SECURITIZATION, THE BANK LENDING CHANNEL AND ASYMMETRIC
MONETARY TRANSMISSION
A Thesis
Presented to the faculty of the Department of Economics
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
Economics
by
Eric Mayes
SPRING
2012
SECURITIZATION, THE BANK LENDING CHANNEL AND ASYMMETRIC
MONETARY TRANSMISSION
A Thesis
by
Eric Mayes
Approved by:
__________________________________, Committee Chair
Kristin Van Gaasbeck
__________________________________, Second Reader
Ta-Chen Wang
____________________________
Date
ii
Student: Eric Mayes
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Graduate Coordinator
Kristin Kiesel
Department of Economics
iii
___________________
Date
Abstract
of
SECURITIZATION, THE BANK LENDING CHANNEL AND ASYMMETRIC
MONETARY TRANSMISSION
by
Eric Mayes
Using a bank lending channel framework, this study investigates how securitization
affects the efficacy of monetary policy. The effectiveness of expansionary and
contractionary policies is evaluated separately on the loan growth of banks divided by
asset size, capital adequacy, and securitized asset holdings. There is evidence that
securitization weakens the bank-lending channel but this result is not robust across all
specifications. The implication of the analysis is that the effect of securitization on the
efficacy of monetary policy depends greatly on the structure of the financial system.
_______________________, Committee Chair
Kristin Van Gaasbeck
_______________________
Date
iv
TABLE OF CONTENTS
Page
List of Tables .............................................................................................................. vi
List of Figures ............................................................................................................ vii
Chapter
1. INTRODUCTION .................................................................................................. 1
2. SECURITIZATION IN THE U.S. FINANCIAL SYSTEM .................................. 5
3. LITERATURE REVIEW ....................................................................................... 8
Monetary Transmission .................................................................................... 8
Securitization .................................................................................................. 11
Asymmetric Policy Effects…...........................................................................13
4. DATA AND EMPIRICAL METHODOLOGY ................................................... 16
Data Sources and Descriptions ....................................................................... 16
Empirical Methodology .................................................................................. 21
5. RESULTS .............................................................................................................. 24
Regression Results .......................................................................................... 24
Robustness Checks.......................................................................................... 31
6. CONCLUSION ...................................................................................................... 34
Appendix A F-Statistics for Differences in Coefficient Sums.................................... 37
Appendix B Model 1 Using Fixed Capital-to-Asset Ratios........................................ 39
References ................................................................................................................... 40
v
LIST OF TABLES
Tables
Page
1.
Cumulative Assets of Securitizing and Non-Securitizing Banks……………....6
2.
Mean Balance Sheet Ratios (1980Q1-2010Q4)………… ............................... .17
3.
Cumulative Bank Assets (1980-2010)…………………………………..…….18
4.
Mean Balance Sheet Ratios By Capital………… .. …………………………. 20
5.
Mean Balance Sheet Ratios (2001Q1-2010Q4)…………………………….....21
6.
Model 1 Using 1996 Break (1980Q1-2010Q4)……………………………….24
7.
Model 1 Using 2001 Break (1980Q1-2010Q4)…………………………….…27
8.
Model 2 (2001Q1-2010Q4)…………………………………………………...30
9.
Degree of Securitization Effects……………………………………………....32
vi
LIST OF FIGURES
Figures
Page
1.
Cumulative Assets of ABS and Non-ABS Banks……………………………...7
2.
Cumulative Bank Assets (1980-2010)……………………………….… . ……19
vii
1
1. Introduction
One of the most important questions in Keynesian macroeconomics is how
monetary policy affects the real economy. In the IS-LM model, monetary policy affects
market equilibrium in the market for real money balances, which leads to a shift in the
LM curve. Shifts in the LM curve change the equilibrium level of output in the IS-LM
model, changing aggregate demand at any given price level. This results in a shift in the
aggregate demand curve. To better understand this process, economists began studying
monetary transmission mechanisms. Monetary transmission mechanisms consist of
different ways that monetary policy affects aggregate demand and reaches the real
economy (Mishkin, 2007).
The traditional view of monetary policy transmission is that the Federal Reserve
(Fed) influences the economy through an interest rate channel. In the interest rate
channel, contractionary policy leads to an increase in real interest rates because of
nominal rigidities, which increases the cost of capital causing a decrease in investment
spending. Since investment spending is a component of Gross Domestic Product (GDP)
this reduces GDP growth and slows down the economy.
An alternative view on monetary transmission is that the Federal Reserve affects
the economy through credit channels that arise from asymmetric information in the
financial system. When asymmetric information exists in financial markets, banks are
necessary financial intermediaries that connect borrowers to lenders that would not
otherwise have access to financial markets. Two forms of credit channels are the bank
lending channel (BLC) and the balance sheet channel (BSC). In the BSC, monetary
2
policy reaches the real economy by affecting firms’ balance sheets. Contractionary
policy can cause a decrease in stock prices which decreases firms’ net worth. This
decrease in net worth increases asymmetric information issues which reduce lending and
investment spending.
In the BLC banks serve as intermediaries connecting borrowers to credit markets.
Expansionary monetary policy operates by increasing bank reserves and deposits which
results in banks making more loans. This increases investment and consumer spending as
borrowers use bank loans to finance their expenditures. In the BLC, small firms are more
sensitive to monetary policy because they depend more on bank finance than large firms
which can more easily acquire funds through stock and bond markets. Even in welldeveloped financial systems, like those in the U.S., Europe, and Japan, banks and other
financial intermediaries remain the most important source of borrowing for households
and firms. The transmission of monetary policy through the BLC potentially magnifies
the effects of monetary policy and redistributes wealth, as households, small banks, and
small firms are disproportionately affected by the contraction in credit that accompanies
monetary policy tightening.
This study focuses on the BLC and how the strength of the BLC varies between
banks based on asset size and capital strength as well as how the rise of securitization has
impacted the bank lending channel. Existing literature has uncovered bank capital as an
important constraint in determining a BLC. Contractionary policy has been shown to be
effective in reducing the loan growth of capital-constrained banks. This is because these
banks must fund lending primarily with deposits and bank reserves, a reduction in
3
reserves due to contractionary monetary policy greatly reducing their ability to generate
new loans. In contrast, contractionary policy should be much less effective in reducing
the loan growth of high-capital banks. Banks holding large amounts of capital can more
easily fund loans when reserves or deposits decrease.
