MFE Program Applied Finance Project Haas School of Business Winter 2002 University of California, Berkeley

Please do not quote, reproduce or distribute this paper without PIMCO’s written consent

Proxy for Lehman Muni Index

Abstract: This paper illustrates a practical implementation of a cell matching

technique for constructing a proxy portfolio with fewer bonds that

closely tracks various factors of a broad Lehman Muni Index

Author: Ika Tsitsishvili

Advisor: Professor Francis Longstaff

Contributors:Ψ Professor David Pyle

Vineer Bhansali

Mihir Worah

Ψ The author would like to thank Pimco’s Mihir Worah for providing continuous directions during this project, as well Vineer Bhansali and Pimco’s Analytics Group members for their suggestions and contribution to this project. The author alone is, however, responsible for errors.

2

Table of Contents

Proxy for Lehman Muni Index ........................................................................................... 3

I. Overview .................................................................................................................... 3

II. Indexing Types.......................................................................................................... 4

III. Project Scope ........................................................................................................... 5

IV. Process of Identifying a Cell ................................................................................... 6

V. Methodology ............................................................................................................. 7

VI. Optimizer ................................................................................................................. 8

VII. Algorithm ............................................................................................................. 11

VIII. Results................................................................................................................. 12

IX. Further Enhancements ........................................................................................... 14

X. Summary ................................................................................................................. 14

3

Proxy for Lehman Muni Index I. Overview

Fixed-income portfolio management strategies can be broadly divided into active and

structured portfolio strategies. Active management strategies are usually driven by

expectations about (1) the direction of future interest rates, (2) changes in yield spreads

between and within market sectors, (3) changes in interest-rate volatility, or (4) changes

in credit quality, etc. 1 On the other hand, a structured portfolio management strategy

emphasizes construction of such portfolios that match various factors of some

predetermined index or benchmark regardless of expectations about the characteristics

that active strategies follow. As a result, the changes in the interest rates do not affect the

performance of the structured portfolio strategy managers as long as they closely track

risk and return of the target benchmark.

Although most fixed-income portfolio managers follow active strategies, this

layer will analyze the structured portfolio management strategies. There are different

types of structured portfolio strategies, including indexing, immunization and cash flow

matching, surplus hedging, etc. Here, we will focus on indexing that is often considered

as a passive strategy despite the fact that it is actually a structured portfolio strategy.

Indexing a portfolio is often referred to as constructing a portfolio with few bonds so that

its performance will match the performance of some broad bond index. Although this

strategy has widely been used in the equity market, it is considered as a relatively new

phenomenon in the fixed-income market. Such strategy is used when an investment is

based on expected market returns and the risk that is relative to the market benchmark

can be removed by investing in an index. Bond indexing has become more popular in

recent years: First, the advisory fees charged by the active managers typically range from

15 to 50 basis points and are substantially higher than the fees for managing indexed

portfolios ranging from 1 to 20 basis points. 2 That is, indexed portfolios mirror the

market and do not require expensive trading strategies or systems. These are portfolios

1 Ravi E. Dattatreya, Frank J. Fabozzi, “Active total return management of fixed-income portfolios,” (Irwin, Professional Publishing), p. 8 2 Sharmin Mossavar-Rahmani, “Understanding and Evaluating Index Fund Management,” p. 434

4

that attempt to replicate risk and return of a market index, thus are more process rather

than people driven. Second, they reduce the risk associated with the market timing

decision or manager selection. Third, active managers may fall back to passive index

strategies, especially when they have no definite views on the market direction or would

like to lock the year-to-date profit and follow the market for the remaining of the year.

Finally, the historical performance of active strategies has not been superior to the market

performance on a large scale.

II. Indexing Types

There are several types of indexing, such as full indexing, stratification sampling (cell

matching), and tracking error minimization. Full index replication occurs when the index

manager owns every security in the target index in proportion to the total index

capitalization. This strategy fully matches all factors of the original index. However, in

most cases where the number of securities in the original index is large to create,

maintaining full index replication with smaller portfolio value would be almost

impossible due to large costs. These costs include overwhelming transactions, the

overheads associated with monitoring a large number of issues, rebalancing, and some

other costs.

In stratification sampling, or “cell matching technique”, one has to match the

original index’ most important components with a few securities. The key for this

strategy is to break down the original index into smaller cells and then match each cell’s

parameters with fewer securities. As a result, the holdings of securities in a particular

cell are usually computed to match the cell’s contribution to overall duration. We intend

to emphasize matching not only duration, but also some other factors that will be

described later in the paper. The goal is to create a proxy portfolio via cell matching

technique that has an average return as close as possible to the benchmark as well as a

similar distribution of returns for other matching factors. The more securities selected in

each cell, the more closely the replicated portfolio will track the index. The drawback of

the cell matching technique is that it completely ignores correlations among cells that

5

could cause risk from overweight in one cell that could be canceled with underestimation

in another one. 3

The Tracking error minimization is used to construct optimal index proxy that

would best track the original index returns using a given number of securities. There are

also other methods of creating proxy for index. Such methods include replication with

derivatives, portfolio optimization, scenario-based optimization, sufficient diversification

in credit portfolios, etc. We will not discuss these strategies because they are not in the

scope of this paper.

III. Project Scope

The goal of the project described in this paper is to create a proxy for the Lehman Muni

Index (LMI) that consists of over 40,000 bonds. Some clients request to match the

performance of LMI. However, it is very difficult and expensive to trade and monitor

over 40,000 bonds due to the overwhelming transaction costs as well as maintaining data

and analytics for this large index. The practical implication of this project is to have an

ability to match risk and return of various factors for LMI by creating a proxy portfolio

with fewer bonds that would be more manageable.

The LMI is an index that consists of only USD denominated bonds, covers all 50

United States plus four regions, such as Puerto Rico, Virgin Islands, etc, includes bonds

from 14 various industries with 14 different quality grades (ratings). The task of the

project is to create a proxy for LMI that should have fewer bonds (the target number is

between 4,000 and 8,000 bonds), but should still match the following factors of the

original index: duration weighted exposure, weighted average coupon, and the market

value percentage for each duration bucket (0-1, 1-3, 3-5, 5-7, 7-8, 8-11, and 11+ years),

state, industry, and rating.

3 Lehman Brothers Quantitative Portfolio Strategies Group, “Quantitative Strategies for Benchmark Portfolios,” Lehman Brothers, May 2001, p. 34

6

IV. Process of Identifying a Cell

Based on the criteria described in the Project Scope, cell-matching technique would be

the best approach among index matching strategies for this project because it will closely

match each factor specified above. However, it was not clear from the very beginning

whether it was possible to create a cell that would match a specified number of factors.

