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FEDERAL RESERVE BANK OF CLEVELAND

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papers

NUMBER 20

By Joseph G. Haubrich and Deborah Lucas

POLICY DISCUSSION PAPER

Who Holds the Toxic Waste?
An Investigation of CMO Holdings

JUNE 2007

POLICY DISCUSSION PAPERS

FEDERAL RESERVE BANK OF CLEVELAND

Who Holds the Toxic Waste?
An Investigation of CMO Holdings
By Joseph G. Haubrich and Deborah Lucas
“Toxic waste” refers to the riskiest derivative structures arising from collateralized mortgage
obligations (CMOs). We use simulations to predict how this risk would manifest itself in
various interest rate environments. We also look for evidence on the total dollar value of
these securities, who holds them, and how much they hold. Very limited public information
is available, but commercial banks are required to report on their holdings, and we
investigate the extent to which the risk is concentrated in that sector.

Joseph G. Haubrich is a
consultant and economist at
the Federal Reserve Bank
of Cleveland, and Deborah
Lucas is a professor at
Northwestern University. The
authors thank Faisal Butt,
Janet Miller, and Brent Meyer
for research assistance,
and seminar participants at
the American Institute for
Economic Research and
the Chicago Bank Structure
Conference, particularly
Mark Flannery, for thoughtful
comments.

Materials may be
reprinted, provided that
the source is credited.
Please send copies of
reprinted materials to the
editor.

POLICY DISCUSSION PAPERS

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Reserve System.

ISSN 1528-4344

FEDERAL RESERVE BANK OF CLEVELAND

Introduction
Home mortgages may seem a rather pedestrian investment, but the mortgage financing
industry has reached a level of maturity and development worthy of the most sophisticated financial engineer. Individual mortgages are bundled together and used as the collateral behind collateralized mortgage obligations (CMOs). CMOs are divided into tranches
of various types, with names such as PACs, TACs, IOs, and sticky jump Zs. This prolifera-

1. See Haubrich (1995) for a basic
introduction to CMO derivatives
and Midanek (1995) for a history
of the market. A detailed, but
still high-level description of
mortgage derivatives can be
found in Oldfield (2000).

tion segments the interest rate and pre-payment risk into different classes of instruments,
creating a class of fairly safe assets with wide appeal.This of course also creates a class of
risky assets, known collectively as toxic waste.1
To what extent is this toxic waste a problem? Held as a hedge, or by well-capitalized
investors who understand the risk, it is not a concern. Held by unsophisticated investors
who do not understand their exposure, or by institutions arbitraging regulatory requirements, it may be a problem.
Unfortunately, the extent of the possible problem has received little attention, either
in the academic literature or the popular press.2 Information on the total amount of risky
CMO constructs is difficult to come by, and public information about the concentration
and eventual disposition of those assets is almost non-existent. In this paper we take a
first look at this aspect of the market, endeavoring to ascertain which portfolios hold
risky CMO debt and the extent to which it poses a problem for investors and regulators.

2. While there is also concern
about the risk of Fannie Mae
and Freddie Mac, and their
holdings of toxic waste are an
aspect of their risk, our focus is
on the pass-through risk to other
institutions.

In the next section we develop a simple pricing model that illustrates how the value of
CMO constructs can change dramatically with interest rates.Then we examine the available data on the size and distribution of risky CMOs. Finally, we take a closer look at CMO
holdings at commercial banks, a sector for which there is more detailed information and
potentially greater regulatory concern.

The Risk in CMOs
Before attempting to track the ownership of risky CMO tranches, we present some examples illustrating the potential risks. Default risk generally is minimal, since most issuers either provide a guarantee of over-collateralize the CMO. The interest rate risk, on
the other hand, can be enormous, particularly as there can be very complicated prepayment effects.
We construct a Monte Carlo model of stochastic interest rates and mortgage cash
flows, and use it to illustrate the risk in the value of several common types of mortgage
derivatives—Zs, IOs and POs. We consider the risks in a variety of interest rate environments, including one of rapidly rising interest rates.To preview the main results, we find
that under conditions of typical interest rate volatility, the value of these derivatives is
highly volatile. For instance, there is a significant probability that losses on toxic waste
holdings will exceed the associated bank capital requirements, even with a 100 percent
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POLICY DISCUSSION PAPERS

NUMBER 20, JUNE 2007

risk weight. We also find that the effect of an unanticipated and unusually rapid increase
in mortgage rates would be to increase the value of some types of derivatives (IOs), and
decrease the value of others (Zs and POs).

Some Illustrations
In the model, stochastic interest rates induce stochastic prepayment rates, and hence variability in the timing, amount and present value of cash flows. More precisely, mortgage
interest rates are assumed to follow a discretized Cox, Ingersoll and Ross (1985) process,
with monthly shocks to annual rates described by:
0.5
(1) r ( t ) = r ( t − 1) + s ( r ( t − 1) − r *) + ( r ( t − 1)) σε ( t ).