The large growth of securitization taking place since the 1970s has important
implications for the BLC. The ability of banks to combine loans into asset-backed
securities has changed the business model of some banks from “originate and hold” to
“originate, repackage and sell” (Altumbas, Fazylov & Molyneux, 2009). In traditional
banking, banks had to finance illiquid loans with liquid deposits and bank reserves. This
created a business model of originating loans and then holding on to loans. The ability to
package loans into securities has allowed banks to originate loans and then sell loans for
cash, making them much less dependent on traditional sources of funding. It follows that
with the rise of securitization the BLC may become weaker. By securitizing and selling
loans banks acquire and additional sources of funding to make more loans independent of
traditional sources.
The effect of widespread asset-backed securities has important implications for
monetary policy. As more and more banks securitize loans the financial system as a
whole can become less responsive to policy actions taken by the Federal Reserve. This
will likely make managing the business cycle more difficult for the Fed as it will have to
take more aggressive open market operations to influence an economy full of banks using
asset-backed securities to fund their lending.
4
This study contributes to the BLC literature in two ways. First, the use of more
recent data will allow for investigation into how banks respond to monetary policy
through the BLC has changed in recent years. Existing literature has shown banks
behave differently in the BLC based on their asset size and capital-to-assets ratio, this
study will follow suit by dividing banks into high and low capital categories as well as
three asset size categories. Second, the study will examine the impact of increased
securitization on the BLC by dividing the sample into two eras, the period from 1980 to
when collateralized debt obligations were developed in 1996, and from 1996 to 2010. In
addition, the sample will be divided into banks that securitized their assets and those that
did not in order to contrast the strength of the BLC between these two categories in
addition to capital strength and asset size.
The rest of this paper is organized as follows; Section 2 presents background
information on the growth of asset-backed securities in U.S. financial system, Section 3
presents review of literature on the topics studied, Section 4 describes the data and
empirical methodologies used, Section 5 reports results and section 6 concludes
5
2. Securitization in the U.S. Financial System
The reasons a bank creates asset-backed securities could be to increase liquidity in
its balance sheet or reduce regulatory capital requirements. The process begins with a
bank originating a financial asset such as a home mortgage, auto loan or credit card
receivable. The asset is then combined with other similar assets into a security that
produces a return equal to the aggregate income of the pooled assets. This asset-backed
security can then be sold to an investor and the cash used to fund more lending activities.
Securitization began in home mortgage markets during the 1970s and has
continued to grow with the trend intensifying in the 1980s and after 1996 (Estrella, 2002).
Estrella (2002) identified the government’s sponsorship of the Federal Home Loan
Mortgage Corporation, the Federal National Mortgage Association and the Government
National Mortgage Association. These organizations issued securities consisting of
pooled home mortgages that were originated by banks. In 1996 collateralized debt
obligations were developed consisting of collateralized loan obligations and
collateralized bond obligations. This intensified the growth of asset-backed securities
which now are composed of not just mortgages but auto loans, student loans, credit card
receivables, commercial and industrial loans as well as others (Estrella, 2002).
Table 1 presents the cumulative total assets of banks that chose to securitize their
assets (ABS Banks) and non-securitizing banks (Non-ABS Banks) from 2001-2007. The
values in the table are the sum of all banks’ total assets in each category with the actual
number of banks in parenthesis. Comparing cumulative assets and the quantities of banks
in both categories it is immediately evident that banks that choose to securitize their
6
assets tend to be much larger than banks that do not. This fact is consistent with Adrian
and Shin (2010), which showed that larger institutions were more likely to have the
resources to create asset-backed securities. Table 1 also shows the asset share of
securitizing banks surpassing non-securitizing banks in 2002 and continuing to grow at a
faster rate through 2007. These trends are also visible in figure 1 which plots cumulative
assets of the two bank categories over time.
Table 1 Cumulative Assets of Securitizing and Non-Securitizing Banks
Year
ABS Banks
Non-ABS Banks
2001
4075873 (181)
4238556 (8817)
2002
4515536 (170)
4324773 (8581)
2003
4891480 (152)
4583138 (8457)
2004
5719328 (134)
4849556 (8297)
2005
6148997 (135)
5427798 (8166)
2006
7120634 (132)
5949914 (8106)
2007
8127334 (130)
6621802 (7967)
Thousands of Real Dollars
7
Figure 1 Cumulative Assets of ABS and Non-ABS Banks
Cummulative Assets of Securitizing and Non-Securitizing
Banks (Thousands of Real Dollars)
9000000
8000000
7000000
6000000
ABS Banks
5000000
Non-ABS Banks
4000000
3000000
2001 2002 2003 2004 2005 2006 2007
Widespread securitization gave birth to the “shadow banking system” which
played a major role in the financial crises beginning in 2007 (Adrian & Shin, 2010).
Adrian and Shin (2010) also determined that securitization increased the fragility of the
financial system by allowing banks to leverage themselves by buying each other’s assetbacked securities.
The growing share of total assets belonging to securitizing banks highlights the
importance of studying how they respond differently to monetary policy. If securitizing
banks are less responsive to monetary policy then these trends suggests the Fed should
become more aggressive in open market operations, adjusting its strategy as the structure
of the banking system changes.
8
3. Literature Review
Monetary Transmission
According to traditional Keynesian and neoclassical macroeconomic theory, monetary
policy affects real economic variables in the short run through the interest rate, or money
channel. Specially, the central bank controls the supply of money and because of nominal
rigidities; changes in money supply affect nominal and real interest rates.
In addition to the interest rate channel of monetary transmission, economists have
completed substantial research identifying alternative transmission mechanisms. These
alternative mechanisms include credit channels such as the bank lending channel (BLC).
With the significant changes in the financial system in the late 1990s and 2000s, and the
2007-2009 financial crisis and recession, these channels of monetary transmission
continue to receive attention in the literature.