As it appears, the problem could be resolved by engineering a set of rules to identify a

cell that would match these factors. However, before we describe the methodology of

how each cell is determined, we must define each specified parameter:

a) Duration buckets are created to separate bonds with the similar duration length.

For example, 1-3 year bucket would include bonds with duration greater than

one year and less or equal to 3 years.

b) Duration Weighted Exposure (DWE) is computed, as follows:

DWE = (Price * Duration * Quantity) / (Total Market Value * 100)

c) Weighted Average Coupon (WAC) is computed, as follows:

WAC = (Coupon * Quantity) / (Total Market Value * 100)

d) Total Market Value (TMV) is computed, as follows:

TMV = (Price + Accrued Interest) * Quantity / 100

e) The market value percentage (% of MV) for each cell is computed, as follows:

% Of MV = Cell MV / Total MV

To use cell matching strategy the first important step is to break down LMI into

such cells that the size of each cell would be large enough to be able to replicate it with

fewer bonds, but still make a significant reduction of the number of bonds in each cell.

As a result, if we target to create proxy with approximately 10 to 20 percent of the

original number of bonds, then most cells should contain more than 10 or 20 bonds, so

we could replicate such cell with at least two to four bonds.

In determining a cell we first identify the individual duration bucket. Then within

each bucket we create a cell that consists of bonds sorted by state and industry (i.e., a cell

would consist from all bonds for a given state in a given industry). We sort and list these

bonds by the market value in the descending order. As you may recall from earlier

discussion, the criteria for proxy required matching DWE, WAC, and % of MV for each

7

state, industry, and rating. If we have identified a cell not only by two parameters - state

and industry, but had also included the rating criterion, then the number of bonds in each

cell would have significantly reduced (where most cells would have less than 10 to 20

bonds), making it impossible to replicate already small cells with fewer bonds.

V. Methodology

After cells are identified within each duration bucket, then we should match all three

specified criteria - DWE, WAC, and % of MV - for each cell. Though it may seem to be

an easy task to do, matching all three criteria with high precision turns out to be a

cumbersome process. It was clear from the beginning that without computer

programming, cell-matching technique would not be an achievable task. For similar

process, PIMCO’s William Sharp and Kenneth Miller used Microsoft Excel Solver. This

suggestion became the mainstream in creating an optimizer for proxy portfolios. Excel

Solver uses the generalized reduced gradient nonlinear optimization with bounds on the

variables. By using this technique we could match a pre-specified target value of one

parameter by changing another parameter that is linked, through a formula, to the Excel

cell with the target value. After all, there was no point in “reinventing the wheel” that

could be utilized in cell matching technique to create a proxy for LMI. In sum, to

implement cell-matching technique and construct LMI proxy, we had to create an

optimizer that would identify target cells and then utilize Excel Solver to match proxy’s

DWE, WAC, and % of MV to LMI’s parameters for each cell. The main obstacle was

that we had to match three criteria by changing only one variable to use Solver. After

careful examination of DWE, WAC, and % of MV formulas, we determined that all three

of them have one parameter in common – the quantity of bonds. Thus, by setting up an

Excel spreadsheet so that we would target to match the values for proxy’s DWE, WAC,

and % of MV with the original values of these three parameters for a specified cell. We

could use Solver to match these formulas for proxy by changing only the quantity of

bonds for the reduced set of bonds of the original index cell. The target number of bonds

in each identified cell would be close to the percentage of the target number of bonds in

the final proxy or the target market value of proxy to the original number of bonds or the

8

original market value of LMI, respectively. The percentage is calculated by an algorithm

that will be described later.

We had to place constraints to the quantity of bonds in Solver, so we would not

get unrealistically large numbers for a given bond or a negative value since we did not

allow short selling. In addition, the precision of our matching requires it to be high in

order to better match specified parameters in the final proxy. As a result, we imposed the

following constraints:

a) No short sell or a negative number of bonds are allowed in proxy. Thus, the

minimum number of bonds would result in zero or eliminate a bond from proxy.

b) A new quantity of each bond that makes into proxy should not exceed its

original quantity given in LMI because the index had the maximum outstanding

quantity for each bond.

c) The new market value with a new quantity for a given bond should exceed

$500. By putting this restriction we would reduce the number of bonds with the

small market value in proxy that would be easily replaced by other bonds.

d) Precision and tolerance level for Solver were set to 10-10 to match proxy’s DWE,

WAC, and % of MV to the 8th decimal place for each cell.

VI. Optimizer

To complete a set of requirements for LMI proxy by using the algorithm described

below, we had to create an optimizer that would achieve the desired results by taking into

account all limitations and constraints. For purposes of simplicity, this optimizer was

created in Excel and a programming code was written in VBA consisting of 44 modules.

The Optimizer contains eight different spreadsheets that had different tasks to perform.

The user must interact only with one spreadsheet, called “Input”. There are four input

variables necessary to run the Optimizer (Appendix I). “Source Key” and “Date, as of”

are input that are used in SQL to retrieve all bonds directly from Pimco’s database

constructing LMI for a given date. Though the source key is unique for LMI, the index is

rebalanced every month and may contain different sets of bonds. As a result, the user has

to choose what month index he/she would like to create proxy for. The user also has to

9

specify either the target number of bonds or the target market value for proxy. These two

input are mutually exclusive and only one of them can be selected, otherwise, the

program would not run requiring the user to make a choice. If the target number of bonds

is chosen by the user, this number would be used to determine the market value for proxy

in the following fashion: First, the algorithm would compute the percentage value that is

equivalent to the ratio of the target number of bonds divided by the original number of

bonds in LMI. Next, this percentage is multiplied by the original market value of LMI

and the outcome would result in proxy’s total market value.

Once all input are entered, the user has to press “Proxy Index” button and the

Optimizer begins creating proxy. As its first step, the Optimizer retrieves all LMI bonds

from the database and places them with required data into “Data” spreadsheet (Appendix

II). The required data includes SSM_ID (each bond’s unique ID in the Blotter 4

corresponding to their cusip numbers), Ratings, State, Industry, Duration, Coupon,

Quantity, Price, Accrued Interest, and the Market Value for a specified date. Then the

algorithm computes DWE, WAC, MV, and % of MV for each bond. Next, the algorithm

sorts all bonds by the largest duration bucket (i.e., 11+ years) and moves only the bonds

in this bucket to another spreadsheet, called “Data (11+)” (Appendix III). Then the

algorithm sorts the largest duration bucket bonds by state and industry, thus creating

cells. If a cell has less then 10 bonds, the bonds in this cell are immediately moved to the

“Remainder” spreadsheet (Appendix IV) for later use. The reason for eliminating small

cells from the beginning is that if we target to replicate LMI with 10% of original bonds

that means that we would be attempting to replicate a small cell with only one bond. This

is an impossible task to do by using Solver considering the constraints and would result

in a waste of running time.

