dummies.
In addition to knowing which characteristics predict issuance, we also want to
determine the effect of issuing SMS on banks. For this, we use the following:
bank characteristici,t = f(CB issue dummyi,t&t-1, MBS issue dummyi,t&t-1, bank
characteristicsi,t-2, other controls)
(2)
where the dummies take the value 1 if bank i issues the appropriate SMS in year t or t-1
and where the bank characteristics are the same as those on the right-hand side of (1).
We include bank fixed effects, so the coefficients on the SMS dummies indicate
whether a particular characteristic is higher or lower after issuance relative to other
times.
The bank characteristics included in the analysis are limited by data availability.
The Bankscope data we use does not have widespread coverage of some balance sheet
and income variables for many of the banks in the sample countries. The variables we
use are intended to cover basic measures of profit and risk while also allowing us to
include as large a sample of banks as possible. Profit is measured using return on
assets, that is, income during divided by total assets at the end of the year (ROA; the
results are robust to using the return on equity).
The first measure of risk we use is the loan-to-deposits ratio. Since loans are
generally illiquid and deposits are generally liquid, higher values of this ratio suggest a
less liquid, and therefore riskier, bank. But, this ratio has a problem when we want to
look at the effect of CB issue on liquidity. The mortgages that back CB remain on a
bank’s balance sheet, thus inflating the bank’s reported loans. From a liquidity
perspective, these mortgages are different from other loans (including other mortgages)
a bank has because they are matched to liabilities with a similar maturity profile.20 For
this reason, we create a CB-free loans-to-deposits ratio by subtracting the mortgages
backing CB from total loans.21 We use this adjusted loans-to-deposits ratio in the
analysis below.
The capital-to-assets ratio (henceforth, the capital ratio) also is used measure to risk.
Clearly, the smaller the capital buffer, the more likely insolvency is. One issue with the
capital ratio is that regulators set minimum capital ratios for banks. We include a
separate variable to indicate banks with low capital on the grounds that low-capital
banks are likely to face more regulatory scrutiny. Since regulatory capital minimums
are based on risk-based capital measures and we do not have these ratios, we define a
low-capital bank as one with a capital ratio in the lowest 25% in a given year. The low20

There may be some minor liquidity issues because the mortgages in the CB pool have the risk of
unexpected default and prepayment.
21
Formally, the numerator of the adjusted loans-to-deposits ratio in year t is the total loans in year t minus
the sum of all covered bonds issued in the years from 2003 to year t, inclusive.

capital variable is the interaction between the capital ratio and a zero-one dummy for
whether a bank has low capital.
The loan-to-deposits ratio and the capital ratio do not separate banks by the riskiness
of the assets they invest in beyond the notion that loans are often riskier than other bank
assets. To further refine our estimate of bank risk, we use the ratio of loan loss
provisions to total loans. Loan loss provisions are the capital that a bank sets aside to
cover changes in future expected losses on loans the bank has made. It is, thus, an ex
ante measure of the risk of a loan portfolio.22 We also include loan growth in our
analysis. Loan growth is the percentage change in loans from one year-end to the next
year-end. More liquid banks should be able to make more loans, thereby growing
faster.
We use these characteristics to capture whether banks are issuing SMS as a line of
business or for balance sheet management. The basic model also allows us to shed
some light on the possible agency reasons for issuance. Table 3 summarizes our
hypotheses about how the regression results are related to the reasons for issuing SMS.
If a bank is issuing SMS as a line of business, then we expect the primary impact of
SMS issuance to be an increase in ROA. This would be reflected in a positive
coefficient on the CB or MBS post-issue dummies in the ROA regression (equation (2)).
Banks could use SMS for different kinds of balance sheet management. First, SMS
can be used to improve liquidity (by bringing forward future profits). We expect that
this means that, all else equal, low-liquidity banks are more likely to use SMS. Support
for this hypothesis would be if either a low ROA or a high loan-to-deposits ratio
predicts SMS issuance. But, SMS are only valuable in this respect if they allow a bank
to increase liquidity. So, we expect that low liquidity banks that issue SMS should see
liquidity improve. A positive coefficient on the loan-to-deposits ratio in equation (1)
and a negative coefficient on a SMS post-issue dummy in the adjusted loan-to-deposits
ratio equation (2) regression are consistent with this. The question then arises as to
whether the CB issue was responsible for the liquidity increase. If the unadjusted loanto-deposit ratio increases after a CB issue when the adjusted loan-to-deposit ratio
decreases, then the CB issue directly increased liquidity.
A second type of balance sheet management would be if banks use SMS to manage
risk. If they do, then high values of the risk measures should predict SMS issue, and
SMS issue should reduce risk. Table 3 gives the coefficients on the capital ratio,
adjusted loan-to-deposit ratio, and provisions variables consistent with this hypothesis.
Here, the inference must be somewhat indirect. We know whether the SMS issue
occurred when a bank was reducing risk, but the data do not allow us to directly tie it to
the bond issue. To examine whether the risk management is due to regulatory pressure,
22

As discussed later, the results are robust to using the ratio of chargeoffs to total loans, which is an ex
post measure of bank risk.

we separately examine a capital ratio variable for low-capital banks. If the coefficient
on this variable in equation (1) is negative and the coefficient on a SMS dummy in the
low-capital regression using equation (2) is positive, then that suggests regulatory
pressure may have played a role in the SMS issue.
There are a number of possible agency problems that could influence the decision to
issue SMS. One that we can indirectly examine using the basic model is empire
building. There is evidence that increasing the size of a bank increases CEO
compensation even if profit does not rise (Bliss and Rosen, 2001; Hubbard and Palia,
1995). If the ability to SMS issuance leads to faster bank growth in the absence of
increased profit, this would be consistent with bank CEOs increasing private benefits
rather than shareholder utility (see Table 3).
The regression results also provide information about what the capital market
requires before it purchases CB from a bank. Recall that the bank that issues CB is
required to replace mortgages that have gone bad or been prepaid with new mortgages.
In addition, if the bank becomes insolvent and the pool backing a CB is insufficient to
cover the bonds, bondholders become a general creditor of the bank. For these reasons,
the purchasers of CB are likely to care about the health of the issuing bank. If market
participants are more likely to purchase CB from a low-risk bank, then banks with low
risk should be more likely to issue CB and risk should not increase after the issue (see
Table 3).
Finally, home prices went up significantly during our sample period (especially in
Spain, the U.K., and the U.S.). This may have led banks to increase loans and to relax
loan quality standards (Dell’Ariccia et al., 2008). If banks are overenthusiastic, then
home price increases should affect both the origination of mortgages that bank CB and
MBS. But, price increases may also have made investing in SMS seem safer to
investors (Rosen, 2010b). Issuing MBS (but not CB) allows banks to exploit
overenthusiastic investors. To examine whether home prices affected SMS issuance,
we include changes in home prices, measured at the national level, as a control.

4. Regression results
This section examines the relationship between SMS issuance and bank
characteristics.
4.1 The impact of bank characteristics on the decision to issue SMS
The first step is to look at what determines whether and when a bank will issue a
SMS. The results of estimating (1) using our sample over the period 2003-2007 are
presented in Table 4A. We choose the case where banks do not issue SMS as our base.
This means that the two other alternatives, issuing CB and issuing MBS, are compared
to not issuing SMS.

