1. finance
2. bank

Why one facility does not …t all? Flexibility and signalling in the
Discount Window and TAF
Céline Gauthiery, Alfred Leharz, Hector Perez Saizxand Moez Souissi{
October 31, 2013
Abstract
In the months before the failure of Lehman Brothers in September 2008, banks were willing to pay a
premium over the more ‡exible discount window (DW) rate to participate in the Term Auction Facility
(TAF). In this paper, we study whether participation to the TAF could be seen as a way for banks to
signal their relative quality during a period of …nancial turbulence, in exchange for lower funding costs
once private funding markets would return to a more normal state. We …rst propose a model where
banks can be of two types, banks with high returns ("good" banks) and banks with low returns ("bad"
banks). Both types of banks can be hit by a liquidity shock but only bad banks can su¤er a run. We
show that in a separating equilibrium good banks hit by a liquidity shock always access the TAF, even
though it is more costly and less ‡exible than the DW, whereas bad banks that su¤er a run access the
DW. In this equilibrium, banks accessing the TAF bene…t from lower funding costs once funding markets
are back to normal. We next show that the main results of our model are consistent with the empirical
evidence observed. TAF rates were on average 24 basis points above the primary DW rate before the
failure of Lehman, and banks that accessed the DW had funding costs after the failure of Lehman up
to 40 basis points higher than banks that accessed the TAF.
We want to thank Hongyu Xiao for excellent research assistance, and we also want to thank for comments and suggestions
Jason Allen, James Chapman, Evren Damar, Randall Morck and participants at seminars at the Bank of Canada, the Canadian Economics Association 2013 Annual Conference, and the International Monetary Fund. Any opinions and conclusions
expressed herein are those of the author(s) and do not necessarily represent the views of the Bank of Canada.
y
Université du Québec en Outaouais. 283, boulevard Alexandre-Taché, Gatineau (Québec) Canada, J8X 3X7. email:
[email protected].
z
University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4. email: [email protected]
x
Bank of Canada, Financial Stability Department, 234 Wellington Street, Ottawa, Ontario, K1V 1V8. email: [email protected]
{
International Monetary Fund. Kuwait City, Kuwait. email: [email protected]
1
1
Introduction
During the recent …nancial crisis, many banks that experienced a liquidity crunch shied away from using
the discount window (DW), the main central bank liquidity facility that was designed to help banks in
that very situation. Banks were concerned that accessing the DW would be perceived by the market as a
signal that they were not only illiquid but also insolvent which would intensify the pressure on them. To
overcome this “stigma e¤ect”of the DW, the US FED created alternative facilities for short term funding
of banks, notably the Term Auction Facility (TAF).
The lending terms of the TAF were in all dimensions worse than at the DW where an unlimited amount
of money is available on demand. Banks needed to wait for three days to access the funds, the funds were
auctioned on a biweekly basis so that availability was not for granted and spaced out, there was a cap on
individual bids, loans cannot be prepaid, and banks had the same collateral requirements than under the
DW. Yet during a substantial period of time, banks were willing to pay a premium over the DW rate to
access TAF funds.
TAF rates were above DW rates during the peak period of …nancial turmoil when uncertainty on the
quality of banks’ assets and their default probability was probably the highest. Indeed, term Libor-OIS
spread was around 350 basis points in the period following the Lehman bankruptcy (see Figure 24) and
banks were willing to pay 150 basis points more to borrow from the TAF rather than from the discount
window (see Figure 10). As the …nancial crisis ebbed, DW lending rates started to exceed TAF rates in
line with what we would expect given the funding terms.
Our paper o¤ers a rationale for o¤ering two di¤erent liquidity facilities and an explanation for the
existence of the TAF rate puzzle mentioned above. We argue that two distinct liquidity facilities allow
banks to signal their quality to the market. Poor quality banks value the ‡exibility of the DW more than
good banks and thus self-select into borrowing from that facility should the need arise. Good quality
banks are willing to pay a higher borrowing rate and subject themselves to more stringent contractual
terms because of the long term funding bene…ts that come with signaling to the market that they are a
good bank.
Speci…cally we assume that banks need to access a liquidity facility because of a random liquidity shock
or because of a “run” caused by concerns about their solvency. Banks can anticipate whether they will
be hit by a liquidity shock, but runs come as a surprise to them. While good banks experience only the
former, bad banks can be hit by both types of shock. In the signaling equilibrium, good banks that expect
a liquidity shock will pay the higher rate to access the TAF facility to signal that they do not need the
‡exibility of the DW to respond to sudden runs because of their higher quality. Bad banks hope not to
experience a run in which case they can pool with the good banks that did not receive a liquidity shock.
In the case of a run, they need the ‡exibility of the DW. The market thus infers that banks that access the
TAF are of better quality than banks drawing on the DW. The stigma of the DW arises endogenously in our
model as a result of the partially separating equilibrium.The immediate success of the TAF is supporting
the hypothesis that less stigma was attached to TAF bidding than to the DW. The TAF constraints,
together with the willingness of banks to pay more for TAF funds, likely helped to decrease the perception
that TAF participants were in desperate need for funding (Bernanke (2009)).
2
Examining a large sample of US banks we …nd evidence that is consistent with our model. Banks that
access the TAF can rely on signi…cantly lower post crisis funding costs than banks that drew from the DW.
Banks accessing TAF during the crisis can also borrow more on the wholesale market after the crisis than
banks accessing the DW.
Our …ndings also have important policy implications for the liquidity provision of central banks during
periods of systemic distress. Instead of forcing banks into a pooling equilibrium by only o¤ering one facility,
the simultaneous provision of several liquidity facilities that allow banks to signal their type can help ensure
the e¢ cient dissemination of liquidity provisions and be bene…cial for systemic stability altogether.
Our paper is related to other papers that have developed theoretical models to analyse the access
to the DW during the crisis. (Ennis and Weinberg (2009) or Klee (2011)). Ennis and Weinberg (2009)
propose a model in which informational asymmetries and asset-quality heterogeneity play a crucial role in
determining equilibrium interest rates. They show under which conditions DW borrowing may be regarded
as a bad signal about the quality of the borrowers. Their goal is to develop a better understanding of the
fundamental nature of the stigma e¤ect and under which conditions it would occur. We rather focus on
some key characteristics of TAF compared to the DW, and derive the conditions under which a separating
equilibrium would occur. Klee (2011) suggests a model to explain why fed funds rates went up as the
spread between the DW rate and the target rate went down. She assumes non pecuniary costs on top of
monetary costs to go to the DW. As the spread went down, banks with the lowest pecuniary costs (or
lowest stigma) went to the DW, leaving the higher stigma banks on the fed funds market. Sellers on the
fed funds market realized that and adjusted rates up accordingly. In this model, the successive decreases
in the DW rate at the start of the liquidity crisis act as a screening device. Sellers can distinguish between
banks with high and low stigma by whether these banks are buying fed funds or borrowing from the DW.
Borrowing on the DW is a signal of quality. We consider a richer setting in which banks can access two
facilities, the TAF and the DW and we …nd theoretical and empirical support that by accessing TAF banks
signal higher quality relative to banks that access the DW.
Armantier et al. (2011) propose a way to measure the costs of accessing the DW in terms of relative
stock market under performance or higher overnight funding rates in the interbank market days around
the DW access. They do not …nd strong evidence of such costs. We think that their approach may
underestimate those costs for many reasons. First, it likely takes time for markets to identify which banks
went to the TAF and which ones to the discount window, in which case, funding costs between the two
types of banks would di¤er after more than a few days of accessing the DW. Second, the authors focus on
the highest volatility period during the crisis, in which period even the healthiest corporations were facing
extreme increase in funding costs. Third, as detailed above, Klee (2011) documents an increase in the Fed
funds rate for banks not accessing the DW, as the spread between the discount rate and the target rate
was decreased. Berger et al. (2013) analyse which banks received funds through the DW and which from
the TAF, how the use of these funds changed the use of other funding sources, and whether it a¤ected
bank lending. They …nd that small banks receiving funds tended to be more capital constrained and riskier
whereas large banks receiving Fed funding generally were not weaker than other large banks; the funds
substituted to a limited degree for other sources; and banks receiving funds increased their lending relative
to other banks. Their …rst result suggest that the Fed did not stricly adhere to a lender-of-last-resort role
3
in that they did not only provide funds to weak large banks. Puddu and Wälchli (2012) concentrate on the
TAF program and assess its impact on U.S. bank liquidity risk using a treatment e¤ects model. They …nd
that banks that bene…ted from the TAF funds exhibit ex ante higher levels of maturity mismatch, have
more illiquid collateral and are more often located in US states that experienced a signi…cant appreciation
of housing prices before the 2006 increase in monetary stance. They also …nd that banks with TAF access
reduced their liquidity risk positions in the quarters after receiving the …nancial support for the …st time,
and that the larger the amount of reserves received, the bigger was the reduction in liquidity risk.
2
Central Bank Liquidity Facilities
During the recent …nancial crisis, the Federal Reserve (the Fed) undertook a series of unusual policy actions
in order to alleviate the strain on bank funding markets. In addition to easing the term of its conventional
liquidity facility, instituting the Term Discount Window (DW), the Fed created a number of unconventional
programs including a new facility for auctioning short-term credit, the Term Auction Facility (TAF). In
this section, we provide a brief historical perspective of DW lending and emphasize the issue of stigma.
Then, we compare the key features of the DW and TAF facilities.
2.1
Discount Window lending: Historical Perspective
Sound depository institutions (DI) facing liquidity shortages can borrow from the Fed’s primary lending
facility — the Discount Window (DW)— at the ‘primary lending rate’.1 Before 2003, the discount rate was
set below the target federal funds rate, which made the borrowing from the Fed cheaper than borrowing
on the interbank market, and created potential arbitrage opportunities.2 Accordingly, DIs were required
to show that they exhausted private sources of funding and that they really need funds for their business
purposes, on top of the regular scrutiny of their soundness. This additional requirement seems to have
created a perception of stigma associated with DW borrowing as it might signal a …nancial weakness of
the borrower if it became known to both peers and the Fed.3 These concerns may have deterred sound DIs
with liquidity shortage from borrowing at the DW even if its terms and amounts were not made public.
