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Federal Reserve Bank of Chicago
Subordinated Debt and Prompt
Corrective Regulatory Action
Douglas D. Evanoff and Larry D. Wall
WP 2003-03
Subordinated Debt and Prompt Corrective Regulatory Action
Douglas D. Evanoff
Federal Reserve Bank of Chicago
devanoff@frbchi.org
Larry D. Wall
Federal Reserve Bank of Atlanta
larry.wall@atl.frb.org
Abstract
Several recent studies have recommended greater reliance on subordinated debt as a tool
to discipline bank risk taking. Some of these proposals recommend using subordinated
debt yield spreads as additional triggers for supervisory discipline under prompt
corrective action (PCA); action that is currently prompted by capital adequacy measures.
This paper provides a theoretical model describing how use of a second market-measure
of bank risk, in addition to the supervisors own internalized information, could improve
bank discipline. We then empirically evaluate the implications of the model. The
evidence suggests that subordinated debt spreads dominate the current capital measures
used to trigger PCA and consideration should be given to using spreads to complement
supervisory discipline. The evidence also suggests that spreads over corporate bonds
may be preferred to using spreads over U.S. Treasuries.
*The authors wish to thank Mark Flannery, Xavier Freixas, Ben Gup, Alan Hess,
George Kaufman, Joe Haubrich, William Perraudin and Mark Vaughan for constructive
comments and suggestions on earlier drafts. The authors also acknowledge the support of
Nancy Andrews, Mark Murawski and George Simler in developing the database used in
the study, and Andy Meyer, Alton Gilbert, and Mark Vaughan for graciously providing
detailed information about their ‘early warning model. The opinions expressed, however,
are those of the authors and not necessarily those of the people mentioned above, the
Federal Reserve Bank of Chicago, Federal Reserve Bank of Atlanta or the Federal
Reserve System.
1
Subordinated Debt and Prompt Corrective Regulatory Action
1. Introduction and overview
Prompt corrective action is based on a simple mandate directed toward bank
supervisors: “resolve the problems of insured depository institutions at the least possible
long-term cost to the deposit insurance fund.”1 The central provisions of prompt
corrective action (PCA) aim to provide a series of interventions as a bank’s financial
condition deteriorates. Moreover, rather than relying solely on supervisory judgment
about a troubled bank’s financial condition, PCA focuses on quantifiable measures of a
bank’s financial condition. In particular, supervisory intervention is currently triggered
by bank capital adequacy ratios.
Although quantifiable measures have a variety of advantages, probably the most
important advantage in the context of PCA is that their use reduces the scope for
supervisors to exercise forbearance. The measure used, however, needs to be closely
associated with the financial condition of the bank if it is to effectively achieve this
purpose. A limitation of focusing almost exclusively on capital adequacy ratios is that
banks and supervisors have substantial influence over the calculation of the numerator
(capital) and denominator (a proxy for risk) of these ratios. The Basel Bank Supervisors
Committee is currently trying to address problems in the calculation of the denominator,
[see BIS (2001)]. Yet the more serious problem lies with the measurement of capital.
Whether capital is measured using historical cost accounting (as is currently the case) or
economic value accounting, a bank’s capital will reflect declines only if the bank
1
Section 131 of FDICIA, titled “Prompt Regulatory Action,” creates a new Section 38 of the Federal
Deposit Insurance Act.
2
voluntarily recognizes the losses or the supervisors force recognition. Given that banks
usually resist recognition of losses that would result in their being undercapitalized under
PCA, responsibility for accurate capital measurement rests with the supervisors. This is
to say, if the supervisors want to exercise forbearance, all they need do is acquiesce to a
bank’s refusal to recognize losses. Prominent examples of such acquiescence by
supervisors include recognition of losses at U.S. banks due to loans to less-developedcountries (mostly in Latin America) in the 1980s and recognition of domestic loan losses
by Japanese regulators throughout the later 1990s.
An alternative to exclusive reliance on capital adequacy ratios would be to use
some market-based risk measure as a trigger for supervisory action. Examples of such
measures would include the spread of subordinated debt obligations over comparable
maturity Treasury obligations as proposed by Evanoff and Wall (2000a) and probabilities
of default calculated using stock returns, such as is done by KMV [see Gunther, Levonian
and Moore (2001)].2 The advantage of using a market-based risk measure is that market
participants have a strong incentive to base their valuations on the expected payouts for
their claim, whether or not a particular price is the one desired by banks and their
regulators. The disadvantage of using a market-based risk measure is that the prices may
incorporate more than the credit risk of the issuer. For example, the price of a bank’s
debt depends in part on factors such as the term structure of interest rates and the liquidity
of the debt obligation.
2
For discussions of the benefits of introducing a mandatory subordinated debt program for large
commercial banks see Evanoff and Wall (2000a,b,c), Kwast, et al. (1999), and Flannery (2001). While
subordinated debt proposals are frequently associated with U.S. banking markets, there is evidence from
European markets suggesting similar potential benefits: see Sironi (2001) and Benink and Wihlborg (2002).
3
The fact that the pricing of market obligations depends on more than the credit
risk of the obligation is one of the primary arguments raised against plans to use marketbased risk measures. Supervisors, it is argued, should not be forced to discipline a bank
based on a market-based measure when they know that the bank is in good financial
condition.3
This paper addresses the criticism of using market-based risk measures in two
ways. First, it provides a simple model to illustrate the underlying wisdom of PCA, that
the use of a quantitative risk measure may produce better outcomes even if the
quantitative risk measure is on average less accurate than supervisory evaluations. The
model considers two cases. The first is one where the supervisor knows the true condition
of some banks, but not others. In this case the quantitative risk measure may improve the
evaluation of banks about which the supervisor is uncertain. In the second case, the
supervisor knows the condition of all banks with certainty and the condition of the bank
is measured with error by the quantitative measure. However, in this situation the
supervisor exercises forbearance toward banks that should be disciplined with positive
probability. The use of a quantitative measure may improve outcomes in this second case
by reducing the probability of forbearance, even though it may result in the disciplining
of a healthy bank.
The second part of the paper provides empirical evidence on the potential use of
subordinated debt yield spreads as a trigger for PCA. This paper extends Evanoff and
Wall (2001) which provided evidence that subordinated debt spreads over both Treasury
and corporate bond indices are better predictors of supervisory ratings than are the risk-
3
Evanoff and Wall (2002, p. 1007) discuss alternative means to incorporate subordinated debt yields in
PCA which some may find less Draconian and more palpable.
4
based capital adequacy ratios. Evanoff and Wall (2002) focus on banks that have high
subordinated debt spreads over Baa yields, but are rated satisfactory by the bank
supervisors. That analysis finds that almost all of the banks with high spreads have some
indication that they are indeed higher risk. In a few cases, the supervisors appeared to be
substituting frequency of exams for reductions in CAMEL or BOPEC ratings. Other
measures of risk considered that indicate that the high spread banks were indeed higher
risk include regulatory early warning scores, market-to-book ratios, credit agency ratings
and recent supervisory ratings.
This paper extends Evanoff and Wall (2002) to examine banks with high
subordinated debt spreads over U.S. Treasury rates. The focus is on spreads over
Treasuries for a couple of reasons. First, spreads over Treasuries may provide more
accurate, albeit more procyclical, measures of banks’ risk than spreads over Baa debt.
The average spread of Baa securities over Treasury securities increases as credit defaults
increase, providing an automatic relaxation of any standard that uses subordinated debt
spreads over a Baa index. The use of a Treasury index may provide a more consistent
test through time. Second, Evanoff and Wall (2002) used spreads over Baa in part for a
practical reason that may no longer be relevant. At the time of their analysis, the stock of
publicly traded Treasury securities was forecast to disappear due to federal government
surpluses. Budgetary developments since the terrorist incidents of September 11, 2001
suggest that the stock of publicly traded Treasury securities may remain substantial.
Evanoff and Wall (2002) discuss the theoretical advantages and disadvantages of the
5
alternative spreads.4 The empirical analysis in this paper may highlight the actual
differences during the sample period.