Bernanke and Blinder (1992) set early foundations for subsequent research on
monetary transmission. The authors identify the federal funds rate as informative of the
stance of monetary policy and link its changes to macroeconomic conditions. The federal
funds rate is now used extensively as a measurement of the Federal Reserve’s policy
stance. The authors also identified two channels of monetary transmission through bank
loans and bank assets. In their analysis, contractionary policy causes banks to sell their
security holdings and become reluctant to make new loans which have a negative effect
on the real economy. This thesis uses the federal funds rate to indicate policy stance, but
differs from Bernanke and Blinder (1992) in that we consider the possibility of
asymmetry in policy shocks.
9
Kashyap and Stein (2000) use the 1976-1993 Report of Condition and Income
Data (call reports) in order to identify a BLC in that period. The authors find the BLC is
stronger among banks with low ratios of cash and securities to assets and these banks are
mainly smaller banks in the sample. The authors use of a panel data set provided strong
evidence in favor of the existence of a BLC, this was important because previous studies
using aggregate data were unable to rule out the possibility of an interest rate channel.
Results from Kashyap and Stein (2000) demonstrated the distributional effects of
monetary policy through the BLC. For this reason, we estimate the strength of the BLC
across banks based on size (measured by total assets).
Kishan and Opiela (2000) also used the call reports to study the BLC. The
authors divide banks by asset size and capital strength and find significant differences in
BLC strength across their sample. The banks were divided into six asset size categories
and three capital strength categories based on capital-to-asset ratios, undercapitalized
(ratio<8%), adequately capitalized (8%<ratio<10%), and well capitalized (ratio>10%).
The authors reasoned that the amount of equity capital bank held would impact its ability
to finance loans and change its sensitivity to monetary policy. In contrast to Kashyap and
Stein (2000) which excluded banks involved in mergers from the sample, Kishan and
Opiela (2000) performed their analysis using both merger-adjusted and non-mergeradjusted versions of the sample. The merger-adjusted data was accomplished using a
method employed by Peek and Rosengren (1995), which treats merged banks as single
banks during the whole sample as if they merged at the beginning. This study used this
approach as a robustness check and found no significant difference in results. The
10
authors’ find that the BLC is strongest among small undercapitalized banks, a result
similar to Kashyap and Stein (2000) which found the BLC was strongest among small
illiquid banks. The authors’ result that undercapitalized banks were more responsive to
policy has important implications for the banking system as a whole. As regulatory
changes call for banks to hold more capital the banking system may become less
responsive to monetary policy. These findings indicate that it is not only important to
consider bank size, but also bank capitalization when measuring the BLC. We similarly
find that bank capitalization matters for monetary transmission through the BLC, when
identified by capital-asset ratios.
Ashcraft (2006) finds evidence consistent with Kashyap and Stein (2000) as well
as Kishan and Opiela (2000) in that a bank lending channel exists and its strength varies
between banks of different sizes and capital strength. However Ashcraft also casts some
doubt on the importance of the BLC to the macroeconomy. Using aggregate data, the
author estimates the elasticity of loan growth to output growth and finds relatively
inelastic estimates. The results of the study suggest that the BLC may not be the whole
picture when it comes to monetary transmission. This is consistent with an earlier survey
by Kashyap and Stein (1994) which finds that the aggregate effects of the BLC are small
in magnitude.
The research surveyed so far has investigated bank lending channels in the United
States; economists have also studied monetary transmission internationally. Using bank
level data from European countries, Altumbas et al. (2002) identified a bank lending
channel and found evidence consistent with Kishan and Opiela (2000) where
11
undercapitalized banks were more responsive to monetary policy. Using bank level data
from 1994-2003, Matousek and Sarantis (2009) find further evidence of a BLC and
obtain results suggesting that liquidity and banks size were most important in determining
loan growth. Data on Polish banks revealed that the strength of the BLC vary by levels
of deposit guarantees (Opiela, 2008).
Bhaumik, Dang, and Kutan (2011) use bank level data from India to study how
different types of banks responded to monetary policy shocks. The authors found that
bank ownership type played a significant role in credit channel strength. The authors also
determined that the BLC was more pronounced during periods of contractionary policy
then expansionary periods.
Securitization
Estrella (2002) uses aggregate data to investigate the growth of securitization in
the U.S. financial system and examine how the spread of asset-backed securities has
influenced the efficacy of monetary policy. The author charts the growth of
securitization in the in the United State from 1965 to 2000. The growth of securitization
intensified in the 1980s and in 1996 when collateralized debt obligations were developed.
The author uses a simple IS curve estimation with aggregate data to assess the effect of
securitization on monetary policy efficacy. Estrella (2002) found that securitization
growth significantly reduced the elasticity of output with respect to the federal funds rate.
In addition, the researcher found results showing mortgage interest rates more closely
following the federal funds rate as the value of securitized assets grew in the economy.
12
Estrella reasons this finding means that the reduction in policy effectiveness is due to
changes in liquidity and credit channel transmission mechanisms. His research suggests
that securitization has significant implications for monetary policy through a variety of
channels.
Loutskina and Strahan (2009) exploited Fannie Mae and Freddie Mac’s inability
to purchase jumbo mortgages to study how securitization has affected banks’ lending
behavior. The authors contrast traditional banking where banks originate and hold loans
with banking under securitization which allows banks to originate loans and then
distribute them. Traditional banks had to fund illiquid loans with liquid deposits which
resulted in deposits and balance sheet liquidity determining the supply of credit. The
authors report that securitizing banks rely only on deposits to make loans and that a
bank’s financial condition has no impact on their supply of credit. The result that
securitizing banks rely mostly on deposits to make loans contrasts with the behavior of
non-securitizing banks. This study will divide banks into securitizing and nonsecuritizing categories to investigate if this behavior difference affects the BLC.
Altumbas, Gambacorta and Marques-Ibanez (2009) use data on European banks
to study securitization’s effect on the BLC. The authors used a unique data set which
allowed investigating how banks that securitize their assets respond differently from
banks that did not. Their results revealed that securitizing banks were less responsive to
monetary policy. Altumbas et al. (2009) also found banks that securitized made more
loans and this result was more pronounced during healthy macroeconomic conditions.
Loutskina and Strahan (2009) finding that securitizing banks rely mostly on deposits
13
could explain why these banks mad more loans during healthy macroeconomic periods as
deposits increase during economic expansions.