4
To address concerns about DW stigma, the Fed changed its approach to DW lending in 2003. It had
1
Primary lending rate is more commonly known as the ‘discount rate’. Prior to the recent …nancial crisis, DW operations
were the Fed’s primary means to implements its lender-of-last-resort function. Lending through the DW allows DIs to borrow
against collateral that is not accepted elsewhere. The Fed would accept virtually anything as collateral, including U.S. Treasury
securities, state and local government securities, AAA-rated collateralized mortgage obligations, consumer loans, commercial
and agricultural loans, and investment-grade certi…cates of deposits. In some cases, the Fed accepted even the bank’s buildings
and furniture (Cecchetti (2009)).
2
Banks could re-lend cheap DW funds on the interbank market at higher rates, potentially leading to larger reserves and
lower rates than levels targeted by the Fed’s monetary policy (Courtois and Ennis (2010)).
3
Courtois and Ennis (2010) argue that, because of the interconnected bilateral nature of the interbank lending market, it
would not be hard for other banks to infer the identity of institutions that borrow from the DW. Fur…ne (2005) …nds evidence
on DW stigma using data from before the recent crisis, while Armantier et al. (2008) provide evidence from the recent crisis.
Ennis and Weinberg (2009) develop a theoretical model for DW stigma, and show that it can be rational for banks to borrow
elsewhere at higher rates if costly repercussions result from going to the DW. They …nd evidence of stigma e¤ects using federal
funds market data during the …rst year of the recent …nancial crisis.
4
DW stigma and associated banks’ reluctance does not necessarily make DW useless. Acharya et al. (2011) show that
stigma e¤ects rather limit the surplus banks can squeeze out of needy banks.
4
put in place a penalty-rate regime, and classi…ed DW loans into primary and secondary credit.5 Primary
credit, the DW main source of short-term liquidity, is available on a “no-questions asked”basis to …nancially
sound DIs that meet a certain capital threshold. These institutions pay the primary credit rate which was
originally set to 100 basis points above the target federal fund rate. Secondary credit is available to DIs not
eligible for primary credit, and entails a higher level of administrative burden. At the outset of program,
secondary credit rate was 150 basis points above the Fed’s target.
In August 2007, as a response to the incipient …nancial crisis, the Fed narrowed the spread in the
DW rate and increased the allowable term for primary credit borrowing to 30 days (instead of typically
overnight). Few months later (on March 16, 2008), in the wake of the takeover of Bear Stearns by JPM
Chase, the Fed narrowed further the spread to 25 basis points and extended the maximum maturity of
term primary credit loans to 90 days. Nevertheless, as shown in Figure 15, total borrowing from the DW
remained low with primary credit loans peaking in late 2008 at just over $100 billion, and secondary loans
at only about $1 billion in late 2009.
2.2
The Term Auction Facility (TAF)
In response to concerns about the DW stigma e¤ects, and in order to improve DIs’access to term funding,
the Fed introduced the TAF, an auction delivery system for DW loans, on December 12, 2007. The TAF
is a series of biweekly auctions for preset amounts of funding available to DIs eligible for primary credit at
the DW. It is a single-price auction, whereby all successful bidders pay the stopout rate, the interest rate
of the last accepted bid which all awarded institutions pay upon maturity.6 TAF loans, which were o¤ered
with a maturity of 28 days, and beginning in August 2008, 84 days, were fully collateralized. Collateral
eligibility and valuation procedures were the same as for the DW.
From its creation, borrowing at the TAF was in high demand. As shown in Figure 11, which contrasts
TAF supplied funds with the total amount of submitted bids; auctions were highly competitive prior to the
bankruptcy of Lehman Brothers. Total bids were 50 percent larger than total o¤ered funds on average over
the pre-Lehman period. Demand for TAF funds continued to rise after the collapse of Lehman exceeding
800 $billion in 2009Q1; however, competition among bidders decreased since the Fed doubled the amounts
supplied. Figure 14 con…rms the trend of a weaker competition among TAF bidders after the collapse of
Lehman. The ratio of bid/cover ratio was continuously lower than one after 2008Q3 (around 50 percent
on average), and stopout rates were equal to TAF minimum rates set by the Fed. In response to continued
improvements in …nancial market conditions, the Fed reduced both the amount and maturity of new TAF
auctions, until March 8, 2010 when the …nal auction was held.
5
There is a third DW program called the seasonal credit that is available for community banks with less than $500 million
in total assets that have yearly swings in deposits and loans that persist for at least four weeks.
6
For each TAF auction, the Fed determines total amount to be auctioned, the maximum amount each individual participant
is allowed to obtain, and the minimum bid interest rate. Bids are ordered from the highest to lowest rate, and bids are accepted
starting with the highest rate submitted, down to successively lower rates, until the o¤ered amount has been allocated or until
the minimum bid rate is reached.
5
2.3
Other liquidity facilities
In addition to the DW and TAF, there were other liquidity facilities that were launched by the Fed. In
March 2008,the Term Securities Lending Facility (TSLF) and the Primary Dealer Credit Facility (PDCF)
were created. The TSLF is weekly and aims to increase the ability of New York Fed’s primary dealers
to obtain cash in the private market by enabling them to pledge securities temporarily as collateral for
Treasuries, which are relatively easy to …nance.7 Like the TAF, the TSLA is based on an auction system,
but it is not o¤ered to DIs and does not provide funding per se. The PDCF aims to provide loans of
cash and securities to primary dealers through overnight loan facilities in exchange for tri-party eligible
collateral. The key di¤erences with the TAF program lie in the type of mechanism of fund allocation
(securities exchange vs. auction) and fund maturities (overnight vs. up to 84 days ). These two programs
were closed on February, 1 2010. Two other programs, the Term Asset-backed Securities loan facilities
(TALF) and the Commercial paper funding facility (CPFF), were created in late 2008. The TALF was a
funding facility that issued loans with a term up to …ve years to holders of eligible ABS. The CPFF was
designed to enhance liquidity in the commercial paper market.
Given the characteristics of all these facilities, and given our objective to focus on small and medium
sized banks, we believe that TAF and DW were the most relevant liquidity facilities for U.S. small and
medium commercial banks, so these two facilities are the main focus of the paper.
3
Data
We use novel data on access to the discount window and TAF for 2008-2010. Data on discount window
usage were released following the Freedon of Information Act requests by Bloomberg News and Fox Business
Network on March 31, 2011. The information on banks that had access to TAF loans was made public on
December 1, 2010 because the Dodd-Frank act mandated the Fed to release it. The data include the user’s
name , Federal Reserve Districct, amount obtained, origination date, and maturity date. We also use FDIC
Call Reports to obtain quarterly …nancial information for U.S. Banks. To combine the di¤erent sources of
information, we use the observed name and the Fed region of the …nancial institution that accessed DW
and TAF. Using these two variables, we could match with very high certainty above 95% of the names.
We discarded all foreign institutions. Finally, we use state level information such as GDP growth or size of
subprime mortgage sector to estimate the exposure of institutions to state level variables. To do that, we
use information from the FDIC with amounts of deposits in branches for every bank to calculate average
exposure of multi-state banks to state-level variables.
4
Relevant stylized facts
In this section, we describe some stylized facts that we aim at explaining in the theoretical model described
below. Table 1 reports descriptive statistics on banks’usage of the DW and TAF facilities (Panel A) before
7
In other worlds, the main objective of the TSLF is to reduce the need for dealers to sell assets into illiquid markets as well
as lessens the likelihood of a loss of con…dence among lenders.
6
and after the collapse of Lehman, and key balance sheet and general macroeconomic indicators (Panel B
and C) as of 2007Q4 (i.e., prior to the announcement of the TAF program) and 2010Q3 (after the conclusion
of the TAF program). Banks are classi…ed according to their usage of the DW and TAF facilities. Banks
that raised more than 95% of their Fed funds from DW (as percentage of total funds from TAF+DW) are
called “DW mainly banks”. Similarly, “TAF mainly banks” are those that obtained more than 95% of
their Fed funds from TAF (as percentage of total funds from TAF+DW).
From the …gures reported in Panel A, we can see that prior to Lehman’s collapse, DW usage was larger
than TAF usage both in terms of access frequency and size of average borrowing. For example, DW mainly
banks went 9 times to the DW facility while TAF mainly banks participated in 7 TAF auctions re‡ecting
the limited number of TAF auctions. Also, the average DW loan was about $7 billion as compared to 6
$billion for the average TAF mainly bank. Following the Lehman bankruptcy, TAF usage dominated DW
usage in terms of outstanding amounts. However, although the amounts borrowed are high in absolute
terms, these amounts are very small for most banks when considered as a fraction of total liabilities (see
Figures 20 and 21).
Prior to the beginning of the TAF program (Panel B, columns 1 to 6 in Table 1), one cannot discriminate
between TAF or DW banks based on either their funding costs or return-on-asset (ROA), which were around
260 bps and 0.6 percent on average, respectively. However, TAF mainly banks were much larger and more
leveraged on average than the other two types of banks, with an average ratio of total assets to Tier 1
capital close to 13 percent, as compared to 11.6 and 10 percent for DW mainly and other banks, respectively.
TAF banks were also relying more on wholesale funding markets, with wholesale funds representing more
than 17 percent of their total liabilities as compared to less than 2.5 percent for DW mainly and other
banks. Banks that did not access either facilities had the lowest funding costs and were also relying less
on wholesale funding markets.
After the conclusion of the TAF program in 2010Q1 (Panel B, culumns 7 to 12 in Table 1), TAF mainly
banks seem to have an easier access to funding markets than DW mainly banks, with an average funding
cost of 220 bps for the former banks and 254 for the latter, and a larger proportion of the funding coming
from the wholesale markets. Others have the highest funding costs among the three groups and also rely
on wholesale funding markets the most.
5
Model
Banks have access to a two period investment project that requires an investment normalized to $1 and
pays either R or zero at the end of the second period. Good banks realize that return with certainty while
bad banks obtain R only with probability 1
as
. Denote the ex-ante probability of a bank being good
. The project is …nanced through two consecutive periods of short term borrowing. In the second
period we assume that markets work frictionless and that banks can borrow from a competitive …nancial
market at the fair market rate given the market’s belief about their type. In the …rst period we assume
that banks face a distressed market and are in potential need of a central bank facility to re…nance the
project. Speci…cally we assume that the bank has no access to market funding sources should a re…nancing
7
need arise.