The first column of Table 4A reports the coefficients for the comparison of CB to
not issuing SMS. The coefficient on ROA of -2.025 is significantly different from zero.
This means that banks with lower ROA in year t-1 are more likely to issue CB than not
issue SMS in year t. To get a feel for the economic significance of this effect, for a
bank with the mean values for all the other variables, increasing the ROA decreases the
probability of issuing a CB by 3.50% per percentage increase in ROA (the marginal
effect, as given in Table 4A). Given that one standard deviation in ROA is 30.3% of the
mean ROA, this suggests that moderate increases in ROA can have a large impact on
the probability of CB.23
The results for the first regression in Table 4A also show that banks with larger
(adjusted) loan-to-deposits ratios, larger capital ratios, lower provision, and larger total
assets are more likely to issue CB than not issue SMS. The largest economic impact in
this group of variables comes from the assets variable, consistent with there being a
substantial fixed cost to issue a covered bond, with the large banks able to spread the
cost over a bigger pool of loans. But the impact of the loan-to-deposits ratio and the
capital ratio are also large. Finally, there is no significant relationship between CB
issuance and any of the low-capital variable, loan growth, or the home price index.
A comparison of issuing MBS to not issuing SMS is given in the second column of
Table 4A. Banks with larger loan-to-deposits ratios, larger provisions, and larger total
assets are more likely to issue MBS than not issue SMS. In addition, MBS are more
common when home prices are higher.
We can also compare CB to MBS. The third column of Table 4A reports the p
value for a test of whether the coefficients in the first column of the table are equal to
those in the second column. Overall, we see significant differences in the effects bank
characteristics have on the decisions to issue CB relative to MBS. For example, the p
value for ROA is 0.001, meaning that the coefficient on ROA in the comparison of CB
to not issuing SMS is significantly smaller than the coefficient on ROA in the
comparison of MBS to not issuing SMS. However, these differences do not fit a simple
pattern such as bank characteristics being more important for one type of SMS.
To fully test our predictions, we have to examine banks both before and after SMS
issuance, but the results in Table 4A give an idea of which banks are issuing SMS.
Banks with low liquidity are more likely to issue SMS, as the coefficients on ROA and
the loan-to-deposits ratio are of the correct signs (although the coefficient on ROA in
the MBS regression is not significant). Banks that issue CB are, by most measures,
safer than average. They have larger capital buffers and lower provisions, although they
also have a larger loans-to-deposit ratio. The banks that issue MBS, on the other hand,
appear riskier than average. They have loans-to-deposits ratios and loan provisions that
23

There is a need to be careful when extrapolating from the marginal effect since it only holds exactly for
a tiny change in ROA

are significantly above those of banks that do not issue SMS. Additionally, as noted
above, it is clear that asset size is an important predictor of which banks issue SMS,
something that we explore in the robustness checks that follow. Finally, it is worth
noting that the house price index affects MBS issuance but not CB issuance, consistent
with agency problems between banks and bond investors.
The results in Table 4A are robust to a number of changes. When the adjusted loanto-deposit ratio is replaced by the balance sheet loans-to-deposits ratio (which includes
the CB loan pool), the qualitative results are similar (see Table 4A). The loan ratio
coefficients have the same signs no matter which ratio is used and the other coefficients
are of about the same magnitudes across the two sets of regressions.
We also examine whether the estimated relationships may vary for banks that are
particularly active issuers. To do this, we interact the independent variables in our
baseline regression with dummies for whether a bank has issued CB or MBS in the last
two-years. The results are shown in Table 4B. The results are completely in line with
those of Table 4A and the only difference is that the estimated coefficients of the
interaction variables are higher than those with no interaction suggesting that the
characteristics that determine issuance of CB and MBS are more economically
significant for more active issuers.
Given the institutional differences between the countries in the sample, it is possible
that the determinants of CB and MBS issuance may vary in countries where both type
of securities are frequently issued compared to those where only one of them is issued.
In order to check for these potential differences we run the same set of regressions of
Table 4A for banks in countries where both MBS and CB are frequently issued (Spain
and the U.K.). The results are shown in Table 4C. Given the similarity between the
coefficients in Tables 4A and 4C, there is no evidence that the factors driving SMS
issuance are different between countries where both types of securities are issued and
those where only one type is.24
Additional robustness tests are reported in Table 5. In order to facilitate
comparison, the first column of the table gives the baseline results from Table 4. One
issue with the baseline specification is that our measure of loan risk, provisions, is
subject to strategic behavior by banks. There is evidence that banks have used
provisions to smooth income, for example (Saurina, 2009; Sacasa, 2011). An
alternative measure of loan risk is the ratio of loan chargeoffs to total loans. This is an
ex post measure of losses, reflecting losses on loans made in the past and therefore
might be less relevant for SMS issuance decisions today. As the results shown in the
second column of Table 5 indicate, the results are very similar when provisions are
replaced by chargeoffs. Also, we control for country effects using dummies, but it is
24

When we run logistic regressions predicting MBS issuance in the U.S. and CB issuance in Germany,
the coefficients on the independent variables are similar to those in Tables 4A and 4C, further supporting
the hypothesis that common factors drive SMS issuance across our sample countries.

possible that the cross-country differences are more subtle. To test this, we subtract
from each of the bank characteristics the average value of that characteristic for banks in
our sample from the same country. Using the netted variables as our controls not
surprisingly affects the magnitudes of the coefficients in the regressions. However, as
shown in the third column of Table 5, the same set of variables is statistically significant
as in the baseline results presented in the first column.
Consistent with there being a large fixed cost to issue SMS, we find that large banks
are more likely to issue these bonds than are small firms. We explore the effect of bank
size in two ways. First, we drop small banks from the sample. The fourth column of
Table 5 reports the results of a regression where all banks with total assets less than the
median value ($40.2 billion) are excluded. The results for the large bank sample are
qualitatively similar to those in the baseline sample. As a further robustness check, we
create interactions between the key controls and our bank size measures. We add these
interaction terms to the baseline model and run a regression on the full sample. The
results are reported in the fifth and sixth columns of Table 5, with the sixth column
giving the coefficients on the interaction terms. The results remain qualitatively similar.

4.2 The impact of SMS issue on bank characteristics
To complete the analysis of why banks issue SMS, we need to examine the effect of
SMS issue on bank characteristics. This is done using fixed-effects regressions based
on equation (2).
There is evidence that issuing CB improves profitability. The coefficient on the CB
dummy in the regression reported in the first column of Table 6 is positive and
significantly different from zero. To get an idea of the magnitude of the effect, a bank
that has issued CB in the past two years has a ROA that is 0.072 larger than that of a
non-issuing bank. This is roughly 9% of the mean and 31% of the standard deviation of
ROA for banks in the sample.
The results are also consistent with banks improving liquidity after issuing CB. The
increase in ROA suggests an increase in liquidity. Another measure of liquidity we use
is the adjusted loan-to-deposits ratio. As shown in the second column of Table 6, the
coefficient on the CB dummy is -0.025, which is significantly less than zero. Since the
standard deviation for the adjusted loan-to-deposit ratio is 0.17, issuing CB leads to a
decrease of 0.15 standard deviations in the ratio. This is consistent with a liquidity
motive for issuing CB. For reference, if we run the same regression with the unadjusted
loan-to-deposit ratio, the coefficient on the CB dummy is 0.028, which is significantly
greater than zero (regression not shown). Thus, while issuing CB increases the loan-todeposit ratio, it does so primarily because the mortgages backing CB stay on the issuing
bank’s balance sheet.

The loan growth regression provides a further check on liquidity changes. As
shown in Table 3, an increase in loan growth after a SMS issue suggests that the
issuance opened up space for the bank to grow. However, we do not find that banks
significantly increase loan growth after CB issuance, although the coefficient on the CB
dummy is of the correct sign for that (column 3 of Table 6).
Issuing CB is associated with lower risk. Following issuance, banks have lower
loan-to-deposit ratios (column 2 of Table 6) and larger capital ratios (column 4 of Table
6). However, the impact on capital ratios is relatively small. Issuing a CB increases a
bank’s capital ratio by 0.080, or 3.7% of the standard deviation of the capital ratio.
Low-capital banks also might be using CB to stay above regulatory capital
minimums. The capital ratio of a low-capital bank increases following a CB issue
(column 5 of Table 6). But this may be no more than the standard risk reduction from
increased capital, as the coefficient on the CB dummy in the low capital ratio regression
(column 5) is significantly smaller than the coefficient on the CB dummy in the full
sample capital ratio regression (column 4).
Profit and liquidity do not increase following issuance for banks that securitize
mortgages. The coefficient on the MBS dummy in the regression in the first column of
Table 6 is small in magnitude and not significantly different from zero. In addition, the
coefficient on the MBS dummy in the provisions regression (column 2 of Table 6) is
significantly positive and the coefficient is significantly negative in the loan growth
regression (column 3 of Table 6), inconsistent with an increase in liquidity.
There is evidence that issuing MBS might reduce risk. Banks that issue MBS have
lower provisions post-issue (column 5 of Table 6) and issuing MBS also leads to slower
loan growth (column 3 of Table 6). Loan growth decreases by 0.17 standard deviations
and provisions decrease by 0.04 standard deviations in the year following a MBS issue.
The results in Table 6 generally are robust to a variety of changes. For brevity,
these results are not shown here. As examples of the robustness of the findings, if we
examine the impact of SMS issuance in the last year (rather than the last two years), the
signs and significance of the coefficients are similar. In addition, the results are also
similar for active and non-active issuers, and for those in countries where both types of
SMS are frequently issued.
There is one area where the robustness checks add some information. We find that
the impact of SMS issue is generally bigger for large banks (see Table 7). But, this
affects only the magnitude of the results. None of the qualitative results differ based on
bank size.