Aysun and Hepp (2011) use call report data to study how securitizing and nonsecuritizing banks respond differently to monetary policy using balance sheet channel
framework. In contrast to Altumbas et al. (2009) which found securitization weakened
the BLC, Aysun and Hepp (2011) find that securitization strengthens the BSC. In their
analysis, Aysun and Hepp (2011) no significant difference between securitizing and nonsecuritizing banks but found that the degree of securitization was positively related to
banks responsiveness to monetary policy. Their results suggest that securitization has had
an ambiguous effect on monetary transmission. To account for the result of Aysun and
Hepp (2011) this study will also restrict the sample to securitizing banks and examine
how the degree of securitization has impacted the BLC.
Asymmetric Policy Effects
While economists have studied asymmetric monetary policy effects for some
time, only recently have they investigated asymmetric effects to alterative transmission
mechanisms. Cover (1992) used aggregate data to examine asymmetric effects of money
supply shocks. Cover found that positive money shocks had no effect on the real
economy while negative shocks had a negative effect on the real economy. This result is
similar to Bhaumik et al. (2011) which found that the BLC was more pronounced during
contractionary policy than expansionary. In contrast to Cover (1992), Weise (1999) found
contractionary and expansionary monetary policy to have nearly symmetric effects in
14
magnitude. Weise (1999) used an autoregression approach with nonlinear vectors using
aggregate data. Given the mixed evidence on the potential for asymmetric effects of
monetary policy, this thesis allows for differences in the effects of contractionary and
expansionary monetary policy.
Kishan and Opiela (2006) studied asymmetric effects of monetary policy on the
BLC. Using call report data the authors split the sample into six asset size categories and
two capital strength categories. Using banks’ capital-to-assets ratios, those with
ratios<8% were undercapitalized and those with ratios>8% were well capitalized. The
researchers found that contractionary policy was most effective in reducing the loan
growth of small, undercapitalized banks while expansionary policy was most effective in
increasing the loan growth of high capital banks. In addition, the authors divided there
sample into pre-Basel and post-Basel eras pertaining to the implantation of the Basel 1
capital regulations. The authors noted that their results only held in the post-Basel era
and concluded that stringent capital constraint enforcement was the reason for
discrepancy between the two eras.
This study extends the literature by combining investigations into the bank
lending channel, securitization and asymmetric policy effects. Expansionary and
contractionary monetary policy stances are isolated and how securitization has impacted
the efficacy of these policy stances will be investigated. The following four hypotheses
are tested in the analysis:
15
(1) Among low-capital banks, contractionary policy should be more effective in reducing
loan growth than expansionary policy is in increasing loan growth.
(2) Among high-capital banks, expansionary policy should be more effective in
increasing loan growth than contractionary policy is in reducing loan growth.
(3) After collateralized debt obligations were developed in 1996, both expansionary and
contractionary policy should be less effective in influencing the loan growth of all banks.
(4) Banks that securitize their assets should be less responsive to both contractionary and
expansionary policy.
16
4. Data and Empirical Methodology
Data Sources and Descriptions
To test the four hypotheses outlined in the previous section, the study uses a combination
of aggregate and bank level data. All aggregate data was taken from the Federal Reserve
Bank of St. Louis’s Federal Reserve Economics Database (FRED), while bank level data
were obtained from the Report of Condition and Income (call reports) that all insured
banks are required to submit to the Federal Reserve. The data set is recorded in quarterly
intervals from the first quarter of 1980 to the fourth quarter of 2010. All balance sheet
variables are adjusted for inflation using the Consumer Price Index.
This study uses methods to distinguish a BLC employed by Kishan and Opiela (2006),
dividing the sample by bank size and capital constraint. Banks are split into three asset
size categories; large (total assets>$1 billion), medium ($1 billion>total assets>$100
million) and small (total assets<$100 million). In order to examine the BLC it is also
necessary to divide banks by capital constraint (Kishan and Opiela, 2000; Altunbus et al.,
2002; Kishan and Opiela, 2006; Matousek and Sarantis, 2009). Using their capital-tototal asset ratios, banks are divided into two categories; constrained banks with a ratio
greater than 8% and unconstrained banks with a ratio less than 8%.
17
Table 2 Mean Balance Sheet Ratios (1980Q1-2010Q4)
Large Banks
Medium Banks
Small Banks
Total Loans/Assets
.5869
.5870
.5661
Real Estate Loans/Assets
.2847
.2847
.2860
Consumer Loans/Assets
.1124
.1128
.0972
Large Time
.1155
.1154
.1110
Total Loans/Assets
.5551
.5316
.5662
Real Estate Loans/Assets
.2867
.2866
.2861
Consumer Loans/Assets
.0891
.0894
.0972
Large Time
.1086
.1085
.1117
Capital Ratio<8%
Deposits/Assets
Capital Ratio>8%
Deposits/Assets
Table 2 presents mean balance sheet ratios for large, medium and small banks
divided by capital to total assets ratio. Large and medium sized banks with high capital
to total asset ratios tended to have smaller total loans to assets ratios, as well as and less
consumer loans relative to their total assets. However this trend is not visible with
smaller banks which varied very little with response to capital to asset ratio.
Cumulative total assets for banks in the three asset size categories from 19802010 is displayed in table 3. In 1980 banks in the small category controlled the majority
of assets in the banking followed by medium banks then large banks; however, the
relative asset shares changed dramatically over time. The cumulative assets of small and
medium banks both increased steadily until 2000 when they begin to taper off. Large
18
banks on the other hand, increased at a rate similar to medium and small banks until
1995, when the growth of their cumulative assets increased dramatically.
It is possible that the large increase in the cumulative assets of large banks is a
result of securitization. It was discussed in section 2 of this study that securitizing banks
tend to be much larger than non-securitizing banks and the growth of asset-backed
securities increased rapidly after collateralized debt obligations were developed in 1996.
Large securitizing banks would be able to increase the size of their balance sheets quickly
by financing loans with asset-backed security sales. Unfortunately data on securitized
assets are only available in the call reports after 2000, preventing any in depth analysis of
securitizations role in the growth of large banks’ asset share after 1995.