In equation (1), r(t) is the mortgage rate at month t, r* is the mean-reverting level of
rates, s is the speed of mean reversion, σ is a volatility parameter, and ε ( t ) is a standard
normal shock. We impose an upper bound on interest rates of 30 percent to reduce the
influence of outliers.The model is roughly calibrated to reflect recent market conditions.
In the base case simulations, s = 0.025 / 12, σ = 0.0378, and r* = 0.07.The assumed volatility is consistent with monthly mortgage rate volatility from January 1990 to July 2004.3
The speed of mean reversion is from Tuckman (1995).The assumed long-run rate is lower
than the average mortgage rate since 1990 of 7.7 percent, implicitly putting more weight
on more recent conditions.
The prepayment rate4 (PSA) varies inversely with the distance between current mortgage rates as given by equation (1) and the weighted average coupon (WAC) of the
underlying mortgage pool. Consistent with mortgages issued in 2003, WAC = .05 in the
base case, and r(0) = 0.045. The relation between prepayment rates and interest rates is
nonlinear, and based on a linear interpolation of recent estimates from investment banks,
reported in table 1.5 In this simple model, prepayments along each path use this rule,
invariant to the pattern of past prepayments.
The cash flows for a given derivative security over its life are determined according to
the rules for that security and the cash flows the underlying 30-year fixed-rate mortgage
pool. In the case of Zs, no cash is received until all other classes of security holders are
repaid in full. Deferred coupons are invested at the current monthly rate implied by the
model, and paid out in full at the time of the first principal repayment to the Z class. Z’s
are assumed to comprise 10 percent of principal. IOs receive all coupon payments as
TABLE 1

PSA RATE SCHEDULE AS A FUNCTION OF INTEREST RATE CHANGES

Coupon Issue
year
5

2003

Avg.
–300*

Avg.
–200

Avg.
–100

Avg.
–50

Avg.
base

Avg.
+50

Avg.
+100

Avg.
+200

Avg.
+300

1470

1400

667

262

170

150

131

111

102

*Avg – 300 indicates a drop in interest rates 300 basis points below the WAC on the mortgage pool.

2

3. This is derived from a monthly
volatility of 1 percent, adjusted
for the square root of the interest
rate in the Cox, Ingersoll,
and Ross (1985) formulation:
σ = (0.01 / 0.07)0.5 .
4. Prepayment rates are expressed
as percentages of the Public
Securities Association standard
conditional prepayment rate,
or PSA. The conditional
prepayment rate (CPR) is the
annualized fraction of outstanding mortgages in a pool that
get prepaid in a given month.
The PSA schedule assumes
the CPR increases from 0 in
month 0 to 6 percent in month
30 (an increase of 0.2 percent
per month), and is constant
thereafter. For example, a PSA
of 150 means that after month
30 the CPR is (6%)(1.5) = 9%.
5. BondMarkets.com monthly
projection survey of PSA rates
(August 16, 2004), for 5 percent,
30-year conventional mortgage
issued in 2003. Participating
dealers include: BS CITI CSFB
DB GC GS LB. At 100 percent
PSA, we assume 0.5 percent
of outstanding mortgages are
prepaid each month.

FEDERAL RESERVE BANK OF CLEVELAND

they arrive, and POs receive all principal payments. Cash flows are discounted at the realized mortgage rates along each Monte Carlo path.6
A histogram of the distribution of the present value of a Z residual, based on 2000
Monte Carlo runs and normalized by the average present value, is given in figure 1. The
mean value is normalized to 1, and the coefficient of variation is 25 percent. However,

6. Discounting along the paths
generated by equation (1)
implicitly equates risk-neutral
and actual probabilities,
imparting some bias to the
estimates.

the asymmetric distribution of the risk reduces the informativeness of variance-based
measures of spread. In fact, if Zs are priced at their expected value, most of the time the
investment will generate a sizable profit (the mode in figure 1 is well above the mean).
The long lower tail, however, indicates that there is a risk of significant losses.
Figure 2 shows the distribution of present values for an IO, and figure 3 shows the
distribution of the corresponding PO, again based on 2000 Monte Carlo runs, with all
outcomes normalized by the average present value.The means of both normalized distri-

FIGURE 1

Z VALUES

Frequency
1000
800
600
400
200
0
0.2

0.4

0.6

0.8

0.9

1

1.1

1.2

1.4

More

Realized value
(relative to mean value)
FIGURE 2

IO VALUES

Frequency
600
500
400
300
200
100
0
0.2 0.4

0.6 0.8

1.2 1.4

1

1.6 1.8

2

2.2 More

Realized value
(relative to mean value)
FIGURE 3

PO VALUES

Frequency
1000
800
600
400
200
0
0.4

0.6 0.8 0.9

1

1.1 1.2 1.4

1.6 1.8

2

More

Realized value
(relative to mean value)

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POLICY DISCUSSION PAPERS

NUMBER 20, JUNE 2007

butions equal 1; the coefficient of variation for the IO is 47.7 percent and 21 percent for
the IO and PO, respectively. The very high risk of the IO is due to the disappearance of
cash flows in the event of prepayments (whereas for the PO prepayment only affects the
timing of the cash flows). Unlike Zs and POs, however, IOs have positive skewness