The paper is organized as follows. The next section contains the theoretical
model describing how use of a second ‘market measure’ of bank risk to initiate PCA may
be beneficial. The third section discusses the data and empirical methodology used to
test the implications of the theory. The fourth presents the results and the final section
summarizes and provides concluding remarks.
2. Disciplining banks with two risk signals
This section develops a model of optimal bank discipline when two risk signals
are available. The first subsection lays out the assumptions of the model. The second
analyzes optimal solutions under the assumption that supervisors act in a socially optimal
manner. The third subsection analyzes the solutions under the assumption that
supervisors sometimes exercise forbearance on weak banks even though the social
welfare would be enhanced if the banks were disciplined. The fourth section discusses
the implications of the results.
2.1
Model assumptions
The objective of the social planner is to establish rules that minimize the cost of
errors in the disciplining of banks. Banks are assumed to be of two quality types Q Î{H,
L}. Type L banks are low quality and should be disciplined by the regulators. Type H
banks are high quality and should not be disciplined. Given that the null hypothesis is
that a bank is solvent, then disciplining a type H bank is a type-1 error that generates total
4
One of the major differences being the extent to which the different spreads ‘bind’ over the business
cycle.
6
social costs of T1. Conversely, failure to discipline a type L bank is a type-2 error that
generates total social costs of T2.
Bank types are not directly observable but bank examiners receive a signal of
each bank’s quality at the end of their examination. The signal to the examiners, R, may
take one of three forms: R Î {A, B, C}. If the examiner receives signals A or C then the
bank’s type is revealed with certainty: signal A signifies a type H bank and signal C
signifies a type L bank. If the signal to the examiner is B, however, then the examiner
knows only that the bank is type H with probability q and type L with probability (1-q).
A second5, independent signal of a bank’s quality, IS, may also be observed:
¥<IS<¥.6 For example, bank capital adequacy ratios are currently used as an
independent signal to prompt PCA. For this signal, the probability that a bank is of high
quality is p(H|IS) and the probability that it is of low quality is (1-p(H|IS)). The
probability that a bank is of high quality is an increasing function of IS:
p’(H|IS) > 0.
This general formulation allows, but does not require that the independent signal be
perfectly correlated with a bank’s condition.
2.2
Model solution without agency problems
If there are no agency problems, the examiner always uses his signal optimally,
and no other signal exists then the decision rule is straightforward. The examiner: (1)
never disciplines banks when the signal is A, (2) always disciplines banks when the signal
5
This second signal could be any one of a number of signals including those that are the focus of this
study: a capital adequacy ratio and the yield on a bank’s subordinated debt. However, the signal could take
a variety of other forms including the cost of “perks” provided to the CEO if these signals contained
information about the bank’s quality.
7
is C, and (3) and disciplines banks when the signal is B only if the expected cost of
incorrectly disciplining type H banks that are rated B is less than the cost of incorrectly
failing to discipline type L banks:
qT1 < (1-q)T2.
If an alternative signal, IS, is available then its use may reduce the social costs of
incorrectly disciplining banks. One way of incorporating IS into the discipline process,
along the lines of PCA, would be to establish a single trigger score for disciplining all
banks, t. That is, all banks with IS values less than t would be disciplined. In addition,
any bank with an IS value greater than t, but that received an examination rating of C
would also be disciplined. In this case the social planner would solve for the value of t
that minimizes the costs of disciplining all banks.
t
t
-¥
-¥
min t SC = ò T 1 p ( H | IS , R = A)dIS + ò T 1 p ( H | IS , R = B)dIS +
ò
¥
t
T 2(1 - p( H | IS , R = B))dIS
(1)
The use of a single trigger score for all banks would result in the cost of Type-1 errors
arising from disciplining some banks that received an examination rating R=A. There
would also be costs associated with the classification errors of some high and low quality
banks that receive an examination rating of B. There would not be errors associated with
failing to discipline banks that received an examination rating of C because all of these
banks would be disciplined.
The strict adherence to a single trigger point with the IS signal for all banks is
inefficient in the absence of agency costs. The examiners know with certainty that type A
6
In the model presented we incorporate a signal that is assumed to be increasing in the quality of the bank,
e.g., bank capital ratios. If the quality of the bank is decreasing as the signal increases, as with
8
banks should not be disciplined and type C banks should be disciplined. Thus, the
independent signal could be applied only to those banks receiving an examination rating
of B, where the banks may be of high or low quality. In this case, the social planner first
finds the optimal trigger point:
t
¥
-¥
t
min t SC = ò T 1 p ( H | IS , R = B )dIS + ò T 2(1 - p ( H | IS , R = B ))dIS
(2)
The social planner then selects the strategy with the lowest cost: (1) disciplining all type
B banks, (2) disciplining none of the type B banks, and (3) disciplining only those banks
with examination ratings of B and an IS > t.
2.3
Model solution with agency problems
Although the application of a single trigger point to all banks may be suboptimal
in the absence of agency problems, PCA was explicitly developed to address agency
problems; see Benston, et al. (1986). Thus, a more thorough analysis of PCA requires an
environment in which supervisors do not always impose discipline on banks even when
examination results indicate that discipline is appropriate. In order to focus on this
agency problem, in this section we assume that the examination signal observed by the
supervisor contains no error, that is all banks receive an examination rating of A or C.
Furthermore, assume that the examiners do not always forbear on banks with an
examination rating of C, but rather exercise forbearance (or leniency) with a probability
of l where 0 < l £ 1. Moreover, supervisors know that if they rate a bank C and fail to
discipline the bank then Congress may learn of the forbearance and impose a penalty on
subordinated debt spreads, the model would need to be adjusted accordingly.
9
them. Thus, as part of the decision to exercise forbearance, supervisors will also claim
that the examination of the bank returned a signal of A.
For a given probability of forbearance, l, the social planner’s problem is to pick a
PCA trigger rate that minimizes the following social costs:
t
¥
-¥
t
min t SC = ò T 1 p( H | IS , R = A)dIS + ò T 2(l )(1 - p( H | IS , R = C ))dIS
(3)
The first order condition for a solution of equation (3) is:
∂SC/∂t =T1p (H |t, R = A) + T2l – T 2(l) p( H | t, R = C) = 0 .
(4)
Let the value of t that solves equation (4) be t*. Then the effect of an increase in the
proportion of C rated banks that receive forbearance, l, on t* is:
¶t
| F .O.C . = 0 = - [T 2 - T 2 p ( H | t , R = C )] [T 1 p '( H | t , R = A) - T 2lp '( H | t , R = C ) ] > 0 (5)
¶l
That is, an increase in the proportion of banks receiving forbearance leads to a decrease
in t*. Given that t is directly related to bank quality, this suggests that an increase in
forbearance leads to more banks being disciplined.
2.4
Implications
The results in the case with no agency costs have implications for the use of an
independent signal. First, a single trigger point for all banks is inefficient. Thus, absent
agency costs, the current structure of PCA with a set of trigger points that applies to all
banks is inefficient if examiners sole goal is to minimize social costs from incorrectly
disciplining banks. Second, the use of an independent signal may improve social welfare
even if it is not always very accurate. The signal need only be better than imposing a
uniform discipline policy for all banks with examination ratings of B in order to improve
10
social welfare. The proportion of banks with examination ratings of A and C that would
be misclassified by the independent signal is irrelevant if the independent signal is only
applied to banks with examination ratings of B.
The results in the case allowing for agency costs also have important implications
for the situation where agency costs are relevant. First, the independent signal need not
be more accurate than the signal observed by examiners. The signal to the examiners in
the model with agency costs does not contain any error, but the use of an independent
signal may, depending on parameter values, nevertheless be preferred from a societal
perspective because supervisors do not always discipline banks with low examination
signals. Second, an increase in the rate of forbearance will lead to a lower trigger point,
t*, for the independent signal. Thus, if the trigger point for PCA using the independent
signal is t* then ex post evaluations of the accuracy of the PCA measure will be biased
down if the probability of forbearance is greater than zero. The optimal trigger point is
set with the intention that supervisors will sometimes act before the trigger forces
supervisory action.
A general implication for the empirical analysis is that we should pay special
attention to cases where the independent signal says the bank is high risk, but the
examination signal says it is low risk—this is where both the costs and benefits of the
independent signal arise. If the independent signal is incorrect then using it generates
costs, if the independent signal is correct then using it generates benefits.