Once all cells are created in the “Data (11+)” spreadsheet, the algorithm looks for

the largest cell and moves all bonds to the “Temp” spreadsheet (Appendix V). Here the

bonds are sorted by the largest market value in descending order and DWE, WAC, and %

of MV are computed. Finally, these bonds are moved to the “Result” spreadsheet

4 Blotter is PIMCO’s central repository database where all data are stored and some analytics are computed for the company’s portfolios and benchmark indexes.

10

(Appendix VI), where Solver would attempt to match DWE, WAC, and % of MV for this

cell.

First, the algorithm selects the target number of bonds (i.e., the target percentage

multiplied by the number of bonds in original cell), which quantities would be the

changing variables in Solver. Then the algorithm with the rules for Solver, described in

the section below, would attempt to match all three parameters with only the target

number of bonds for proxy. If the parameters were matched, then the selected bonds with

new quantities would be moved to the “Proxy” spreadsheet (Appendix VII), where we

would build proxy through using recursively the steps described above for each cell in

each duration bucket. However, if Solver fails to match all three parameters, then it

would move all bonds in the original cell to the “Remainder” spreadsheet and adds them

to the bonds that earlier made small cells.

This process is repeated for all generated cells in “Data (11+)” until there are no

more bonds left in this spreadsheet. Then the algorithm moves all bonds from

“Remainder” to “Data (11+)” again and sorts them, this time only by state, thus creating

larger cells. This would give an opportunity to better match DWE, WAC, and % of MV

for each state. Then the remaining of the process is repeated, as described above. If there

are still unmatched cells left in “Remainder”, then they are moved to “Data (11+)” again,

but this time they are sorted by industry and the process is repeated again. If there were

still unmatched bonds left, they would be sorted by the market value in descending order,

thus creating one large cell. For the most part, this would result in matching DWE,

WAC, and % of MV for the entire duration bucket, but in case of unsuccessful outcome,

the remaining bonds would be moved to the “Rest” spreadsheet (Appendix VIII) where

all unmatched cells with different durations would be sorted by the market value to create

the final large cell. Thought it is possible that the Optimizer may not be able to fully

replicate LMI, the residual value of the three parameters would be marginal and could be

ignored. Based on empirical observations, the Optimizer has always matched all cells

before reaching the “Rest” spreadsheet for LMI.

Afterwards, this process is recursively repeated for the rest of duration buckets

until the proxy is completed. It should be noted that if proxy has successfully been

created, the target market value would always be proxy’s market value, however, the

11

number of bonds in proxy may not be the exact number that was originally targeted by

the user. Although, in most cases, the number of bond in proxy is close enough to the

target number.

VII. Algorithm

Though the idea of using the Solver was instrumental, the Solver, by itself, could not find

a desired match for each cell. Clearly, there was a need to engineer an algorithm that

would evaluate the Solver’s outcome after each run and would add or take out bonds that

would facilitate matching the three criteria. As a result, an algorithm was created

consisting of nine sets of rules and four to eight sub rules. After each run, the Solver tries

to match original cell’s DWE, WAC, and % of MV with fewer bonds. However, in most

instances it might match only one parameter leaving the other two greater or smaller than

the target value. To accelerate the matching process, the algorithm evaluates the outcome

values and then finds a bond(s) (depending on the magnitude of the difference) in a

selected set of bonds for proxy that has unmatched parameters way off of the target value

range. Next, the algorithm eliminates the bond(s). At the same time, the algorithm finds

other bond(s) from the original cell that were not used previously in Solver with the

values of unmatched parameters closer to the target value. To clarify this algorithm, let’s

consider an example (Appendix IX). As is observed, after the first try, Solver matched

DWE and % of MV, but overvalued WAC (Excel cells “U6:W6”). As a result, the

algorithm would find a bond among the chosen for proxy with the highest coupon and

replaces it with a new bond from the original cell that has a lower coupon, but with the

same (or as close as available) DWE and % of MV. By doing so, Solver has a better

chance to match all three factors at the very next attempt and accelerates proxy creation.5

5 Note: This is a simplified example. The algorithm for Solver’s rules is very complex and consists of more than 80 rules, in total. It evaluates all possible outcomes of Solver and fits the best possible alteration to match all three factors at the very next attempt.

12

VIII. Results

After the Optimizer was created, the next step was to test how well the proxy matches the

LMI’s factors that were originally specified in this project. There were only a few

months worth of available data for the LMI. In addition, this index gets rebalanced at the

beginning of each month. As a result, because of these and the time constraints, we

tested the results only for November 2001 LMI (for detailed results, see Appendix X).

Also, we used the December 2001 LMI’s beginning prices because the prices at the end

of November were not available (most bonds in the November index are carried into the

December index, so only 463 bonds of the original index and 108 bonds of proxy were

not priced and were left unchanged for this month).

The original November LMI consisted of 41,841 bonds. The Optimizer replicated

it with 7,946 bonds (originally we targeted 8,000 bonds). At the beginning of the month

DWE, WAC, and % of MV were perfectly matched for all seven duration buckets (see

Appendix X). At the end of the month the maximum difference in DWE, WAC, and %

of MV for one of the buckets were –0.031, -0.018, and –0.003, respectively, with

weighted square deviation of 1.35%, 0.56%, and 0.09%, respectively.

Below are the summaries of the differences in DWE, WAC, and % of MV by

States, Industries, and Ratings (for detailed stats, see Appendix X):

November 1, 2001 States DWE WAC % Of MV

MAX Difference -0.0050 0.0032 0.0005 Weighted Sq. Dev. 0.07% 0.03% 0.01% November 30, 2001

States DWE WAC % Of MV MAX Difference -0.0051 0.8216 0.1598 Weighted Sq. Dev. 0.06% 0.03% 0.01% November 1, 2001

Industry DWE WAC % Of MV MAX Difference -0.0277 0.0160 -0.0030 Weighted Sq. Dev. 1.15% 0.38% 0.07% November 30, 2001

Industry DWE WAC % Of MV MAX Difference -0.0274 0.0162 -0.0031 Weighted Sq. Dev. 1.32% 0.40% 0.07%

13

November 1, 2001 Ratings DWE WAC % Of MV

MAX Difference 0.0496 -0.0273 0.0049 Weighted Sq. Dev. 0.96% 0.31% 0.07% November 30, 2001

Ratings DWE WAC % Of MV MAX Difference -0.0616 -0.0378 -0.0066 Weighted Sq. Dev. 1.06% 0.38% 0.07%

As expected, the states matched the best among the three criteria, followed by the

industries. Though we did not make an effort to match the LMI’s and the proxy’s ratings,

they were still matched reasonably well due to the bonds dominated LMI by their market

value made to proxy.