Losses and Bank Capital Requirements
As discussed below in the section on CMO holdings at commercial banks (Bank Risk),
the rules governing the capital held against these securities by commercial banks are
complicated. It is reasonable to assume that for many banks, particularly the smaller
ones, toxic waste will be assigned a risk weight of 1, with a corresponding 8 percent
capital requirement.
Under the base case assumptions, the Monte Carlo results suggest that an 8 percent
capital requirement for these securities is often inadequate. For the Zs, losses exceed
required capital 27.3 percent of the time; for the IOs, losses exceed capital a striking
54.8 percent of the time; and for POs losses exceed capital 24.9 percent of the time.
Larger banks may hold capital based on the more complicated rules for measuring
market risk, although they may also follow the 8 percent rule if the securities are not
held in a trading account. The market risk rule is based on value at risk (VaR) for a 10day period and a 99 percent confidence level. Assessing the VaR for these securities is
tricky. If historical price data were available (which it is not), it could be used to create a
probability distribution of conditional price changes.The model used in the Monte Carlo
experiments provides the conditional distribution of future interest rate and cash flow
paths from a given starting point. How should this be used this to represent the distribution of changes in expectation over 10 days about the entire future path of cash flows
and their present value?
The approach taken here is to compare the average present value of cash flows at the
initial interest rate with the average present value for an adverse change in interest rates
at the 99 percent level over 10 days.The 99 percent confidence interval for interest rates
in the model, centered on the initial value of 0.045, is (0.0356, 0.0544).An adverse change
for the IOs implies that rates fall to 0.0356, while for POs and Zs it implies that rates rise
to 0.0544. For IOs, the VaR is 17.7 percent of the original price, for POs it is 8.8 percent
of the original price, and for Zs it is 6.5 percent of the original price. Multiplying each by
the factor of 3.5 implies a capital requirement far in excess of the 8 percent required for
smaller institutions.To the extent that bank portfolios contain securities whose risk fully
or partially offsets (for example, an IO plus a PO has risk identical to a whole mortgage),
evaluating capital adequacy one security at a time overstates the risk.

4

FEDERAL RESERVE BANK OF CLEVELAND

Rapidly Rising Rates
Given the low interest rate environment of recent years and the expectation that rates
could rise sharply, it is interesting to ask what would happen to the value of toxic waste
if those expectations were realized or exceeded. As a test of this, we assume that the
path of interest rates over 14 months follows the pattern of rates from October of 1993
to December of 1994. At that time, interest rates rose abruptly after a long period of
gradual decline, climbing a total of 2.37 percent. The episode revealed the vulnerability of several major institutions to large and unhedged derivative positions, including
Orange County and Proctor and Gamble. By way of comparison, home mortgage rates
only rose 0.30 percent during the fed funds increases of 2004-2006.
To reproduce the 1994 experience, we assume those historical rate changes for the
first 14 months of the simulations, with stochastic rates and their corresponding prepayment rates simulated in the Monte Carlo thereafter. We assume that security prices
start at the average value predicted by the base case model, and that rates initially are
at 4.5 percent.We then calculate the percentage change in the value of each security at
the end of 14 months, assuming payments received along the way are reinvested and
rolled over at current rates. For Zs, the average present value falls to 91.3 percent of
the base case starting value. The average present value of POs falls to 94.7 percent of
the base case starting value. IOs, on the other hand, significantly increase in value, to
179.5 percent of the base case. While these price changes are unlikely to threaten the
viability of well-capitalized banks, they could have a significant adverse affect on poorly
capitalized institutions with concentrated positions in these securities.
It is possible that the market is already pricing a more rapid increase in interest rates
into mortgage derivatives than in the base case model. If so, the above calculations
exaggerate the gains or losses likely to be realized.An alternative that takes this into account is to assume a more rapid rate of mean reversion to the long-run 7 percent rate
than in the base case.To implement this, we recalculate the distribution of present values under the assumption that s, the mean reversion parameter, increases by a factor of
10 (going from 0.025 to 0.25 on an annual basis).This implies that rates on average are
expected to rise by about 1 percent in the first two years the mortgages are outstanding, roughly consistent with implied forward rates. All other parameters are as before.
Figures 4 to 6 illustrate the effect of faster mean reversion in rates on the distribution of
present values. For the Zs, the average present value falls to 97.5 percent of the original
base case.The value is depressed due to slower repayments as rates rise, and payments
discounted at a higher average rate. For the IOs, value increases to 116 percent of the
original base case. The dominant effect causing IO value to rise is that slower prepayments result in more coupon payments being received. For the POs, value decreases
on average to 96.6 percent of the base case, as principal is returned more slowly and
discounted at higher average rates.
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POLICY DISCUSSION PAPERS

NUMBER 20, JUNE 2007

Using this alternate base case, the losses to Zs and POs that would result from an
interest rate experience like that in 1994 are considerable smaller—with values at the
end of 14 months equal to about 99 percent of the modified base case values for both
Zs and POs.