We model two possible re…nancing needs re‡ecting the idea that banks in need of assistance can either
be illiquid due to the general closure of the market for re…nancing, which we refer to as a liquidity shock,
or due to unwillingness of counter-parties to extend …nancing because of concerns about the bank’s asset
quality, which we refer to as run. Banks receive a liquidity shock with probability
type, in which case they need to re…nance their
project.8
independent of their
We think of liquidity shocks as the inability of
a bank to rollover its …nancing due to adverse market conditions speci…c to its funding structure. A bank
that relied heavily on wholesale funding or short term paper might not be able to roll over its debt while a
bank that is …nanced through retail deposits will …nd it easier to maintain its funding. Since banks know
their funding structure and can observe market conditions we assume that banks learn whether they will
receive a liquidity shock or not. Speci…cally we assume that a bank knows with certainty at the beginning
of the …rst period whether it will be exposed to a liquidity shock or not.
The second possible reason for banks to need re…nancing is based on adverse information about the
bank’s credit quality which does not allow them to roll over very short interbank …nancing or commercial
paper, or causes a sudden withdrawal of callable interbank or retail deposits. We refer to the case of
information driven re…nancing needs as a ’run’, and to simplicity the exposition we assume that only bad
banks can be subject to a run with probability
while good banks will never be run. Runs occur in the
middle of the …rst period. Since runs can be based on informational cascades and rumors we assume for
simplicity that the bad bank has no advance information whether a run will occur or not.
The central bank provides two funding facilities that banks can access to re…nance their project, the
term auction facility (TAF) and the discount window (DW).9 We capture two stylized facts about these
facilities in our model: …rst, they o¤er funds at di¤erent rates, speci…cally banks can borrow funds at rates
rT and rD for the TAF and DW, respectively. Second, the TAF facility is less ‡exible than the DW. TAF
funds cannot be accessed instantly because the federal reserve requires three business days to transfer funds
to successful bidders and because TAF auctions are not held on a daily basis. We capture this institutional
feature by assuming that TAF funds are only available at the beginning of the period.
We assume the following timeline for our model (see …gure 2): In period 1a, the bank observes whether
it will receive a liquidity shock or not. In period 1b, the central bank o¤ers access to TAF funding. Banks
can also access DW in that period. After banks decide to use TAF/DW or not, bank runs are realized
(period 1c). A bank experiencing a bank run that has not secured TAF or DW funding in period 1b is
forced to re…nance through the DW in period 1c. In period 1d the market can observe whether a bank
has accessed the TAF, the DW, or did not use a central bank liquidity facility, and updates their belief
about the bank’s type based on that information. In period 2 markets are open and banks can borrow in
the market at a rate that depends on their perceived type. At the end of period 2 the project return is
realized, the bank repays its obligations if possible and closes.
8
To simplify the exposition of the paper we assume that the bank needs to re…nance the whole project.
Note that in this model we assume that all banks are quali…ed by the central bank to access both facilities. This is
consistent with the facts observed previously about the low use of the secondary DW and the low number of troubled banks
before the failure of Lehman.
9
8
5.1
Separating equilibrium
We propose that banks use the TAF as a signaling device and hence conjecture a separating equilibrium in
which banks that learn that they will be hit by a liquidity shock access the TAF, bad banks that experience
a run access the DW, and banks that experience neither a run nor a liquidity shock do not access a central
bank facility. We solve the model by backward induction.
Banks that do not access a central bank liquidity facility are either good banks that did not receive a
liquidity shock or bad banks that received neither a liquidity shock nor a run. Denote by
0
the probability
that a bank is good given that it has not accessed any central bank facility, which is
0
=
(1
(1
)
)(1
) + (1
)(1
)
=
+ (1
)(1
(1)
)
Since both types of banks access the TAF upon receiving a liquidity shock the market cannot learn from
observing a bank accessing the TAF and thus sets the probability of being a good bank upon accessing
TAF equal to the unconditional probability,
T
= . Since we assumed that only bad banks are subject
to runs, accessing the DW fully reveals the bank’s type and thus
D
= 0. The market’s belief of the bank
being of the good type depends on the bank’s actions as follows.
Lemma 1 The markets belief that a bank is of the good type is highest for banks that do not access any
central bank liquidity facility and lowest for banks that access the discount window, i.e.
D
T
0.
The …nding in Lemma 1 is consistent with the widely cited stigma e¤ect that banks face when accessing
the discount window. In the second period the market will set the competitive interest rate to break even
given a belief
that the bank is good. The interest rate r2 for the second period will solve the equation
1 = (1
)(1
or
r2 =
where – because of the separating equilibrium –
case that the bank has accessed TAF, or
0
)(1 + r2 ) + (1 + r2 )
(1
(2)
)
+1
(3)
is either zero, if the bank has accessed the DW,
in
if the bank did not access a central bank facility.
The following lemma studies with detail the e¤ect of the parameters on r2 and
0
(see Appendix for
proofs of all theoretical results):
Lemma 2 The second period interest rate r2 is increasing in the PD of the bad bank , and decreasing in
the market’s belief that the bank is good . The second period interest rate for banks that do not access a
central bank funding facility, r2 ( 0 ), is decreasing in the fraction of good banks
, and the probability of a
run, . The markets belief of a bank being of the good type given that it has not accessed any central bank
funding,
0,
is increasing in
and .
Most comparative statics in Lemma 2 are intuitive. The set of banks that do not access a central bank
9
facility are comprised of good banks without liquidity shock and bad banks that have neither a liquidity
shock nor a run. The quality of this pool increases with the ex-ante number of good banks
and the
probability of a run, , as more runs reduce the number of bad banks in the pool. As the pool quality
increases the second period interest rate decreases.
From Lemmas 1 and 2 we can rank the second period interest rates as a function of the banks’ …rst
period …nancing needs and obtain
r2 (
D)
=
r2 (
1
T)
r2 ( 0 )
r2 ( = 1) = 0:
(4)
Banks that access the DW are assessed worse by the market and pay higher …nancing rates on the
second period. Nevertheless the DW can be attractive for the bad bank due to its ‡exibility. If the bad
bank has no liquidity shock it can speculate that it will not experience a run and can pool with the good
banks that do not need to access central bank funding and thus receive a favorable interest rate in the
second period as r2 ( 0 ). In the case of a run the bad bank’s type get revealed and it has to pay a higher
second period rate. If the probability of a run is not too high the opportunity to pool with the good banks
in the absence of a run can create enough value for the bad bank to prefer the ‡exible DW over the more
rigid TAF.
The good bank’s pro…t function is then as follows: if it gets a liquidity shock then it will access the
TAF at cost rt , it will be revealed to the market that it is a good bank with probability
T
= , and the
funding cost for the second period is r2 ( ). Therefore, the good bank that receives a liquidity shock has
the following pro…t function:
g;l
=R
(rt + r2 ( )):
(5)
If the good bank is not hit by a liquidity shock then it does not need any funding in the …rst period
but needs access to funding at r2 ( 0 ) in the second period. The pro…t of the good bank without liquidity
shock is then:
g;n
=R
r2 ( 0 ):
(6)
The bad bank, which will realize the payo¤ of R only with probability (1
), can also learn that it
will realize a liquidity shock in which case it would access the TAF and face the same funding costs as the
good bank.
b;l
= (1
)(R
(rt + r2 ( ))):
(7)
Otherwise it can experience a run in which case it has to access the DW at cost rd and is identi…ed as
bad bank resulting in a funding cost of r2 (0) in the second period, or it can have no run, in which case it
will pay r2 ( 0 ) in the second period. Its expected pro…t is then
b;n
= (1
)(R
(rd + r2 (0))
(1
)r2 ( 0 )):
(8)
In the separating equilibrium, the good bank with a liquidity shock (GL), and the bad bank with a
liquidity shock (BL) go to TAF. The rest of the banks, the bad bank without a liquidity shock (BN ), and
10
the good bank without a liquidity shock (GN ), do not go to TAF.
5.2
Characterization of separating equilibrium
For this equilibrium to be stable the both types of banks must not have an incentive to deviate from the
conjectured strategies. First neither the good nor the bad bank should …nd it pro…table to access the DW
rather than the TAF when receiving a liquidity shock. By accessing the DW instead of the TAF banks
could pro…t from lower …nding costs if rD < rT but su¤er from being identi…ed as bad banks by the market
and thus paying a higher interest rate in the second period. The corresponding conditions are
R
(rt + r2 ( ))
R
rd + r2 (0)
(rd + r2 (0)) ,
r2 ( )
(9)
rt :
(10)
for the good bank and
(1
)(R
(rt + r2 ( )))
(1
)(R
(rd + r2 (0)));
(11)
which is identical, for the bad bank, respectively.
Second, if the good bank does not receive a liquidity shock it could still access the TAF and invest
the proceeds in a riskless storage technology for which we assume a normalized return of zero.. The
corresponding incentive compatibility constraint is
R
r2 ( 0 )
R
r2 ( 0 )
(rt + r2 ( )) ,
r2 ( )
(12)
rt :
(13)
Third, the bad bank could access the TAF even if it has no liquidity shock and store the proceeds.
The bank could then avoid accessing the DW and being identi…ed as bad bank in case there is a run. The
corresponding incentive compatibility constraint is
(1
)(R
(rd + r2 (0))
(1
)r2 ( 0 ))
rd + r2 (0) + (1
)r2 ( 0 )
(1
)(R
r2 ( )
(rt + r2 ( ))) ,
rt :
(14)
(15)
Note that Equations (10) and (11) are identical, so we can discard one of them. Also, because we
assume that rd
0 and rt
0, and r2 (0)
r2 ( )
r2 ( 0 ) Equation (13) can be discarded. Therefore, a
separating equilibrium is de…ned by (10) and (15), which leads to
Proposition 1 The separating equilibrium is fully characterized by equations (10) and (15).
The interest rates for the central bank facilities, rd and rt that support the separating equilibrium are
illustrated in the shadow region in Figure 3. Because
1 and r2 (0)
r2 ( 0 ) from Equation (4) it is easy
to show that the line corresponding to constraint (10) is above the one of constraint (15) opening up the
11
equilibrium region between them.
We can see that the equilibrium TAF rate exceeds the DW rate as long as the latter is not too high,
which is consistent with the rate pattern observed in the recent …nancial crisis. Banks are willing to pay an
extra premium to access the TAF over the discount window because of the associated signaling bene…ts. It
is also straightforward to show that the equilibrium region is shrinking in the probability of an information
driven run
such that it collapses to a single line when
= 1. As the bad banks are more likely to be caught
in the market through a run the opportunity of pooling with the good banks that have no liquidity shock
and enjoying a low second period rate vanishes, which makes the DW less attractive. The rate di¤erential
between rt and rd then merely mirrors the rate di¤erential of TAF and DW banks for the second period.