3.
Measuring bank risk
The empirical analysis below focuses on the cases where subordinated debt yield
spreads are high but supervisory ratings are satisfactory. However, to motivate the
11
potential use of subordinated debt yield spreads as a PCA trigger, we contrast the relative
accuracy of alternative capital ratios and subordinated debt spreads in predicting bank
condition. We measure bank condition as subsequent CAMEL or BOPEC ratings.7
Evanoff and Wall (2001) found that subordinated debt spreads outperformed most of the
capital measures, including the measure currently used to trigger PCA, in predicting bank
condition. Capital measures were shown to add very little predictive power once the debt
spread was accounted for.8 Below we summarize those findings.
For the current analysis we introduce results from the estimation of alternative
single-variable logit models in Evanoff and Wall (2001) and use them as a starting point
to more fully evaluate observations where predictions from debt markets do not coincide
with supervisory ratings.9 As emphasized in our theory section, these are the very
observations where adding a second signal to the PCA process could have the most
impact.
3.1 Data
We use the data developed in Evanoff and Wall (2001). It includes a sample of
bank subordinated debt yields between 1985 and 1999 that satisfy two criteria: (1) the
issuer must be among the 100 largest domestic banking organizations in the United
7
These are measures of the composite financial condition of the bank or bank holding company,
respectively, as summarized by the federal banking agencies.
8
It could be argued that one limitation of this analysis is the possibility that capital ratios, debt spreads and
supervisory ratings may not be independent. Investors in subordinated debt certainly care about
supervisor’s evaluations and supervisors have demonstrated a strong interest in capital ratios. Supervisors
may also care about subordinated debt spreads. Evanoff and Wall (2001) attempted to minimize the
potential for supervisory evaluations to influence capital ratios and yield spreads by using the risk measures
to predict supervisory ratings in the following quarter. Whether spreads influenced examiners is impossible
to determine without additional information; however, discussions with examiners suggest that supervisors
were not placing much weight on subordinated debt spreads in the late 1990s. However, since we are
analyzing observations where the two measures differ, whether the two measures are independent is less
important.
12
States, and (2) the bond must be listed on Bloomberg with quarterly yield data. If
sufficient trading occurs in a bond, then Bloomberg reports volume-weighted average
transaction prices. If trading is not sufficient, then matrix-generated prices based on price
quotes from informed market traders are reported. Prices are weighted averages based on
a minimum of two price sources and they must be within an "acceptable" tight range.10
Much of the analysis in Evanoff and Wall (2002) uses subordinated debt yield
spreads over maturity matched Baa corporate bonds. The current analysis evaluates
spreads over maturity matched Treasury security yields. Treasury yields are obtained
from the Board of Governors of the Federal Reserve System's web site and the spread
over Treasuries is calculated as the difference between the subordinated debt yield and
the calculated yield on a comparable maturity Treasury security.11
We also obtained confidential supervisory ratings from the Federal Reserve
Supervision Department database. The ratings are the composite CAMEL(S) rating for
banks and the composite BOPEC rating for bank holding companies. About 70% of the
observations are at the bank holding company level. Capital adequacy ratios are
calculated using data from the Reports of Condition and Income filed by banks (Call
Reports) and bank holding companies (FR Y-9C) with their respective federal supervisor.
We examine a subsample of 452 observations from the data in Evanoff and Wall
(2001) that contains complete information on the bond spreads over Treasuries. Within
this subsample there are 13 banks with a supervisory rating of 3 or lower.
9
Readers interested in a more thorough critical analysis of the use of capital ratios for triggering PCA are
referred to Tables 1-4 of Evanoff and Wall (2001).
10
For a more detailed description of the data see Evanoff and Wall (2001, 2002).
11
Comparable maturity Treasury obligations are obtained via linear interpolations of the term structure
across 3 month, 6 month, 1 year, 2 year, 3 year, 5 year, 7 year, 10 year and 30 year securities.
13
3.2
Results
The results of estimating logit models to predict supervisory examination ratings
are presented in Table 1 and are from Evanoff and Wall (2001). They indicate that
subordinated debt spreads over Treasuries (Sub-debt spread over Treasuries) provide
greater predictive power than the current PCA capital adequacy standard (PCA capital
adequacy status). Although the “percentage correct” is relatively high for the PCA
capital adequacy status, the high level of “tied” observations suggests the model is not
very confident of its assignment of individual observations and the low percentage of
correct 3-4 classifications suggests the model is doing very poorly in identifying problem
banks. The rather low concordance found using the alternative capital measures raise
concerns about their usefulness to forecast future problem banks. Therefore, with one
exception, the alternative capital adequacy ratios are generally inferior to the spread
measures at predicting supervisory rating; the exception being the Tier-1 leverage ratio.
Evanoff and Wall (2001), Table 3, present further results from estimating models that
include both the debt spreads and capital adequacy measures. Their results indicate that
inclusion of the capital measures appear to add little to the explanatory power of the
models. The performance of subordinated debt spreads satisfies an important prerequisite
for tying PCA to debt market information.
While the spread measures perform better than the current PCA triggers at
predicting troubled banks, as discussed below, they still result in a number of
misclassifications; i.e., the spread suggests the bank is high-risk while the supervisory
rating suggests otherwise. As discussed earlier, these differences are a prerequisite for
generating cost and/or benefits from initiating PCA with debt spreads. Additional
14
analysis is therefore needed to associate the spread and risk measures and to determine
the extent of the potential gains from such a program.
4.
A more detailed analysis of “misclassifications”
This section discusses the empirical methodology used to analyze the
misclassifications resulting from using the debt spreads to predict risk levels as proxied
by supervisory ratings. Can we delineate the source of the differences in bank
classification between debt spreads and supervisory ratings?12 In particular, we consider a
priori reasons for expecting that in certain circumstances the spread may be a poor
indicator of bank condition. We also evaluate situations in which subordinated debt
spreads may be a better indicator of bank risk than the model projects.
4.1
Misclassifications and liquidity issues
The spread on a bank’s subordinated debt over comparable maturity Treasury
securities may depend on a variety of factors, perhaps the largest of which is the reduced
liquidity of bank issues, see Hancock and Kwast (2001). If this premium is
approximately the same for all banks, its presence simply requires an adjustment to the
spread used to trigger supervisory action. However, if it varies across banks, some
additional adjustment needs to be made. Most subordinated debt proposals recognize that
bond issues by smaller banks are likely to have higher liquidity premiums because the
issue size is smaller and may attract less attention from bond analysts. Hence, smaller
banking organizations are typically excluded from mandatory subordinated debt issuance
12
Evanoff and Wall (2002) address the issue of whether debt spreads and supervisory ratings are measuring
the same risk. The concern is that subordinated debt yields reflect only expected losses to the holders of
the debt while the primary concern of supervisors is the probability of failure. Thus, any misclassifications
may simply result from measuring different risks.
15
requirements.13 As a rough first approximation, we consider any debt issue by a bank not
among the largest thirty in that year to be likely to have a relatively high liquidity
premium. These banks are then excluded from further analysis.14
4.2
Additional factors to consider in evaluating the accuracy of the yield spreads
and examination ratings
After accounting for the above mentioned liquidity concerns, the remaining
observations will be evaluated to determine when the debt yields identified a problem
that was apparently not being reflected in the examination ratings. This part of the
empirical analysis is made more complicated by our inability to objectively determine the
true condition of the bank. Unfortunately, the problem of not having the true measure of a
bank’s condition is unavoidable in analyzing an independent signal of bank risk. Thus,
we examine a variety of measures while being careful to note any limitations on the
conclusions that can be drawn from the results. Our procedure is to use supervisory
information and a variety of accounting and financial market measures to identify banks
that appear to be financially distressed around the time of the examination although the
regulatory rating suggests the bank is low risk.
We use two measures of supervisory concern about the bank. The first is whether
the bank has been re-examined within a six-month period. While bank supervisors
ordinarily go at least one year between examinations, in some cases in our sample the
13
For example, Evanoff and Wall’s (2000a) proposal would require only the largest 25 banks to issue
subordinated debt. Similarly, most of the discussion in Kwast et al. stresses the debt of the top 50 U.S.
banking organizations.
