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Transcript of AD Prepay Model Meth
MARCH 1999QUAN
TITA
TIVE
PERS
PECT
IVES
..
FIXED RATE MORTGAGE PREPAYMENT MODEL Version 4.1
Eknath Belbase
QUANTITATIVE PERSPECTIVES .. MARCH 1999
FIXED RATE MORTGAGE PREPAYMENT MODEL Version 4.1
Eknath Belbase
Introduction
In 1998, the mortage market experienced dramatic spikes in
prepayments resulting from structural changes in the refinancing
process. This environment created a difficult challenge to prepayment
models. Prepayment analysts made a variety of changes to their models
to account for these high prepayment speeds. Andrew Davidson & Co.,
Inc. has updated our fixed-rate mortgage prepayment models using data
through November 1998. The updated model incorporates the data from
this difficult period.
This article discusses our modeling approach and underlying
philosophy, the model factors, and statistical issues. It also compares the
updated model with the old one and presents some OAS analyses based
on the updated prepayment model.
There are two aims in modeling prepayment behavior that do not necessarily
coincide. The first is to be able to explain and fit observed historical
prepayment behavior in terms of meaningful variables. The second is to be
able to forecast future prepayment behavior under a range of future
environments with the same model.
Historical fit can be increased by increasing the number of factors, from MBS
pool characteristics such as WAC, term, loan type, and age to loan-level
characteristics such as LTV and geographical location. It can also be increased
by adding economic variables, such as GNP growth, unemployment rate and
inflation.
Our Approach
3
Improving historical fit, however, does not imply better forecasts. This can
occur because the perceived relationship between the additional variables and
prepayments is not sufficiently fundamental to act in the future; it can also be
a historical quirk. Furthermore, forecasts often have to be made for
environmental scenarios that simply have not been observed before. Finally,
each variable used in forecasting also has to be predicted. For example, with
recent GNP growth forecasts by economists significantly off the mark even on
a quarterly basis, requiring a GNP forecast input for the next 15 or 30 years
seems undesirable.
The Andrew Davidson & Co., Inc. prepayment model attempts to explain and
forecast prepayment behavior using a small set of pool-level factors, which
have a persistent relationship with prepayments. In this sense it is a
parsimonious model. All of our inputs except for interest rate are deterministic;
hence the level of uncertainty is reduced to one input. The form of the model
has not had to change over time, though parameters are regularly updated with
new data. In general, the parameters have been fairly robust over time.
In 1998, aggressive refinancing campaigns, improved efficiency in the
refinancing process, an increase in the number of alternative loan types and
point-payment combinations made available to borrowers along with falling
rates combined to produce extreme prepayment spikes. Structural changes like
these are very difficult to predict. In addition, after they have occurred, some
time is required to accumulate data and understand which changes are likely to
persist. Simply re-estimating with more historical data can lead to parameters
which are in some sense a weighted average of dynamics before and after the
structural change but which describe neither set of dynamics well.
Prepayment modeling is thus a dynamic problem that requires constant monitoring
and refinement. Though no model is perfect, we believe our prepayment model is
a robust and parsimonious model that captures all the major pool-level factors
affecting MBS prepayments. It is a valuable tool for performing sensitivity analysis
and is an integral component of an option-adjusted risk and valuation framework.
QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
The collateral types supported by the fixed-rate model are:
1. GNMA I and II 15 and 30 year loans
2. Fannie and Freddie Gold 15 and 30 year loans
3. Fannie 20 year loans
4. Fannie and Freddie 5 and 7 year balloons
5. 15 and 30 year Whole loans
6. 30 year Relo loans.
The index rate driving prepayments for a collateral type is the current coupon
rate for that type of collateral. This is defined to be the (net) coupon that would
give a par-priced security. For historical purposes these rates can be gotten
from financial systems (e.g. MTGEGNSF<Index> on Bloomberg for GNMA
30s) and for forecasting are usually modeled in terms of short and long-term
treasury rates with a spread.