We characterize some of the properties of the equilibrium in the following proposition :
Proposition 2 Properties of separating equilibrium:
If rd is small enough, then rt > rd .
If
! 1, then rt = rd + r2 (0)
r2 ( ) and rt > rd for any rd
is shifted up (i.e, rt increases) as
If
! 1.
increases, the equilibrium region of rt does not have a clear behavior (I2 increases, but I1 does
not have a clear pattern). However, if
If
6
0. Also, the equilibrium region of rt
! 1 then rd ! +1.
increases, the equilibrium region of rd is shifted down (i.e, rt decreases)
Empirical evidence
6.1
Empirical strategy
Our objective in this section is to test some of the implications of the model presented previously. The
model presents predictions about the …rst period with high market turbulence, and the second period where
this …nancial turbulence disappears. Our empirical strategy is based on assuming that in the months before
the failure of Lehman Brothers in September 2008 the market turbulence was very high (the …rst period
of our model), whereas the late months of 2009 and the year 2010 had a signi…cantly smaller turbulence
(the second period of our model) and markets reacted to the observed access of banks to these facilities.
In previous sections, we have provided some evidence that these two periods approximate well to period 1
and 2 of our model.
The model …rst provides a prediction about the relationship between the TAF rate and the DW rate
in a context of low DW rates (see Proposition 2) which is a reasonable assumption to be considered in the
months before the failure of Lehman. In …gure 10 we compare these two rates. TAF rates are consistently
higher than DW rates in the months before the failure of Lehman. In addition, we do not observe a
di¤erential e¤ect when comparing TAF rates for 28-day and 84-day terms. After the failure of Lehman,
the relationship between DW and TAF rates is just reverse, with rates being approximately ‡at for about
12
two years. Figure 14 provides additional information about these auctions. Total demand of funds in TAF
auctions before the Lehman failure was signi…cantly higher than the funds supplied, which implies that
bidding behavior by banks had necessarily an e¤ect on rates. After the failure of Lehman, the supply of
funds was signi…cantly higher than the observed demand.
Second, the model provides predictions about the funding cost in the post Lehman period in Lemma
1 and equation (4). These predictions depend also on parameters such as ,
and
that can be to some
extent represented in the econometric model by key variables such as bank pro…ts, leverage ratios, liquidity
ratios, or other solvency ratios. To test these predictions, we use …rst a simple regression to provide more
convincing evidence (than the stylized facts we have so far) about good banks using the access to TAF to
signal themselves. We use the following simple econometric speci…cation:
FundingCosti;post =
0
+
T AF T AFi;pre
+
DW DWi;pre
+
B Bi;post
+
X Xi;post
+ "i ;
(16)
where FundingCosti;post is the cost of funding reported by bank i in a period posterior to the Lehman
Brothers failure, Bi;post are bank-level variables, and Xi;post are market-level variables. T AFi;pre is a
dummy variable equal to one if the bank i was mainly a user of the TAF before the Lehman Brothers
failure, and DWi;pre is a dummy variable equal to one if the bank i was mainly a user of the DW before
the Lehman Brothers failure. In order to isolate the observed funding cost from the period of …nancial
turbulence, a quarter that is distant enough from the failure of Lehman (such as late 2009 or 2010) would
be appropriate to be used in this regression. For bank-level variables we use leverage ratios, liquidity ratios,
wholesale funding dependence and other relevant variables. For market-level variables we use percent of
subprime mortgages, unemployment ratios. To control for potential multimarket presence of banks, we
use the weighted average of these market level variables, using the share of branch deposits in every state
as the weight. Many of these variables could represent to some extent the parameters
,
and
in the
model. All variables are de…ned in the appendix.
The econometric model in (16) can be signi…cantly improved. Unobserved bank and time e¤ects
correlated with the use of DW or TAF could bias the estimations. Therefore, in a second speci…cation, we
use two pooled cross-sections, one for the pre-Lehman period, and one for the post-Lehman period, so we
can include …xed e¤ects for banks and periods. We need to consider two periods that are distant enough
so we have a cleaner identi…cation of the e¤ects. We take di¤erences between the two periods for every
bank so we can estimate the following regression in di¤erences:
FundingCosti =
0
0
+
T AF T AFi;pre
+
DW DWi;pre
+
0
B
Bi +
0
X
Xi + "0i
(17)
This model is equivalent to the standard di¤erence-in-di¤erence estimator widely used in economic
research. Alternatively, to verify the robustness of our results we can use other estimators to identify these
e¤ects. We use a matching estimator from (Abadie and Imbens (2006)) (see Abadie et al. (2004) for a
practical implementation) that tries to match banks that do not use any facility to banks that use TAF
or DW based on the closest vector of observable variables, and then estimate the treatment e¤ect of using
every facility. We use similar control variables as in (16) and calculate not only the average treatment
e¤ect, but also the average treatment e¤ect on the treated, and we use several periods for the post-Lehman
13
period:
Average E¤ ect of facility F = E [FundingCostF -FundingCostN o F ]
(18)
Average E¤ ect of facility F (on treated) = E [FundingCostF -FundingCostN o F j F is used] ;
(19)
where F = T AF; DW
In the next section we show the results of applying this empirical strategy to estimate the e¤ect of
accessing TAF or DW before the failure of Lehman on the cost of funding in the period after the failure of
Lehman.
6.2
Results
We …rst show the results of the simple cross-section regression from (16) in Table 5 using quarter 2010Q4.
We use controls lagged by one quarter (2010Q3) to predict funding cost in 2010Q4 (we have also used
longer lags and we still obtain similar results). We observe a negative and signi…cant e¤ect of banks that
go to DW. The e¤ect is economically not very large, about 7 to 10 basis points lower than banks that do
not go to DW. For the case of TAF, we …nd a more negative and signi…cant e¤ect that can vary between
16 to 33 basis points. The di¤erence in the two dummy variables shows that banks that go to TAF have a
lower funding cost than the banks that go to the DW, con…rming the predictions of our model in Lemma
1 and equation (4). However, our model predicts that banks that do not go to either facility have a lower
funding cost and we obtain the reverse result from our regression. We consistently obtain this result in
all the other econometric speci…cations used. The rest of the terms in the regression give mostly intuitive
signs when we consider how key bank indicators a¤ect the beliefs of external investors about the quality
of the bank. Most variables we use are related either with solvency risk, or with liquidity risk, which
should have a signi…cant e¤ect on the external investors beliefs and therefore on the risk premium charged
to the banks when obtaining external funding. For example, banks that are larger have lower funding
costs. Intuitively, scale economies may arise from the fact that large banks have access to more funding
markets, or large banks can be considered as too-big-to-fail, and therefore are regarded as less risky by
funding markets. Also, banks with lower leverage ratio tend to have lower funding costs. More pro…table
banks (using return over assets) and more liquid banks have also a lower funding cost. The dependence
of banks to wholesale funding is a key variable that directly a¤ects the cost of funding because wholesale
funds tend to be more expensive. When controlling for a broad de…nition of wholesale funds (that consider
all non-deposits funding), we still obtain the expected signs in the TAF and DW dummy variables. A
50% increase in the use of wholesale funds by banks increases the cost of funding between 30 and 37 basis
points. When using a more narrow de…nition of wholesale funds, the expected e¤ects of TAF and DW
prevail, but the sign of the control becomes negative, which is not intuitive. The signs corresponding
to non performing assets and importance of real estate assets are also intuitive. However, we …nd a not
very intuitive sign for the e¤ect of the market level variables, such as exposure to subprime mortgages
and unemployment. Finally, we also take into account the access of banks to the Troubled Asset Relief
14
Program (TARP) that injected capital in troubled banks. We …nd a positive and signi…cant e¤ect in most
cases. The positive sign suggests that …rms that received capital injections were still perceived as not safe
despite their participation in this program. The e¤ect is economically small (about 4 basis points).
For robustness, we show same regressions for quarter 2010Q2 in Table 6. We …nd similar results to the
previous regression for the key dummy variables and the controls considered.
As we explained in the previous section, the cross section model is too simple and the key dummy
variables parameters estimated could be biased. We show equations in di¤erences in Table 7 for quarter
2010Q4 and we obtain similar results. When using di¤erences, we …nd the e¤ect of DW is more economically
signi…cant than in the case of pure cross section (between 14 and 17 basis points). The e¤ect of TAF is
also more economically signi…cant (between 39 and 46 basis points). The constant term of the regression
is negative, which shows that di¤erences in funding cost for all banks are negative between 2010Q4 and
2007Q2. This is probably due to the decrease in nominal interest rates for this period (see Figure 26).
Some of the estimates corresponding to some control variables (size,leverage) give intuitive signs as before,
whereas other variables (leverage, liquidity ratio, non performing assets) give opposite signs to the estimates
obtained before. More importantly, the e¤ect of wholesale funds, both in the wide or narrow de…nitions,
are positive as we would expect. Also, we …nd that the e¤ect of exposure to subprime mortgages and
unemployment in the state is positive which is intuitive. Another change with respect to the cross section
analysis is the sign of TARP, which is negative, and economically of similar importance to the e¤ect of DW
and TAF (about 24 basis points) suggesting that banks that received capital injections were considered as
safer by …nancial markets. For robustness, we show same regressions for quarter 2010Q2 in Table 10 and
we …nd similar results.
Finally, we use a matching estimator from (Abadie and Imbens (2006)) to verify if these results still
hold after using a di¤erent methodology. For robustness, we use two di¤erent periods as before, 2010Q4
and 2010Q2, and we look at the average e¤ect and also just the e¤ect on the banks that use the facility
(e¤ect on the treated). To do the matching between …rms that go to TAF or DW and the banks that do
not go, we use similar variables that we used in the previous regressions. Again, we …nd similar e¤ects to
the previous regressions. The e¤ect of going to TAF is negative and signi…cantly higher in absolute value
that the e¤ect of DW, which is typically small and in some cases, not signi…cant. For example, we …nd
e¤ects for TAF between 15 and 33 basis points, whereas the e¤ect of the DW is between 1 and 5 basis
points. These e¤ects are in general smaller than the regression in di¤erences.