Table 3 Cumulative Bank Assets (1980-2010)
Year
Small
Medium
Large
1980
1434512
690304
433199
1985
2018824
1121523
603290
1990
2726674
1694430
792223
1995
3139996
1975441
1338404
2000
3209028
1756916
3257754
2005
3268455
1915595
5680753
2007
3422783
1892225
7011202
2010
3111419
1761482
8943852
Thousands of Real Dollars
19
Figure 2 Cumulative Bank Assets (1980-2010)
Cumulative Bank Assets (Thousands of Real Dollars)
10000000
9000000
8000000
7000000
6000000
Small
5000000
Medium
4000000
Large
3000000
2000000
1000000
0
1980 1985 1990 1995 2000 2005 2007 2010
In order to examine the effects of securitization the sample is further divided into
pre-1996/CDO eras and post-1996/CDO eras to examine the effect of the large growth in
asset backed securities after the development of collateralized debt obligations in 1996.
In addition, the sample was also divided into pre-2001 and post-2001 eras to examine the
effects of when asset-backed securities were required in the call reports. To investigate
the effects of securitization more closely the sample was also divided into banks that held
asset-backed securities and banks that did not, allowing for a more in depth analysis of
behavior differences between securitizing and non-securitizing banks.
20
Table 4 Mean Balance Sheet Ratios by Capital
Capital/Assets <8%
Capital/Assets >8%
Total Loans/Assets
.5473
.5594
Real Estate Loans/Assets
.2473
.2750
Consumer Loans/Assets
.1094
.0994
Large Time Deposits/Assets
.1032
.1090
Total Loans/Assets
.5984
.5648
Real Estate Loans/Assets
.3283
.3005
Consumer Loans/Assets
.0919
.0873
Large Time Deposits/Assets
.1221
.1113
Pre-1996/CDO
Post-1996/CDO
Table 4 contrasts mean balance sheet ratios between low and high capital banks during
the pre-1996/CDO and Post-1996/CDO eras. There is little noticeable difference
between low capital and high capital banks. Moving between the two eras both
classifications slightly increased their total loans to asset ratios while real estate loans
became a larger proportion of total assets relative to consumer loans. Thus, securitization
lead to an expansion of mortgages that is statistically significant.
Total loans/assets and large time deposits/assets ratios for ABS and Non-ABS banks are
reported in table 5. The table does not show any real differences between the two
categories and none of the means were statistically different from each other.
21
Table 5 Mean Balance Sheet Ratios (2001Q1-2010Q4)
ABS Banks
Non-ABS Banks
Total Loans/Assets
.6466
.6326
Large Time Deposits/Assets
.1415
.1373
Total Loans/Assets
.6270
.6315
Large Time Deposits/Assets
.1359
.1373
Capital Ratio<8%
Capital Ratio>8%
Empirical Methodology
The analysis uses two dynamic panel regression models to investigate the
relationship between loan growth and monetary policy. The first model explores possible
structural breaks in 1996 and 2001 and the second model examines how banks that held
asset-backed securities respond differently to monetary policy compared to banks with no
asset-backed securities. The dependent variable is the growth rate of total loans in both
models. The contemporary change in the federal funds rate and four lagged values of the
federal funds rate are included as policy regressors. To account for asymmetric responses
to the federal funds rate reported by Cover (1992), Morgan (1993), Bliss and Kaufman
(2003) as well as Kishan and Opiela (2006), the federal funds rate variable is divided into
contractionary and expansionary variables. The contractionary variable consists of
positive changes in the federal funds rate or zero otherwise while the expansionary
variable contains negative changes in the federal funds rate or zero otherwise.
22
For non-policy regressors, seasonal dummy variables and a time trend dummy are
included in both models. To control for demand shocks caused by macroeconomic
conditions, contemporary and four lagged values of real GDP growth are included.
Kishan and Opiela (2006) identified a structural break affecting the BLC in the third
quarter of 1990 caused by the Basel 1 capital requirements. To control for this regulatory
change a dummy variable equal to zero before 1990q3 and one afterwards is added to
both regressions. Both models also include a dummy variable to control for changes in
variable definitions in the first quarter of 1984 and four lagged values of the dependent
variable.
To mitigate potential endogeneity bias and funding effects, the analysis will
employ a two-stage regression process outlined by Kishan and Opiela (2006) which
includes unexpected changes in large time deposits and securities as independent
variables. In the first stage, the growth in large time deposits and securities are regressed
on all other independent variables. In the second stage, the residuals from these
regressions are included in the loan growth equations.
To examine how the growth of asset-backed securities has impacted the BLC, the
first model will use a dummy variable equal to zero for all observations before 1996Q1
and one for all observations after. This date was chosen because of the large increase in
asset-backed security growth after collateralized debt obligations were developed in
1996. This dummy variable will then be interacted with the federal funds rate variables
to measure the effect on the BLC. In addition to the 1996/CDO break, the study will also
check how requiring asset-backed securities to be reported in the call reports affected the
23
BLC. A dummy variable will be set equal to one for all observations before 2001Q1 and
equal to one for all observations afterwards and then interacted with all federal funds rate
variables. This specification will allow us to compare the strength of the BLC across
periods of varying asset-backed security presence.
In addition to how the growth of asset-backed securities has affected the BLC, the
second model will investigate how the strength of the BLC varies between banks that
securitized their assets and those that did not. This analysis restricts the time period to
2001Q1-2010Q4. The restriction is necessary since data on asset-backed securities
appears in the call reports only after 2000Q4. To separate the banks a securitization
variable was created. This variable is the sum value of securitized assets appearing on
the balance sheet including home equity lines, credit card receivables, auto loans,
commercial and industrial loans, other consumer loans and all other loans. If the
securitization variable is any value greater than zero the bank is categorized as a bank
holding securitized assets. The loan growth equation is then regressed again using the
new divided sample. This specification will allow us to test whether the BLC’s strength
varies between banks the hold asset-backed securities and those that do not.
24
5. Results
Regression Results
Table 6 presents the results of the first empirical model using the 1996/CDO break.
This analysis tests how both contractionary and expansionary policy stances impact the
loan growth of banks based on their size and capital strength. The model then tests how
the development of collateralized debt obligations in 1996 and the following growth in
asset-backed securities affected this relationship. The table presents the sums of
coefficients corresponding to changes in the federal funds rate. Expansionary policy
measurements consist of the coefficient sums of negative changes in the federal funds
rate and contractionary policy measurements are the corresponding sums of positive
changes in the federal funds rate. F-Statistics for differences in Coefficient sums can be
found in Appendix A.