Although the specified matching criteria for proxy tracked the LMI well, it was

important to evaluate the proxy’s performance. Below is the summary of the proxy’s

monthly risk and returns versus the original November LMI’s numbers:

November Index* Proxy** Difference MTD Price Return -1.329% -1.345% -0.02% MTD Coupon Return 0.416% 0.416% 0.00% MTD Total Return -0.913% -0.929% -0.02% DWE 7.51 7.52 (0.01) # Of Bonds 41,841 7,946 Note: * 463 bonds prices are not available and were kept constant over this month ** 108 bonds prices are not available and were kept constant over this month

As one could notice, proxy’s price return was lower than LMI’s by roughly two

basis points, coupon return matched perfectly, and proxy’s DWE was higher only by 0.01

for November LMI.

At the same time, we obtained the results for November LMI published by

Lehman Brothers. Here is the summary how proxy did in comparison to Lehman’s

numbers:

14

The difference in price return was roughly nine basis points and the difference in

coupon return was insignificant, whereas the proxy’s DWE was lower by 0.16 compared

to the Lehman’s Return Duration. 6

IX. Further Enhancements

After the LMI proxy results were evaluated, it was suggested to further enhance the

Optimizer, making it usable for any bogie 7 in the Blotter. As a result, the user interface

was modified to allow the user to choose one of the four criteria to match DWE, WAC,

and % of MV of the original bogie. These criteria are: issuer country, security sector,

ratings, and currency. The user would have a choice to either create a proxy for a bogie

with one primary criterion or to specify the second criterion. Furthermore, many bogies

contain foreign bonds. As a result, we had to add the foreign currency conversion

(retrieved directly from the database) to the algorithm in order to convert the bonds

denominated in foreign currencies into USD.

X. Summary

The goal of this project was successfully achieved. The Optimizer that utilizes the cell-

matching technique to replicate the proxy for the LMI by matching specified factors was

developed. The results for the November LMI were encouraging. However, before one

6 We are not sure how Lehman Brothers compute “Other Return” or whether Lehman’s “Return Duration is calculated in a similar fashion, as DWE. 7 Bogie is another name for an index that is used, as a benchmark, in the Blotter.

November Lehman Proxy** Difference MTD Price Return -1.255% -1.345% -0.09% MTD Coupon Return 0.414% 0.416% 0.00% MTD Other Return -0.001% 0.00% MTD Total Return -0.842% -0.929% -0.09% Returns Duration/DWE 7.68 7.52 (0.16) # of Bonds 41,846 7,946 Note: **108 bonds prices are not available and were kept constant over this month

15

fully trusts the Optimizer, an extensive testing has to be performed for a larger set of data.

In addition, the modified version of the Optimizer has to be tested with different bogies

on a larger scale to assure that the proxy closely tracks risk, return and other factors of

original bogies.

The advantage of this Optimizer is that it is completely automated. After the

input parameters are entered, the user only needs to press the button and the proxy is

immediately created. Here the technology chooses the best fit for proxy and reduces the

management’s involvement in making the decision about which bonds should be

included in that proxy.

Therefore, what are the benefits of having a proxy from a fixed-income portfolio

management point of view? First, it allows clients to track risk and return of large

indexes, such as LMI. Second, it significantly reduces the transaction costs of trading the

entire index. Third, it is easier and less expensive to maintain and monitor any portfolio

with fewer bonds. Fourth, it is process oriented rather than people-driven. I hope

PIMCO will greatly benefits from this Optimizer.

16

Appendices:

I. Appendix: User Interface (“Input”)

II. Appendix: “Data”

17

III. Appendix: “Data (11+)”

IV. Appendix: – “Remainder”

18

V. Appendix: “Temp”

19

VI. Appendix: “Result”

20

VII. Appendix: “Proxy”

21

VIII. Appendix: “Rest”

IX. Appendix: Example

22

X. Appendix: Proxy Results

November 1, 2001 Index 41,841 Bonds MV = $799,800,395

Bucket DWE WAC % of MV Bucket11+ 3.42181181 1.29605702 26.428745%Bucket8-11 2.03982239 1.09124956 21.672133%Bucket7-8 0.57463908 0.38097678 7.754026%Bucket5-7 0.82727731 0.67475615 13.826668%Bucket3-5 0.51259031 0.64276106 13.047082%Bucket1-3 0.27256218 0.69610557 13.676833%Bucket0-1 0.01913132 0.21076364 3.594513%Total 7.66783440 4.99266978 100.0000% Proxy 7,946 Bonds MV = $156,760,877

Bucket DWE WAC % of MV Bucket11+ 3.42181181 1.29605702 26.428745%Bucket8-11 2.03982239 1.09124956 21.672133%Bucket7-8 0.57463908 0.38097678 7.754026%Bucket5-7 0.82727731 0.67475615 13.826668%Bucket3-5 0.51259031 0.64276106 13.047082%Bucket1-3 0.27256218 0.69610557 13.676834%Bucket0-1 0.01913132 0.21076364 3.594513%Total 7.66783440 4.99266978 100.0000% Difference

Bucket DWE WAC % of MV Bucket11+ 0.00000000 0.00000000 0.000000%Bucket8-11 0.00000000 0.00000000 0.000000%Bucket7-8 0.00000000 0.00000000 0.000000%Bucket5-7 0.00000000 0.00000000 0.000000%Bucket3-5 0.00000000 0.00000000 0.000000%Bucket1-3 0.00000000 0.00000000 0.000000%Bucket0-1 0.00000000 0.00000000 0.000000%Total 0.00000000 0.00000000 0.0000%

23

November 30, 2001 Index 41,841 Bonds * MV = $789,167,355

Bucket DWE WAC % of MV Bucket11+ 3.00493693 1.17792167 23.849650% Bucket8-11 2.28283620 1.24614630 24.211560% Bucket7-8 0.57665019 0.38798951 7.778309% Bucket5-7 0.83912172 0.69658966 14.007396% Bucket3-5 0.51958590 0.66198638 13.263463% Bucket1-3 0.26552196 0.69103680 13.455620% Bucket0-1 0.01887712 0.19826689 3.434002% Total 7.50753002 5.05993721 100.0000% Note: * 463 bonds prices are not available and kept constant over this month Proxy 7,946 Bonds ** MV = $154,652,543

Bucket DWE WAC % of MV Bucket11+ 3.03587396 1.19540994 24.104347% Bucket8-11 2.26441980 1.23363530 24.008479% Bucket7-8 0.56038328 0.37591893 7.563799% Bucket5-7 0.85191150 0.70602395 14.198333% Bucket3-5 0.51720137 0.65700210 13.177566% Bucket1-3 0.27014926 0.70278715 13.675438% Bucket0-1 0.01729092 0.18995605 3.272039% Total 7.51723010 5.06073343 100.0000% Note: ** 108 bonds prices are not available and kept constant over this month Difference