The inputs required by the model are loan type, WAC, age, monthly current
coupon forecast, and a history of monthly current coupon rates. The historical
data is provided by Andrew Davidson & Co., Inc. and is updated monthly. The
functional forms for all the sub-models are very similar, with slight differences
for balloons. The parameters were estimated using historical pool-level
prepayment data from 1987 through November 1998. The newer collateral
types, such as balloons, were estimated with considerably less historical data,
though in all cases at least five years of data were used. In some cases where a
majority of the data was old, some lags were estimated using new data to
reflect recent structural changes in the refinancing process. In general, the
updated model shows faster aging, higher prepayment speeds and shorter lags
between interest rate movements and changes in prepayment rates.
4 QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
Model Overview
5 QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
Figure 1 shows a scatter plot of age versus observed prepayment speed in CPR for
some GNMA 1 30 pools. Such an aggregate graph contains a wide range of
prepayment speeds at any given age. This is partly because we are putting loans that
were premiums and discounts together, but is also the result of some inherent noise
in pool behavior.
Figure 2 shows the current coupon rate graphed together with the prepayment
speeds for a relatively “clean” Fannie Mae pool with a WAC of approximately
8.7.
Figure 1
GNMA 1 30
Prepayments
0
5
10
15
20
25
30
35
40
45
50
0 20 40 60 80 100 120
Age
CP
R
Figure 2
Current Coupon
vs. CPR
4
5
6
7
8
9
10
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81
Age(Months)
Cur
rent
Cou
pon
(%)
0
10
20
30
40
50
60
70
80C
PR
(%)
Coupon CPR
The Historical Data
In general, prepayments rise as the current coupon rate falls, with a short lag
between the troughs for rates and the peaks for prepayments. However, there
also seem to be minor fluctuations in prepayment speeds, which are not
mirrored in the current coupon movements. In contrast, Figure 3 graphs
current coupon against CPR for a very noisy GNMA pool with WAC of 8.5.
The CPR stays mostly at zero with occasional leaps.
There are several periods when the spikes in prepayment rates correspond to
rises in current coupons instead of falls and in general the pool behaves
erratically. This could be due to small pool size, bad data, or a number of other
factors.
For modeling purposes, the first type of pool is very useful, and leads to fast
parameter convergence due to the lack of noise. The second type of pool makes
it difficult to obtain parameter estimates, displays what seems to be
occasionally irrational behavior with respect to current coupon rates, and
changes the parameters which would be estimated with the first type of pool.
Because of this, model estimation is always done with a clean sub-sample of
the original data.
6 QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
Figure 3
A Noisy GNMA
Pool
4
5
6
7
8
9
10
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76
Age(Months)
Cur
rent
Cou
pon(
%)
0
10
20
30
40
50
60
70
CP
R (%
)
Coupon CPR
We give a brief overview of the components before going over each one in
detail. The Andrew Davidson & Co., Inc. fixed-rate mortgage prepayment
model has four main factors that interact with each other for all collateral
types: the interest-rate, age, burnout,andseasonality factors. It also has a
points effectfor some collateral types, which acts by modifying the first three
of these four factors.
The interest-rate factor models the effect of current coupon rates as they
affect refinancing as well as a base prepayment speed due to turnover. The
age (or seasoning) factor models the observed tendency of prepayment
speeds to initially rise and then level off regardless of the level of the current
coupon (though how fast they age may depend on coupon level). The
burnout factor adjusts for the fact that as fast prepayers leave a pool during
times when it is a premium, it will prepay more slowly during subsequent
such periods. The seasonality effect takes into account the observed rise in
summer and fall in winter of prepayments. Finally, the points effect adjusts
the interest rate, aging and burnout effect for loans which were originated at
a high WAC relative to rates prevalent at origination.
The number of months a given coupon takes to reach its peak depends on the
collateral type and whether it is a discount, current coupon or premium.
Whether something is a premium or discount at any month is measured by
taking the ratio of the WAC of the pool to the weighted average of a number
of lagged current coupon rates.
Interest Rate Factor
Figure 4 displays the relationship between the ratio (of WAC to weighted
lagged current coupon rates) and the strength of the interest rate effect. When
the ratio is very low, we have a deep discount, which has some base
prepayment speed due to turnover.
Model Features in Depth
QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 19997
As the ratio increases past one, we have a premium and the refinancing
incentive produces prepayments at an increasing rate. At some fairly high ratio
the interest rate effect caps at its maximum value. The exact shape of this curve
is determined from the historical data for each collateral type using appropriate
statistical techniques.