Table 6 Model 1 Using 1996 Break (1980Q1-2010Q4)
Policy
Pre-1996 Low
Stance/Bank
Capital Banks
Size
Large Banks
-2.96***
(Expansionary)
Large Banks
-3.54*
(Contractionary)
Medium Banks
-.812***
(Expansionary)
Medium Banks
-3.47**
(Contractionary)
Small Banks
-.776***
(Expansionary)
Small Banks
-3.21**
(Contractionary)
Significant at 10%*, 5%**, 1%***
Pre-1996 High
Capital Banks
-2.46***
Post-1996
Low Capital
Banks
7.48**
Post-1996
High Capital
Banks
1.81
8.527*
-.533***
-6.51*
3.46***
-.689**
-6.61**
4.20***
-.739***
13.04**
3.05***
1.077
-1.25*
2.258**
-2.53*
-1.25
25
Table 6 presents evidence consistent with the first hypothesis, that contractionary
policy should have a stronger impact with low-capital banks. In the pre-1996 era the
model shows both expansionary and contractionary policy for all banks showing negative
and statistically significant relationships. Contractionary coefficient sums were greater in
magnitude for all three bank sizes which suggests increases in the federal funds rate were
more effective in reducing loan growth than decreases in the federal funds rate were at
increasing loan growth. This relationship is more pronounced for small and medium
banks than for large banks.
The second hypothesis posits that expansionary policy would have a stronger
effect then contractionary among high capital banks. Table 6 presents results compatible
with this hypothesis for large banks but not for medium or small banks. For large highcapital banks, the model shows expansionary policy having a positive effect on loan
growth during the pre-1996 era while banks in this category were able to bypass
contractionary policy and continue to increase loan growth. The model shows that both
small and medium sized banks were able to bypass both contractionary and expansionary
policy.
The third hypothesis is that both expansionary and contractionary policy became
less effective in controlling loan growth after collateralized debt obligations were
developed in 1996. In the post-1996 columns for both low and high capital banks Table
6 presents the sums of federal funds rate coefficients and their interaction terms in the
Post-1996 columns for both low and high capital banks. For low capital banks the results
are consistent with Hypothesis 3 for all rows except small banks facing expansionary
26
policy. For large and medium sized banks coefficient sums decreased in magnitude after
the break, suggesting monetary policy became less effective in controlling loan growth.
The estimated model shows large banks being able to bypass expansionary policy entirely
by reducing loan growth when the federal funds rate decreases.
For high capital banks, the model yielded evidence contrary to hypothesis 3 for all
bank sizes. Among large banks, contractionary policy was more effective in reducing
loan growth after 1996 if their capital to assets ratio was greater than 8%. Expansionary
policy became more effective in stimulating loan growth in both medium and small banks
with high capital to asset ratios. For medium sized banks with high capital to asset ratios,
the sums of coefficients were significant and positive when facing contractionary policy
both before and after 1996. However the coefficient sum after the break is greater in
magnitude which may suggest the banks were even better able to bypass monetary policy
by increasing loan growth under contractionary policy stances.
It also needs to be noted whether first two hypotheses held during the post break
era. Table 6 does present some evidence consistent with hypothesis 1. Contractionary
policy was more effective than expansionary for both large and medium sized banks
although this effect was small for medium banks. For small banks contractionary policy
was negative and significant but expansionary policy was not statistically significant.
The model shows significant evidence fitting with hypothesis 2 for only medium sized
banks. In the post 1996 era the model shows medium banks with high capital-to-asset
ratios able to increase loan growth during contractionary policy while expansionary
policy was effective in increasing loan growth. Coefficient sums were also significant
27
and negative for large, high capital banks facing contractionary policy and small, high
capital banks facing expansionary policy. Although these sums were significant and of
the expected sign, not having statistically significant results for large, high-capital banks
facing expansionary policy and small, low-capital banks facing contractionary policy
gives little guidance for hypothesis 2.
Table 7 Model 1 Using 2001 Break (1980Q1-2010Q4)
Policy
Pre-2001 Low
Stance/Bank
Capital Banks
Size
Large Banks
-.174***
(Expansionary)
Large Banks
-.427***
(Contractionary)
Medium Banks
-.841**
(Expansionary)
Medium Banks
1.69
(Contractionary)
Small Banks
.464***
(Expansionary)
Small Banks
9.61*
(Contractionary)
Significant at 10%*, 5%**, 1%***
Pre-2001 High
Capital Banks
-1.59***
Post-2001 Low Post-2001
Capital Banks High Capital
Banks
-3.36**
1.87*
5.89***
-1.11*
.923
.791**
9.09**
.851
2.94**
-5.55**
-5.82*
-1.18***
3.81*
1.36
4.52**
-1.03
0.822
Table 7 presents mixed evidence from the first model using the 2001 break when
data on asset backed securities begins being recorded in the call reports. In the Pre-2001
low-capital bank column evidence in favor of hypothesis 1 can be seen only with large
banks. For medium sized banks expansionary policy was effective in stimulating loan
growth but the coefficient sum was not significant for contractionary policy. The model
shows small banks bypassing both expansionary and contractionary policy. Both large
28
and small banks show evidence in favor of hypothesis 2 while medium banks appear able
to bypass both expansionary and contractionary policy stances, although the sum of
expansionary coefficients being a smaller magnitude then the contractionary sum may
suggest hypothesis held for medium banks.
The post-2001 era columns show mixed evidence as well. Among low-capital
banks, contractionary policy was less effective than expansionary which contrasts with
our first hypothesis. However, among medium sized banks with low capital
contractionary policy was effective in reducing loan growth while expansionary policy
was not effective in increasing loan growth which is consistent with the first hypothesis.
For small banks the expansionary coefficient sum was positive and significant while the
contractionary sum was not significant.
For high-capital banks in the post-2001 era only large banks facing expansionary
policy and medium banks facing contractionary policy were statistically significant.
Large banks were able to bypass expansionary policy and reduce their loan growth while
the model shows contractionary policy being effective in reducing the loan growth of
medium sized banks.