14
While this partially addresses the liquidity issue there may still be a time dimension to liquidity (e.g., ad
hoc events or cyclical effects) that we are not capturing. In earlier work we attempted to account for this by
allowing for fixed time effects in the empirical analysis and the results were not appreciably changed.
However, this is an important issue that merits future work to develop a more accurate signal of firm
condition.
16
supervisors appeared to be substituting frequent exams for lower CAMEL ratings.
Discussions with examiners indicate that this procedure has been followed for some
banks at certain times in the past.15 The second measure of supervisory concern is
whether the bank has been or will be rated less than satisfactory within one year of the
current rating.
We also consider alternative accounting measures of the condition of the bank.
Accounting ratios are accounted for via parameter estimates from econometric
forecasting models aimed at identifying problem banks—early warning models. The
econometric model approach is more likely to yield an objective risk measure. The
models were created to use accounting data to identify potential problem institutions to
help guide the use of examination resources.16 If these models indicate that the banks
identified by the yield spreads had a higher probability of failure or were good candidates
for a rating downgrade, that result would be consistent with debt yield spreads correctly
signaling that the bank is high risk.
We use two early warning models to identify banking organizations that were
good candidates for downgrades both at the time of the examination and over several
subsequent quarters. The first is that of Gilbert, Meyer, and Vaughan [GMV (2000)].
The model is designed to predict future bank supervisory rating downgrades to less than
15
This indicator is not perfect. A follow-up examination may occur for other reasons such as to evaluate
the safety and soundness implications of a proposed takeover. However, in most cases in our sample there
appears to be confirmation of supervisory concern in the form of repeated re-examinations over the same
time period, or signals from one or more of our other indicators. In addition, the fact that the supervisors
are substituting examination frequency for lower CAMEL ratings does not necessarily imply that the
supervisors are failing to impose adequate discipline. We do not have information on the recommendations
made by supervisors to these banks or on what action the supervisors threatened to take if their
recommendations were not followed
17
satisfactory condition (i.e., CAMEL 3, 4 or 5) based on accounting data. It is more
flexible than other early warning models commonly used by supervisors in that it
emphasizes the potential for deterioration in bank condition rather than bank failure;
downgrades being a much more common phenomenon during our sample period. As
such, parameter estimates are allowed to vary through time to more accurately account
for changing influences on bank condition. Where appropriate, we also augment the
results of the GMV model with those of the Federal Reserve’s “System to Estimate
Examination Ratings,” or SEER risk rank model, which is used to predict the probability
of bank failure.17 The GMV ratings are obtained from the authors and the SEER risk
rankings are obtained from confidential Federal Reserve supervision files.
Common stock prices may also yield information on the financial condition of
banking organizations. As a rather crude proxy for bank condition we consider a marketto-book ratio of less than one as being indicative of the equity market’s concern about the
financial condition of a bank. An alternative measure would be market-adjusted equity
returns. We do not include this measure in our analysis, however, because negative equity
returns may be caused merely by a firm’s transition from having great earnings prospects
to having mild difficulties that should not be sufficient to trigger PCA. Another
alternative is the price-to-earnings ratio, but the interpretation of this ratio is less clear
when a bank suffers losses. Thus, results consistent with the supervisors exercising
forbearance would be: (1) low debt ratings, (2) the equity market measure indicating
16
Krainer and Lopez (2001) augment these models with information from equity markets and find the
additional information to be of value in predicting future regulatory ratings.
17
For a discussion of the model see Cole and Gunther (1995) and GMV (2000). The SEER model is less
flexible in that bank failures have been so rare during the 1990s that the parameter estimates have been
frozen throughout the period.
18
problems before the satisfactory examination rating is assigned, and (3) the equity market
measure indicating a problem after the examination rating is assigned. The bond ratings
are obtained from Moody’s Banking and Finance Manual, various issues. Market-tobook ratios are obtained from the American Banker for the last business day in each
quarter.
5.
Empirical results
Based on the model presented in column 5 of Table 1, and employing probability
assumptions similar to those employed in Evanoff and Wall (2002), the analysis correctly
predicts 288 observations, misclassifies (misses) three high risk institutions and
misclassifies (erroneously classifies as high risk) 183 observations. The 183 type-2
errors can be significantly improved upon by raising the spread threshold from an 84
basis point spread to 90 or 100 basis points.18 However, we base our analysis on the 84
basis point spread to approximately match the cut-off point suggested by the logit
estimation results in column 5 of Table 1 and to enable us to compare results using
spreads over alternative debt instruments.
The cut-off point of 84 basis points correctly classifies 10 of the 13 banks with
CAMEL ratings of 3 or 4. The bulk of the following analysis emphasizes the 183
additional banks (35% of the highly rated banks) classified as problem institutions
although the supervisory rating suggested otherwise. As mentioned above, the
misclassification may occur for a number of reasons including the possibility that
subordinated debt markets simply incorrectly classify low-risk institutions as high-risk.
We evaluate this possibility. The first subsection compares the high-spread banks with
19
the remainder of the sample, the next subsection further analyzes the characteristics of the
high-spread banks and the final subsection summarizes the lessons learned from the
empirical analysis.
5.1
Contrasting high- and low-spread banks
A comparison of certain characteristics of banks with high spreads versus those
with low spreads is found in Table 2. The results presented in the first three rows suggest
there is little difference in capital adequacy ratios between the high-and low-spread
groups, in either a statistical or economic sense. We also found that there was essentially
no difference in an ordinal PCA capital adequacy measure taking on values from 1 to 5
depending on whether the bank is considered Well Capitalized, Adequately Capitalized,
Under Capitalized, Significantly Undercapitalized or Critically Undercapitalized,
respectively, under the guidelines introduced in the early 1990s. This index is of
particular interest since it is the measure currently being used to trigger PCA.
Substantial differences exist, however, across the two subgroups using alternative
criteria to measure potential bank problems. To incorporate information from early
warning models, two alternative measures based on the GMV (2000) regulatory
downgrade model are presented in Table 2. The first, GMV-1, is a measure of the
probability of a downgrade based on beginning of year projections. GMV-2 is based on
end-of-year data; thus it measures conditions realized during the year and can be
considered more of a current-condition measure while the GMV-1 measure is more
forward looking. Each measure is indexed to the mean of the non-problem bank
subsample.
18
The type-2 errors decline to 153 and 97 for a 90 and 100 basis points spread, respectively. One’s choice
of the appropriate threshold depends on the relative trade off in unnecessarily disciplining problem banks
20
The GMV-1 measure of bank condition for our high-yield spread subsample is
over four times that for the other group, suggesting a substantial difference in the
potential for downgrades to less than satisfactory status across the two groups.19 Results
using the GMV-2 measure suggest even greater differences. There are also differences
using the regulator’s SEER model. Thus, there is some evidence that the “misclassified”
observations may be different from satisfactory rated banks.
5.2
Analysis of high spread banks
In this section we further delineate the high spread banks. We first separate the
banks with high spreads into three groups: those that we can explain as either being in
agreement with supervisory ratings, those that may have significant noise in their yield
spread measures, and the remaining banks. This remaining group, the ‘unexplained’
observations, is then further investigated.
The observations that are easily explained are summarized in Table 3. The 193
observations are first ranked by spread with a ranking of 1 assigned to the highest spread
and a ranking of 193 assigned to the lowest spread (but greater than 84 basis points). The
observations are then separated by these rankings into eight sets of 25, with the last set
including the 17 observations with the smallest spreads (but greater than 84 basis points).
This procedure allows an evaluation of the accuracy of the spread measure as yield
versus failing to discipline troubled institutions (i.e., the relative cost of type-2 and type-1 errors).
19
This forward-looking measure best serves the intended role of the model: to predict future downgrades.
The alternative measure (GMV-2) is included for robustness since the actual date of the examination
information may be closer to the end of the year. Individual year subsamples were also analyzed since the
period could be divided into particularly tranquil and less-tranquil subperiods. There was still a consistent
difference across subsamples. One caveat, however, concerns the population of U.S. banks used to
generate the GMV (and SEER discussed later) prediction model. Although it includes larger banks, the
sample is dominated by smaller institutions. Thus, our sample of debt-issuing banks would not be a
representative sample of banks from which the model parameter estimates were generated.