4.3 The reasons for issuing SMS
We can use the results above along with the predictions in Table 3 to examine why
banks issue CB and MBS. As shown in Table 8, the analysis supports the hypothesis
that banks issue CB at least in part for profit and liquidity reasons. There is also
evidence consistent with banks issuing SMS for risk management and possibly because
of agency problems.
If banks are issuing SMS as a line of business, then the main effect of issuance
should be an increase in profit. We find that issuing CB significantly increases profit
while issuing MBS is associated with a statistically insignificant and economically
small increase in profit. Of course, our evidence indicates that issuing either CB or
MBS leads to changes in bank balance sheets. This implies that there are other reasons
bank issue SMS, especially CB, beyond viewing them as a line of business.
Banks can use SMS for balance sheet management, including increasing liquidity
and capital ratios (as suggested by Packer, et al., 2007). We find evidence consistent
with both CB and MBS being used for balance sheet management, but of different
kinds. Our results strongly suggest that liquidity increases when CB are issued but not
when MBS are issued. There is also evidence indicating the issuance of MBS for risk
management reasons.
Banks can issue SMS for reasons related to agency problems between bank
managers and bank owners. For example, bank CEOs might want to build an empire to
increase their compensation. The results are consistent with MBS being used, at least in
part, for empire building. Issuing MBS is associated with increases in asset size,
notwithstanding the movement of mortgages off the balance sheet to fund the SPE, but
not with increases in profit.
There can be other agency reasons for banks to issue SMS. The rapid increase in
banks that issued MBS (see Figure 1) might be a sign of herd behavior. Banks may
have decided to securitize loans because securitization markets were hot. Hot markets
may mean that bankers can take advantage of bond buyers (or the principals of the
buyers) by issuing bonds at interest rates below their steady state (or fair) value, perhaps
because bond purchasers are not paying close attention to markets (Rosen, 2010b). To
test for herd behavior, we examine whether, all else equal, SMS issuance in year t at
bank i was affected by SMS issuance at other banks in country c, the home of bank i,
during years t-1 and t. Specifically, we add variables measuring CB issuance and MBS
issuance to our SMS issuance regression, modifying (1) to:
SMS issuei,c,t = f(CB issue dummyi,t&t-1, MBS issue dummyi,t&t-1, CB total
issue volumec,t&t-1, MBS total issue volumec,t&t-1, bank characteristicsi,t-2,
other controls)
(3)

where CB and MBS total issue volume is the total dollar volume of either CB or MBS
issued by banks in country c during years t and t-1. The results of the regression are
reported in Table 9. They show evidence of herd behavior among MBS-issuing banks
but not for CB-issuing banks.
Overall, banks appear to be issuing CB for very different reasons than they issue
MBS. In addition to being profitable, CB issues are associated with liquidity increases.
Banks that issue MBS are reducing risk and may be taking advantage of agency
problems. These differences between CB and MBS are consistent with a key difference
in the structures of the two types of SMS. MBS but not CB offer banks an opportunity
to transfer risk. Once mortgages are placed in a MBS pool, the issuing bank has no
(direct) interest in them. On the other hand, the bank issuing CB must replace the
defaulted mortgages in the bond pool. Thus, issuing MBS can reduce bank risk more
than issuing CB. This ability to shed risk also makes moral hazard problems more
severe. A bank that “fools” investors by putting mortgages that are riskier than the
market thinks into a CB pool gets little benefit from this because if the mortgage holders
default, the bank must replace the defaulted mortgages with new ones.25 However, once
mortgages go into the SPE backing MBS, all risk is borne by bondholders. This is
consistent with MBS be more useful the CB for both risk management and exploiting
certain kinds of agency problems.
While structural differences between MBS and CB are consistent with the risk
management results, it is more difficult to come up with a reason why CB but not MBS
are useful for liquidity. Issuing a SMS can add to liquidity by bringing forward future
revenues or by financing mortgages with long-term bonds (those backing the mortgage
pool) rather than with deposits. Both of these are available whether the SMS is CB or
MBS.

5. Impact of the financial crisis
The recent financial crisis was exceptionally harmful. Many financial markets,
including the private securitization market, were essentially shut down during the crisis.
This caused problems for a number of banks. In order to mitigate the impact of the
crisis, many governments took extraordinary actions to restart financial markets and to
bail out troubled banks. In this section, we look at how SMS issuance in the pre-crisis
period was related to bank bailouts.
To examine whether SMS issuance made a bank more likely to be bailed out, we
define a bail out dummy that takes the value one if and only if a bank was bailed out by

The only benefit comes because there are some states where the bank fails and the CB mortgage pool is
insufficient to pay bondholders.

its government in 2008. 26 In our sample, 11% of the banks received a bailout (see
Table 1). For each bank, we ask how the bailout dummy is related to whether the bank
issued SMS:
bail out in 2008 = f(dummy for CB issue in 2006-7, dummy for MBS issue in
2006-7, bank characteristics in 2006)
(4)
The results of this regression are reported in the first column of Table 10. The
coefficient on the CB dummy is small and not significantly different from zero while
the coefficient on the MBS dummy is positive and significantly different from zero.
This implies that banks issued CB were no more likely to be bailed out than other banks
while those that that issued MBS were more likely to be bailed out.
The coefficients on the bank size and capital ratio variables suggest that bank size
and capital affected the chances of a bailout. This opens the possibility that the
correlation between issuing MBS and being bailed out might be because the banks that
issued MBS were larger or had lower capital than other banks. To test this, the
regressions reported in columns 2-5 of Table 10 split the sample by bank size and
capital ratio. The results show that for both large and small banks and for both low
capital banks, issuing MBS is associated with a greater chance of being bailed out.
We do not know to what extent the need to be bailed out was related to the issue of
MBS. One possibility is that banks that issued MBS also were involved in a lot of the
complex financial products at the center of the financial crisis. It is possible that the
MBS dummy is a proxy for a bank being involved in these other activities.

6. Concluding comments
Covered bonds and mortgage-backed securities are similar in the main economic
function they perform: allowing banks to finance mortgages using duration-matched
bonds. This has led some to suggest that, given the troubles in MBS markets following
the recent financial crisis, that CB could be a good substitute for MBS. We examine
whether banks, prior to the crisis, were using CB and MBS for the same reasons.
We find no evidence that CB and MBS were being used by banks for similar
reasons. Both types of SMS seem to increase profit, although only weakly in the case of
MBS. But, our results are consistent with liquidity improvement being a primary
benefit of CB issuance, but not of MBS issuance. There is some indication, albeit
indirect, that banks used MBS when they were attempting to reduce risk. Finally,
agency problems may have pushed banks to issue MBS as there is evidence of herd
behavior in their issue. The same is not true for CB.
26

To identify the receipt of aid by European Union banks, we accept the European Commission (EC)
definition of State aid. This includes capital injections/recapitalization and debt guarantees. To identify
the recipients of bailouts in the US, we rely on US Treasury data covering participation in the Asset
Guarantee Program, the Capital Assistance Program and the Capital Purchase Program.