In summary, our empirical results show that when we control for other relevant variables, the banks that
go to TAF have typically a funding cost lower than bank that go to the DW. The di¤erential e¤ect between
the two groups of banks is typically of the order of 20-40 basis points, depending on the speci…cation used.
We also usually …nd intuitive results for the e¤ects of other relevant variables, such as the e¤ect of the
funding structure, the level of riskiness of the banks, the liquidity and leverage ratios, and other relevant
demographic variables. These controls have intuitive e¤ects in terms of the solvency and liquidity risks of
the banks. These results con…rm our predictions from the model about the perceived riskiness of banks
by the …nancial markets after they accessed the TAF and DW. However, we do not …nd that banks that
do not use these facilities have a lower funding cost as the model predicts. These results are robust for
di¤erent speci…cations of the econometric model used.
15
7
Conclusion
[TO BE COMPLETED]
16
References
Abadie, A., Drukker, D., Herr, J. L. and Imbens, G. W.: 2004, Implementing matching estimators for
average treatment e¤ects in stata, Stata journal 4, 290–311.
Abadie, A. and Imbens, G. W.: 2006, Large sample properties of matching estimators for average treatment
e¤ects, Econometrica 74(1), 235–267.
Acharya, V., Gromb, D. and Yorulmazer, T.: 2011, Imperfect competition in the interbank market for
liquidity as a rationale for central banking.
Armantier, O., Ghysels, E., Sarkar, A. and Shrader, J.: 2011, Stigma in …nancial markets: Evidence from
liquidity auctions and discount window borrowing during the crisis, FRB of New York Sta¤ Report (483).
Armantier, O., Krieger, S. and McAndrews, J.: 2008, The federal reserve’s term auction facility, current
issues in economics and Finance 14(5).
Berger, A. N., Black, L. K., Bouwman, C. H. and Dlugosz, J.: 2013, The federal reserve’s discount window
and taf programs: Pushing on a string?, Technical report, Working Paper.
Bernanke, B.: 2009, The crisis and the policy response, Stamp Lecture, London School of Economics,
January 13.
Cecchetti, S. G.: 2009, Crisis and responses: the federal reserve in the early stages of the …nancial crisis,
The Journal of Economic Perspectives 23(1), 51–76.
Courtois, R. and Ennis, H. M.: 2010, Is there stigma associated with discount window borrowing?, Richmond Fed Economic Brief (May).
Ennis, H. and Weinberg, J.: 2009, Over-the-counter loans, adverse selection, and stigma in the interbank
market, Federal Reserve Bank of Richmond Working Paper pp. 10–07.
Fur…ne, C.: 2005, Discount window borrowing: Understanding recent experience, Chicago Fed Letter 212.
Klee, E.: 2011, The …rst line of defense: The discount window during the early stages of the …nancial crisis.
Puddu, S. and Wälchli, A.: 2012, Taf e¤ect on liquidity risk exposure, Technical report, Working Paper.
17
A
Theory model …gures
Figure 1: Timeline of the model
Figure 2:
Figure 3: Separating equilibrium with ‡exible discount window.
18
Figure 4: Comparative statics of separating equilibrium (‡exible discount window).
B
Proofs
B.1
Proof of Lemma 2
We know r2 ( ) =
@
@
0
=
+ (1
(1
)
+1
)(1
[ + (1
and
0
=
+(1
)(1
).
Note that
) (1 1 + )
(1
) + (1
)(1
)
1
=
=
)(1
)]2
[ + (1
)(1
)]2
[ + (1
(1
)
@ 0
=
> 0:
@
[ + (1
)(1
)]2
)(1
)]2
> 0 (20)
(21)
Note that r2 ( ) is increasing in :
@r2
(1
=
@
(1
=
=
which is intuitive: If
)(
)(
(1
(
+ 1)
(
1)(
2
(
+ 1)
) + (1
)+(
(
+ 1)2
)
> 0;
+ 1)2
)
1)(
=
)
(22)
increases, the probability of having a bad outcome is higher, and therefore the risk
19
Figure 5: Separating equilibrium without ‡exible discount window.
Figure 6:
premium increases. We can also obtain the cross partial derivative:
@ 2 r2
=
@ @
=
+ 1)2 2(
+ 1) (1
4
(
+ 1)
+ 1)[ (
+ 1) 2 (1
(
+ 1)4
(
(
)
)]
< 0:
(23)
Also we can show that r2 ( ) is increasing in :
(
@r2
=
@
(
=
=
(
+ 1)
(
(
+ 1)2
)
+ (
(
+ 1)2
+ 1)2
20
< 0:
)
=
)
(24)
Therefore, we obtain the following comparative statics
dr2 ( =
d
dr2 ( =
d
dr2 ( =
d
B.2
0)
@r2 ( =
@
@r2 ( =
0)
=
@
@r2 ( =
0)
=
@
=
0) @ 0
<0
@
0) @ 0
<0
@
0)
> 0:
(25)
(26)
(27)
Proof of Proposition 2
In …gure 3 it is easy to see that when rd is small enough, then the equilibrium region for rt is such that
rt > rd .
In order to …nd comparative statics results of the equilibrium rates with respect to ; and , we
need to study how the equilibrium region de…ned by (10) and (15) changes with these parameters. The
equilibrium conditions can be written as rt rd + I2 and rt
rd + I1 . We …nd a simple expression for
the intercept of (15), I1
r2 (0) + (1
)r2 ( 0 ) r2 ( ) :
I1
r2 (0)
=
r2 ( ) + (1
)r2 ( 0 ) =
1
1
+1
+ (1
)
(1
1
1
0
0
+1
)
+ (1
+1
)
(1
0)
0
+1
:
(28)
Also I1 can be written as
I1 = r2 (0)
To study the sign of
@I1
@
r2 ( ) + (1
)r2 ( 0 ) = (r2 (0)
r2 ( 0 ) + (1
Since r2 (0) r2 ( 0 ) > 0 and from (26) we have
1
if ! 1, then @I
@ > 0.
@I1
@
r2 ( ):
(29)
we di¤erentiate (29):
@I1
= r2 (0)
@
To study the sign of
r2 ( 0 )) + r2 ( 0 )
dr2 ( 0 )
d
)
dr2 ( 0 )
:
d
< 0; then the sign of
@I1
@
is ambiguous. However,
we di¤erentiate (29):
@I1
@r2 (0)
=
@
@
@r2 ( )
+ (1
@
)
@r2 ( 0 )
:
@
This can be positive or negative depending on the value of parameters. Because (23) is satis…ed, if
1
1
! 0 then @I
! 1 then @I
! 1 then , I1 ! +1 and if ! 0
@ > 0; and if
@ < 0. Also, using (28), if
then I1 ! 0.
Finally, using (25), we obtain
@I1
@
< 0.
We also study the intercept of (10), r2 (0)
r2 ( );
I2
r2 (0)
21
r2 ( ):
(30)
It easy to show
@I2
=0
@
@I2
= 0:
@
(31)
(32)
Also,
@I2
@r2 (0)
=
@
@
@r2 ( )
:
@
Because (23) is satis…ed, then we have
@I2
> 0:
@
(33)
Using the derivatives of I1 and I2 , and using a graphical argument (see Figure 4), we show the following:
If
! 1, then rt = rd + r2 (0)
r2 ( ); and the equilibrium region of rt is shifted up (i.e, rt increases).
If increases, the equilibrium region of rt does not have a clear behavior (I2 increases, but I1 does not
have a clear pattern). However, if ! 1 then rd ! +1.
If
C
C.1
increases, the equilibrium region of rd is shifted down (i.e, rt decreases)
Access to TAF and Discount Window
Characteristics of banks that accessed discount window and TAF
22
23
obtained by averaing quarterly reports after the failure. Source: FDIC Call Reports.
NOTE: We show some relevant distribution of characteristics of banks that mainly accessed DW or TAF one year before failure of Lehman Brothers. Values shown are
Figure 7: Relevant bank-level and market-level variables, 2009Q3
24
obtained by averaing quarterly reports after the failure. Source: FDIC Call Reports.
NOTE: We show some relevant distribution of characteristics of banks that mainly accessed DW or TAF one year before failure of Lehman Brothers. Values shown are
Figure 8: Relevant bank-level and market-level variables, 2009Q3
25
obtained before the failure. Source: FDIC Call Reports.
NOTE: We show some relevant distribution of characteristics of banks that mainly accessed DW or TAF one year after failure of Lehman Brothers. Values shown are
Figure 9: Relevant bank-level and market-level variables, 2007Q4
C.2
Discount window and TAF rates
Figure 10: Borrowing cost of DW and TAF. This …gure displays weekly DW primary rates (in blue) and
stopout rates for TAF auctions with maturities of 13 days (in red), 28 days (in green), and 84 days (in
yellow). Also, the …gure reports the event of Lehman failure.
C.3
Other statistics of access about discount window and TAF
26
27
1.27
2.22
8.57
11.74
13.41
5.19
(6)
(7)
(8)
(9)
5.69
-.38
-8.64
9.76
12.69
-.14
10.91
2.54
.
2.81
.05
2.55
2.55
7.66
24.11
1.26
2.23
8.55
92.40
13.02
2.25
27.1
.65
.
20.2
.07
29.27
15.69
17.22
332.25
Other banks
DW banks
mean
sd
mean
sd
to Funding Facilities
28.22 73.91
(5)
16.62 7.58
Panel B. Balance Sheet Indicators
65.01 203.14 .57
6.62
14.34 16.13
20.39 79.22
.62
1.52
.6
4.36
12.77 3.08
10.17 3.57
2.59
.76
2.66
.74
.
.
.
.
20.61 54.16
.25
2.42
.03
.02
.05
.07
17.06 56.72
.12
2.08
16.32 40.28
.26
2.36
Panel C. Macroeconomic Indicators
5.46
1.56
5.41
1.15
-.9
2.16
-.24
2.1
-9.09 7.72
-6.42 6.89
30.71
6.18
7.43
(3)
(4)
Pre-Lehman
TAF banks
mean
sd
Panel A. Access
5.46
-.93
-9.11
66.90
16.25
-.01
12.84
2.21
.
19.53
.1
18.91
18.24
8.86
11.66
8.63
1.58
2.17
7.8
202.75
23.62
1.65
3.23
.87
.