Market Data
The mortgage market in the United States is large: As of the third quarter of 2005, the
value of mortgage debt outstanding was $11.50 trillion, of which $8.82 trillion was residential mortgages (one to four family residences). This compares with federal debt in
private hands of $3.86 trillion and total assets of domestically chartered commercial
banks of $7.73 trillion for the same time period. (Federal Reserve Bulletin, tables 1.54,
1.41 and 1.26.)
FIGURE 4

Z VALUES, ACCELERATED MEAN REVERSION

Frequency
700
600
500
400
300
200
100
0
0.2

0.4

0.6

0.8

0.9

1

1.1

1.2

1.4 More

Realized value
(relative to mean value)
FIGURE 5

IO VALUES, ACCELERATE MEAN REVERSION

Frequency
500
400
300
200
100
0
0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

2 2.2 More

Realized value
(relative to mean value)
FIGURE 6

PO VALUES, ACCELERATED MEAN REVERSION

Frequency
800
700
600
500
400
300
200
100
0
0.4 0.6 0.8 0.9

1

1.1 1.2 1.4 1.6 1.8

Realized value
(relative to mean value)

6

2

More

FEDERAL RESERVE BANK OF CLEVELAND

Much of the mortgage debt is securitized: $4.69 trillion of the total mortgage debt,
and $4.22 trillion of the residential mortgages, most of it securitized by the major government sponsored enterprises: Ginne Mae did $474 billion, Freddie Mac did $1,148
billion, and Fannie Mae did $1,787 billion, leaving $842 billion to the private mortgage
conduits. Not all of these are CMOs/REMICs (Real Estate Mortgage Investment Conduits).
The Mortgage Market Statistical Annual (2004) reports that as of the fourth quarter of
2003, agency-backed CMO/REMICs were at $955 billion. Interestingly, they report total
mortgage securities at $4,207 billion, while the Federal Reserve Bulletin reports 2003
pool and trusts as $4,692 billion).

How Much Toxic Waste?
Of the CMO/REMICs out there, how much is extremely risky and should count as toxic
waste? This is a difficult question to answer. The mortgage debt outstanding number is
a stock measure, and as such it combines securities issued in many different years. Furthermore, a major characteristic of CMOs is that tranches may be of short or variable
duration. To our knowledge, there is no accurate aggregate estimate of the number or
value of outstanding tranches. There is somewhat more information on the flow variable, CMO issuance.
One source for this is Bloomberg, with the ICMO function.The “Deal Structure” part of
this splits the CMO tranches issued in a particular month into eight classes: PAC (Planned
Amortization Class),AD (Accretion Directed), Z (accrual), FLT (Floater), INV (inverse floater), IO/PO (Interest Only/Principal Only), SUB (Subordinate), and Other (all others, but
mostly standard sequential pay classes). What exactly counts as toxic waste is a matter
of judgment, but a reasonable definition would be Z+INV+IO/PO+SUB and this is the
definition we use here.Accretion-directed bonds, PACs, and to a lesser extent, floaters, are
designed to be safe, and most of the other, as generic sequential pay bonds, will also be
relatively safe (see Amerman, 1996, for a discussion). The Federal Reserve’s Trading and
Capital-Markets Activities Manual (section 4110.1, p. 12) states that “prepayment risk
is concentrated within a few volatile classes, most notably residuals, inverse floaters, IOs
and POs, Z bonds, and long-term support bonds.”As an example, in April 2000, Bloomberg
lists 46 CMO issuances that total $16.6 billion, with toxic waste of $3.0 billion, most of it
($2.4 billion) as IOs and POs.
Figures 7 and 8 plot the time series flow of total toxic waste value and toxic waste
value as a fraction of total value from Bloomberg. One possible concern about our measure is that it on occasion exceeds 50 percent. Can there be that many highly risky CMO
constructs? As it turns out, for the three months where the fraction exceeds one-third, in
two of them (May 1995 and October 1999) the high number results from components
about which we have the most confidence that they are risky: Zs and Subs. Usually, these

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POLICY DISCUSSION PAPERS

NUMBER 20, JUNE 2007

are much lower; the median of their sum is only 4 percent. In the other (July 1996) the
cause was a high level of IO/POs, which also are generally regarded as high risk.
Another source on the extent of toxic waste arises from regulatory concern about
the risk in bank portfolios. In 1992, the Federal Reserve Board issued a Supervisory Policy Statement on Securities Activities that defined the “high-risk mortgage securities”
deemed unsuitable investments for banks. This became known as the FFIEC test, and
CMO bonds that passed (deemed not high risk) became known as FFIEC-qualified.Those
deemed high risk had to be carried in the institution’s trading account or as assets held
for sale. In practice, a mortgage-derivative product that met any of the following three
criteria was deemed high risk:
• Average Life Test: expected weighted average life greater than 10.0 years
• Average Life Sensitivity Test: expected weighted average life extends by
more 4.0 years if the yield curve shifts up 300 basis points or shortens by more
the 6.0 years if the yield curve shifts down 300 basis points (both shifts sustained
and parallel).
• Price Sensitivity Test: the estimated change in the price of the security exceeds 17 percent with a shift in the yield curve of 300 basis points.
FIGURE 7