Bucket DWE WAC % of MV Bucket11+ -0.03093703 -0.01748827 -0.254696% Bucket8-11 0.01841640 0.01251100 0.203080% Bucket7-8 0.01626691 0.01207058 0.214510% Bucket5-7 -0.01278979 -0.00943430 -0.190936% Bucket3-5 0.00238453 0.00498429 0.085897% Bucket1-3 -0.00462730 -0.01175035 -0.219818% Bucket0-1 0.00158619 0.00831083 0.161963% Total -0.00970009 -0.00079622 0.0000% November 1, 2001

Buckets DWE WAC % of MV MAX Difference 0.0000 0.0000 0.0000 Weighted Sq. Dev. 0.00% 0.00% 0.00% November 30, 2001

Buckets DWE WAC % of MV MAX Difference -0.0309 -0.0175 -0.0025 Weighted Sq. Dev. 1.35% 0.56% 0.09%

24

November 1, 2001

Index 41,841 Bonds

MV = $799,800,395 Proxy

7,946 Bonds MV = $156,760,877 Difference

States DWE WAC % of MV States DWE WAC % of MV States DWE WAC % of MV AK 0.0554 0.0275 0.60% AK 0.0604 0.0301 0.65% AK -0.0050 -0.0026 -0.05%AL 0.0483 0.0285 0.57% AL 0.0483 0.0287 0.57% AL 0.0001 -0.0003 0.00%AR 0.0137 0.0086 0.16% AR 0.0108 0.0075 0.14% AR 0.0029 0.0011 0.02%AZ 0.0845 0.0702 1.39% AZ 0.0845 0.0702 1.39% AZ 0.0000 0.0000 0.00%CA 1.0750 0.6454 13.18% CA 1.0725 0.6445 13.16% CA 0.0025 0.0010 0.02%CO 0.1510 0.0835 1.77% CO 0.1505 0.0832 1.77% CO 0.0005 0.0003 0.01%CT 0.1171 0.0930 1.92% CT 0.1171 0.0930 1.92% CT 0.0000 0.0000 0.00%DC 0.0672 0.0488 0.96% DC 0.0651 0.0472 0.93% DC 0.0021 0.0016 0.03%DE 0.0091 0.0081 0.17% DE 0.0112 0.0090 0.18% DE -0.0021 -0.0010 -0.02%FL 0.5068 0.3229 6.45% FL 0.5068 0.3229 6.45% FL 0.0000 0.0000 0.00%GA 0.1983 0.1328 2.64% GA 0.1983 0.1328 2.64% GA 0.0000 0.0000 0.00%GU 0.0112 0.0068 0.13% GU 0.0120 0.0080 0.14% GU -0.0008 -0.0011 -0.02%HI 0.0779 0.0584 1.14% HI 0.0804 0.0599 1.16% HI -0.0024 -0.0016 -0.03%IA 0.0095 0.0052 0.10% IA 0.0096 0.0061 0.11% IA -0.0001 -0.0008 -0.01%ID 0.0043 0.0025 0.04% ID 0.0041 0.0025 0.04% ID 0.0002 0.0000 0.00%IL 0.4073 0.2443 5.12% IL 0.4073 0.2443 5.12% IL 0.0000 0.0000 0.00%IN 0.0661 0.0418 0.82% IN 0.0662 0.0433 0.84% IN -0.0001 -0.0014 -0.02%KS 0.0267 0.0204 0.39% KS 0.0264 0.0172 0.34% KS 0.0003 0.0032 0.05%KY 0.0675 0.0416 0.85% KY 0.0666 0.0411 0.84% KY 0.0009 0.0005 0.01%LA 0.0491 0.0399 0.78% LA 0.0514 0.0413 0.81% LA -0.0023 -0.0014 -0.02%MA 0.3381 0.2286 4.62% MA 0.3381 0.2286 4.62% MA 0.0000 0.0000 0.00%MD 0.0941 0.0689 1.41% MD 0.0941 0.0689 1.41% MD 0.0000 0.0000 0.00%ME 0.0174 0.0100 0.19% ME 0.0172 0.0097 0.18% ME 0.0002 0.0004 0.01%MI 0.2149 0.1288 2.52% MI 0.2149 0.1288 2.52% MI 0.0000 0.0000 0.00%MN 0.0880 0.0559 1.12% MN 0.0879 0.0556 1.12% MN 0.0001 0.0002 0.00%MO 0.0749 0.0472 0.90% MO 0.0748 0.0470 0.90% MO 0.0001 0.0003 0.00%MS 0.0251 0.0181 0.36% MS 0.0247 0.0184 0.37% MS 0.0003 -0.0003 -0.01%MT 0.0138 0.0085 0.15% MT 0.0162 0.0099 0.18% MT -0.0023 -0.0014 -0.03%NC 0.1398 0.0952 1.88% NC 0.1366 0.0935 1.84% NC 0.0031 0.0018 0.03%ND 0.0086 0.0041 0.08% ND 0.0050 0.0026 0.05% ND 0.0036 0.0015 0.03%NE 0.0391 0.0292 0.59% NE 0.0389 0.0291 0.59% NE 0.0002 0.0001 0.00%NH 0.0131 0.0077 0.15% NH 0.0101 0.0062 0.12% NH 0.0030 0.0015 0.03%NJ 0.2313 0.1717 3.41% NJ 0.2313 0.1717 3.41% NJ 0.0000 0.0000 0.00%NM 0.0216 0.0183 0.35% NM 0.0216 0.0183 0.35% NM 0.0000 0.0000 0.00%NV 0.0868 0.0577 1.12% NV 0.0876 0.0591 1.14% NV -0.0008 -0.0014 -0.02%NY 1.1817 0.8107 15.94% NY 1.1777 0.8090 15.91% NY 0.0039 0.0017 0.03%OH 0.1806 0.1192 2.40% OH 0.1805 0.1190 2.39% OH 0.0000 0.0002 0.00%OK 0.0372 0.0272 0.54% OK 0.0400 0.0276 0.55% OK -0.0028 -0.0003 -0.01%OR 0.0472 0.0296 0.59% OR 0.0463 0.0288 0.58% OR 0.0009 0.0008 0.01%PA 0.3042 0.2023 3.96% PA 0.3042 0.2023 3.96% PA 0.0000 0.0000 0.00%PR 0.2493 0.1367 2.75% PR 0.2493 0.1367 2.75% PR 0.0000 0.0000 0.00%