21
spreads decline while at the same time providing ample protection for the confidentiality
of individual bank examination ratings.
The second column contains the number of observations in each set where the
bank is rated less than satisfactory (i.e., a CAMEL or BOPEC rating of 3 or 4). These are
the observations where the yield spreads and the supervisors appear to be reaching
similar conclusions. For example, among the banks with the largest 25 yield spreads, two
banks were rated less than satisfactory by the supervisors.
The third column contains the number of observations with potentially noisy data
because the observations came from a smaller bank. For example, eight banks with
spreads ranked between 26 and 50 in our sample fall into the category of banks with
potentially noisy data. The fourth column contains the unexplained observations where
the bank received a satisfactory rating and there is no obvious indication that the spread
measure contains unusual noise.
Table 4 provides an analysis of the unexplained misclassifications. Columns 3
through 7 of Table 4 provide information on the extent to which other risk measures
suggest the bank was high-risk. A high early warning score is defined as one at least four
times the mean value of the GMV-1 model, GMV-2 model or SEER model. In some
cases there may be multiple reasons for classifying the bank as potentially high-risk, thus
a bank may be included in more than one of the columns. For example, a bank that was
re-examined within six months and had a high early warning score would be included in
both of those columns. Column 8 gives the number of banks for which, after accounting
or these additional factors, there is still no indication of higher risk.
22
The results in Table 4 suggest that a significant proportion of the unexplained
observations with high yield spreads had one or more indicators that the bank was
actually high risk. Combined with the banks that were assigned less-than-satisfactory
regulatory ratings, this suggests that many of the banks that subordinated debt markets
suggested were high-risk, were also banks about which the supervisors or market had
some concerns.
After accounting for these indicators of above-average risk, we are left with
thirty-nine bank observations with no obvious indication of problems; from an original
183 misclassifications. While a number of these would have been eliminated if we had
adjusted our threshold criteria to 90 or 100 basis points, whether one chooses to do that
depends on how one weighs the costs of disciplining a sound bank versus the costs of
failing to discipline a weak bank. However, we still would have been left with some
banks misclassified as risky banks as a result of their relatively high spreads.
Comparisons of these results with those found using a spread over Baa rated debt
[Evanoff and Wall (2002)] indicates that use of the spread over Treasuries results in more
misclassifications as type-2 errors. In the analysis using spreads over Baa yields, 21% of
the unexplained observations (after accounting for thin market trading and data concerns)
showed no indication of having higher risk characteristics. In the current analysis (after
accounting for thin market trading) 40% of the observations showed no indication of
having higher risk characteristics---i.e., contrasting columns 2 and 8 of Table 4.
Additionally, the unexplained observations using the Baa spreads were generally bunched
near the threshold; that is these observations had relatively low spreads in the group of
misclassified observations. In the current analysis, the misclassifications are more evenly
23
spread across various spread-groupings (rows in Table 4) instead of being bunched at or
near the threshold. Thus, the spread over Treasuries does not appear to perform as well
as the spread over Baa rated corporate debt in identifying high-risk banks.
One important difference between the sample used in this study and that in
Evanoff and Wall (2002) is that the current study incorporates a longer sample period.
The earlier study was limited to the 1990 to mid-1998 period due to limitations of their
corporate bond yield spread data. The longer sample period used in this study
incorporates the collapse and restructuring of Long-Term Capital Management (LTCM)
in September 1998. An important part of LTCM’s strategy was to earn profits by
supplying liquidity to a number of relatively illiquid markets. LTCM’s positions were
unwound and its operations were ultimately shut down after September 1998, resulting in
a reduction in liquidity and an increase in liquidity premiums in a number of markets [see
President’s Working Group (1999)]. If the collapse and restructuring of LTCM caused
an overall increase in liquidity premiums in corporate bond markets, the spread of bank
subordinated debt issues over Treasury securities during this period would also have
increased. Such an increase would be mistaken as an increase in credit risk premiums for
the post-LTCM sample of bank subordinated debt in Tables 3 and 4.
5.3
Post Long-Term Capital Management
This section explores the possible impact of LTCM on the information content of
subordinated debt yield spreads by dropping those observations from September 1998 to
the end of the sample period. If the collapse and restructuring of LTCM increased
liquidity premiums then deleting observations after its collapse should reduce the
proportion of unexplained observations for which there are no obvious indicators of risk.
24
Deleting all of the observations from September 1998 to the end of the sample
reduces the number of high-spread observations by 75 to 118. The results from the preLTCM period are presented in Tables 5 and 6 in a format similar to that of the entire
sample in Tables 3 and 4.
In contrast to the results for the entire sample, most of the observations in Table 5
with less than satisfactory supervisory ratings are in the group of banks with the highest
spreads. Also, the number with less than satisfactory ratings declines uniformly as the
spread increases.
The number of observations with no indicator of problems in Table 6 (row 8) is
less than the number with no indicator in Table 4 for each of the rows. The number with
no indication of problems in the group of banks with the highest spreads drops from 3 to
1, the number in the group ranked from 26-50 drops from 7 to 5, and the remaining
declines are even more dramatic. The fall also occurs as a proportion of the unexplained
observations (contrasting columns 2 and 8 of Tables 4 and 6). The proportion of
unexplained observations with no indicator of risk drops to 23% (14/62), which is
essentially the same as Evanoff and Wall (2002) found using Baa spreads. However, the
results in Table 6 differ from those obtained using spreads over Baa debt in that over onethird of the observations with no indicator or risk are in a category of banks with
relatively high spreads (see the row for banks with the 26-50 highest spreads in Table 6).
Taking a closer look at observations in this row, however, a majority of them were
obtained from a single quarter in 1987. This suggests that closer analysis of these
observations may yield additional insight into potential issues surrounding market
25
disruptions that need to be considered when using subordinated debt yield spreads;
particularly spreads over Treasuries.
While the collapse and restructuring of LTCM appears to have influenced the
liquidity premiums on corporate bonds, its collapse and restructuring may also have
influenced the credit risk spreads on these bonds. The collapse may have increased credit
risk premiums to the major banks that were LTCM’s creditors and counterparties in overthe-counter (OTC) derivative transactions due to the risk that LTCM would not be able to
honor these debts. The collapse may also have threatened the value of the trading and
investment portfolios of banks that held positions similar to LTCM if portfolios had to be
liquidated at distressed prices. While such a credit risk increase in spreads is potentially
important for some banks, it appears not to be very important for our sample. The banks
that were most exposed to LTCM were the money center banks that had credit exposure
to LTCM and may also have had similar trading positions. Given that LTCM was
restructured without major losses to creditors and its positions unwound in an orderly
manner, its creditors suffered, at most, minor losses. If some bank’s subordinated debt
yields experienced large increases in credit risk premiums due to LTCM, the jump in
yields should have happened to the largest banks in the September or possibly December
1998 periods. Yet, looking at our sample during this period, there is only one large bank
that could have experienced such an increase in credit spreads.20
A second way in which LTCM may have influenced credit spreads is by
reinforcing market perceptions that some financial firms are too-big-to-fail. In LTCM’s
20
Recall that in order for a subordinated debt issue to be included in our sample, the issuer must have
received a CAMEL(S) or BOPEC rating from its federal supervisor in the following calendar quarter.
Thus, many potentially relevant debt issues are not included because the bond issuer did not receive a
rating during the quarter.
26
case, the Federal Reserve Bank of New York organized the meetings to restructure
LTCM. While the Reserve Bank did not provide any direct support to LTCM, its role in
facilitating the meetings might lead some market participants to expect Federal Reserve
intervention should a large bank encounter financial problems. This may be a particular
concern given that LTCM was a hedge fund, not a bank, and accordingly did not have a
federal supervisor or an important role in the payments system. If credit spreads declined
due to a strengthening in the conjectural value of a too-big-to-fail policy, that effect is
swamped in our data by the liquidity impact of LTCM.