When contrasting the pre-2001 and post-2001 eras, evidence conflicts with the
third hypothesis. The model shows both expansionary and contractionary policy
becoming more effective with large low capital banks. However, large high-capital
banks became less responsive to expansionary policy and medium sized low-capital
banks became less responsive to expansionary policy, both results are inconsistent with
hypothesis 3. All other rows in the post-2001 era were not statistically significant.
29
In addition to the first analysis the second analysis investigates how the strength
of the BLC varies between banks that held asset-backed securities and those that did not.
This model restricts the sample to 2001Q1 to 2010Q4 due to data limitations and restricts
the banks to two size categories, large and small. The asset size categories were reduced
due the very small presence of medium sized banks during the 2001-2010 periods. Any
banks that would have fallen into the medium sized category are in the large category
while banks that held asset-backed securities are designated ABS banks and those that
did not are Non-ABS banks. The results of the analysis are displayed in table 8.
First looking at the large bank rows, there are noticeable differences in coefficient
sums for both high and low capital banks with respect to asset-backed securities. Both
expansionary and contractionary policy was effective in influencing the loan growth of
low capital banks that did not hold asset-backed securities. This contrasts with low
capital banks that did hold asset-backed securities which were able to bypass both
expansionary and contractionary policy. This result is consistent with hypothesis 4 which
stated banks holding asset-backed securities would be less responsive to monetary policy.
This result is also visible among large high-capital banks and although it appears less
pronounced the coefficient sums are statistically different from each other. Both policy
stances had an effect on high-capital banks and the magnitude of the coefficients
decreased among those that held asset-backed securities, providing evidence in support of
the fourth hypothesis.
30
Table 8 Model 2 (2001Q1-2010Q4)
Policy Stance/
Bank Size
Low-Capital
Non-ABS
Banks
-2.44**
Large Banks
(Expansionary)
Large Banks
-4.55***
(Contractionary
Small Banks
5.37***
(Expansionary)
Small Banks
-3.57**
(Contractionary)
Significant at 10%*, 5%**, 1%***
Low-Capital
ABS Banks
High-Capital
ABS Banks
4.836**
High-Capital
Non-ABS
Banks
-6.03***
2.97***
-3.40***
-3.029***
5.134***
-4.357***
5.023***
-3.146***
-2.99***
-3.102***
-4.834***
The rows containing coefficient sums of small banks show conflicting evidence
regards to our fourth hypothesis. No statistically significant difference exists between
small low-capital banks with respect to asset-backed securities. The model shows both
Non-ABS and ABS banks able to reduce loan growth during expansionary policy and
contractionary policy was effective in reducing loan growth. Small banks with high
capital-to-asset ratios were much less responsive to expansionary policy if they held
asset-backed securities. The results show expansionary policy effective in increasing the
loan growth of Non-ABS banks while ABS banks were able to reduce loan growth during
expansionary policy, fitting with hypothesis 4. However, like the low-capital small banks
there is no statistically significant difference between Non-ABS and ABS high-capital
small banks with regards to contractionary policy.
Examining coefficient sums for low-capital Non-ABS banks, contractionary
policy was more effective in reducing loan growth than expansionary policy was in
increasing loan growth, a result consistent with hypothesis 1. Low-capital ABS banks
31
also showed results consistent with hypothesis 1. Small low-capital banks holding assetbacked securities were able to bypass expansionary policy but responded to
contractionary. While large low-capital banks holding asset-backed securities were able
to bypass both policy stances the coefficient sum for contractionary policy was a lesser
magnitude (and statistically different) which may be evidence in favor of hypothesis 1.
The high-capital bank columns in table 8 show evidence in favor of hypothesis 2
in all categories except small banks holding asset backed securities. Small banks holding
asset backed securities were not affected by expansionary policy but did respond to
contractionary policy which is at odds with hypothesis 2. For all other categories of high
capital banks expansionary policy was more effective in increasing loan growth than
contractionary policy was in reducing loan growth which is consistent with hypothesis 2.
Robustness Checks
Over time some banks in the sample switch between the high-capital and lowcapital categories. This is a possible source of bias as some variation in loan behavior
between high and low-capital banks may change over time as a result of banks switching
categories as opposed to in response to the federal funds rate. To address this issue a
third model was estimated using data from 1980Q1-2010Q2. The model specification
was identical to model 1 except for the high and low-capital categories. New categories
were created by locking banks into a category based on their capital-to-assets ratio in
1995. The date of 1995 was chosen because it was 5 years after the Basel 1 capital
accord which resulted in many banks changing increasing the levels of capital they held
32
on their balance sheets. The results were consistent with model 1 and the results of the
regression are presented in the appendix.
The results in table 8 are divided into securitizing and non-securitizing banks but
the results show no evidence regarding how the degree of securitization affects banks’
responsiveness to monetary policy. To examine how the level of securitization on a
bank’s balance sheet impacts in responsiveness to policy a fourth model was estimated.
In this analysis a variable was created equal to banks’ securitized assets to total assets
ratio. This variable was then interacted with all monetary policy variables. The sample
is restricted to only securitized banks using data from 2001Q1-2010Q4. The results are
presented in table 9.
Table 9 Degree of Securitization Effects
Bank/Policy
Low-Capital Banks
Large Banks
-1.300**
(Expansionary)
Large Banks
-2.009*
(Contractionary)
Small Banks
-.4938
(Expansionary)
Small Banks
.9286
(Contractionary)
Significant at 10%*, 5%**, 1%***
High-Capital Banks
.9021
-.6025*
-.6346
.9021
The values in table 7 are the sums of the interaction term coefficients. The only
statistically significant findings are for large low-capital banks and large high-capital
banks facing contractionary policy. All statistically significant coefficients are negative
suggesting that the larger a bank’s securitized assets to total assets ratio the more
33
responsive the bank is to monetary policy. This result suggests that differences between
securitizing and non-securitizing banks reported in table 6 could be due to unobserved
traits that vary between the two bank categories. It is also possible that the model is
showing variation resulting from the balance sheet channel. Aysun and Hepp (2011)
found evidence that the degree of securitization has a positive effect on the balance sheet
channel of monetary transmission. The responsiveness of loans reported in the
coefficient sums could be a result of the balance sheet channel overpowering the bank
lending channel.