Aging
Figure 5 compares two hypothetical aging ramps, one for a GNMA pool with net
coupon 6.0 and another for a GNMA with net coupon 8.5. The premium takes
about 10 months for its age effect to reach peak level while the discount takes
about 50.
8 QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
0
10
20
30
40
50
60
0.7 1.2 1.7
Ratio(WAC/Lagged CC)
CP
R(%
)
Figure 4
Interest Rate
Factor
0
20
40
60
80
100
1 7 13 19 25 31 37 43 49
Age(Months)
Per
cent
Premium Discount
Figure 5
Aging Factor
Burnout
The burnout factor adjusts prepayment speeds to take into account the fact that
after each wave of refinancing, the pool becomes slightly less sensitive to
subsequent interest rate drops. This is because every pool consists of borrowers
with different degrees of interest rate sensitivity; when rates drop, the more rate-
sensitive ones leave, leaving the pool less rate-sensitive for future rate drops.
The burnout effect can be conceptualized as in Figure 6 via the aging effect.
Having a burnout effect decreases the strength of the aging effect some time after
its peak occurs. Implementing the burnout effect via the aging curve as in the
figure would lead to some decrease in interest rate sensitivity after age 36.
However, the amount of burnout in a pool depends on the entire history of its
prepayment behavior, and thus on the entire past path of current coupons.
Implementing burnout via the aging curve would not capture the path-dependent
nature of burnout. Rather than implement it through the aging curve, the Andrew
Davidson & Co., Inc. fixed-rate prepayment model computes burnout using a
path-dependent, non-linear method which is more flexible than the approach
displayed in Figure 6. In addition, the burnout factor has a reversion component,
which takes into account the tendency of burnout todecrease due to additional
factors.
9 QU A N T I TAT I V E PE R S P E C T I V E S MA R CH 1999
0
20
40
60
80
100
120
1 6 11 16 21 26 31 36 41 46
Age(Months)
Per
cent
Without Burnout With Burnout
Figure 6
Burnout Factor
Seasonality
There are changes in prepayment speeds that depend on the time of year but not on
the interest rate, aging or burnout effects. These are modeled using procedure X11
in SAS by only using discounts to remove the interest rate effect. Some sample
seasonality effects are graphed in Figure 7. In general, speeds tend to rise during
spring months, peak over the summer, drop over the fall and are at their lowest over
the winter.
As with all the model factors, each collateral type has its own characteristics,
obtained from the historical data.
The Points Effect
The points effect was added to the Andrew Davidson & Co., Inc. fixed-rate
prepayment model in a previous release to account for observed differences in
the behavior of high relative WAC pools. The relative WAC is the difference
between the WAC of the pool and the prevailing mortgage rate for that type of
collateral at the time of and just prior to the time that the pool was originated.
A high relative WAC would indicate that the mortgage holders in the pool
obtained their mortgages at higher than market rates. This serves as a proxy for
a low point loan, credit constraints or poor documentation availability on the
part of the mortgage holder.
Figure 7
Seasonality
10
0.4
0.6
0.8
1
1.2
1.4
1.6
Jan
Feb
Mar
chApr
ilMay
June Ju
lyAug
Sept
OctNov Dec
Month
GN1-30 Conventional 20 7-year Balloon
QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
The shapes of the interest rate and aging curves, as well as the burnout effect,
are modified for high relative WAC pools. For some collateral types the points
effect does not exist in the data and hence was not modeled; for a few the
amount of data available did not allow accurate estimation of this affect due to
the paucity of both high relative WAC data and clean data. Figure 8 illustrates
how the points effect reshapes the interest rate factor. Low point loans
generally have higher base speeds but less of a refinancing component until
they become seasoned.
In general, the updated model exhibited faster aging ramps than the old one,
particularly for premiums. Figure 9 compares the two versions for a
conventional 30 year loan with a WAC of 8. The graph compares projected
monthly CPR over the life of the loans assuming that the current coupon stays
at 6.3.
The updated model reaches its peak prepayment speed considerably earlier
than the previous version. It also remains at a higher speed for most of the life
of the loan. The periodic wave-like changes come from the seasonality factor.
Finally, the steady decline due to burnout is curved in a way that modeling
burnout by modifying the aging ramp (as in Figure 6) does not allow.