Thus, the finding that LTCM may have increased the liquidity spreads on bank
subordinated debt tends to support the recommendation of Evanoff and Wall (2000a,
2000c) to set the threshold for supervisory action based on the Baa spread.21 However, as
discussed in those studies, there are data issues that should be further evaluated and
further work looking at the Baa spread during this period would be necessary to confirm
the benefits of using the Baa spread.
6.
Summary and policy implications
The ability of PCA to limit supervisory forbearance is substantially weakened by
its reliance on capital adequacy ratios where the measure of capital is under the control of
banks and their supervisors. One potential complement to capital adequacy ratios would
be to use a risk measure extracted from market prices. Perhaps the biggest objection
from supervisors to the use of a market-based risk measure is that the measure may signal
that a bank should be disciplined even though the supervisor’s information suggests the
bank is low risk. The risk measures extracted from market prices may contain such
21
For a discussion of data issues see Evanoff and Wall (2002).
27
errors, largely because financial market prices incorporate more than the credit riskiness
of the issuer.
This study addresses the concern with using market risk measures both
theoretically and empirically. The theoretical section of the paper shows that the use of
an imperfect risk measure for PCA can improve outcomes even if the supervisor has
perfect information. The reason for the improvement is that the benefits from limiting
forbearance may more than offset the gains from the supervisor’s superior information.
The empirical analysis focuses on one particular risk measure, the yield spread of
subordinated debt securities over comparable maturity Treasury securities. This part of
the study uses the methodology that Evanoff and Wall (2002) applied to subordinated
debt spreads over Baa bonds. The advantage of using spreads over Treasury securities is
that the spread over Treasuries may more accurately capture the probability of failure
over the business cycle, albeit at the cost of potentially making the standard more procyclical. The current quality of the data may also be superior when Treasury spreads are
considered. A potential cost of using the spread over Treasuries is that short-term market
disruptions may affect private debt yields differently than that of Government debt.
Thus, spreads over Treasuries may be more sensitive to short-term changes in liquidity
and credit risk than would spreads over private debt yields (e.g., Baa bonds).
The results of examining spreads over Treasury securities suggest that
subordinated debt yield spreads have substantial predictive power. However, the results
also suggest that the spreads are an imperfect indicator, some high spreads were not
accompanied by any other signs that the bank was high risk. Further analysis suggested
that a substantial fraction of the apparent classification errors from using spreads over
28
Treasury securities may be due to an increase in the liquidity premiums on corporate
bond issues in the wake of the collapse of LTCM. This finding suggests that not only do
subordinated debt spreads appear to dominate the current capital measures used to trigger
PCA, but that careful consideration should be given to determining which spread to use.
The findings suggest that the merits of using a spread over a corporate bond index, such
as Baa bonds, may exceed those of using alternative spreads and therefore deserves
further consideration.
29
REFERENCES
Bank for International Settlement. 2001. “The New Basel Capital Accord.” Consultative
Paper Issued by the Basel Committee on Banking Supervision, June.
Benink, H. A. and C. Wihlborg, 2002. “The New Basel Capital Accord: Making it
Effective with Stronger Market Discipline.” European Financial Management, 8, pp.
103-115.
Cole, R.A. and J.W. Gunther. 1995. “FIMS: A New Monitoring System for Banking
Institutions.” Federal Reserve Bulletin, 81, pp.1-15.
Evanoff, D.D. and L.D. Wall. 2000a. “Subordinated Debt as Bank Capital: A Proposal
for Regulatory Reform.” Economic Perspectives, Federal Reserve Bank of Chicago,
Second Quarter, pp. 40-53.
_____. 2000b. “The Role of SND in Bank Safety and Soundness Regulations.”
Proceedings of a Conference on Bank Structure and Competition, Federal Reserve Bank
of Chicago, pp. 480-93.
_____. 2000c. “Subordinated Debt and Bank Capital Reform.” Research in Financial
Services: Private and Public Policy. edited by George Kaufman, Volume 12, JAI Press,
pp. 53-120.
_____. 2001. “SND Yield Spreads as Bank Risk Measures.” Journal of Financial
Services Research, 20, pp. 121-46.
_____. 2002. “Measures of the Riskiness of Banking Organizations: Subordinated Debt
Yields, Risk-based Capital, and Examination Ratings.” Journal of Banking and Finance,
26, pp. 989-1010.
Flannery, M.J. 2001. “The Faces of ‘Market Discipline’.” Journal of Financial Services
Research, 20, pp. 107-20.
Gilbert, R.A., A.P. Meyer and M.D. Vaughan. 2000. “The Role of a CAMEL Downgrade
Model in Bank Surveillance.” Research in Financial Services: Private and Public
Policy. edited by George Kaufman, Volume 12, JAI Press, pp. 265-86.
Gunther, J. W., M. E. Levonian and R. R. Moore. 2001. “Can the Stock Market Tell
Bank Supervisors Anything They Don’t Already Know?” Economic and Financial
Review, Federal Reserve Bank of New York, pp. 1-9.
Hancock, D. and M.L. Kwast. 2001. “Using Subordinated Debt to Monitor Bank Holding
Companies: Is it Feasible?” Journal of Financial Services Research, 20, pp. 147-88.
30
Krainer, J., and J.A. Lopez. 2001. “Incorporating Equity Market Information into
Supervisory Monitoring Models.” Presented at a Conference on Banks and Systemic
Risk, sponsored by the Bank of England, London (May 24).
Kwast, M.L., D.M. Covitz, D. Hancock, J.V. Houpt, D.P. Adkins, N. Barger, B.
Bouchard, J.F. Connolly, T.F. Brady, W.B. English, D.D. Evanoff, and L.D. Wall. 1999.
Using SND as an Instrument of Market Discipline. Report of a study group on
subordinated notes and debentures. Board of Governors of the Federal Reserve System,
M. Kwast (chair), Staff Study No. 172, December.
President’s Working Group on Financial Markets. 1999. Hedge Funds, Leverage, and the
Lessons of Long-Term Capital Management.
Sironi, A. 2001. “An Analysis of European Bank’s SND Issues and its Implications for
the Design of a Mandatory Subordinated Debt Policy.” Journal of Financial Services
Research, 19, pp. 233-66.
31
Table1: Binomial model predicting CAMEL ratings as a function of capital ratios and debenture spreads over the Treasury rate
Variable
Intercept
Parameter
Estimate
(1)
Parameter
Estimate
(2)
Parameter
Estimate
(3)
Parameter
Estimate
(4)
Parameter
Estimate
(5)
-5.2195
(0.0001)
-3.0839
(0.1008)
1.2028
(0.5061)
-1.3070
(0.4499)
-4.5877
(0.0001)
1.1047
(0.0038)
Sub-debt spread over Treasuries
PCA capital adequacy status
1.5700
(0.0525)
-0.0352
(0.8155)
Total risk based capital ratio
-0.7052
(0.0119)
Tier 1 leverage ratio
-0.2622
(0.2083)
Tier 1 capital to risk-weighted exposure
Association of Predicted Probabilities and Observed Responses
Concordant
Discordant
Tied
Gamma
14.8%
3.1%
82.1%
0.656
43.2%
35.4%
21.4%
0.099
65.9%
30.8%
3.3%
0.363
54.8%
40.3%
4.9%
0.152
76.8%
19.0%
4.2%
0.604
% Correct
% 3 - 4 Correct
% 1 - 2 Correct
94.0%
15.4%
96.4%
23.2%
0%
23.9%
55.3%
69.2%
54.9%
38.9%
53.8%
38.5%
58.0%
69.2%
57.6%
2.259
(0.0967)
0.058
(0.8098)
6.402
(0.0114)
1.797
(0.1801)
6.475
(0.0109)
Chi-square for covariates
(p - value)
The results are from Evanoff and Wall (2001). The dependent variable takes a value of 0 for CAMEL (or BOPEC) ratings 1 and 2, and a value of 1 for ratings
3 and higher. The PCA capital adequacy status ranges from 1 for the best capitalized banks (well capitalized) to 5 for the least well capitalized (critically
undercapitalized). The p values for the maximum likelihood parameter estimates are in parentheses below the coefficients. The “Chi
square for covariates” statistic is based on the log likelihood statistic, and tests the marginal explanatory power of the independent variables relative to a model
with only a constant term. The associated p values are included in parentheses.