49.69
.18
61.89
45.44
5.37
19.32
6.71
(10)
(11)
Post-Lehman
TAF banks
mean
sd
5.4
-.24
-6.4
.59
19.68
.26
10.58
2.47
.
.26
.06
.14
.27
26.51
13.57
6.21
.16
1.07
5.75
47.51
1.14
2.1
6.88
7.2
78.11
3.87
6.21
.72
.
2.48
.08
2.63
2.56
14.72
32.16
5.4
.66
5.24
12.68
25.95
Other banks
mean
sd
(12)
NOTE: We show key statistics for TAF mainly, DW mainly and all other banks. We also show statistics for the pre-Lehman period and the post-Lehman period.
5.68
-.37
-8.66
Unemployment
GDP Growth (%)
Change in Housing Prices (%)
16.44
6.89
73.15
11.72
1.27
4.37
.66
.
11.67
.04
24.63
10.14
89.31
6.87
8.27
13.09
.59
11.67
2.74
.
2.3
.04
2.28
2.03
22.96
8.95
Total Assets (billions)
Capital bu¤er (Tier1 ratio)
ROA
Leverage Ratio
Funding Cost
Non-performing Assets
Real estate loans (billions)
Liquidity
Wholesale Funding (billions)
Insured Deposits (billions)
DW Access frequency
TAF Access frequency
DW Loan Amount (billions)
TAF Loan Amount (billions)
DW Term (days)
TAF Term (days)
DW Borrowers/Day
TAF Borrowers/Day
(2)
DW banks
mean
sd
(1)
Table 1: Summary Statistics for mainly TAF, mainly DW and all other banks
28
mean
29
660
17
12
55
923
11
21
17
832
31
7
9
1,352
57
9
sd
35
2,901
42
63
20
3,866
68
114
8
1,354
12
5
5
2,600
28
7
min
1
0
0
1
15
0
0
1
5
5
28
1
1
5
13
2
p1
1
0
1
1
16
0
1
1
5
5
28
1
1
5
17
2
p25
16
5
1
1
40
3
1
1
12
50
28
2
4
60
28
4
p50
22
32
1
2
54
9
1
3
16
150
28
7
8
250
70
6
p75
29
148
14
6
68
37
3
11
23
1,000
28
10
11
1,500
84
12
p90
38
617
56
22
83
600
27
50
25
3,000
28
16
18
3,500
84
18
p99
197
14,200
140
154
107
23,500
93
188
32
5,000
84
19
20
15,000
85
28
max
209
44,110
729
1,156
118
61,000
1,753
3,097
32
7,500
84
19
20
15,000
85
28
N
160
4,617
4,617
387
337
18,502
18,502
899
21
349
349
48
37
329
329
38
periods.
NOTE: We show key statistics for banks that accessed DW mainly and banks that accessed TAF mainly. We show statistics for the before-Lehman and after-Lehman
Before Lehman, DW mainly: Borrowers/Day
Before Lehman, DW mainly: Amount (millions)
Before Lehman, DW mainly: Term (days)
Before Lehman, DW mainly: Access Frequency
After Lehman, DW mainly: Borrowers/Day
After Lehman, DW mainly: Amount (millions)
After Lehman, DW mainly: Term (days)
After Lehman, DW mainly: Access Frequency
Before Lehman, TAF mainly: Borrowers/Day
Before Lehman, TAF mainly: Amount (millions)
Before Lehman, TAF mainly: Term (days)
Before Lehman, TAF mainly: Access Frequency
After Lehman, TAF mainly: Borrowers/Day
After Lehman, TAF mainly: Amount (millions)
After Lehman, TAF mainly: Term (days)
After Lehman, TAF mainly: Access Frequency
Table 2: Summary Statistics for Banks that are DW mainly or TAF mainly
Figure 11: TAF o¤ered vs. submitted amounts. This …gure plots supplied funds (in red) and total
submitted bids (in blue) in each quarter of the TAF program.
Table 3: How many banks accessed DW and TAF?
Number
Frequency
DWMain
994
77
29
TAFMain
146
11
Neither
158
12
Figure 12: Number of banks accessing DW per day.
Figure 13: Evolution number of banks accessing TAF
30
Figure 14: Outcome of TAF auctions. Figure plots the average bid-to-cover ratio (red columns) which is
computed as the ratio of total submitted bids to total o¤ered TAF funds. Stopout rate in excess of the
minimum bid rate (in blue) is the di¤erence between the stopout rate and the minimum accepted bid rate
as set by the Fed. Number of bidders (in green) is the average number of bidders in auctions held during
a given quarter (right hand scale). Finally, the …gure reports several market and policy events.
Figure 15: Primary credit in DW and TAF loans. The …gure plots weekly outstanding Federal Reserve
credit for the primary DW (in blue) and TAF programs (in red). The …gure is generated using data on
DW loans to individual DIs that were released by the Fed in March 2011 in response to a Freedom of
Information Act request and subsequent court ruling. The data include loans to individual institutions
made between August 20, 2007 and March 1, 2010.
31
Figure 16: Secondary and seasonal loans in DW.
Figure 17: Funding structure of TAF mainly banks.
32
Figure 18: Funding structure of DW banks.
Figure 19: Funding structure of "other" banks.
33
Figure 20: Share of DW loans on total liabilities per bank.
Figure 21: Share of TAF loans on total liabilities per bank.
34
Figure 22: TAF borrowing by type of bank
Figure 23: DW borrowing by type of bank
35
C.4
Failures and access to DW and TAF
Table 4: How many banks accessed DW and TAF before Lehman and failed after Lehman?
Discount Window
TAF
Total access
387
45
36
Total fail
50
3
% fail
12.9%
6.67%
D
Regressions of econometric model
De…nition of variables
Liquidity access variables:
DW main: Dummy variable equal to 1 if the bank accessed the discount window before Lehman
Brothers failure.
TAF main: Dummy variable equal to 1 if the bank accessed TAF before Lehman Brothers failure.
TARP: Dummy variable equal to 1 if the bank accessed the TARP program.
Bank level variables (call reports):
Assets: Total bank assets in billion US$.
Leverage: Ratio of total assets to Tier 1 core capital.
ROA: Return on assets (Net income after taxes as % of assets).
Liquidity ratio: Ratio of cash & Balances due from depository institutions to total assets.
% wholesale funds: Ratio of total liabilities (excluding insured deposits) to total liabilities.
% wholesale funds (narrow): Ratio of ( total liabilities excluding insured deposits, subordinated debt,
and other borrowed money) to total liabilities. "other borrowed money" excludes deposits, federal
funds purchased, securities sold under agreements to repurchase in domestic o¢ ces of the bank, and
trading liabilities)
% Risk weighted assets: Ratio of risk weighted assets to total assets.
% Non perf assets: Ratio of (other real estate owned + assets past due 30-89 days + assets past due
90+ days + nonaccrual asset) to total assets.
% Uninsured deposits: Ratio of uninsured deposits to total liabilities.
% Real estate: Ratio of real estate loans to total assets.
Market level variables:
% subprime mortgages: Ratio of subprime mortgages to total mortgages.
% unemployment: State level unemployment.
37
Table 5: Funding cost in 2010q4 as function of variables in 2010q3 and DW/TAF access
VARIABLES
DW main
TAF main
TARP
Assets
Leverage
ROA
Liquidity ratio
% wholesale funds
% Uninsured deposits
% Real estate
% Non perf assets
% subprime mortgages
% unemployment
(1)
intexpy
(2)
intexpy
(3)
intexpy
(4)
intexpy
(5)
intexpy
-0.0800***
(0.0267)
-0.128*
(0.0688)
0.0307*
(0.0183)
-0.000292**
(0.000147)
0.00592***
(0.00224)
-0.0344***
(0.0120)
-0.862***
(0.0700)
0.00684***
(0.00148)
-0.0182***
(0.00165)
0.00461***
(0.000512)
0.0160***
(0.00217)
-0.00957***
(0.00322)
-0.00113
(0.00413)
-0.0406
(0.0268)
-0.0511
(0.0770)
0.0565***
(0.0186)
-9.47e-05
(0.000139)
0.00670***
(0.00231)
-0.0236*
(0.0136)
-0.901***
(0.0713)
-0.0765***
(0.0269)
-0.107
(0.0714)
0.0188
(0.0184)
-0.000296**
(0.000148)
0.00871***
(0.00241)
-0.0444***
(0.0112)
-0.701***
(0.0758)
0.00704***
(0.00147)
-0.0191***
(0.00166)
0.00408***
(0.000625)
-0.0785***
(0.0268)
-0.124*
(0.0697)
0.0417**
(0.0177)
-0.000296**
(0.000148)
0.00822***
(0.00198)
-0.0699**
(0.0280)
-0.139*
(0.0791)
0.0261
(0.0197)
-0.000233*
(0.000138)
0.00699***
(0.00235)
-0.0246**
(0.0125)
-0.995***
(0.0693)
-0.00448***
(0.000609)
% wholesale funds (narrow)
-0.00429***
(0.00144)
0.00433***
(0.000493)
0.0171***
(0.00235)
-0.00956***
(0.00325)
0.000781
(0.00416)
-0.00719***
(0.00141)
% Risk weighted assets
Constant
Observations
R-squared
1.257***
(0.0490)
1.298***
(0.0523)
-0.00598*
(0.00336)
0.00326
(0.00425)
-0.810***
(0.0655)
0.00591***
(0.00144)
-0.0172***
(0.00164)
0.00485***
(0.000489)
0.0211***
(0.00205)
-0.00967***
(0.00324)
0.000612
(0.00413)
4.37e-07***
(6.69e-08)
0.945***
(0.0590)
1.172***
(0.0337)
1.247***
(0.0511)
6,916
0.269
6,916
0.220
6,916
6,916
6,916
0.279
0.272
0.277
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
0.00514***
(0.000507)
0.0196***
(0.00231)
-0.0116***
(0.00336)
-0.00438
(0.00426)
NOTE: Regression of levels of funding cost in 2010Q4 as function of variables as 2010Q3. DW_main= Dummy equal to 1 if
bank was DW mainly in before Lehman period. TAF_main= Dummy equal to 1 if bank was TAF mainly in before Lehman
period. TARP= Dummy equal to 1 if bank was part of the TARP program.