IO VALUES, ACCELERATE MEAN REVERSION

Percent
25
20
15
10
5
0
1991 1993 1995 1997 1999 2001 2003 2005
FIGURE 8

PO VALUES, ACCELERATED MEAN REVERSION

Percent
60
50
40
30
20
10
0
1991 1993 1995 1997 1999 2001 2003 2005

8

FEDERAL RESERVE BANK OF CLEVELAND

This regulation also led to a revision of the FFIEC Call Reports, having banks report
the amount of high risk mortgage securities they held. In April 1998, the constraints were
rescinded, and shortly thereafter banks stopped reporting.
Bloomberg reports some aggregate FFIEC test results for CMOs in the “Bloomberg
universe.”7 For May 16, 2000, this had a value of $2,728.2 billion, of which $884.4 was a
solid pass and $1,110.2 was a solid fail. For May 3, 2004, out of a total market value of the
Bloomberg universe of $3,753 billion, $1,301 billion were solid pass and $1,053 billion,
or 28 percent, were solid fail. Should a fail count as toxic waste? In some sense it is an

7. Some measure of the coverage
of the Bloomberg universe
can be gained by noting that
excluding re-REMICs, Bloomberg
lists $836.8 billion in CMOs as
of June 30, 2000. Compare this
with the $690 billion for mid-year
2000 from the Mortgage Market
Statistical Annual so Bloomberg
appears to cover much of the
market.

objective criterion, in that regulators deemed these securities high risk for banks.

Who Holds CMOs and Their Constructs?
Who holds the risky CMO constructs—the exotic tranches, the toxic waste? That question is not so easy to answer. Anecdotally, much goes to private partnerships and hedge
funds, entities with little regulation a few reporting requirements (Passmore, et al., 2002).
A preliminary step is to establish which investors hold CMOs in their portfolios, though
the distribution of risky CMO constructs may differ. Furthermore, concentration matters:
a sector’s aggregate holdings may be low, but that does not preclude an unhealthy concentration in a few firms.Table 2 lists the mortgage-related security holdings by investor
type for year-end 2003 reported in the Mortgage Market Statistical Annual (MMSA).
Notice from table 2 that the three largest holders of CMOs appear to be commercial
banks, life insurance companies, and foreign investors (with Fannie Mae, and presumably
Freddie Mac, although they are not listed by MMSA, in close fourth place). Details on holdings of foreigners is exceedingly difficult to come by, and we will have little more to say
about them. According to recent press reports, however, foreign holdings of mortgage-

TABLE 2

CMO HOLDINGS, (BILLIONS OF DOLLARS, BY INVESTOR TYPE, YEAR-END 2003)

Investor type
FDIC commercial banks

CMOs

Percent of
assets

263.1

3.46

S&Ls

45.5

3.09

Federal credit unions

12.9

2.11

FHL banks

40.0

4.86

funds1

42.5

0.65

155.0

4.11

Fannie Mae & Freddie Mac

N.A.

N.A.

investors2

182.0

N.A.

18.0

N.A.

Pension

Life insurance companies
Foreign

MBS dealer inventory
(Sub)total

709.8

1. year-end 2004 estimate.
2. No longer reported; 2001 estimate.
Source: Mortgage Market Statistical Annual, 2004; and FNMA 10-k, 2004.

9

POLICY DISCUSSION PAPERS

NUMBER 20, JUNE 2007

backed securities have been growing rapidly. A small amount of information exists about
life insurance companies, but little of that is centralized.The best data exist for commercial banks, though even there the breakdown of portfolios is sketchy.

Life Insurers
Although as table 2 shows, life insurers are major investors in CMOs, detailed portfolio
data are difficult to come by, particularly because insurers are regulated at the state level. One rating agency, at least, occasionally reports more detailed information. Weiss Ratings reported on what they termed the “riskiest types of CMOs…multiclass, nondefined,
mortgage and asset-backed securities,” putting these holdings among the nation’s life and
health insurers at $105 billion at the end of 1998 and $123 billion at the end of 1999. Anecdotal evidence suggests that at least some life insurers overinvest in risky CMO constructs.The most prominent example is the failure of Coastal States Life Insurance, which
was seized by the Georgia Department of Insurance in January 1993. Coastal States had
invested heavily in mortgage-backed securities, and its portfolio of CMOs had a book value nearly $9 million less than what the company reported (Knowles, 1993).

Banks
Banks hold many CMOs, and this raises two concerns. First, under current capital requirements, it may be advantageous to hold the riskier forms of any given asset class to increase return on equity. Secondly, the expense of bank failures may be borne by the public because of the safety net.
How much toxic waste do banks hold? At the end of December 1998, banks held a
total of $7.50 billion of FFIEC-risky mortgage securities.This excludes what they held in
their trading accounts (as it includes only RCON 8781 in the call report data). Even adding all the CMOs in the trading accounts (RCON 3535 and 3536) only brings the number
up to $14.2 billion.While banks hold some risky CMO residuals, in general, they account
for a modest portion of the total. But do some individual banks hold too much?