25

RI 0.0214 0.0154 0.30% RI 0.0214 0.0154 0.30% RI 0.0000 0.0000 0.00%SC 0.0940 0.0600 1.17% SC 0.0950 0.0606 1.18% SC -0.0010 -0.0006 -0.01%SD 0.0100 0.0055 0.10% SD 0.0117 0.0068 0.12% SD -0.0017 -0.0013 -0.02%TN 0.0892 0.0548 1.10% TN 0.0887 0.0535 1.08% TN 0.0006 0.0013 0.02%TX 0.4954 0.3118 6.33% TX 0.4990 0.3135 6.36% TX -0.0036 -0.0017 -0.03%UT 0.0538 0.0364 0.73% UT 0.0581 0.0384 0.76% UT -0.0043 -0.0020 -0.03%VA 0.1160 0.0813 1.62% VA 0.1161 0.0813 1.63% VA -0.0001 0.0000 0.00%VI 0.0082 0.0046 0.08% VI 0.0091 0.0045 0.08% VI -0.0009 0.0000 0.00%VT 0.0070 0.0037 0.07% VT 0.0042 0.0023 0.04% VT 0.0028 0.0014 0.03%WA 0.2121 0.1388 2.83% WA 0.2120 0.1392 2.84% WA 0.0001 -0.0003 -0.01%WI 0.0835 0.0581 1.14% WI 0.0835 0.0581 1.14% WI 0.0000 0.0000 0.00%WV 0.0189 0.0128 0.25% WV 0.0185 0.0122 0.24% WV 0.0005 0.0006 0.01%WY 0.0057 0.0038 0.07% WY 0.0043 0.0033 0.06% WY 0.0014 0.0005 0.01%Total 7.6678 4.9927 100.00% Total 7.6678 4.9927 100.00% Total 0.0000 0.0000 0.00%

November 30, 2001

Index 41,841 Bonds

MV = $789,167,355 Proxy

7,946 Bonds MV = $154,652,543

States DWE WAC % of MV States DWE WAC % of MV DWE WAC % of MV AK 0.0540 0.0278 0.60% AK 0.0591 0.0305 0.65% AK -0.0051 -0.0027 -0.05%AL 0.0470 0.0288 0.56% AL 0.0471 0.0291 0.57% AL -0.0002 -0.0003 0.00%AR 0.0133 0.0087 0.16% AR 0.0105 0.0076 0.14% AR 0.0028 0.0011 0.02%AZ 0.0839 0.0712 1.39% AZ 0.0849 0.0712 1.39% AZ -0.0010 0.0000 0.00%CA 1.0499 0.6541 13.20% CA 1.0480 0.6532 13.18% CA 0.0019 0.0009 0.02%CO 0.1474 0.0846 1.76% CO 0.1459 0.0844 1.76% CO 0.0014 0.0002 0.00%CT 0.1154 0.0943 1.92% CT 0.1161 0.0943 1.92% CT -0.0007 0.0000 0.00%DC 0.0664 0.0494 0.94% DC 0.0644 0.0479 0.91% DC 0.0021 0.0016 0.03%DE 0.0089 0.0082 0.17% DE 0.0107 0.0091 0.19% DE -0.0018 -0.0010 -0.02%FL 0.4974 0.3272 6.44% FL 0.5022 0.3273 6.45% FL -0.0048 -0.0001 -0.01%GA 0.1933 0.1346 2.64% GA 0.1932 0.1346 2.64% GA 0.0001 0.0000 0.00%GU 0.0109 0.0069 0.12% GU 0.0118 0.0081 0.14% GU -0.0008 -0.0011 -0.02%HI 0.0759 0.0591 1.14% HI 0.0782 0.0608 1.17% HI -0.0024 -0.0016 -0.03%IA 0.0091 0.0053 0.10% IA 0.0091 0.0061 0.11% IA 0.0000 -0.0008 -0.01%ID 0.0041 0.0026 0.04% ID 0.0038 0.0025 0.04% ID 0.0002 0.0000 0.00%IL 0.3997 0.2476 5.10% IL 0.4008 0.2476 5.09% IL -0.0011 0.0000 0.00%IN 0.0650 0.0424 0.82% IN 0.0653 0.0439 0.84% IN -0.0004 -0.0015 -0.02%KS 0.0275 0.0207 0.38% KS 0.0264 0.0175 0.34% KS 0.0011 0.0032 0.05%KY 0.0658 0.0421 0.85% KY 0.0652 0.0416 0.85% KY 0.0006 0.0005 0.01%LA 0.0482 0.0404 0.78% LA 0.0502 0.0418 0.80% LA -0.0021 -0.0014 -0.02%MA 0.3286 0.2317 4.62% MA 0.3290 0.2318 4.62% MA -0.0004 0.0000 0.00%MD 0.0930 0.0698 1.41% MD 0.0938 0.0698 1.41% MD -0.0008 0.0000 0.00%ME 0.0172 0.0102 0.19% ME 0.0170 0.0098 0.18% ME 0.0002 0.0004 0.01%MI 0.2083 0.1305 2.52% MI 0.2054 0.1305 2.52% MI 0.0030 0.0000 0.00%MN 0.0866 0.0566 1.12% MN 0.0862 0.0564 1.12% MN 0.0003 0.0002 0.00%MO 0.0729 0.0479 0.90% MO 0.0725 0.0476 0.90% MO 0.0003 0.0003 0.00%MS 0.0247 0.0183 0.36% MS 0.0242 0.0187 0.37% MS 0.0005 -0.0004 -0.01%MT 0.0136 0.0086 0.15% MT 0.0158 0.0100 0.18% MT -0.0023 -0.0014 -0.03%