Since our results suggest that banks used CB and MBS for different reasons, the two
may not be substitutes. As we refine this study, we plan to examine whether the real
and regulatory differences between CB and MBS can explain the varied uses.

References
Adrian, Tobias, and Hyun Song Shin, 2009, “Money, Liquidity, and Monetary Policy.” American
Economic Review Papers and Proceedings 99(2), 600–605.
Bannier, Christina E. and Dennis N. Hänsel, 2009, “Determines of Banks’ Engagement in Loan
Securitization” Goethe-University Frankfurt, Working Paper N0. 171.
Benveniste, Lawrence M. and Allen N. Berger, 1987, “Securitization With Recourse: An Instrument That
Offers Uninsured Bank Depositors Sequential Claims,” Journal of Banking and Finance 11, 403-24.
Brunnermeier, Markus K., 2009, “Deciphering the Liquidity and Credit Crunch 2007-08”, Journal of
Economic Perspectives, 23(1), 77-100.
Calomiris, Charles and Joseph Mason, 2004, “Credit Card Securitization and Regulatory Arbitrage,”
Journal of Financial Services Research, 26, 5-27.
Casu, Barbara, Andrew D. Clare, Anna Sarkisyan and Steve H. Thomas, 2010, “Does Securitization
Reduce Credit Risk Taking? Empirical Evidence from US Bank Holding Companies,” working paper.
Available at SSRN: http://ssrn.com/abstract=1548361.
Dell’Ariccia, G., Igan, D., Laeven, L., 2008, “Credit Booms and Lending Standards: Evidence from the
Subprime Mortgage Market”. International Monetary Fund. Working Paper 106.
Elul, Ronel, 2005, “The economics of asset securitization,” Business Review, Federal Reserve Bank of
Philadelphia, Third Quarter, pp. 16–25.
European Central Bank, 2011, “Recent Developments in Securitization”, report by the Banking
Supervision Committee. February.
Hänsel, D. and Krahnen J.P., 2007, “Does Credit Securitization Reduces Bank Risk? Evidence from the
European CDO Market”, mimeo, Goethe-University Frankfurt.
Gorton, Gary and Andrew Metrick, 2009, “Securitized Banking and the Run on Repo,” working paper.
Greenbaum, Stuart and Anjan Thakor, 1987, “Bank Funding Modes: Securitization Versus Deposits”,
Journal of Banking and Finance 11-3, September 1987, 379-401.
Hubbard, R.G., Palia, D., 1995. Executive pay and performance: evidence from the U.S. banking
industry.. Journal of Financial Economics 39, 105–130.
Jiangli, Wenying and Matt Pritsker, “The Impacts of Securitization on US Bank Holding Companies,”
working paper, 2008.
Keys, B., J., Mukherjee, T.K. Seru, A. and V. Vig, (2010): “Did securitization lead to lax screening?
Evidence from subprime loans,” Quarterly Journal of Economics, 125, forthcoming.
Kohler, K. E., 1998, "Collateralised Loan Obligations: A Powerful New Portfolio Management Tool For
Banks," The Securitization Conduit, 1(2), 6-15.
Kothari, Vinod, 2006, Securitization: The financial instrument of the future, John Wiley and Sons,
Singapore.
Martin-Oliver, Alfredo and Jesús Saurina, 2007, “Why Do Banks Securitize Assets?” working paper.
Minton, Bernadette A., Anthony B. Sanders, and Philip E. Strahan, 2004, “Securitization by Banks and
Finance Companies: Efficient Contracting or Regulatory Arbitrage?" working paper.
Packer, Frank, Ryan Stever. and Christian Upper, 2007, “The covered bond market”, BIS Quarterly
Review, September 2007, 43-55.

Panetta, F. and A. Pozzolo (2010), “Why Do Banks Securitize Their Assets? Bank-Level Evidence from
Over One Hundred Countries”, mimeo.
Pennacchi, George, 1988, “Loan sales and the cost of bank capital,” Journal of Finance, 43, 375-96.
Purnanandam, Amiyatosh, 2009, “Originate-to-Distribute Model and the Subprime Mortgage Crisis,”
working paper.
Rosen, Richard J., 2010a, “The impact of the originate-to-distribute model on banks before and during the
financial crisis,” working paper.
Rosen, Richard J., 2010b, “Demasiados aciertos pueden llevar al error: Preparando el escenario para la
crisis financiera” [“Too much right can make a wrong: Setting the stage for the financial crisis”], Papeles
de Economia Española, 122.
Sacasa, N., 2011, "Implementing Rules-Based Stabilizers for Banks: A Simplified Simulation for the
United States 1992-2007", IMF Working Paper, forthcoming (Washington: International Monetary Fund).
Saurina, J., 2009, “Loan-loss provisions in Spain. a working macroprudential tool”, Financial Stability
Review- Bank of Spain 17, 9-26.

Figure 1. MBS evolution in the US and Europe
United States

European Union

Source: ECB (2011)

Figure 2. MBS: origination vs. securitization
Bank securitizes its own loans
Securitizing bank
Borrower
Loan

Bank loan
office

Bank-run
MBS pool

Loan
sale

Bond
sale

MBS bond
holders

Bank purchases loans to securitize
Securitizing bank
Borrower

Originator
Loan

Loan
sale

Alternative
path for loan

MBS pool

Financial
firm

Bond
sale

MBS bond
holders

23
Figure 3. Covered bond issuance in the EU

Note: self-funded issunce is the issuance made solely for the purpose of
creating eligible collateral for Eurosystem credit operations

Source: ECB (2011)

Figure 4. CB funding steps

Borrower

Lo
pa an
ym
en
ts

Lo
an

Bank
Pool of loans,
bank can’t
take loans out

Bonds

Investor

Buys
bond

Table 1. Descriptive statistics
ALL SAMPLE

RoA (%)
Loans-to-deposits ratio
(%)
Capital ratio (%)
% of banks in the Low
CR group
Capital ratio of banks
in the Low CR group
(%)
Provisions-to-loans
ratio (%)
Net charge-offs ratio
(%)
Loan growth (%)
Total assets ($ bil.)
Total assets (log)
Bail-out dummy

MBS ISSUERS

CB ISSUERS

Mean

Median

Std.
Dev.

0,76

0,72

0,23

0,68

0,64

0,19

0,81

0,78

0,26

0,83

0,79

0,17

0,86

0,82

0,19

0,79

0,77

0,13

5,94

5,56

2,16

5,61

5,51

2,20

6,32

6,24

2,08

4,03

3,99

2,04

3,94

3,86

2,27

4,15

4,08

2,16

8,26

8,06

3,27

7,16

7,12

3,32

9,03

8,85

3,22

0,37

0,44

0,21

0,40

0,45

0,19

0,32

0,36

0,18

8,07

9,31

1,93

12,16

12,19

1,85

9,18

9,37

1,96

38,51

40,22

6,13

39,81

40,66

6,52

36,23

37,96

5,3

10,54

10,39

2,17

10,83

10,60

2,08

10,68

10,42

2,23

0,11

0,10

0,33

0,13

0,11

0,35

0,08

0,09

0,29

109

107

111

109

106

104

House price index
(base: 2003= 100)
Observations

Mean Median

2170

Std.
Dev.

Mean

Median Std. Dev.

193

221

Note: Information on CB issuers and MBS issuers is shown only for the year prior to when a security was issued.

Table 2. SMS issue by country
MBS

Number
(per
year)

Average
issue size
($ mil.)

Average
issuer size
($bil)

Number
(per
year)

Average
issue size
($ mil.)

Average
issuer size
($bil)

3,2

1,130

0,863

3,60

0,576

0,451

0,87

0,735

0,514

Germany

0,25

0,030

0,026

14,21

0,627

0,553

Italy

3,2

0,110

0,084

0,25

0,158

0,114

Spain

4,75

0,286

0,218

6,62

0,445

0,332

U.K.

3,14

0,375

0,321

3,12

0,602

0,406

U.S.