Another significant change in the updated model is in the lag structure. Figure 10
shows the response to an immediate 100 bp drop in rates a year from now for a
GNMA 30 year loan with a WAC of 7 from the old and updated version of the
model.
Model Factors: Old vs. New
11 QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9
Ratio (WAC/CCRate)
SM
M
No point High Point
Figure 8
Impact of Points
The models predict somewhat similar behavior (with the new model predicting
slightly higher speeds) until the rate drop occurs in month 12. At that point, the
subsequent rise in prepayments due to increased refinancing occurs earlier in
version 4.1 due to the change in lag structure.
During our initial qualitative analysis of the aggregate data, we found an aging
pattern that differs from that of non-balloons. This is shown in Figure 11 for 7-year
balloons.
Figure 10
GNMA 30
Lagged Response
12 QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
0
10
20
30
40
50
1 6 11 16
Month
CP
R(%
)
4
4.5
5
5.5
6
6.5
7
Cur
rent
Cou
pon
%
version 4.0 version4.1 coupon
Change in Balloon Aging
0
10
20
30
40
50
1 23 45 67 89 111
133
155
177
199
221
243
265
287
309
331
353
MonthC
PR
(%)
4
4.5
5
5.5
6
6.5
7
Cur
rent
Cou
pon%
version 4.0 version 4.1 Coupon
Figure 9
A Conventional 30
with WAC=8
Somewhere between month 50 and 60 prepayments begin to pick up. This
behavior was not taken into account in version 4.0 of the model because at the
time that model was estimated, there were not enough balloons of age greater
than 40 to exhibit a trend. Closer statistical analysis revealed that this behavior
is almost independent of the ratio of WAC to current coupon rate. This indicates
that the decision to refinance as the age approaches the balloon date becomes
less and less dependent on the interest rate and deterministically dependent on
the age alone. To take into account this observed behavior, the aging component
of our functional form was modified. The basic idea is illustrated in Figure 12
for a hypothetical example.
13 QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
0
20
40
60
80
100
0 10 20 30 40 50 60 70 80
Age(Months)
CP
R(%
)
Figure 11
Seven-Year
Balloons
0
5
10
15
20
25
30
35
40
45
50
1 6 11 16 21
Age(Months)
CP
R(%
)
Non-Balloons Balloons
Figure 12
Balloon Aging
Both balloons and non-balloons have an initial rise in prepayments due to age (the
slope of which depends on the WAC ratio). While non-balloons flatten out thereafter,
there is a later point at which balloons rise once again as their age approaches the
balloon date. The exact point at which this change occurs depend on the type of
balloon, as does the slope with which it rises.
Table 1 shows some fit statistics for each model subtype: R-squared with and
without the points effect, the regression sum of squares (SSR), the sum of squares
from the error (SSE) and the total degrees of freedom (number of observations).
One interpretation of the R-squared numbers is that they measure the percent of
variation in the observed data that is explained by the independent variables using
the Andrew Davidson & Co., Inc. prepayment model with the optimized
parameters. Thus, with an R-squared of 80%, the fitted model cannot account for
20% of the observed variation in actual prepayments. One reason for this residual
component of variation is usually due to the presence of loan-level variables (such
as LTV, geographical location, etc.) which were not taken into account.
The R-squared numbers range from 77 to 94 percent without the points
effect and the points effect typically adds 2 percent. The points effect was
not estimated for GNMA II 15s and Conventional 20 year loans because
there was not enough data. For the others it was not found to improve the fit.
14 QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
Model Fit: Actual vs. Fitted
Collateral Type without points with points SSR SSE DF
GNMA I 30 year 89 % 90 % 3.9185 0.5023 11530
GNMA II 30 year 85 % 86 % 3.1837 0.5661 8642
Conventional 30 year 89 % 91 % 3.999 0.4780 6971
GNMA I 15 year 87 % 89 % 3.0940 0.4774 12654
GNMA II 15 year 77 % -- 0.6867 0.2105 2707
Conventional 15 year 88 % 90 % 2.5400 0.3360 9050
Conventional 20 year 84 % -- 0.2285 0.0427 1999
Conventional 10 year 86 % -- 2.5000 0.3860 9081
5 Year Balloon 77 % -- 8.3632 2.4460 4132
7 Year Balloon 94 % -- 4.1459 0.2822 5612
Table 1
Fit Statistics by
Sub-Model
Figures 13-18 display some actual vs predicted graphs for version 4.1.