Bank Risk
Bank capital requirements are designed to differentiate between different instruments according to risk and, to a large extent, are directed at credit rather than interest rate risk.
Accordingly, for risk-based capital purposes, any sort of mortgage-backed security falls
into one of several broad categories. Agency CMOs get generally favorable treatment. Securities backed by Ginne Mae, Freddie, and Fannie get a 20 percent weight (though passthroughs from Ginnie get a zero weight). Privately-issued CMOs have a weight dependent
on the weights of the underlying assets, and thus often get a risk weight of 50 or 100 percent. Instruments viewed as risky, such as strips, get a 100 percent weight. The criterion

10

FEDERAL RESERVE BANK OF CLEVELAND

for a 100 percent weighting is “any class of an MBS that can absorb more than its pro rata
8

share of loss without the whole issue being in default”8
While the most obvious form of regulatory arbitrage may be banks amassing risky
CMO constructs, which, for some reason, get a 20 percent weight, that is not the only

Commercial Bank Eamination
Manual, Nov. 1998, section
3020.1, p. 14. This is essentially
the language in Regulation H, 12
CFR 208 appendix A.

possibility. Roughly speaking, banks must hold eight percent capital against their riskweighted assets. As shown above, it is entirely possible that eight percent capital is not
enough, given the risk of some CMO constructs. Thus, even with a 100 percent risk
weighting, holding some CMOs may constitute regulatory arbitrage.
Since 1998 however, there has been another capital requirement on market risk for
banks with large trading activity. These banks must increase their credit-risk-weighted
assets by a “market-risk-equivalent” factor based on the value at risk (VaR) of the bank’s
trading account (and commodity position).9
In organizing the data on bank CMO holdings, we take two approaches.The first looks
for particularly high concentrations of CMOs or risky CMOs. The other looks into the
determinants of CMO holdings, the factors influencing the CMO component of bank
portfolios. The hope is to uncover the reasons (evading capital requirements, etc.) that
lead some banks to a high, or inappropriate, level of holdings.

9. The market-risk-equivalent
assets are defined as 12.5 (the
reciprocal of 8 percent) times
the larger of the the 60-day
average VaR (99 percent level,
calculated on 10 days) times a
factor between three and four
and the previous day’s VaR, plus
an additional charge for specific
risk.

Here we should be precise about exactly what we are reporting. CMOs listed in the
call reports are in two sections, the portfolio (RC-B) and the trading account. For the measure of total CMOs, we report the sum of the portfolio measures (RCON A561 and RCON
A562) and the trading account measures (RCON 3535 and RCON 3536). For a narrow
measure of risky CMOs, we report those held in the portfolio that failed the FFIEC test,
RCON 8781 (technically, the maximum of RCON 8781 and RCFD8781). An alternative
would be to report RCONA562, which includes other mortgage-backed securities with
an expected average life of over three years, and although this gives perhaps too broad a
measure of what is risky, it has the advantage of still being reported. Our broader measure
adds in RCON 8781 plus mortgage-backed securities other than pass-throughs held in the
trading accounts (RCON3535 and RCON 3536).Trading account assets are not necessarily risky, but they are where regulations mandated that risky CMOs be housed.Table 3 lists
these definitions for easier reference.
TABLE 3

CALL REPORT DEFINITIONS

Variable

Call mnemonic

Description

Total CMOs

RCON A561 + RCON A562 +
RCON 3535 + RCON 3536

Portfolio plus trading account

Risky CMOs
(narrow measure)

Max
(RCON8781,RCFD8781)

Portfolio, failing FFIEC test

Risky CMOs
(broad measure)

Max
(RCON8781,RCFD8781) +
RCON3535+RCON3536

Narrow measure, plus MBS other
than pass-throughs in trading account

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POLICY DISCUSSION PAPERS

NUMBER 20, JUNE 2007

Table 4 lists the ten banks that hold the most CMOs for the fourth quarter of 2005. Because we have a more detailed breakdown for risky CMOs in 1998, we also report those
figures in table 5. Similarly, table 6 lists the ten banks with the largest ratio of CMOs to
total assets for the fourth quarter of 2005, and table 7 lists the ten banks with the largest
ratio of CMOs to total assets for the fourth quarter of 1998.
The portfolios with a high concentration of CMOs are rarely large in an absolute sense:
Only one bank in tables 6 and 7 is among the top-ten CMO holders for the quarter. But
the concentration seems quite impressive.
Table 8 lists the ten banks with the most risky CMOs (under the broad definition). Not
surprisingly, several of the largest CMO holders are also among the largest holders of risky
CMOs. Compass and North Fork appear among the top-ten CMO holders. Some others,
however, concentrated their holdings more in risky CMOs.

TABLE 4

LARGEST CMO HOLDINGS AMONG BANKS, 2005

Name
Commerce Bank, NA

CMOs
(thousands of
dollars)

Percentage of
total assets

Percentage of
capital

14,551,881

41.89

724.96

Merrill Lynch Bank

7,930,545

13.14

138.34

Countrywide Bank, NA

4,946,132

6.76

92.56

Merrill Lynch B&T

4,460,239

42.47

583.85

Fifth Third Bank

3,700,011

7.77

75.17

Charles Schwab Bank, NA

3,424,178

50.13

619.49

New York Community Bank

2,684,223

10.48

132.87

Branch B&T, Virginia

2,577,437

11.45

162.71

HSBC Bank USA, NA

2,339,228

1.66

24.03

Associated Bank, NA

2,251,297

10.31

150.99

TABLE 5

LARGEST CMO HOLDINGS AMONG BANKS, 1998

Name

CMOs
(thousands of
dollars)

Percentage of
total assets

Percentage of
capital

Washington Mutual

5,009,473

15.43

279.77

Compass Bank

4,487,579

27.22

390.55

Morgan Guaranty Trust, NY

2,892,712

4.61

27.98

North Fork Bank

2,454,078

23.23

347.44

National City, MI/IL

2,060,031

10.39

119.36

Apple Bank for Savings

1,849,563

34.45

478.41

Investors Savings Bank

1,546,131

39.05

625.57

Citizens Bank, MA

1,502,782

25.05

390.22

Citizens Bank, RI

1,440,246

24.17

358.34

First Union N.B.