14,21

1,734

1,116

0,25

0,553

0,376

Full sample
France

Table 3. Predicted signs on coefficients
Possible reason for issuing SMS:
Before issue

Direct effect of issuing SMS:
After issue

Indirect effect of issuing SMS:
After issue

Line of business:
Profit

ROA +

ROA +
L/D – and unadj. L/D +
--

Loan growth +

Balance sheet management:
Liquidity

ROA –, L/D +

Capital for
regulatory reasons

K/A – and low relative to reg.
standards

Risk management

Paired before and after:*
K/A –, L/D +, Prov/L+

K/A + given it was low
relative to reg. standards
before
Paired before and after:*
K/A +, L/D –, Prov/L–

Agency reasons:
Empire building

Both TA + and ROA not +

If the CB market requires (both of these are measures of safety for the CBs):
Low risk

K/A +, Prov/L-

* - Paired means both K/A changes, both L/D changes, and/or both Prov/Lchanges.
TA = total assets.
L/D = loan-to-deposits ratio (adjusted to net out CB issue).
Unadj. L/D = loan-to-deposits ratio (not adjusted to net out CB issue).
K/A = capital-to-asset ratio.
Prov/L = provisions-to-loans ratio.

Not K/A –, not Prov/L+

26
Table 4A
Determinants of the use of securitization (baseline specification)
dum(non-issuer=0,CB=1,MBS=2)
CB

Comparison
of CB to MBS

MBS

Comparison
of CB to
MBS

MBS

Coeff.

m.e (%).

p-value

Coeff.

m.e (%).

p-value

Coeff.

m.e (%).

p-value

Coeff.

m.e (%).

p-value

RoA t-1

-2.025**

3.50

0.018

-0.482

-0.74

0.113

0.001

-1.907**

3.39

0.022

-0.485

-0.72

0.149

0.001

(Loans-covered bonds)
to Deposits t-1

1.359***

2.63

0.004

5.101***

8.61

0.005

0.003

---

0.329

1.322***

2.59

0.004

2.827***

4.91

0.004

0.304

Loans to Deposits t-1
Capital ratio t-1

---

1.228**

2.38

0.012

0.520

0.90

0.243

0.001

0.821**

1.28

0.007

0.140

0.21

0.412

0.003

0.130

0.18

0.362

0.359

0.59

0.548

---

0.155

2.76

0.407

0.035

0.05

0.526

0.082

-0.515**

-0.90

0.015

0.270**

0.37

0.031

0.001

-0.412**

-0.69

0.012

0.267**

0.36

0.037

0.001

-1.932

-3.42

0.158

0.791

1.20

0.508

0.014

-1.690

2.90

0.151

0.700

1.08

0.461

0.010

Total assets (log) t-1

2.046***

3.64

0.003

1.014***

1.99

0.001

0.004

2.018***

3.51

0.003

1.014***

2.20

0.001

0.003

House price index t-1

0.349

0.56

0.285

0.508**

0.79

0.026

0.003

0.369

0.60

0.293

0.494**

0.75

0.022

0.002

Low CR t-1
Provisions to Loans t-1
Loan growth t-1

Base category:
Number of
observations
Number of groups
legend: * p<.1; ** p<.05; *** p<.01

Non-issuer

2170

711

27
Table 4B (Active vs. non-active issuers)
Determinants of the use of securitization (baseline specification)
dum(non-issuer=0,CB=1,MBS=2)
CB

Comparison
of CB to MBS

MBS

Comparison
of CB to
MBS

MBS

Coeff.

m.e
(%).

pvalue

Coeff.

m.e
(%).

pvalue

p-value

Coeff.

m.e
(%).

pvalue

Coeff.

m.e
(%).

pvalue

p-value

RoA t-1

-2.017**

3.43

0.016

-0.479

-0.72

0.115

0.001

-1.802**

3.28

0.018

-0.510

-0.77

0.152

0.001

(Loans-covered bonds) to Deposits t-1

1.352***

2.61

0.002

5.118***

8.60

0.008

0.003

---

1.244***

2.71

0.002

2.691***

4.87

0.003

0.221

Loans to Deposits t-1
Capital ratio t-1

---

1.222**

2.37

0.012

0.540

0.94

0.261

0.370

0.819***

1.39

0.004

0.136

0.19

0.433

0.004

0.129

0.18

0.348

0.371

0.63

0.533

0.001

0.136

0.19

0.429

0.040

0.06

0.511

0.076

-0.523**

-0.91

0.017

0.254**

0.34

0.051

0.001

-0.417**

-0.70

0.015

0.268**

0.38

0.022

0.001

-1.927

-3.40

0.160

0.819

1.27

0.455

0.020

-1.692

2.70

0.155

0.714

1.12

0.460

0.014

Total assets (log) t-1

2.060***

3.69

0.003

1.016***

2.04

0.002

0.003

2.017***

3.66

0.006

1.007***

2.17

0.001

0.003

House price index t-1

0.351

0.54

0.283

0.506**

0.77

0.023

0.002

0.362

0.58

0.315

0.485**

0.73

0.020

0.002

Low CR t-1
Provisions to Loans t-1
Loan growth t-1

(continued on the next page)

28
Table 4B (continued)
Have you ever issued CB in the last 2 years?

2.024**

3.47

0.220

0.023

0.01

0.667

0.001

2.019**

3.25

0.207

0.027

0.04

0.514

0.001

Have you ever issued CB in the last 2 years? X RoA t-1

-2.710**

4.81

0.014

0.119

0.17

0.431

0.001

-2.677**

4.68

0.024

0.113

0.17

0.639

0.001

2.011***

3.52

0.003

0.014

0.01

0.721

0.001

---

2.008***

3.41

0.027

0.009

0.01

0.623

0.001

Have you ever issued CB in the last 2 years? X (Loans-covered
bonds) to Deposits t-1
Have you ever issued CB in the last 2 years? X Loans to Deposits
t-1
Have you ever issued CB in the last 2 years? X Capital ratio t-1

---

----

---

2.018**

3.55

0.022

0.018

0.03

0667

0.001

1.266**

2.78

0.012

0.028

0.04

0.318

0.001

0.146

0.21

0.217

0.035

0.05

0.201

0.001

0.146

0.20

0.411

0.139

0.19

0.199

0.001

-1.115**

-2.20

0.014

0.024

0.04

0.119

0.001

-0.882**

-1.43

0.037

0.020

0.03

0.115

0.001

Have you ever issued CB in the last 2 years? X Loan growth t-1

-2.770

-4.71

0.279

0.008

0.01

0.210

0.001

-1.667

2.77

0.216

0.031

0.04

0.228

0.001

Have you ever issued CB in the last 2 years? X Total assets (log)
t-1

2.927***

5.03

0.002

0.018

0.02

0.114

0.001

2.816**

4.94

0.017

0.015

0.02

0.332

0.001

Have you ever issued MBS in the last 2 years?

0.032

0.05

0.837

2.638**

4.05

0.013

0.001

0.030

0.05

0.917

1.002***

2.16

0.001

Have you ever issued MBS in the last 2 years? X RoA t-1

0.004

0.01

0.689

-0.023

0.03

0.277

0.001

0.002

0.01

0.632

-0.039

0.04

0.277

0.001

0.002

0.01

0.544

6.113***

9.01

0.006

0.001

---

0.001

0.01

0.521

3.104**

5.15

0.022

0.001

Have you ever issued CB in the last 2 years? X Low CR t-1
Have you ever issued CB in the last 2 years? X Provisions to
Loans t-1

Have you ever issued MBS in the last 2 years? X (Loans-covered
bonds) to Deposits t-1
Have you ever issued MBS in the last 2 years? X Loans to
Deposits t-1