Concordance is a measure of the correlation between the observed and predicted probabilities of the dependent variable. A pair of observations is said to be
concordant if, based on the model, the observation that has a particular rating has a sufficiently higher probability of receiving that rating than does the other
observation. A pair is discordant if the reverse is true. A pair is tied if the probability interval between the two observations is sufficiently small, 0.002. A
correlation index, the Goodman-Kruskal Gamma index, is also included for assessing the predictive power of the model and for making comparisons across
models. If nc is the number of concordant pairs and nd the number of discordant pairs, then the Goodman-Kruskal Gamma = (nc - nd) / (nc + nd). See
Goodman and Kruskal (1972). Generally, the index approaches zero as independence between the two measures increases. Number of observations = 452.
32
Table 2
Means of bank capital and downgrade probabilities across subsamples
Predicted ‘good’ banks*
Predicted ‘problem’
banks*
(1)
(2)
Risk based capital
12.5%
12.2%
Tier-1 leverage
7.08%
6.99%
Tier-1 risk-based
8.85%
8.56%
Early-warning model:
GMV-1
Early-warning model:
GMV-2
1.0
4.77
1.0
7.9
* A “problem bank” is one which has a subordinated debt issue trading at a
yield spread equal to or greater than 84 basis points over a maturity matched
Treasury bond. The early warning model measure is normalized to one for the
predicted ‘good’ banks; thus the other measures are relative to the normalized
category.
33
Table 3
Analysis of projected problem banks based on subordinated debt yields:
Identification of banks with explicit reasons for high spreads
Ranking by spread
Less than satisfactory
Potentially noisy data
examine rating
Unexplained observations with
satisfactory regulatory ratings and no
noisy data (type-2 error)
(1)
(2)
(3)
(4)
1-25
2
14
9
26-50
4
8
13
51-75
0
8
17
76-100
2
6
17
101-125
1
7
17
126-150
0
12
13
151-175
1
7
17
176-193
0
5
13
34
Table 4
Analysis of projected problem banks based on subordinated debt yields:
Debt spreads of at least 84 basis points over Treasury yields
Ranking by
Unexplained
Remaining observations with one or more indicators of a problem
No indicator
spread
observations*
(All relevant categories noted)
of a
Re-
Rated less
High early
Market–to-
examined
than
warning
book less
within 6
satisfactory
score
than 1.0
months
within 1 year
Rated Baa
problem or
data noise
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
1-25
9
3
1
9
2
2
3
26-50
13
1
1
3
2
1
7
51-75
17
5
2
4
0
4
8
76-100
17
4
0
7
1
5
8
101-125
17
6
0
5
0
2
7
126-150
13
7
0
5
1
3
5
151-174
17
5
1
4
1
4
6
176-193
13
6
2
3
1
3
3
* Observations remaining after deleting observations with unsatisfactory examine ratings and those observations from
banking organizations not among the largest 30 by asset size in the year of the observation.
35
Table 5
Subsample analysis of projected problem banks based on subordinated debt yields:
Identification of banks with explicit reasons for the high spread
--excluding the post-Long-Term Capital Management period--
Ranking by spread
Less than satisfactory
Potentially noisy data
examine rating
Unexplained observations with
satisfactory regulatory ratings and no
noisy data (type-2 error)
(1)
(2)
(3)
(4)
1-25
6
10
9
26-50
2
6
17
51-75
1
9
15
76-100
1
16
8
101-118
0
5
13
36
Table 6
Subsample analysis of projected problem banks based on subordinated debt yields:
Debt spreads of at least 84 basis points over Treasury yields
--excluding the post-Long-Term Capital Management period-Ranking by
Unexplained
Remaining observations with one or more indicators of a problem
No indicator
spread
observations*
(All relevant categories noted)
of a
Re-
Rated less
High early
Market–to-
examined
than
warning
book less
within 6
satisfactory
score
than 1.0
months
within 1 year
Rated Baa
problem or
data noise
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
1-25
9
1
2
5
4
1
1
26-50
17
5
2
6
1
4
5
51-75
15
9
0
8
1
4
3
76-100
8
4
0
3
3
2
1
101-118
13
5
1
2
1
4
4
* Observations remaining after deleting observations with unsatisfactory examine ratings and those observations from
banking organizations not among the largest 30 by asset size in the year of the observation.
37
Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Dynamic Monetary Equilibrium in a Random-Matching Economy
Edward J. Green and Ruilin Zhou
WP-00-1
The Effects of Health, Wealth, and Wages on Labor Supply and Retirement Behavior
Eric French
WP-00-2
Market Discipline in the Governance of U.S. Bank Holding Companies:
Monitoring vs. Influencing
Robert R. Bliss and Mark J. Flannery
WP-00-3
Using Market Valuation to Assess the Importance and Efficiency
of Public School Spending
Lisa Barrow and Cecilia Elena Rouse
Employment Flows, Capital Mobility, and Policy Analysis
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Does the Community Reinvestment Act Influence Lending? An Analysis
of Changes in Bank Low-Income Mortgage Activity
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WP-00-5
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Subordinated Debt and Bank Capital Reform
Douglas D. Evanoff and Larry D. Wall
WP-00-7
The Labor Supply Response To (Mismeasured But) Predictable Wage Changes
Eric French
WP-00-8
For How Long Are Newly Chartered Banks Financially Fragile?
Robert DeYoung
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Bank Capital Regulation With and Without State-Contingent Penalties
David A. Marshall and Edward S. Prescott
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Why Is Productivity Procyclical? Why Do We Care?
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Oligopoly Banking and Capital Accumulation
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Puzzles in the Chinese Stock Market
John Fernald and John H. Rogers
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The Effects of Geographic Expansion on Bank Efficiency
Allen N. Berger and Robert DeYoung
WP-00-14
Idiosyncratic Risk and Aggregate Employment Dynamics
Jeffrey R. Campbell and Jonas D.M. Fisher
WP-00-15
1
Working Paper Series (continued)
Post-Resolution Treatment of Depositors at Failed Banks: Implications for the Severity
of Banking Crises, Systemic Risk, and Too-Big-To-Fail
George G. Kaufman and Steven A. Seelig
WP-00-16
The Double Play: Simultaneous Speculative Attacks on Currency and Equity Markets
Sujit Chakravorti and Subir Lall
WP-00-17
Capital Requirements and Competition in the Banking Industry
Peter J.G. Vlaar
WP-00-18
Financial-Intermediation Regime and Efficiency in a Boyd-Prescott Economy
Yeong-Yuh Chiang and Edward J. Green
WP-00-19
How Do Retail Prices React to Minimum Wage Increases?
James M. MacDonald and Daniel Aaronson
WP-00-20
Financial Signal Processing: A Self Calibrating Model
Robert J. Elliott, William C. Hunter and Barbara M. Jamieson
WP-00-21
An Empirical Examination of the Price-Dividend Relation with Dividend Management
Lucy F. Ackert and William C. Hunter
WP-00-22
Savings of Young Parents
Annamaria Lusardi, Ricardo Cossa, and Erin L. Krupka
WP-00-23
The Pitfalls in Inferring Risk from Financial Market Data
Robert R. Bliss
WP-00-24
What Can Account for Fluctuations in the Terms of Trade?
Marianne Baxter and Michael A. Kouparitsas
WP-00-25
Data Revisions and the Identification of Monetary Policy Shocks
Dean Croushore and Charles L. Evans
WP-00-26
Recent Evidence on the Relationship Between Unemployment and Wage Growth
Daniel Aaronson and Daniel Sullivan
WP-00-27
Supplier Relationships and Small Business Use of Trade Credit
Daniel Aaronson, Raphael Bostic, Paul Huck and Robert Townsend
WP-00-28
What are the Short-Run Effects of Increasing Labor Market Flexibility?
Marcelo Veracierto
WP-00-29
Equilibrium Lending Mechanism and Aggregate Activity
Cheng Wang and Ruilin Zhou
WP-00-30
Impact of Independent Directors and the Regulatory Environment on Bank Merger Prices:
Evidence from Takeover Activity in the 1990s
Elijah Brewer III, William E. Jackson III, and Julapa A. Jagtiani
Does Bank Concentration Lead to Concentration in Industrial Sectors?