38
Table 6: Funding cost in 2010q2 as function of variables in 2010q1 and DW/TAF access
VARIABLES
DW main
TAF main
TARP
Assets
Leverage
ROA
Liquidity ratio
% wholesale funds
% Uninsured deposits
% Real estate
% Non perf assets
% subprime mortgages
% unemployment
(1)
intexpy
(2)
intexpy
(3)
intexpy
(4)
intexpy
(5)
intexpy
-0.0615**
(0.0286)
-0.180**
(0.0794)
0.0406**
(0.0181)
-0.000353**
(0.000150)
-0.00493
(0.00849)
-0.0380***
(0.00971)
-0.829***
(0.0705)
0.00758***
(0.00161)
-0.0190***
(0.00177)
0.00497***
(0.000454)
0.0245***
(0.00366)
-0.00647*
(0.00365)
-0.00331
(0.00447)
-0.0485
(0.0301)
-0.149*
(0.0809)
0.0238
(0.0182)
-0.000336**
(0.000151)
-0.00179
(0.00962)
-0.0492***
(0.0107)
-0.657***
(0.0777)
0.00782***
(0.00162)
-0.0202***
(0.00185)
0.00506***
(0.000585)
-0.0603**
(0.0290)
-0.178**
(0.0810)
0.0488***
(0.0181)
-0.000352**
(0.000151)
-0.00359
(0.00950)
-0.0517*
(0.0302)
-0.164*
(0.0887)
0.0451**
(0.0195)
-0.000279**
(0.000142)
-0.00339
(0.00978)
-0.785***
(0.0697)
0.00659***
(0.00157)
-0.0179***
(0.00173)
0.00519***
(0.000458)
0.0300***
(0.00469)
-0.00657*
(0.00377)
-0.000793
(0.00439)
-0.949***
(0.0654)
-0.00449***
(0.000625)
-0.0522*
(0.0299)
-0.165*
(0.0862)
0.0383**
(0.0195)
-0.000277**
(0.000140)
-0.00448
(0.00902)
-0.0308***
(0.0107)
-0.993***
(0.0733)
-0.00422***
(0.000605)
0.00580***
(0.000441)
0.0320***
(0.00486)
-0.00829**
(0.00389)
-0.00438
(0.00447)
0.00565***
(0.000438)
0.0277***
(0.00393)
-0.00830**
(0.00379)
-0.00661
(0.00464)
1.301***
(0.0850)
1.311***
(0.0875)
1.379***
(0.0943)
7,075
0.211
7,075
0.223
% Risk weighted assets
Constant
Observations
R-squared
1.384***
(0.0886)
-0.00474
(0.00371)
0.00316
(0.00438)
4.13e-07***
(6.68e-08)
1.089***
(0.105)
7,075
7,075
7,075
0.284
0.264
0.266
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
NOTE: Regression of levels of funding cost in 2010Q2 as function of variables as 2010Q1. DW_main= Dummy equal to 1 if
bank was DW mainly in before Lehman period. TAF_main= Dummy equal to 1 if bank was TAF mainly in before Lehman
period. TARP= Dummy equal to 1 if bank was part of the TARP program.
39
Table 7: Di¤erences of funding cost (2010q4 vs 2007q4) as function of DW/TAF access
VARIABLES
DW main
TAF main
TARP
Asset
Leverage
ROA
Liquidity ratio
% wholesale funds
% Uninsured deposits
% Real estate
% Non perf assets
% subprime mortgages
% unemployment
(1)
Di¤Funding
(2)
Di¤Funding
(3)
Di¤Funding
(4)
Di¤Funding
(5)
Di¤Funding
-0.149***
(0.0330)
-0.431***
(0.144)
-0.235***
(0.0243)
-0.00216***
(0.000771)
0.00229***
(0.000818)
0.00723
(0.00961)
0.182
(0.152)
0.0119***
(0.00246)
-0.00862***
(0.00264)
0.0103***
(0.00118)
-0.00643**
(0.00298)
0.0400**
(0.0162)
0.0326***
(0.00779)
-0.144***
(0.0326)
-0.411***
(0.144)
-0.223***
(0.0246)
-0.00208***
(0.000715)
0.00209***
(0.000796)
0.0110
(0.00858)
0.324**
(0.159)
0.0120***
(0.00249)
-0.00878***
(0.00267)
0.00762***
(0.00140)
-0.161***
(0.0337)
-0.471***
(0.140)
-0.240***
(0.0242)
-0.00221***
(0.000781)
0.00235***
(0.000829)
-0.177***
(0.0334)
-0.476***
(0.140)
-0.251***
(0.0242)
-0.00222***
(0.000769)
0.00235***
(0.000812)
-0.177***
(0.0334)
-0.476***
(0.140)
-0.251***
(0.0242)
-0.00222***
(0.000769)
0.00235***
(0.000812)
0.191
(0.151)
0.00540***
(0.00103)
0.132
(0.153)
0.132
(0.153)
0.0111***
(0.00118)
-0.00693***
(0.00243)
0.0354**
(0.0152)
0.0320***
(0.00748)
0.0107***
(0.00119)
-0.00916***
(0.00242)
0.0377**
(0.0152)
0.0278***
(0.00753)
0.0107***
(0.00119)
-0.00916***
(0.00242)
0.0377**
(0.0152)
0.0278***
(0.00753)
-1.672***
(0.0386)
0.00181*
(0.00109)
-1.688***
(0.0383)
0.00181*
(0.00109)
-1.688***
(0.0383)
6,709
0.060
6,709
0.060
% Risk weighted assets
0.0385**
(0.0163)
0.0298***
(0.00769)
5.51e-07***
(1.58e-07)
% wholesale funds (narrow)
Constant
Observations
R-squared
-1.673***
(0.0395)
-1.662***
(0.0393)
6,709
6,709
6,709
0.076
0.079
0.068
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
NOTE: Di¤erences equation in funding cost from 2010q4 to 2007q4. DW_main= Dummy equal to 1 if bank was DW mainly
in before Lehman period. TAF_main= Dummy equal to 1 if bank was TAF mainly in before Lehman period. TARP=
Dummy equal to 1 if bank was part of the TARP program.
40
Table 8: Di¤erences of wholesale funding (2010q4 vs 2007q4) as function of DW/TAF access
VARIABLES
DW main
TAF main
TARP
Asset
Leverage
ROA
Liquidity ratio
% Real estate
% Non perf assets
% subprime mortgages
% unemployment
(1)
Di¤Whole
(2)
Di¤Whole
(3)
Di¤Whole
(4)
Di¤Whole
(5)
Di¤Whole
-3.606***
(0.692)
-1.134
(3.085)
-2.535***
(0.501)
-0.00823
(0.00713)
-0.00392
(0.0132)
0.162
(0.138)
-15.94***
(2.835)
-0.143***
(0.0238)
-0.581***
(0.0517)
0.591***
(0.204)
-1.125***
(0.114)
-3.638***
(0.716)
-0.780
(3.103)
-2.548***
(0.510)
-0.00659
(0.00765)
-0.0270*
(0.0151)
0.603***
(0.153)
-11.86***
(2.739)
-0.207***
(0.0283)
-3.638***
(0.693)
-1.137
(3.083)
-2.537***
(0.501)
-0.00818
(0.00712)
-0.00567
(0.0132)
-3.638***
(0.693)
-1.137
(3.083)
-2.537***
(0.501)
-0.00818
(0.00712)
-0.00567
(0.0132)
-3.638***
(0.693)
-1.137
(3.083)
-2.537***
(0.501)
-0.00818
(0.00712)
-0.00567
(0.0132)
-16.34***
(2.793)
-0.138***
(0.0234)
-0.612***
(0.0439)
0.592***
(0.203)
-1.124***
(0.113)
-16.34***
(2.793)
-0.138***
(0.0234)
-0.612***
(0.0439)
0.592***
(0.203)
-1.124***
(0.113)
-16.34***
(2.793)
-0.138***
(0.0234)
-0.612***
(0.0439)
0.592***
(0.203)
-1.124***
(0.113)
-4.505***
(0.598)
-4.505***
(0.598)
-4.505***
(0.598)
6,709
0.136
6,709
0.136
% Risk weighted assets
Constant
Observations
R-squared
-4.503***
(0.601)
0.453**
(0.209)
-1.506***
(0.113)
1.35e-05***
(3.04e-06)
-3.867***
(0.618)
6,709
6,709
6,709
0.136
0.114
0.136
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
NOTE: Di¤erences equation of percent of wholesale funding from 2010q4 to 2007q4. DW_main= Dummy equal to 1 if bank
was DW mainly in before Lehman period. TAF_main= Dummy equal to 1 if bank was TAF mainly in before Lehman
period. TARP= Dummy equal to 1 if bank was part of the TARP program.
41
Table 9: Di¤erences of wholesale funding (narrow) (2010q4 vs 2007q4) as function of DW/TAF access
VARIABLES
DW main
TAF main
TARP
Asset
Leverage
ROA
Liquidity ratio
% Real estate
% Non perf assets
% subprime mortgages
% unemployment
(1)
Di¤WholeN
(2)
Di¤WholeN
(3)
Di¤WholeN
(4)
Di¤WholeN
(5)
Di¤WholeN
-2.186***
(0.687)
-0.651
(3.094)
-1.905***
(0.501)
-0.0169
(0.0120)
-0.0180
(0.0138)
0.0761
(0.133)
-16.22***
(2.880)
-0.186***
(0.0244)
-0.581***
(0.0482)
0.482**
(0.203)
-1.027***
(0.112)
-2.163***
(0.709)
-0.133
(3.078)
-1.795***
(0.509)
-0.0147
(0.0100)
-0.0405**
(0.0176)
0.505***
(0.142)
-11.13***
(2.715)
-0.270***
(0.0281)
-2.200***
(0.688)
-0.653
(3.094)
-1.905***
(0.501)
-0.0169
(0.0120)
-0.0188
(0.0138)
-2.200***
(0.688)
-0.653
(3.094)
-1.905***
(0.501)
-0.0169
(0.0120)
-0.0188
(0.0138)
-2.200***
(0.688)
-0.653
(3.094)
-1.905***
(0.501)
-0.0169
(0.0120)
-0.0188
(0.0138)
-16.41***
(2.841)
-0.184***
(0.0239)
-0.596***
(0.0405)
0.482**
(0.203)
-1.026***
(0.112)
-16.41***
(2.841)
-0.184***
(0.0239)
-0.596***
(0.0405)
0.482**
(0.203)
-1.026***
(0.112)
-16.41***
(2.841)
-0.184***
(0.0239)
-0.596***
(0.0405)
0.482**
(0.203)
-1.026***
(0.112)
-4.444***
(0.619)
-4.444***
(0.619)
-4.444***
(0.619)
6,709
0.132
6,709
0.132
% Risk weighted assets
Constant
Observations
R-squared
-4.444***
(0.621)
0.344*
(0.208)
-1.392***
(0.112)
1.77e-05***
(2.76e-06)
-3.774***
(0.641)
6,709
6,709
6,709
0.132
0.114
0.132
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
NOTE: Di¤erences equation of percent of wholesale funding (narrow de…nition) from 2010q4 to 2007q4. DW_main=
Dummy equal to 1 if bank was DW mainly in before Lehman period. TAF_main= Dummy equal to 1 if bank was TAF
mainly in before Lehman period. TARP= Dummy equal to 1 if bank was part of the TARP program.