---

Have you ever issued MBS in the last 2 years? X Capital ratio t-1

0.022

0.04

0.626

0.593

1.01

0.144

0.001

0.011

0.01

0.889

0.119

0.17

0.422

0.001

Have you ever issued MBS in the last 2 years? X Low CR t-1

0.008

0.01

0.776

0.375

0.64

0.382

0.001

0.019

0.03

0.644

0.044

0.06

0.773

0.001

Have you ever issued MBS in the last 2 years? X Provisions to
Loans t-1

0.017

0.03

0.884

0.469**

0.78

0.021

0.001

0.044

0.06

0.338

0.588**

1.00

0.014

0.001

Have you ever issued MBS in the last 2 years? X Loan growth t-1

0.037

0.05

0.256

0.822

1.33

0.505

0.001

0.016

0.003

0.115

0.722

1.12

0.253

0.001

Have you ever issued MBS in the last 2 years? X Total assets
(log) t-1

0.019

0.03

0.188

2.027***

3.46

0.004

0.001

0.032

0.05

0.382

2.006**

3.44

0.017

0.001

Base category:
Number of observations
Number of groups
legend: * p<.1; ** p<.05; *** p<.01

Non-issuer

2170

711

29
Table 4C (Subsample UK and Spain)
Determinants of the use of securitization (baseline specification)
dum(non-issuer=0,CB=1,MBS=2)
CB

Comparison
of CB to MBS

MBS

Comparison
of CB to
MBS

MBS

Coeff.

m.e (%).

p-value

Coeff.

m.e (%).

p-value

Coeff.

m.e (%).

p-value

Coeff.

m.e (%).

p-value

RoA t-1

-3.239**

5.33

0.012

-0.464

-0.70

0.114

0.001

-3.115**

5.18

0.013

-0.429

-0.56

0.133

0.001

(Loans-covered bonds) to
Deposits t-1

1.440***

2.70

0.002

5.351**

8.78

0.013

0.001

---

1.415***

2.68

0.002

3.415**

5.44

0.018

0.001

Loans to Deposits t-1
Capital ratio t-1

---

1.228**

2.39

0.025

0.153

2.53

0.252

0.001

1.250**

2.46

0.014

0.149

2.50

0.264

0.001

0.147

0.23

0.541

0.491

0.69

0.531

0.005

0.135

0.20

0.522

0.461

0.56

0.522

0.003

Provisions to Loans t-1

-0.738*

-0.98

0.079

0.258*

0.32

0.079

0.001

-0.720*

-0.93

0.070

0.257*

0.32

0.090

0.001

Loan growth t-1

-2.118

-3.69

0.192

0.706

0.87

0.505

0.032

-2.106

-3.66

0.194

0.709

0.88

0.533

0.024

Total assets (log) t-1

2.017**

3.49

0.032

1.324**

2.60

0.039

0.005

2.092**

3.12

0.028

1.306**

2.58

0.031

0.005

House price index t-1

0.257

0.32

0.299

0.739**

0.99

0.016

0.002

0.470

0.72

0.196

0.744**

1.00

0.025

0.002

Low CR t-1

Base category:
Number of observations
Number of groups
legend: * p<.1; ** p<.05; *** p<.01

Non-issuer

496

140

30
Table 5- Determinants of the use of securitization (baseline specification) (loans are net of covered bonds)
Chargeoffs as a control
Coeff.

m.e.

p-value

CB versus no issue
RoA t-1

-1.314**

2.50

(Loans-covered bonds) to Deposits t-1

2.260***

4.20

Capital ratio t-1

Profitability w.r.t. country
average
Coeff.

m.e.

p-value

0.036

-0.914**

1.48

0.004

3.083***

4.99

Large bank subsample

Size-based interaction terms

Coeff.

m.e.

p-value

0.041

-0.908**

-1.45

0.040

Coeff.
m.e.
p-value
Coefficients for interaction of
variables with log(TA)
-1.026** 2.14
0.040

0.001

2.316***

4.36

0.001

1.147**

2.25

0.022

1.301**

2.38

0.012

1.306**

2.45

0.013

1.353**

2.55

0.019

1.311**

2.46

0.037

Low CR t-1

0.201

0.28

0.284

0.234

0.33

0.366

0.100

0.14

0.371

0.024

0.02

0.214

Provisions to Loans t-1

---

-0.312**

0.43

0.013

-0.427**

0.76

0.010

-0.065** 0.09

0.023

Net charge-offs ratio t
Loan growth t-1

-1.106***

2.27

0.002

---

-2.420

4.53

0.167

0.592

0.90

0.117

-0.980

1.55

0.127

-0.013

Total assets (log) t-1

4.620***

7.53

0.001

1.214***

2.33

0.001

1.645***

2.98

0.001

---

House price index t-1

0.304

0.42

0.265

0.350

0.53

0.278

0.308

0.46

0.235

MBS versus no issue
RoA t-1

0.02

0.746

0.349

0.53

0.322

-0.196

-0.28

0.097

-0.250

0.32

0.133

-0.381

0.62

0.134

0.019

0.02

0.417

5.154***

8.72

0.002

2.511***

4.65

0.002

4.220***

8.17

0.002

1.498**

2.79

0.040

Capital ratio t-1

0.480

0.80

0.384

0.563

0.91

0.324

0.426

0.40

0.352

0.363

0.59

0.261

Low CR t-1

0.295

0.44

0.286

0.271

4.75

0.511

0.293

0.45

0.501

0.074

0.11

0.257

0.014**

0.019

0.122**

0.15

0.026

0.017

0.02

0.325

0.02

0.069

0.68

0.019

(Loans-covered bonds) to Deposits t-1

Provisions to Loans t-1

---

Net charge-offs ratio t

-0.604

0.98

0.161

Loan growth (covered bonds excluded) t-1

---

0.835

1.27

0.327

0.904

1.48

0.273

0.613

1.03

0.318

0.016*

Total assets (log) t-1

3.090***

5.01

0.001

1.035***

2.19

0.001

0.914***

1.48

0.001

---

House price index t-1

0.515**

0.79

0.023

0.506**

0.70

0.025

0.511**

0.76

0.018

0.497**

Base category:
Number of observations
Number of groups
legend: * p<.1; ** p<.05; *** p<.01

non-issuer
2170
711

non-issuer
1787
622

non-issuer
2170
711

31
Table 6
Determinants of the use of securitization (forward looking regressions)
Panel data with fixed effects
RoA t

Coeff.

RoA t-2

Loans to deposits t

p-value

---

Loan growth t

Capital ratio t

Coeff.

p-value

Coeff.

p-value

Coeff.

0.025

0.129

0.022

0.218

0.169

0.030

-0.124

Total assets
(log) t

Provisions to loans t

p-value

Coeff.

p-value

Coeff

0.015

0.129

0.009

p-value

0.017

0.059

0.015

0.044

0.024

-0.027

0.027

***

0.004

Loans to deposits t-2

0.024

0.259

---

Capital ratio t-2

0.163

0.162

-0.207

***

0.003

0.067

0.134

---

0.030

0.028

0.054

Low CR t-2

-0.017

0.246

0.043

0.019

0.030

0.211

---

0.021

0.026

0.036

0.252

Provisions to Loans t-2

-0.019

0.025

1.817

0.013

0.817

0.084

0.011

0.007

0.518

Loan growth t-2

0.047

0.054

0.050

0.184

---

Total assets (log) t-2

0.074

***

0.007

-0.026

***

0.001

0.006

House price index t-2
Have you ever issued CB in the last 2
years?
Have you ever issued MBS in the last 2
years?

0.019

0.110

0.228

0.015

0.562

0.030

-0.025

0.023

0.084

0.144

0.010

0.051

-0.030

Adjusted R2

0.526

legend: * p<.1; ** p<.05; *** p<.01
ºNote: Last two years are years t-1 and t.

0.072
0.004

0.123

1.443

0.627

0.007

***

0.065

---

0.447

0.019

0.106

0.085

0.013

0.015

0.003

-0.017

0.051

0.031

0.509

0.080

0.020

0.062

0.021

0.011

0.319

-0.007

0.515

0.523

0.038

0.014

0.359

---

0.024

0.011

0.281

0.008

0.084

0.040

0.053

0.019

0.617

0.060

0.291

Table 7
Determinants of the use of securitization (with interaction terms)
Panel data with fixed effects
Loans (covered
bonds excluded) to
deposits t

RoA t

Coeff.