1,438,575

0.70

10.02

12

FEDERAL RESERVE BANK OF CLEVELAND

TABLE 6

RATIO OF CMO HOLDINGS TO TOTAL ASSETS, 2005

Name

Percentage of
total assets

Percentage of
capital

First Signature B&T

91.03

1127.99

HSBC Trust (Delaware), NA

85.66

86.18

Frontier State Bank

77.75

1075.74

Washita State Bank

75.84

1078.65

Firstbank North

55.64

879.81

Firstbank, Arvada

902.73

53.90

851.25

Fiserv Trust

53.37

578.89

Firstbank, Evergreen

52.23

802.01

Firstbank, Parker

TABLE 7

55.19

Firstbank, Douglas County

51.99

893.74

RATIO OF CMO HOLDINGS TO TOTAL ASSETS, 1998

Name

Percentage of
total Assets

Percentage of
capital

First National Bank, Okeene

59.22

184.92

Lincoln Trust Company

56.87

591.49

Bank of Beulah

47.87

512.44

Rochester Bank

45.91

347.68

First Trust Corporation

607.45

42.27

416.04

Merrill Lynch B&T

41.24

339.66

Firstbank, Parker

39.26

360.52

Investors Savings Bank

39.05

625.57

Firstbank, Parker

TABLE 8

45.72

Citizens’ and People’s Bank, NA

51.99

893.74

LARGEST HOLDERS OF RISKY CMOS, 1998

Name
Compass Bank

CMOs
(thousands of
dollars)

Percentage of
total assets

Percentage of
capital

1,681,433

10.20

146.33

First Midwest Bank, NA

391,596

7.75

116.15

Lafayette American Bank

192,725

7.63

127.37

North Fork Bank

190,622

1.80

26.99

Harris Savings Bank

150,157

6.02

93.66

Southside Bank

127,740

14.60

236.60

National City, MI/IL

127,419

0.64

7.38

Citizens Bank NH

94,771

2.09

36.30

Citizens 1st Bank

90,076

21.49

162.26

Community Bank, NA

85,452

5.07

90.64

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POLICY DISCUSSION PAPERS

NUMBER 20, JUNE 2007

Table 9 lists the ten banks with the largest ratio of risky CMOs to total assets. Southside
and Compass stand out as banks with a position in risky CMOs that is large both absolutely and relative to their assets.
One thing to notice in table 9 is the relatively quick drop-off in holding concentration.
Few banks hold much more than 10 percent of their assets as risky CMOs.The top banks
show surprisingly high concentrations, though. In eight of sixteen quarters for which
we have these data, at least one bank is holding more than one-quarter of its total assets
as risky CMOs. The peak is nearly 43 percent. Possibly, these are just special-purpose vehicles and not “real banks.” If they are conduits for mortgage firms or securitization, there
may be less of a problem, if management knows the relevant risks.
TABLE 9

LARGEST HOLDERS OF RISKY CMOS BY PERCENT OF ASSETS, 1998

Name

CMOs
(thousands of
dollars)