34
6. Conclusion
Recent research into the bank lending channel has emphasized capital constraints
and bank size as factors in the efficacy of monetary policy. It has also been shown that
monetary policy impacts the BLC asymmetrically not just by bank size and capital
constraint but by the direction of the policy as well. Research has uncovered that
contractionary policy is especially effective in reducing the loan growth of small lowcapital banks but expansionary policy is not effective in increasing the loan growth of
these banks. It has also been shown that contractionary policy is ineffective in curtailing
the loan growth of high-capital banks while expansionary policy is effective in increasing
the loan growth of these banks. Economists have also uncovered evidence that
securitization can shut down the bank lending channel, giving banks alternative methods
to finance loans by pooling assets into securities and selling these securities to investors.
This study empirically investigated these asymmetric trends in the bank lending
channel and how the rise of securitization has impacted these relationships. Among lowcapital banks, contractionary policy was more effective than expansionary policy for all
bank asset size categories. After collateralized debt obligations were developed in 1996,
the analysis shows evidence that low-capital banks became less responsive to both
contractionary and expansionary policy. The policy stance asymmetry among lowcapital banks held after the 1996 break for all but small banks which did not have a
statistically significant result for expansionary policy. The empirical analysis produced
results showing small and medium high-capital banks being able to bypass both policy
stances before 1996. This result was inconsistent with expectations formed by surveying
35
existing literature; however, large high-capital banks did reveal expected behavior by
being much more responsive to expansionary policy. High-Capital banks revealed
evidence completely at odds with the third hypothesis. After 1996, large banks were
more responsive to contractionary policy and both medium and small banks became more
responsive to expansionary policy.
In the second empirical model estimated, banks were divided into those that
securitized their assets and those that did not. This analysis revealed some evidence in
favor with hypothesis 4. Among large banks, those that securitized their assets were less
responsive to monetary policy than those that did not. While large banks behaved as
expected the behavior of smaller banks was much more conflicting. There was very little
difference in the behavior between securitizing and non-securitizing small low-capital
banks and small banks with high capital-to-assets ratios showed conflicting behavior.
Securitizing banks in this category were less responsive to expansionary policy but more
responsive to contractionary policy.
The results of this study suggest the notion that securitization weakens the bank
lending channel is more complex than previous research has suggested. The impact of
securitization varies depending on banks’ capital strength and size which suggests that
the structure of the banking system is an important factor in determining how the growth
of asset-backed securities influences the bank lending channel. For example, this
analysis suggests that a banking system heavily populated by large securitizing banks
would be less responsive to monetary policy. The results also suggest that securitization
36
affects the difference in effectiveness of expansionary and contractionary monetary
policy, a result with implications for business cycle management.
Future research would do well to directly contrast how the large growth of assetbacked securities has impacted different channels of monetary transmission. While there
is evidence suggesting the bank lending channel has become weaker, Aysun and Hepp
(2011) found evidence that the balance sheet channel may have been made stronger by
securitization. To truly understand how securitization has affected the efficacy of
monetary policy the relative strength of different transmission mechanisms and how these
mechanisms have been impacted needs to be compared and contrasted.
37
Appendix A: F-Statistics for Differences in Coefficient Sums
Pre and Post 1996 (Table 4)
Category
Small High Capital Bank
Small Low Capital Bank
Medium High Capital Bank
Medium Low Capital Bank
Big High Capital Bank
Big Low Capital Bank
Contractionary Policy
4.21***
0.71
3.43***
2.71**
5.68***
8.15***
Expansionary Policy
7.08***
8.20**
9.31***
5.3***
0.21
50.14***
Contractionary and Expansionary Policy (Table 4)
Category
Small High Capital Bank
Small Low Capital Bank
Medium High Capital Bank
Medium Low Capital Bank
Big High Capital Bank
Big Low Capital Bank
Pre-1996
2.69**
12.31***
1.01
15.27***
8.44***
5.35***
Post-1996
0.89
0.43
4.21***
0.88
3.33***
17.81***
Contractionary Policy
4.88**
0.47
1.00
0.73
5.73**
4.88**
Expansionary Policy
1.29
5.87***
0.56
23.73***
8.00***
53.23***
Pre and Post 2001 (Table 5)
Category
Small High Capital Bank
Small Low Capital Bank
Medium High Capital Bank
Medium Low Capital Bank
Big High Capital Bank
Big Low Capital Bank
38
Contractionary and Expansionary Policy (Table 5)
Category
Small High Capital Bank
Small Low Capital Bank
Medium High Capital Bank
Medium Low Capital Bank
Big High Capital Bank
Big Low Capital Bank
Pre-2001
17.48***
6.91***
2.68***
1.00
9.11***
7.24**
Post-2001
0.13
0.47
1.32
7.47**
0.87
2.99**
ABS and Non-ABS Banks (Table 6)
Category
Contractionary Policy
Small High Capital Bank
Small Low Capital Bank
Big High Capital Bank
Big Low Capital Bank
0.56
0.91
0.72
15.26***
Expansionary
Policy
10.03***
0.09
1.35
21.21***
Contractionary and Expansionary Policy (Table 6)
Category
Contractionary Policy
Small High Capital Bank
Small Low Capital Bank
Big High Capital Bank
Big Low Capital Bank
5.02**
4.63**
4.01**
8.88**
Expansionary
Policy
16.52***
13.22***
5.12**
4.24***
39
Appendix B: Model 1 Using Fixed Capital-to-Asset Ratios
Policy
Pre-1996 Low
Stance/Bank
Capital Banks
Size
Large Banks
-2.87***
(Expansionary)
Large Banks
-3.55*
(Contractionary)
Medium Banks
-.547***
(Expansionary)
Medium Banks
-4.12**
(Contractionary)
Small Banks
-1.20***
(Expansionary)
Small Banks
-3.87**
(Contractionary)
Significant at 10%*, 5%**, 1%***
Pre-1996 High
Capital Banks
Post-1996 Low
Capital Banks
-3.11***
7.25**
Post-1996
High Capital
Banks
4.22
8.522*
-1.49***
-7.47**
3.00***
-.689**
-3.13**
3.99***
-.742***
3.20**
3.58***
1.81
-.232*
2.101**
-2.53*
-.041
40
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