RoA t-2

p-value

---

Loan growth t

Capital ratio t

Coeff.

p-value

Coeff.

p-value

Coeff.

0.030

0.126

0.015

0.164

0.171

0.027

---

Coeff.

p-value

0.015

0.141

0.017

-0.124 **

0.014

0.035

0.032

0.020

0.237

Capital ratio t-2

0.167

0.144

-0.207 ***

0.004

0.054

0.119

---

0.030

0.024

Low CR t-2

-0.014

0.266

0.043

0.024

0.015

0.242

---

0.022

0.029

Provisions to Loans t-2

-0.027

0.026

1.816

0.013

0.027

0.066

0.017

Loan growth t-2

0.039

0.059

0.064

0.193

---

0.033

Total assets (log) t-2

0.060

***

0.005

-0.024 ***

0.002

0.003

House price index t-2

0.024

0.307

0.202

***

0.007

0.632

Have you ever issued CB in the last 2 years?

0.086

0.030

-0.029

0.014

0.151

Have you ever issued MBS in the last 2 years?

0.015

0.141

0.008

0.051

-0.014

Log assets t-2 * Have you ever issued CB in the last 2
years?

0.016

0.164

-0.006

0.897

0.073

Log assets t-2 * Have you ever issued MBS in the last 2
years?

0.004

0.172

0.001

0.131

-0.028

Adjusted R2

0.513

legend: * p<.1; ** p<.05; *** p<.01
ºNote: Last two years are years t-1 and t.

0.749

0.632

p-value

Loans ((covered bonds excluded) to deposits t-2

1.359

Provisions to loans t

0.006

***

0.034

---

0.335

0.014

0.120

0.087

0.011

0.009

0.004

-0.014 **

0.046

0.031

0.011

0.497

0.074

0.024

-0.074

0.017

0.026

0.011

0.350

0.012

0.044

0.433

0.075

0.021

0.056

0.021

0.017

0.334

-0.006

0.533

0.528

0.402

0.364

0.037

33
Table 8. Predicted signs on coefficients

Before After

CB
Consistent with
Profit,
Liquidity
Liquidity,
Risk management

MBS
Before After
Consistent with

ROA

–

Loan-to-deposit ratio

–

Capital ratio

Low capital

(+)

Provisions

(+)

–

Risk management

–

(+)

–

Risk management

Loan growth

Low risk

(–)

(+)

Empire building

(+)

Parentheses indicate coefficients that are not significantly different from zero.

34
Table 9. Herd behavior

Full sample
CB

CB issue in the last 2 years

Large banks only
MBS

Coeff. p-value
0.035** 0.001

Coeff.
0.018

MBS

p-value Coeff. p-value
0.136 0.038** 0.001

Coeff.
0.014

p-value
0.128

MBS issue in the last 2 years

0.053

0.136

0.115***

0.003

0.038

0.159

0.120***

0.002

Growth of CB issuance in your country
in the last year

0.073

0.184

0.007

0.198

0.044

0.168

0.016

0.263

Growth of MBS issuance in your
country in the last year

-0.059

0.139

0.138***

0.001

-0.014

0.127

0.143***

0.001

Observations
legend: * p<.1; ** p<.05; *** p<.01

2170

1767

Table 10
Bailout regressions

Full sample

Coeff.

RoA 2006
Loans (covered bonds excluded) to
Deposits 2006
Capital ratio 2006
Low CR 2006
Provisions to Loans 2006
Net charge-offs ratio 2006
Loan growth 2006
Total assets (log) 2006
House price index 2006
Have you ever issued CB in the
last 2 years (2006-07)
Have you ever issued MBS in the
last 2 years (2006-07)
Adjusted R2
legend: * p<.1; ** p<.05; *** p<.01

Large banks

pvalue

Coeff.

Small banks

p-value

Coeff.

Top 75% capital

p-value

Coeff.

Bottom 25% capital
(low CR)

p-value

Coeff.

p-value

0.054

0.037

0.070

0.041

0.092

0.050

0.016

0.061

0.071

0.024

1.166

0.024

0.862

0.022

1.214

0.028

0.632

0.043

1.363

0.040

-0.089

0.013

-0.037

0.007

-0.091

0.014

-0.084

0.027

-0.132

0.014

0.062

0.034

0.066

0.024

0.043

0.034

0.054

0.026

0.034

0.026

0.014

0.059

0.040

0.027

0.010

0.013

0.043

0.033

0.087

0.030

0.070

0.021

0.091

0.032

0.062

0.018

0.097

0.056

1.231

***

0.008

1.047

***

0.004

1.309

0.013

0.914

0.023

1.123

***

0.005

0.258

0.032

0.216

0.018

0.211

0.018

0.258

0.028

0.319

0.053

0.011

0.685

0.010

0.750

0.013

0.132

0.031

0.225

0.172

0.064

0.135

0.026

0.191

0.054

0.216

0.033

0.111

0.013

0.176

0.013

0.133

0.015

0.037

0.035

0.182

0.020
0.065

0.033
0.154

0.819

0.728

0.717

0.623

0.608

0.026

Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
A Leverage-based Model of Speculative Bubbles
Gadi Barlevy

WP-08-01

Displacement, Asymmetric Information and Heterogeneous Human Capital
Luojia Hu and Christopher Taber

WP-08-02

BankCaR (Bank Capital-at-Risk): A credit risk model for US commercial bank charge-offs
Jon Frye and Eduard Pelz

WP-08-03

Bank Lending, Financing Constraints and SME Investment
Santiago Carbó-Valverde, Francisco Rodríguez-Fernández, and Gregory F. Udell

WP-08-04

Global Inflation
Matteo Ciccarelli and Benoît Mojon

WP-08-05

Scale and the Origins of Structural Change
Francisco J. Buera and Joseph P. Kaboski

WP-08-06

Inventories, Lumpy Trade, and Large Devaluations
George Alessandria, Joseph P. Kaboski, and Virgiliu Midrigan

WP-08-07

School Vouchers and Student Achievement: Recent Evidence, Remaining Questions
Cecilia Elena Rouse and Lisa Barrow

WP-08-08

Does It Pay to Read Your Junk Mail? Evidence of the Effect of Advertising on
Home Equity Credit Choices
Sumit Agarwal and Brent W. Ambrose

WP-08-09

The Choice between Arm’s-Length and Relationship Debt: Evidence from eLoans
Sumit Agarwal and Robert Hauswald

WP-08-10

Consumer Choice and Merchant Acceptance of Payment Media
Wilko Bolt and Sujit Chakravorti

WP-08-11

Investment Shocks and Business Cycles
Alejandro Justiniano, Giorgio E. Primiceri, and Andrea Tambalotti

WP-08-12

New Vehicle Characteristics and the Cost of the
Corporate Average Fuel Economy Standard
Thomas Klier and Joshua Linn

WP-08-13

Realized Volatility
Torben G. Andersen and Luca Benzoni

WP-08-14

Revenue Bubbles and Structural Deficits: What’s a state to do?
Richard Mattoon and Leslie McGranahan

WP-08-15

Working Paper Series (continued)
The role of lenders in the home price boom
Richard J. Rosen

WP-08-16

Bank Crises and Investor Confidence
Una Okonkwo Osili and Anna Paulson

WP-08-17

Life Expectancy and Old Age Savings
Mariacristina De Nardi, Eric French, and John Bailey Jones

WP-08-18

Remittance Behavior among New U.S. Immigrants
Katherine Meckel

WP-08-19

Birth Cohort and the Black-White Achievement Gap:
The Roles of Access and Health Soon After Birth
Kenneth Y. Chay, Jonathan Guryan, and Bhashkar Mazumder

WP-08-20

Public Investment and Budget Rules for State vs. Local Governments
Marco Bassetto

WP-08-21

Why Has Home Ownership Fallen Among the Young?
Jonas D.M. Fisher and Martin Gervais

WP-09-01

Why do the Elderly Save? The Role of Medical Expenses
Mariacristina De Nardi, Eric French, and John Bailey Jones