42
Table 10: Di¤erences of funding cost (2010q2 vs 2007q2) as function of DW/TAF access
VARIABLES
DW main
TAF main
TARP
Asset
Leverage
ROA
Liquidity ratio
% wholesale funds
% Uninsured deposits
% Real estate
% Non perf assets
% subprime mortgages
% unemployment
(1)
Di¤Funding
(2)
Di¤Funding
(3)
Di¤Funding
(4)
Di¤Funding
(5)
Di¤Funding
-0.176***
(0.0365)
-0.523***
(0.114)
-0.252***
(0.0266)
-0.00269***
(0.000755)
0.00313***
(0.000582)
0.0268***
(0.00401)
0.141
(0.0977)
0.00750***
(0.00115)
-0.00496***
(0.00118)
0.0146***
(0.000822)
-0.00152
(0.00216)
0.0469***
(0.00951)
0.0372***
(0.00604)
-0.171***
(0.0364)
-0.497***
(0.114)
-0.241***
(0.0266)
-0.00261***
(0.000753)
0.00307***
(0.000569)
0.0259***
(0.00379)
0.254**
(0.0992)
0.00754***
(0.00114)
-0.00514***
(0.00117)
0.0116***
(0.000969)
-0.180***
(0.0367)
-0.543***
(0.115)
-0.252***
(0.0267)
-0.00280***
(0.000758)
0.00286***
(0.000582)
-0.188***
(0.0368)
-0.536***
(0.115)
-0.254***
(0.0268)
-0.00276***
(0.000759)
0.00283***
(0.000583)
-0.188***
(0.0368)
-0.536***
(0.115)
-0.254***
(0.0268)
-0.00276***
(0.000759)
0.00283***
(0.000583)
0.0859
(0.0978)
0.00385***
(0.000740)
0.0616
(0.0980)
0.0616
(0.0980)
0.0162***
(0.000798)
-0.00555***
(0.00204)
0.0442***
(0.00952)
0.0372***
(0.00606)
0.0157***
(0.000804)
-0.00791***
(0.00205)
0.0451***
(0.00954)
0.0340***
(0.00608)
0.0157***
(0.000804)
-0.00791***
(0.00205)
0.0451***
(0.00954)
0.0340***
(0.00608)
-1.585***
(0.0301)
0.000198
(0.000756)
-1.599***
(0.0301)
0.000198
(0.000756)
-1.599***
(0.0301)
6,864
0.090
6,864
0.090
% Risk weighted assets
0.0473***
(0.00948)
0.0368***
(0.00586)
5.48e-07***
(9.51e-08)
% wholesale funds (narrow)
Constant
Observations
R-squared
-1.585***
(0.0299)
-1.575***
(0.0298)
6,864
6,864
6,864
0.101
0.106
0.093
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
NOTE: Di¤erences equation of funding cost from 2010q2 to 2007q2. DW_main= Dummy equal to 1 if bank was DW mainly
in before Lehman period. TAF_main= Dummy equal to 1 if bank was TAF mainly in before Lehman period. TARP=
Dummy equal to 1 if bank was part of the TARP program.
43
Table 11: Di¤erences of wholesale funding (2010q2 vs 2007q2) as function of DW/TAF access
VARIABLES
DW main
TAF main
TARP
Asset
Leverage
ROA
Liquidity ratio
% Real estate
% Non perf assets
% subprime mortgages
% unemployment
(1)
Di¤Whole
(2)
Di¤Whole
(3)
Di¤Whole
(4)
Di¤Whole
(5)
Di¤Whole
-2.191***
(0.682)
1.779
(2.839)
-0.738
(0.498)
0.0117
(0.00780)
-0.00669
(0.0169)
0.0506
(0.109)
-6.272**
(2.587)
-0.128***
(0.0217)
-0.640***
(0.0484)
0.230
(0.330)
-0.884***
(0.139)
-2.619***
(0.718)
1.643
(2.973)
-0.903*
(0.512)
0.0122
(0.00926)
-0.0422
(0.0295)
0.446***
(0.114)
-4.148
(2.667)
-0.168***
(0.0262)
-2.197***
(0.680)
1.776
(2.837)
-0.740
(0.498)
0.0117
(0.00779)
-0.00730
(0.0169)
-2.197***
(0.680)
1.776
(2.837)
-0.740
(0.498)
0.0117
(0.00779)
-0.00730
(0.0169)
-2.197***
(0.680)
1.776
(2.837)
-0.740
(0.498)
0.0117
(0.00779)
-0.00730
(0.0169)
-6.362**
(2.573)
-0.125***
(0.0212)
-0.649***
(0.0424)
0.231
(0.329)
-0.884***
(0.139)
-6.362**
(2.573)
-0.125***
(0.0212)
-0.649***
(0.0424)
0.231
(0.329)
-0.884***
(0.139)
-6.362**
(2.573)
-0.125***
(0.0212)
-0.649***
(0.0424)
0.231
(0.329)
-0.884***
(0.139)
-3.714***
(0.674)
-3.714***
(0.674)
-3.714***
(0.674)
6,865
0.116
6,865
0.116
% Risk weighted assets
Constant
Observations
R-squared
-3.716***
(0.675)
0.132
(0.340)
-1.350***
(0.143)
6.68e-06**
(3.01e-06)
-3.101***
(0.697)
6,865
6,865
6,865
0.116
0.074
0.116
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
NOTE: Di¤erences equation of percent of wholesale funding from 2010q2 to 2007q2. DW_main= Dummy equal to 1 if bank
was DW mainly in before Lehman period. TAF_main= Dummy equal to 1 if bank was TAF mainly in before Lehman
period. TARP= Dummy equal to 1 if bank was part of the TARP program.
44
Table 12: Di¤erences of wholesale funding (narrow) (2010q2 vs 2007q2) as function of DW/TAF access
VARIABLES
DW main
TAF main
TARP
Asset
Leverage
ROA
Liquidity ratio
% Real estate
% Non perf assets
% subprime mortgages
% unemployment
(1)
Di¤WholeN
(2)
Di¤WholeN
(3)
Di¤WholeN
(4)
Di¤WholeN
(5)
Di¤WholeN
-1.822***
(0.688)
0.441
(2.849)
-0.949**
(0.478)
0.00693
(0.00745)
-0.0113
(0.0181)
0.0467
(0.110)
-5.384*
(2.785)
-0.168***
(0.0221)
-0.673***
(0.0481)
0.0595
(0.331)
-0.948***
(0.139)
-2.219***
(0.727)
0.535
(2.887)
-1.016**
(0.489)
0.00813
(0.00754)
-0.0483
(0.0316)
0.446***
(0.108)
-2.179
(2.893)
-0.237***
(0.0274)
-1.828***
(0.686)
0.439
(2.849)
-0.950**
(0.478)
0.00691
(0.00746)
-0.0118
(0.0180)
-1.828***
(0.686)
0.439
(2.849)
-0.950**
(0.478)
0.00691
(0.00746)
-0.0118
(0.0180)
-1.828***
(0.686)
0.439
(2.849)
-0.950**
(0.478)
0.00691
(0.00746)
-0.0118
(0.0180)
-5.467**
(2.767)
-0.166***
(0.0216)
-0.681***
(0.0428)
0.0607
(0.330)
-0.948***
(0.139)
-5.467**
(2.767)
-0.166***
(0.0216)
-0.681***
(0.0428)
0.0607
(0.330)
-0.948***
(0.139)
-5.467**
(2.767)
-0.166***
(0.0216)
-0.681***
(0.0428)
0.0607
(0.330)
-0.948***
(0.139)
-3.864***
(0.702)
-3.864***
(0.702)
-3.864***
(0.702)
6,865
0.135
6,865
0.135
% Risk weighted assets
Constant
Observations
R-squared
-3.866***
(0.703)
-0.0384
(0.343)
-1.430***
(0.143)
1.19e-05***
(3.09e-06)
-3.144***
(0.735)
6,865
6,865
6,865
0.135
0.093
0.135
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
NOTE: Di¤erences equation of percent of wholesale funding (narrow de…nition) from 2010q2 to 2007q2. DW_main=
Dummy equal to 1 if bank was DW mainly in before Lehman period. TAF_main= Dummy equal to 1 if bank was TAF
mainly in before Lehman period. TARP= Dummy equal to 1 if bank was part of the TARP program.
45
Table 13: RESULTS FROM MATCHING ESTIMATOR
Treatment e¤ect
2010,Q4:
Average Treatment
Average Treatment
Average Treatment
Average Treatment
Funding cost
E¤ect
E¤ect
E¤ect
E¤ect
(TAF)
on Treated (TAF)
(DW)
on Treated (DW)
-0.326
-0.137
-0.017
-0.048
0.090
0.072
0.032
0.026
2010,Q2:
Average Treatment
Average Treatment
Average Treatment
Average Treatment
E¤ect
E¤ect
E¤ect
E¤ect
(TAF)
on Treated (TAF)
(DW)
on Treated (DW)
-0.358
-0.152
0.012
-0.025
0.094
0.091
0.032
0.026
NOTE: Calculation of e¤ects of DW and TAF on funding cost using Abadie and Imbens (2002) matching estimator. We use
total assets, leverage ratio, liquidity ratio, percent of wholesale funds, percent of real state and non performing assets as
matching variables.
46
E
Other …gures
Figure 24: LIBOR-OIS Spread
Figure 25: Number of bank failures.
47
Figure 26: E¤ect of Fed rate on funding cost.
48