26

NC 0.1381 0.0965 1.88% NC 0.1347 0.0947 1.85% NC 0.0034 0.0018 0.03%ND 0.0082 0.0041 0.08% ND 0.0048 0.0027 0.05% ND 0.0034 0.0015 0.03%NE 0.0384 0.0296 0.60% NE 0.0382 0.0295 0.59% NE 0.0002 0.0001 0.00%NH 0.0126 0.0078 0.15% NH 0.0101 0.0063 0.12% NH 0.0025 0.0015 0.03%NJ 0.2257 0.1740 3.42% NJ 0.2249 0.1740 3.42% NJ 0.0008 0.0000 0.00%NM 0.0213 0.0185 0.35% NM 0.0210 0.0185 0.35% NM 0.0004 0.0000 0.00%NV 0.0851 0.0585 1.12% NV 0.0863 0.0599 1.14% NV -0.0012 -0.0014 -0.02%NY 1.1585 0.8216 15.98% NY 1.1601 0.8200 15.94% NY -0.0016 0.0016 0.04%OH 0.1766 0.1208 2.39% OH 0.1772 0.1207 2.39% OH -0.0005 0.0002 0.00%OK 0.0363 0.0276 0.54% OK 0.0380 0.0279 0.55% OK -0.0017 -0.0003 -0.01%OR 0.0462 0.0300 0.59% OR 0.0453 0.0292 0.58% OR 0.0009 0.0008 0.01%PA 0.2990 0.2050 3.96% PA 0.2984 0.2050 3.97% PA 0.0007 0.0000 -0.01%PR 0.2412 0.1385 2.75% PR 0.2415 0.1385 2.76% PR -0.0003 0.0000 0.00%RI 0.0211 0.0156 0.30% RI 0.0212 0.0156 0.30% RI -0.0001 0.0000 0.00%SC 0.0922 0.0608 1.17% SC 0.0937 0.0615 1.19% SC -0.0016 -0.0006 -0.01%SD 0.0096 0.0055 0.10% SD 0.0113 0.0068 0.12% SD -0.0017 -0.0013 -0.02%TN 0.0869 0.0556 1.10% TN 0.0863 0.0542 1.08% TN 0.0005 0.0013 0.02%TX 0.4851 0.3160 6.33% TX 0.4891 0.3178 6.35% TX -0.0040 -0.0018 -0.02%UT 0.0531 0.0369 0.73% UT 0.0571 0.0389 0.77% UT -0.0041 -0.0020 -0.04%VA 0.1148 0.0824 1.63% VA 0.1149 0.0824 1.63% VA -0.0001 -0.0001 0.00%VI 0.0078 0.0047 0.08% VI 0.0085 0.0046 0.08% VI -0.0007 0.0000 0.00%VT 0.0068 0.0038 0.07% VT 0.0041 0.0023 0.04% VT 0.0027 0.0014 0.03%WA 0.2091 0.1407 2.83% WA 0.2091 0.1411 2.84% WA 0.0000 -0.0003 -0.01%WI 0.0815 0.0589 1.15% WI 0.0816 0.0589 1.15% WI -0.0001 0.0000 0.00%WV 0.0187 0.0130 0.25% WV 0.0185 0.0124 0.24% WV 0.0003 0.0006 0.01%WY 0.0056 0.0038 0.07% WY 0.0042 0.0034 0.06% WY 0.0014 0.0005 0.01%Total 7.5075 5.0599 100.00% Total 7.5172 5.0607 100.00% Total -0.0097 -0.0008 0.00%

November 1, 2001 Index 41,841 Bonds MV = $799,800,395

Industry DWE WAC % of MV Education 0.18490673 0.11856122 2.371445% Electric 0.27082051 0.20330701 3.929966% Hospital 0.29313004 0.16365614 2.985477% Housing 0.34768778 0.20103541 3.587280% IDR/PCR 0.16378129 0.10608494 1.927170% Insured 3.89589442 2.21766431 45.396312% Leasing 0.08698653 0.05950475 1.159186% Local Gen Oblig 0.77759391 0.55337944 11.134738% Pre-refunded 0.36039314 0.43280952 8.429446% Resource Recovery 0.02233908 0.01980322 0.346429% Special Tax 0.04353465 0.03597921 0.751420% State Gen Oblig 0.67798397 0.51760865 10.823939% Transportation 0.32175441 0.22250886 4.352183% Water & Sewer 0.22102794 0.14076709 2.805010% Total 7.66783440 4.99266978 100.0000%

27

Proxy 7,946 Bonds MV = $156,760,877

Industry DWE WAC % of MV Education 0.17624328 0.11186453 2.253282% Electric 0.29847493 0.21885611 4.232580% Hospital 0.29364374 0.16360156 2.982194% Housing 0.32540682 0.18502774 3.320038% IDR/PCR 0.16396059 0.10713059 1.931238% Insured 3.91841617 2.22576642 45.547698% Leasing 0.08603680 0.05875893 1.144955% Local Gen Oblig 0.75888898 0.54434651 10.955111% Pre-refunded 0.35283594 0.42962746 8.354444% Resource Recovery 0.01883745 0.01895882 0.331931% Special Tax 0.04729297 0.03949892 0.814836% State Gen Oblig 0.68142212 0.51850452 10.837529% Transportation 0.33010629 0.23194235 4.533092% Water & Sewer 0.21626832 0.13878532 2.761071% Total 7.66783440 4.99266978 100.0000% Difference

Industry DWE WAC % of MV Education 0.00866346 0.00669669 0.118163% Electric -0.02765442 -0.01554910 -0.302614% Hospital -0.00051370 0.00005458 0.003284% Housing 0.02228096 0.01600768 0.267241% IDR/PCR -0.00017929 -0.00104565 -0.004068% Insured -0.02252175 -0.00810210 -0.151387% Leasing 0.00094973 0.00074582 0.014232% Local Gen Oblig 0.01870493 0.00903293 0.179627% Pre-refunded 0.00755720 0.00318206 0.075002% Resource Recovery 0.00350163 0.00084439 0.014498% Special Tax -0.00375832 -0.00351971 -0.063417% State Gen Oblig -0.00343815 -0.00089587 -0.013590% Transportation -0.00835188 -0.00943349 -0.180909% Water & Sewer 0.00475962 0.00198178 0.043939% Total 0.00000000 0.00000000 0.0000% November 30, 2001 Index 41,841 Bonds * MV = $789,167,355

Industry DWE WAC % of MV Education 0.1807 0.1202 2.359700% Electric 0.2700 0.2060 3.941291% Hospital 0.2846 0.1659 2.977553% Housing 0.3367 0.2037 3.599417% IDR/PCR 0.1605 0.1075 1.926120% Insured 3.8029 2.2475 45.342909% Leasing 0.0857 0.0603 1.161063%

28

Local Gen Oblig 0.7673 0.5608 11.149719% Pre-refunded 0.3534 0.4386 8.448404% Resource Recovery 0.0228 0.0201 0.344342% Special Tax 0.0433 0.0365 0.752831% State Gen Oblig 0.6691 0.5246 10.828747% Transportation 0.3141 0.2255 4.362143% Water & Sewer 0.2165 0.1427 2.805760% Total 7.50753002 5.05993721 100.0000% Note: * 463 bonds prices are not available and kept constant over this month Proxy 7,946 Bonds ** MV = $154,652,543

Industry DWE WAC % of MV Education 0.1725 0.1134 2.2378% Electric 0.2975 0.2218 4.2472% Hospital 0.2857 0.1658 2.9743% Housing 0.3148 0.1876 3.3320% IDR/PCR 0.1608 0.1086 1.9329% Insured 3.8293 2.2561 45.4859% Leasing 0.0852 0.0596 1.1452% Local Gen Oblig 0.7513 0.5518 10.9732% Pre-refunded 0.3461 0.4355 8.3724% Resource Recovery 0.0194 0.0192 0.3293% Special Tax 0.0471 0.0400 0.8158% State Gen Oblig 0.6733 0.5256 10.8444% Transportation 0.3224 0.2351 4.5461% Water & Sewer 0.2118 0.1407 2.7636% Total 7.51723010 5.06073343 100.0000% Note: ** 108 bonds prices are not available and kept constant over this month Difference