Nicola Cetorelli
WP-00-31
WP-01-01
2
Working Paper Series (continued)
On the Fiscal Implications of Twin Crises
Craig Burnside, Martin Eichenbaum and Sergio Rebelo
WP-01-02
Sub-Debt Yield Spreads as Bank Risk Measures
Douglas D. Evanoff and Larry D. Wall
WP-01-03
Productivity Growth in the 1990s: Technology, Utilization, or Adjustment?
Susanto Basu, John G. Fernald and Matthew D. Shapiro
WP-01-04
Do Regulators Search for the Quiet Life? The Relationship Between Regulators and
The Regulated in Banking
Richard J. Rosen
Learning-by-Doing, Scale Efficiencies, and Financial Performance at Internet-Only Banks
Robert DeYoung
The Role of Real Wages, Productivity, and Fiscal Policy in Germany’s
Great Depression 1928-37
Jonas D. M. Fisher and Andreas Hornstein
WP-01-05
WP-01-06
WP-01-07
Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy
Lawrence J. Christiano, Martin Eichenbaum and Charles L. Evans
WP-01-08
Outsourcing Business Service and the Scope of Local Markets
Yukako Ono
WP-01-09
The Effect of Market Size Structure on Competition: The Case of Small Business Lending
Allen N. Berger, Richard J. Rosen and Gregory F. Udell
WP-01-10
Deregulation, the Internet, and the Competitive Viability of Large Banks
and Community Banks
Robert DeYoung and William C. Hunter
WP-01-11
Price Ceilings as Focal Points for Tacit Collusion: Evidence from Credit Cards
Christopher R. Knittel and Victor Stango
WP-01-12
Gaps and Triangles
Bernardino Adão, Isabel Correia and Pedro Teles
WP-01-13
A Real Explanation for Heterogeneous Investment Dynamics
Jonas D.M. Fisher
WP-01-14
Recovering Risk Aversion from Options
Robert R. Bliss and Nikolaos Panigirtzoglou
WP-01-15
Economic Determinants of the Nominal Treasury Yield Curve
Charles L. Evans and David Marshall
WP-01-16
Price Level Uniformity in a Random Matching Model with Perfectly Patient Traders
Edward J. Green and Ruilin Zhou
WP-01-17
Earnings Mobility in the US: A New Look at Intergenerational Inequality
Bhashkar Mazumder
WP-01-18
3
Working Paper Series (continued)
The Effects of Health Insurance and Self-Insurance on Retirement Behavior
Eric French and John Bailey Jones
WP-01-19
The Effect of Part-Time Work on Wages: Evidence from the Social Security Rules
Daniel Aaronson and Eric French
WP-01-20
Antidumping Policy Under Imperfect Competition
Meredith A. Crowley
WP-01-21
Is the United States an Optimum Currency Area?
An Empirical Analysis of Regional Business Cycles
Michael A. Kouparitsas
WP-01-22
A Note on the Estimation of Linear Regression Models with Heteroskedastic
Measurement Errors
Daniel G. Sullivan
WP-01-23
The Mis-Measurement of Permanent Earnings: New Evidence from Social
Security Earnings Data
Bhashkar Mazumder
WP-01-24
Pricing IPOs of Mutual Thrift Conversions: The Joint Effect of Regulation
and Market Discipline
Elijah Brewer III, Douglas D. Evanoff and Jacky So
WP-01-25
Opportunity Cost and Prudentiality: An Analysis of Collateral Decisions in
Bilateral and Multilateral Settings
Herbert L. Baer, Virginia G. France and James T. Moser
WP-01-26
Outsourcing Business Services and the Role of Central Administrative Offices
Yukako Ono
WP-02-01
Strategic Responses to Regulatory Threat in the Credit Card Market*
Victor Stango
WP-02-02
The Optimal Mix of Taxes on Money, Consumption and Income
Fiorella De Fiore and Pedro Teles
WP-02-03
Expectation Traps and Monetary Policy
Stefania Albanesi, V. V. Chari and Lawrence J. Christiano
WP-02-04
Monetary Policy in a Financial Crisis
Lawrence J. Christiano, Christopher Gust and Jorge Roldos
WP-02-05
Regulatory Incentives and Consolidation: The Case of Commercial Bank Mergers
and the Community Reinvestment Act
Raphael Bostic, Hamid Mehran, Anna Paulson and Marc Saidenberg
Technological Progress and the Geographic Expansion of the Banking Industry
Allen N. Berger and Robert DeYoung
WP-02-06
WP-02-07
4
Working Paper Series (continued)
Choosing the Right Parents: Changes in the Intergenerational Transmission
of Inequality Between 1980 and the Early 1990s
David I. Levine and Bhashkar Mazumder
WP-02-08
The Immediacy Implications of Exchange Organization
James T. Moser
WP-02-09
Maternal Employment and Overweight Children
Patricia M. Anderson, Kristin F. Butcher and Phillip B. Levine
WP-02-10
The Costs and Benefits of Moral Suasion: Evidence from the Rescue of
Long-Term Capital Management
Craig Furfine
WP-02-11
On the Cyclical Behavior of Employment, Unemployment and Labor Force Participation
Marcelo Veracierto
WP-02-12
Do Safeguard Tariffs and Antidumping Duties Open or Close Technology Gaps?
Meredith A. Crowley
WP-02-13
Technology Shocks Matter
Jonas D. M. Fisher
WP-02-14
Money as a Mechanism in a Bewley Economy
Edward J. Green and Ruilin Zhou
WP-02-15
Optimal Fiscal and Monetary Policy: Equivalence Results
Isabel Correia, Juan Pablo Nicolini and Pedro Teles
WP-02-16
Real Exchange Rate Fluctuations and the Dynamics of Retail Trade Industries
on the U.S.-Canada Border
Jeffrey R. Campbell and Beverly Lapham
WP-02-17
Bank Procyclicality, Credit Crunches, and Asymmetric Monetary Policy Effects:
A Unifying Model
Robert R. Bliss and George G. Kaufman
WP-02-18
Location of Headquarter Growth During the 90s
Thomas H. Klier
WP-02-19
The Value of Banking Relationships During a Financial Crisis:
Evidence from Failures of Japanese Banks
Elijah Brewer III, Hesna Genay, William Curt Hunter and George G. Kaufman
WP-02-20
On the Distribution and Dynamics of Health Costs
Eric French and John Bailey Jones
WP-02-21
The Effects of Progressive Taxation on Labor Supply when Hours and Wages are
Jointly Determined
Daniel Aaronson and Eric French
WP-02-22
5
Working Paper Series (continued)
Inter-industry Contagion and the Competitive Effects of Financial Distress Announcements:
Evidence from Commercial Banks and Life Insurance Companies
Elijah Brewer III and William E. Jackson III
WP-02-23
State-Contingent Bank Regulation With Unobserved Action and
Unobserved Characteristics
David A. Marshall and Edward Simpson Prescott
WP-02-24
Local Market Consolidation and Bank Productive Efficiency
Douglas D. Evanoff and Evren Örs
WP-02-25
Life-Cycle Dynamics in Industrial Sectors. The Role of Banking Market Structure
Nicola Cetorelli
WP-02-26
Private School Location and Neighborhood Characteristics
Lisa Barrow
WP-02-27
Teachers and Student Achievement in the Chicago Public High Schools
Daniel Aaronson, Lisa Barrow and William Sander
WP-02-28
The Crime of 1873: Back to the Scene
François R. Velde
WP-02-29
Trade Structure, Industrial Structure, and International Business Cycles
Marianne Baxter and Michael A. Kouparitsas
WP-02-30
Estimating the Returns to Community College Schooling for Displaced Workers
Louis Jacobson, Robert LaLonde and Daniel G. Sullivan
WP-02-31
A Proposal for Efficiently Resolving Out-of-the-Money Swap Positions
at Large Insolvent Banks
George G. Kaufman
WP-03-01
Depositor Liquidity and Loss-Sharing in Bank Failure Resolutions
George G. Kaufman
WP-03-02
Subordinated Debt and Prompt Corrective Regulatory Action
Douglas D. Evanoff and Larry D. Wall
WP-03-03
6
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