15 QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
Figure 13
Sept 93
Origination FNCL
30-Year with
Gross WAC=7.5
and
Net Coupon=7 0
10
20
30
40
50
60
70
Sep-93 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98
DateC
PR
(%)
Actual Fitted
0
10
20
30
40
50
60
70
Mar-96 Mar-97 Mar-98
Date
CP
R(%
)
Actual Fitted
Figure 14
May 96
Origination FNCL
30-Year with
Gross WAC=9.1
and
Net Coupon=8.5 0
10
20
30
40
50
60
70
May-95 May-96 May-97 May-98
Date
CPR
(%)
Actual Fitted
Figure 15
Mar 96
Origination GNMA
30-Year
with
Gross WAC=7.5
and
Net Coupon=7
In general, the model captures the average behavior of these cohorts quite well.
16QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
0
10
20
30
40
50
60
70
Sep-92 Sep-93 Sep-94 Sep-95 Sep-96 Sep-97 Sep-98
Date
CP
R(%
)Actual Fitted
Figure 16
Sept 92
Origination
7-Year FRDG
Balloon with
Gross WAC=7.6
and
Net Coupon=7
0
10
20
30
40
50
60
70
Mar-97 Mar-98
Date
CP
R(%
)
Actual Fitted
Figure 17
Mar 97
Origination
15-Year GNMA
with Gross WAC=7
and
Net Coupon=6.5
0
10
20
30
40
50
60
70
Sep-95 Sep-96 Sep-97 Sep-98
Date
CP
R(%
)
Actual Fitted
Figure 18
Sept 95
Origination
15-Year FNMA
with
Gross WAC=7.5
and
Net Coupon=7
However, it is clear that the model does not precisely predict all the monthly
variation in prepayments. Some of these differences are a result of sampling error,
as prepayments vary from month to month. Other errors are a result of the model’s
inability to capture certain effects. Of particular note is that not all of the increase
in prepayment speeds in 1998 is reflected by the model. We believe some of the
prepayments during this period reflected unique events which are not likely to
reoccur in a predictable manner. Therefore, altering the model to capture those
spikes would reduce the predictive power of the model.
In particular, we believe that the fast prepayment speeds during early 1998, were
the result of several factors. Introduction of streamlined refinancing programs by
Fannie Mae and Freddie Mac may have contributed to increased prepayment
speeds. Very low intra-month interest rates in January may have also led to faster
prepayments during the period. While these particular factors may not lead to
prepayment spikes in the future, this date clearly indicates the potential volatility
of prepayments.
All parameter estimation was done using the procedure NLIN (non-linear) in
SAS. Since the model is non-linear in both the factors and parameters, as well
as path-dependent, standard regression methodology is insufficient for model
estimation. Grids were formed in the parameter space around suitable values to
obtain initial fits which were then optimized by the NLIN procedure using an
iterative empirical derivative based search technique. Some parameters have to
be constrained in order that factor effects and the final output remain in
reasonable ranges. This methodology leads to finding values of the parameters
which are both meaningful and which minimize the sum of square errors
between predicted and observed values.
The seasonality weights were estimated by eliminating premiums and hence
the interest rate effect. This was done using PROC X11 in SAS (which was
developed by the U.S. Census Bureau). Lags and the rest of the parameters
were estimated using NLIN. In this update, separate models were estimated
with and without the points effect even for those collateral types where the
points effect exists in the data and can be estimated. This is because some
systems on which the model is implemented do not have the ability to
implement the points effect. On such systems, it is better to have the model
parameters optimized without the points effect.
Statistical Issues and Techniques
QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 199917
The Lag Structure
Table 2 shows lag weights for some collateral types:
The lag weights determine what percent of the current coupon used in the ratio
(which determines premium or discount status) come from lags of one, two and
three months respectively. This ratio affects how the aging, interest rate and
burnout factors behave; the lags determine how quickly rate changes lead to
prepayment changes.
Tuning
The tuning feature allows users to change components of our prepayment
model to examine how altering assumptions affects prepayment behavior.
Version 3.0 allowed access to the slide, steepness, burnoutand scale
parameters. In version 4.1 we also allow tuning of the lag. In general,
increasing the tuning parameters increase prepayment speeds.