Industry DWE WAC % of MV Education 0.00821517 0.00676913 0.121894% Electric -0.02744431 -0.01579340 -0.305939% Hospital -0.00104618 0.00002931 0.003221% Housing 0.02191351 0.01619395 0.267450% IDR/PCR -0.00036313 -0.00107677 -0.006732% Insured -0.02634598 -0.00856510 -0.143031% Leasing 0.00043396 0.00074653 0.015909% Local Gen Oblig 0.01596365 0.00906810 0.176547% Pre-refunded 0.00726893 0.00315410 0.076030% Resource Recovery 0.00340989 0.00085276 0.015086% Special Tax -0.00377736 -0.00357342 -0.062973% State Gen Oblig -0.00424749 -0.00099036 -0.015632% Transportation -0.00836435 -0.00959746 -0.183978% Water & Sewer 0.00468361 0.00198642 0.042148% Total -0.00970009 -0.00079622 0.0000%

29

November 1, 2001 Index 41,841 Bonds MV = $799,800,395

Ratings DWE WAC % of MV A 0.40694988 0.28395603 5.393742% A- 0.11658048 0.07853040 1.478569% A+ 0.28947317 0.20757629 4.007646% AA 0.71756196 0.52472786 10.702897% AA- 0.46101026 0.35181476 7.046766% AA+ 0.45748678 0.31977699 6.442986% AAA 4.60806196 2.84195307 57.893996% B+ 0.00010784 0.00045936 0.006572% BB 0.00185322 0.00175108 0.026879% BB+ 0.00116890 0.00085973 0.013187% BBB 0.09906296 0.07073594 1.205765% BBB- 0.09543471 0.04681521 0.863297% BBB+ 0.07716408 0.05865669 1.055286% NR 0.33591820 0.20505636 3.862413% Total 7.66783440 4.99266978 100.0000% Proxy 7,946 Bonds MV = $156,760,877

Ratings DWE WAC % of MV A 0.35876325 0.26034694 4.993436% A- 0.11330259 0.08175775 1.527934% A+ 0.31114917 0.22256689 4.323711% AA 0.74750085 0.53556016 10.923658% AA- 0.50428673 0.37909972 7.504450% AA+ 0.46320921 0.31561550 6.327921% AAA 4.62247793 2.84424292 57.977832% B+ 0.00011520 0.00049071 0.007021% BB 0.00122311 0.00121984 0.017959% BB+ 0.00164757 0.00095252 0.013518% BBB 0.09564314 0.06593521 1.137966% BBB- 0.08966369 0.04498625 0.825118% BBB+ 0.07253964 0.05855828 1.051754% NR 0.28631232 0.18133711 3.367721% Total 7.66783440 4.99266978 100.0000% Difference

Ratings DWE WAC % of MV A 0.04818663 0.02360909 0.400306% A- 0.00327789 -0.00322735 -0.049366% A+ -0.02167600 -0.01499060 -0.316065% AA -0.02993889 -0.01083230 -0.220761% AA- -0.04327647 -0.02728496 -0.457684% AA+ -0.00572243 0.00416149 0.115065% AAA -0.01441597 -0.00228985 -0.083837% B+ -0.00000736 -0.00003135 -0.000449%

30

BB 0.00063010 0.00053124 0.008920% BB+ -0.00047867 -0.00009279 -0.000331% BBB 0.00341982 0.00480072 0.067799% BBB- 0.00577102 0.00182896 0.038179% BBB+ 0.00462444 0.00009842 0.003532% NR 0.04960588 0.02371926 0.494691% Total 0.00000000 0.00000000 0.0000% November 30, 2001 Index 41,841 Bonds * MV = $789,167,355

Ratings DWE WAC % of MV A 0.39927116 0.28781875 5.401154% A- 0.11480542 0.08044958 1.493645% A+ 0.25645831 0.18937860 3.607992% AA 0.70799463 0.53277716 10.729073% AA- 0.48153371 0.37563262 7.420625% AA+ 0.45155072 0.32378742 6.426518% AAA 4.50533330 2.88059826 57.882246% B+ 0.00010781 0.00046555 6.68852E-05 BB 0.00184900 0.00177467 0.027271% BB+ 0.00100535 0.00087131 0.012546% BBB 0.09688417 0.07168901 1.212663% BBB- 0.09151841 0.04744599 0.866276% BBB+ 0.07458847 0.05768265 1.026508% NR 0.32462956 0.20956562 3.886794% Total 7.50753002 5.05993721 100.0000% Note: * 463 bonds prices are not available and kept constant over this month Proxy 7,946 Bonds ** MV = $154,652,543

Ratings DWE WAC % of MV A 0.35449734 0.26389617 5.004273% A- 0.11416979 0.08605568 1.586262% A+ 0.26000237 0.19368168 3.703700% AA 0.73814103 0.54446690 10.963578% AA- 0.54315991 0.41344768 8.085260% AA+ 0.45660400 0.31991820 6.321549% AAA 4.52108360 2.88286708 57.930386% B+ 0.00011518 0.00049739 0.007146% BB 0.00123447 0.00123647 0.018226% BB+ 0.00139651 0.00096550 0.012411% BBB 0.09274873 0.06683409 1.144316% BBB- 0.08628060 0.04559953 0.829590% BBB+ 0.06835578 0.05571969 0.985670% NR 0.27944078 0.18554736 3.407632% Total 7.51723010 5.06073343 100.0000% Note: ** 108 bonds prices are not available and kept constant over this month

31

Difference Ratings DWE WAC % of MV

A 0.04477381 0.02392258 0.396881% A- 0.00063562 -0.00560609 -0.092616% A+ -0.00354405 -0.00430307 -0.095707% AA -0.03014640 -0.01168974 -0.234505% AA- -0.06162619 -0.03781506 -0.664635% AA+ -0.00505328 0.00386922 0.104969% AAA -0.01575030 -0.00226882 -0.048141% B+ -0.00000738 -0.00003185 -0.000458% BB 0.00061453 0.00053821 0.009044% BB+ -0.00039117 -0.00009419 0.000135% BBB 0.00413544 0.00485492 0.068347% BBB- 0.00523780 0.00184646 0.036686% BBB+ 0.00623269 0.00196296 0.040838% NR 0.04518878 0.02401826 0.479162% Total -0.00970009 -0.00079622 0.0000%

32

References: Lehman Brothers Quantitative Portfolio Strategies Group, “Quantitative Strategies for Benchmark Portfolios,” Lehman Brothers, May 2001, p. 34 Ravi E. Dattatreya, Frank J. Fabozzi, “Active total return management of fixed-income portfolios,” (Irwin, Professional Publishing), p. 8 Sharmin Mossavar-Rahmani, “Understanding and Evaluating Index Fund Management,” p. 434