WP-09-02

Using Stock Returns to Identify Government Spending Shocks
Jonas D.M. Fisher and Ryan Peters

WP-09-03

Stochastic Volatility
Torben G. Andersen and Luca Benzoni

WP-09-04

The Effect of Disability Insurance Receipt on Labor Supply
Eric French and Jae Song

WP-09-05

CEO Overconfidence and Dividend Policy
Sanjay Deshmukh, Anand M. Goel, and Keith M. Howe

WP-09-06

Do Financial Counseling Mandates Improve Mortgage Choice and Performance?
Evidence from a Legislative Experiment
Sumit Agarwal,Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff

WP-09-07

Perverse Incentives at the Banks? Evidence from a Natural Experiment
Sumit Agarwal and Faye H. Wang

WP-09-08

Pay for Percentile
Gadi Barlevy and Derek Neal

WP-09-09

The Life and Times of Nicolas Dutot
François R. Velde

WP-09-10

Regulating Two-Sided Markets: An Empirical Investigation
Santiago Carbó Valverde, Sujit Chakravorti, and Francisco Rodriguez Fernandez

WP-09-11

Working Paper Series (continued)
The Case of the Undying Debt
François R. Velde
Paying for Performance: The Education Impacts of a Community College Scholarship
Program for Low-income Adults
Lisa Barrow, Lashawn Richburg-Hayes, Cecilia Elena Rouse, and Thomas Brock
Establishments Dynamics, Vacancies and Unemployment: A Neoclassical Synthesis
Marcelo Veracierto

WP-09-12

WP-09-13

WP-09-14

The Price of Gasoline and the Demand for Fuel Economy:
Evidence from Monthly New Vehicles Sales Data
Thomas Klier and Joshua Linn

WP-09-15

Estimation of a Transformation Model with Truncation,
Interval Observation and Time-Varying Covariates
Bo E. Honoré and Luojia Hu

WP-09-16

Self-Enforcing Trade Agreements: Evidence from Antidumping Policy
Chad P. Bown and Meredith A. Crowley

WP-09-17

Too much right can make a wrong: Setting the stage for the financial crisis
Richard J. Rosen

WP-09-18

Can Structural Small Open Economy Models Account
for the Influence of Foreign Disturbances?
Alejandro Justiniano and Bruce Preston

WP-09-19

Liquidity Constraints of the Middle Class
Jeffrey R. Campbell and Zvi Hercowitz

WP-09-20

Monetary Policy and Uncertainty in an Empirical Small Open Economy Model
Alejandro Justiniano and Bruce Preston

WP-09-21

Firm boundaries and buyer-supplier match in market transaction:
IT system procurement of U.S. credit unions
Yukako Ono and Junichi Suzuki
Health and the Savings of Insured Versus Uninsured, Working-Age Households in the U.S.
Maude Toussaint-Comeau and Jonathan Hartley

WP-09-22

WP-09-23

The Economics of “Radiator Springs:” Industry Dynamics, Sunk Costs, and
Spatial Demand Shifts
Jeffrey R. Campbell and Thomas N. Hubbard

WP-09-24

On the Relationship between Mobility, Population Growth, and
Capital Spending in the United States
Marco Bassetto and Leslie McGranahan

WP-09-25

The Impact of Rosenwald Schools on Black Achievement
Daniel Aaronson and Bhashkar Mazumder

WP-09-26

Working Paper Series (continued)
Comment on “Letting Different Views about Business Cycles Compete”
Jonas D.M. Fisher

WP-10-01

Macroeconomic Implications of Agglomeration
Morris A. Davis, Jonas D.M. Fisher and Toni M. Whited

WP-10-02

Accounting for non-annuitization
Svetlana Pashchenko

WP-10-03

Robustness and Macroeconomic Policy
Gadi Barlevy

WP-10-04

Benefits of Relationship Banking: Evidence from Consumer Credit Markets
Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, and Nicholas S. Souleles

WP-10-05

The Effect of Sales Tax Holidays on Household Consumption Patterns
Nathan Marwell and Leslie McGranahan

WP-10-06

Gathering Insights on the Forest from the Trees: A New Metric for Financial Conditions
Scott Brave and R. Andrew Butters

WP-10-07

Identification of Models of the Labor Market
Eric French and Christopher Taber

WP-10-08

Public Pensions and Labor Supply Over the Life Cycle
Eric French and John Jones

WP-10-09

Explaining Asset Pricing Puzzles Associated with the 1987 Market Crash
Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein

WP-10-10

Prenatal Sex Selection and Girls’ Well‐Being: Evidence from India
Luojia Hu and Analía Schlosser

WP-10-11

Mortgage Choices and Housing Speculation
Gadi Barlevy and Jonas D.M. Fisher

WP-10-12

Did Adhering to the Gold Standard Reduce the Cost of Capital?
Ron Alquist and Benjamin Chabot

WP-10-13

Introduction to the Macroeconomic Dynamics:
Special issues on money, credit, and liquidity
Ed Nosal, Christopher Waller, and Randall Wright

WP-10-14

Summer Workshop on Money, Banking, Payments and Finance: An Overview
Ed Nosal and Randall Wright

WP-10-15

Cognitive Abilities and Household Financial Decision Making
Sumit Agarwal and Bhashkar Mazumder

WP-10-16

Complex Mortgages
Gene Amromin, Jennifer Huang, Clemens Sialm, and Edward Zhong

WP-10-17

Working Paper Series (continued)
The Role of Housing in Labor Reallocation
Morris Davis, Jonas Fisher, and Marcelo Veracierto

WP-10-18

Why Do Banks Reward their Customers to Use their Credit Cards?
Sumit Agarwal, Sujit Chakravorti, and Anna Lunn

WP-10-19

The impact of the originate-to-distribute model on banks
before and during the financial crisis
Richard J. Rosen

WP-10-20

Simple Markov-Perfect Industry Dynamics
Jaap H. Abbring, Jeffrey R. Campbell, and Nan Yang

WP-10-21

Commodity Money with Frequent Search
Ezra Oberfield and Nicholas Trachter

WP-10-22

Corporate Average Fuel Economy Standards and the Market for New Vehicles
Thomas Klier and Joshua Linn

WP-11-01

The Role of Securitization in Mortgage Renegotiation
Sumit Agarwal, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff

WP-11-02

Market-Based Loss Mitigation Practices for Troubled Mortgages
Following the Financial Crisis
Sumit Agarwal, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff

WP-11-03

Federal Reserve Policies and Financial Market Conditions During the Crisis
Scott A. Brave and Hesna Genay

WP-11-04

The Financial Labor Supply Accelerator
Jeffrey R. Campbell and Zvi Hercowitz

WP-11-05

Survival and long-run dynamics with heterogeneous beliefs under recursive preferences
Jaroslav Borovička

WP-11-06

A Leverage-based Model of Speculative Bubbles (Revised)
Gadi Barlevy

WP-11-07

Estimation of Panel Data Regression Models with Two-Sided Censoring or Truncation
Sule Alan, Bo E. Honoré, Luojia Hu, and Søren Leth–Petersen

WP-11-08

Fertility Transitions Along the Extensive and Intensive Margins
Daniel Aaronson, Fabian Lange, and Bhashkar Mazumder

WP-11-09

Black-White Differences in Intergenerational Economic Mobility in the US
Bhashkar Mazumder

WP-11-10

Can Standard Preferences Explain the Prices of Out-of-the-Money S&P 500 Put Options?
Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein

WP-11-11

Working Paper Series (continued)
Business Networks, Production Chains, and Productivity:
A Theory of Input-Output Architecture
Ezra Oberfield

WP-11-12

Equilibrium Bank Runs Revisited
Ed Nosal

WP-11-13

Are Covered Bonds a Substitute for Mortgage-Backed Securities?
Santiago Carbó-Valverde, Richard J. Rosen, and Francisco Rodríguez-Fernández

WP-11-14

Full text of Working Papers (Federal Reserve Bank of Chicago) : Are Covered Bonds a Substitute for Mortgage-Backed Securities?, Working Paper 2011-14

FRASER