The slide tuning parameter allows the user to shift the S-shaped interest rate
curve to the right or left. Steepallows the curve to be made steeper or flatter.
Burn is used to make burnout occur faster or slower. Scalecan be used to
multiply the output SMMs by a constant factor, magnifying or damping
speeds. Finally,lag can be used to increase or decrease the amount of time
taken for prepayments to respond to interest rate changes.
OAS Analysis
The Andrew Davidson & Co., Inc. prepayment model is used to obtain option-
adjusted value and risk measures for MBS and CMOs using our proprietary OAS
tool. The two main components of the OAS methodology for MBS are the interest
rate generation process and the prepayment model. Changes to either will affect all
option-adjusted measures, including average WAL, OAS, option cost, duration and
convexity.
18
Additional Features
Collateral One Month Two Months Three Months
GNMA 1 - 30 15 % 40 % 45 %
GNMA 1 - 15 30 % 45 % 25 %
Conventional 30 15 % 30 % 55 %
5-year Balloon 0 % 85 % 15 %
Table 2
Lag Weights by
Month
QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
To produce these measures, the required inputs are the yield curve, volatility
and mean reversion assumptions along with the type and age of collateral.
Based on these inputs, 512 arbitrage free short rates and 2 and 10 year rates are
forecast monthly out to the maturity of the collateral using our Monte Carlo
interest rate process. The mortgage current coupon is computed as a weighted
average plus spread of the 2 and 10 year rates. Prepayment vectors (of SMMs)
are forecast for each current coupon path and these give us 512 cash flow
paths. The short rates are used to discount cashflows along each rate path.
Table 3 displays WAL, OAS, duration and convexity for 4 securities based on
their prices on February 22, 1999 and the yield curve and volatility
assumptions.
Because running an OAS analysis with a large number of paths can be
potentially time-consuming, it is important that each component work as
efficiently as possible. Our prepayment model version 4.1 has several
enhancements to improve its performance speed.
19
Security Coupon Age price OAS WAL duration convexity
GNI 30 8 21 104-10 105 3.7 1.4 -1.3
FN15 7 24 102-09 86 3.8 1.8 -1.4
FN7 Balloon 6.5 8 100-16 114 1.9 1.6 -0.6
FN20 6.5 10 102-31 103 2.8 1.9 -0.6
Table 3
Some Value And Risk
Measures Calculated
Using the Prepayment
Model
Rate Process On-the-Run Treasuries
Volatility Reversion 3 mo 6 mo 1 yr 2 yr 5yr 10 yr 30 yr
15 % 3% 4.56 4.57 4.65 4.91 4.94 5.02 5.35
QU A N T I TAT I V E PE R S P E C T I V E S MA R C H 1999
The prepayment model is available under the product name VectorsTM for
analyzing mortgage-backed securities through a number of vendor systems
including: Algorithmics, ALLTEL/CPI/Busch Analytics, Bloomberg, Carolina
Capital, Derivative Solutions, Intex, MIAC, Polypaths, QRM, Reuters,
RF/ Spectrum, SS&C Technologies and Wall Street Analytics. The model is
also available in a stand-alone format and is callable from ExcelTM and
MATLAB.
For Windows 95,98 or NT systems, it is available as a dynamically linked
library (dll) which plugs into these vendor systems. It is also available as a
shared object for HP/UX, OS/2 and Sun/Solaris environments. Finally, a
standard subroutines version is available which can be called from users’ in-
house analytical systems on any of these platforms.
The Andrew Davidson & Co. fixed-rate MBS prepayment model is a
robust and parsimonious pool-level model. It has been updated with
new data taking into account recent behavior and structural changes.
There have been significant changes to the lags, aging structure, and
functional form for balloons. In addition, there have been programming
changes, which will speed up OAS analysis. The model has been tested
across a range of scenarios and extreme behavior analyses and works
with a number of vendor systems under different platforms and
operating systems. We continually monitor its behavior and market
dynamics to keep it up to date. Our client input is an integral
component of this process and we welcome your comments.
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Contents set forth from sources deemed reliable, but Andrew Davidson & Co., Inc. does notguarantee its accuracy. Our conclusions are general in nature and are not intended for use asspecific trade recommendations.
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Quantitative Perspectives