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Default models are a
category of models that assess the likelihood of default by an
obligor. They differ from
credit scoring models in two
ways:
Credit scoring is usually (but not always)
applied to smaller credits—individuals
or small businesses. Default models are applied more to larger
credits—corporation or sovereigns.
Credit scoring models are largely
statistical, regressing instances of default against various risk
indicators, such as a obligor's income, home renter/owner status, etc.
Default models directly model the default process, and are typically
calibrated to market variables, such as the obligor's
stock price or the
credit spread on its
bonds.
Default models find many applications within financial
institutions. They are used
to support
or supplant
credit analysis;
to calculate utilization of counterparty credit
risk
limits;
to extend standard
financial engineering techniques to
value credit derivatives or other credit sensitive instruments.
Default models may be integrated with some sort of
correlation model to facilitate
modeling the credit risk of portfolios with exposures to multiple obligors. Such extensions of default models—called
portfolio credit risk models—can
be used
to calculate utilization of industry, country
or portfolio credit risk limits;
to price
collateralized debt
obligations (CDOs) or other
securitizations;
to support
capital calculations.
Consider a time horizon staring at the current time 0 and
ending at some future time t. A one year horizon is typical, but
financial institutions usually consider credit risk over several
horizons. Let L represent the financial loss, if any, due to
default on a particular obligation—a bond, loan,
derivative instrument,
etc.—over the horizon. L is a random variable. Its
expected value E(L) is a
metric of the
credit risk of the obligation. It can be calculated as the
product
|
E(L) = Pr(default) EAD LGD |
[1] |
where
Pr(default) is the probability of
default on the obligation during the horizon—what is called the
default probability.
EAD is
exposure at default—the
credit exposure on the obligation at the time of default. In [1],
this is treated as a known constant.
LGD is loss
given default—the fraction of EAD that will not be recovered
following default. EAD is simply 1 minus the
recovery rate. In [1],
it too is treated as a known constant.
The essential purpose of a default model is to calculate
the default probability. However, sophisticated models may do more than
this. For example, models might treat EAD and LGD as random, and
substitute their expectations into [1]. Treating both
in this manner requires an assumption that they are independent. Such an
assumption is difficult to justify, but it may be made to simplify models.
A simple default model can be constructed by calibrating
credit ratings to historical frequencies of migrations between ratings.
Exhibit 1 indicates a ratings
transition matrix. constructed by Standard & Poor's. It
indicates one-year ratings migration probabilities based upon bond rating
data from the period 1981-2000.
|
|
 |
|
original rating |
probability of migrating to rating by year end
(%) |
|
AAA |
AA |
A |
BBB |
BB |
B |
CCC |
Default |
|
AAA |
93.66 |
5.83 |
0.40 |
0.08 |
0.03 |
0.00 |
0.00 |
0.00 |
|
AA |
0.66 |
91.72 |
6.94 |
0.49 |
0.06 |
0.09 |
0.02 |
0.01 |
|
A |
0.07 |
2.25 |
91.76 |
5.19 |
0.49 |
0.20 |
0.01 |
0.04 |
|
BBB |
0.03 |
0.25 |
4.83 |
89.26 |
4.44 |
0.81 |
0.16 |
0.22 |
|
BB |
0.03 |
0.07 |
0.44 |
6.67 |
83.31 |
7.47 |
1.05 |
0.98 |
|
B |
0.00 |
0.10 |
0.33 |
0.46 |
5.77 |
84.19 |
3.87 |
5.30 |
|
CCC |
0.16 |
0.00 |
0.31 |
0.93 |
2.00 |
10.74 |
63.96 |
21.94 |
|
Default |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
100.00 |
|
|
One-year ratings migration probabilities
based upon bond rating data from 1981-2000. Data is adjusted for
rating withdrawals. Numbers in each row should sum to 100%. Due to
round-off error, they may not do so exactly. Source: Standard & Poor's. |
For example, based upon the matrix, a BBB-rated bond has a
4.44% probability of being downgraded to a BB-rating by the end of one
year. The matrix is based upon raw data, so it exhibits statistical
anomalies. A CCC-rated bond is given a 0.16% probability of
being upgraded to AAA, but a B-rated bond has a 0.00% probability of such
an upgrade. If it were used to model defaults, the numbers in the matrix
might be smoothed.
To use a ratings transition matrix as a default model, we
simply take the default probabilities indicated in the last column and
ascribe them to bonds of the corresponding credit ratings. For example,
with this approach, we would ascribe an A-rated bond a 0.04% probability
of default within one year.
If we want two-year default probabilities, we simply
multiply the matrix by itself once (i.e. employ matrix multiplication as
defined in linear algebra) to obtain a two-year ratings transition matrix.
The last column of that matrix will provide the desired default
probabilities. For three-year default probabilities, we multiply the
matrix by itself three times, etc. Exhibit 2 indicates a five-year ratings
transition matrix obtained by multiplying the one-year matrix of Exhibit 1
by itself five times.
|
|
 |
|
original rating |
probability of rating after five years
(percent) |
|
AAA |
AA |
A |
BBB |
BB |
B |
CCC |
Default |
|
AAA |
72.39 |
21.69 |
4.74 |
0.86 |
0.20 |
0.08 |
0.01 |
0.02 |
|
AA |
2.49 |
66.45 |
25.05 |
4.45 |
0.75 |
0.51 |
0.09 |
0.18 |
|
A |
0.39 |
8.19 |
68.22 |
18.05 |
3.19 |
1.32 |
0.18 |
0.50 |
|
BBB |
0.16 |
1.72 |
16.80 |
60.61 |
13.16 |
4.68 |
0.79 |
2.08 |
|
BB |
0.13 |
0.53 |
3.81 |
19.50 |
44.77 |
19.84 |
3.09 |
8.34 |
|
B |
0.06 |
0.42 |
1.62 |
4.15 |
15.18 |
46.97 |
6.54 |
25.15 |
|
CCC |
0.34 |
0.20 |
1.21 |
3.05 |
6.33 |
18.10 |
12.36 |
58.51 |
|
Default |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
100.00 |
|
|
Five-year ratings migration probabilities
obtained by multiplying the matrix of Exhibit 1 by itself five
times. |
Default models that base default probabilities on
empirical ratings transition matrices are called
ratings migration models.
CreditMetrics is an example of a commercial portfolio credit risk model
that calculates default probabilities with a ratings migration model.
CreditMetrics also uses its ratings migration matrices to model the
evolution of bonds' credit spreads based upon migrations in their ratings.
This allows for the modeling of bond portfolios' market (or mark-to-model) values over time.
Ratings migration models have a number of shortcomings.
First, credit ratings reflect overall
credit quality, which depends on
both probabilities of default as well as likely recovery rates. If two
bonds have the same credit rating, but one bond is senior and the other is
subordinated, the senior bond is likely to have a higher default
probability offsetting its likely higher recovery rate.
Second, ratings migration models are not dynamic. Because
they are based upon long-term empirical probabilities of ratings
transitions, they are not sensitive to business cycles or other
fluctuations in the business environment.
Ratings migration models are just one type of default
model. Many different default models have been proposed in the literature
or implemented by financial institutions. With few exceptions, those that are not ratings
migration models are implementations of:
asset value
models, or
intensity models.
Both types of models are sophisticated, flexible
approaches to credit risk modeling that support a variety of analyses.
They can be calibrated to current business conditions (typically using a
firm's stock price or bond spreads for this purpose). They can be
implemented with "real probabilities" to support credit risk measurement
or with risk neutral
probabilities to support financial engineering applications.
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