Percentage of
total assets

Percentage of
capital

Kentucky-Farmers, Catlettsburg

36,602

30.55

101.64

Citizens 1st Bank

90,076

21.49

162.26

Southside Bank

127,740

14.60

236.60

Watertown Savings Bank

56,269

10.99

121.60

South Shore Bank of Chicago

85,216

10.86

195.42

Compass Bank

1,681,433

10.20

146.33

First United Security Bank

36,965

8.29

76.22

First Bank Richmond, NA

36,281

8.16

92.31

First National Bank, Chillicothe

6,040

8.12

113.79

First National Bank, Okeene

5,246

8.11

25.34

TABLE 10

TOBIT VARIABLE DESCRIPTIONS, 1998

Variable

Description

Intercept
Dum1
Dum2

1 if total assets between $50 and $100 million

Dum3

1 if total assets between $100 and $500 million

Dum4

1 if total assets between $500 and $1000 million

Dum5

1 if total assets between $1 and $5 billion

Caprat

Ratio of bank capital to total assets

Hotrat

Hot funds to total assets

Bhc

Dummy for bank-holding-company affiliation

Tass

Log of total assets

Chrat

Ratio of total charge-offs, net recoveries, to total assets

Nlrat

Net loans and leases to total assets

Netmar

Net interest margin

Tsprd

Spread between 30-year T-bond and 3-month T-bill

Baasp

Spread between Baa portfolio and 3-month T-bill

Offrat

14

Dummy for size. 1 if total assets < $50 million

Ratio of off-balance-sheet activities to total assets

FEDERAL RESERVE BANK OF CLEVELAND

Tobits
Which factors lead a bank to invest in CMOs? As a natural beginning to answering this
question, we conduct a Tobit analysis. The procedure controls for zero holdings and includes standard control variables in addition to variables related to risk, such as the capital ratio, net interest margin, and charge-offs. The full set of variables is listed in table 10.
The idea is to test the hypothesis that riskier banks have a higher propensity to hold risky
CMOs (as a share of assets).
Table 11 reports the results for the period where we have the most detailed data
(1994:Q1 to 1998:Q4). Notice that most coefficients are highly significant.The size dummies are mostly significant and positive. Since the excluded group is banks with total
assets above $5 billion, this result indicates that smaller banks tend to hold more CMOs
as a fraction of total assets, the exception being the smallest banks with assets below $50
million.The charge-off ratio enters negatively, which suggests that banks investing in risky
CMOs are not particularly risky on other dimensions.
The capital ratio has a negative coefficient, suggesting that a higher capital ratio implies lower holdings of CMOs.This is consistent with the story that some CMO holdings
might be for gaming capital regulations. However, the effect is rather small. Increasing
the capital ratio of the bank by one percentage point (say from 8 percent to 9 percent)
should decrease the percentage of CMOs in the bank’s portfolio by 0.03 percent (see

TABLE 11

TOBIT: RISKY(BROAD) CMO/TOTAL ASSETS, 1998

Noncensored: 3,194
Censored: 38,504

Variable

Estimate

STD Err

Pval

Intercept

–0.1818207

0.012523

0.0001

Dum1

–0.3501799

1332.627

0.9998

Dum2

0.01513873

0.004572

0.0009

Dum3

0.01094492

0.003285

0.0009

Dum4

0.0050788

0.002777

0.0674

Dum5

0.00109907

0.002368

0.6426

Caprat

–0.0318325

0.009439

0.0007

Hotrat

0.01525661

0.0026

0.0001

Bhc

0.00101018

0.00087

0.2457

Tass

0.01111653

0.000746

0.0001

Chrat

–0.1418837

0.061826

0.0217

Nlrat

–0.0364996

0.002168

0.0001

Netmar

0.08664372

0.007874

0.0001

Tsprd

–0.002567

0.001157

0.0264

Baasp

0.00552368

0.00146

0.0002

Offrat

–0.0159142

0.002578

0.0001

15

POLICY DISCUSSION PAPERS

NUMBER 20, JUNE 2007

Maddala, 1983, section 6.6). Since a big change in bank capital of 3 percent would decrease the risky CMO percentage by only one-tenth of one percent, this does not appear
to be highly important.

Conclusion
Although we find no smoking gun in call report data, CMO constructs can be dangerous. That became obvious ex post when interest rates rose dramatically in 1994. Then,
the losses from CMO constructs made the headlines, with multimillion-dollar losses at
Askin Capital Management, Piper Jaffray, the Louisiana State Retirement Plan and Yamachi Securities, among others (Canadian Institute of Actuaries, 1996). Unfortunately, the
institutions that have assumed this risk in recent years are opaque, and it is impossible
to determine whether and where there are concentrated exposures. At a time when
interest rates are again rising, understanding who is exposed to such risk is a question
investors—and taxpayers—should ponder.

16

FEDERAL RESERVE BANK OF CLEVELAND

References
Amerman, D. R. 1996. Collateralized Mortgage Obligations. New York: McGraw Hill.
Canadian Institute of Actuaries. 1996. Management, Risks, Regulation and Accounting
of Derivatives. (March).
Cox, J., J. Ingersoll, and S. A. Ross. 1985.“A Theory of the Term Structure of Interest Rates,”
Econometrica, vol. 53, 385–408.
Haubrich, J. G. 1995. “Derivative Mechanics: The CMO,” Federal Reserve Bank of Cleveland, Economic Commentary, September.
Inside Mortgage Finance Publications, Inc. 2004. The 2004 Mortgage Market Statistical
Annual. Bethesda, MD: Inside Mortgage Finance Publications Inc.
Knowles, R. G. 1993. “Coastal States’ Operations Terminated,” National UnderwriterLife
&Health-Financial Services Edition. (January 25).
Maddala, G. S. 1983. Limited-Dependent and Qualitative Variables in Economics, “Econometric Society Monographs No. 3, Cambridge, UK.: Cambridge University Press.
Midanek, D. H. and J. I. Midanek. 1995. The Development of the Mortgage-Backed Securities Market:A Short History,” Derivatives Quarterly, fall, pp. 25–34.
Oldfeld, G. S. 2000.“Making Markets for Structured Mortgage Derivatives,” Journal of Financial Economics, vol. 57, pp. 445–71.
Passmore, W., R. Sparks, and J. Ingpen. 2002.“GSEs, Mortgage Rates, and the Long-Run Effects of Mortgage Securitization,” Journal of Real Estate Finance and Economics , vol.
25, pp. 215–42.
Tuckman, Bruce. 1995. Fixed Income Securities, New York: John Wiley and Sons.

17

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