Yield Book Mortgage Research

The impact of ESG on MBS
performance and prepayment
characteristics
April 2023
performance

The incorporation of sustainability and social responsibility measures into
investment practices has often been handled separately from traditional
financial assessment; however, in this publication we demonstrate that ESG
considerations can provide significant value when integrated into workflows.
Last year, Yield Book introduced a quantitative-based methodology foragency residential mortgage-backed security (RMBS) assets, producing
ESG-focused metrics that analyzed and measured entities’ Responsible
Lending and Responsible Servicing behaviors and applying this at a security
level. Market participants can use this information to identify mortgage poolsthat meet certain criteria. In this paper we show that the same information
correlates to investment performance.
This publication focuses on prepayment behavior across the dimension ofthese socially oriented lending and servicing traits. We present the results
through several common visualizations, including a correlation study, Scurves, weighted average loan age (WALA) ramps and a total return
analysis. Our goal is to assist investors in identifying mortgage-backed
security (MBS) pools that contain loans originated or serviced alongcommonly accepted social criteria and to support investment decisions withthe benefit of observing prior results.
The study includes performance and prepayment information for eachindividual ESG metric, as well as aggregated loan attributes outside of ESG.
Our research highlights significant dispersion in the prepayment behavior of
ESG cohorts and cannot be attributed to other collateral characteristics
typically used in prepayment models. We find that investing based on Yield
Book’s ESG metrics generally leads to better financial performance and, inmost cases, ESG MBS cohorts are observed to prepay more slowly.

AUTHORS
Marina Manoim
Quantitative MBS Modeling Data Leadmarina.manoim@lseg.com
Irene Shi
Quant Data Analyst Manager
Irene.shi@lseg.com

CONTRIBUTORSOctavian Ceban
Senior Analyst, Research
Joseph Corrado
RMBS Data Scientist
Hui Ding
Head of Agency RMBS Research
Vlad-Ionut Ene
Senior Analyst, Product
Jake Katz
Head of Non-Agency RMBS Researchand Data Science
Katie PrideauxHead of Sustainable Investment
Analytics Productsolutions.yieldbook.com

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Contents
Yield Book’s ESG Metric Methodology overview ................................................ 3
Total return analysis............................................................................................ 7
S-curves and WALA ramps............................................................................... 10
Metric distribution and CPR regression by entity .............................................. 15
Conclusion ........................................................................................................ 18

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Yield Book’s ESG Metric Methodology overview
Last year, Yield Book launched a set of ESG metrics intended to facilitate detection of negative and positive behaviors ina servicing and lending context based on loan-level data; a summary of each metric is given below. We then investigate
these metrics' connection to financial performance.
For additional information on our Sustainable Investment Analytics solutions, please visit Sustainable Investment
Analytics | Yield Book

Responsible servicing
Responsible behavior in a servicing context is the practice of extending fair and reasonable terms to borrowers during theservicing process. To facilitate the measurement of – and to highlight the extent of – potentially predatory practices withina deal or market segment, we have developed quantitative-based metrics.
Responsible delinquency policy
Positive behaviors can be observed when the servicer actively attempts to seek modification with the borrower to prevent
foreclosure and other routes such as default and buyout, particularly prior to legally enforced deadlines. This measureshows servicers’ willingness to provide positive outcomes for the borrower by helping them avoid incurring further debt or
losing their home.
Relative default rate
Foreclosure is the ultimate worst outcome for both the borrower and the servicer. It is a costly legal process usually
avoided by the servicer and becomes a major credit issue for the borrower; however, a relatively high value incombination with other characteristics could also indicate an unwillingness to engage in alternative options for the
borrower.
Loan churn
Loan churn is understood to be the process of encouraging borrowers to refinance repeatedly in a short timeframe
following the origination of their loan with little benefit to the borrower, with the servicer benefiting from theseadministrative fees at each refinance.

Responsible Lending
Responsible behavior in a lending context is the practice of extending fair and reasonable terms to borrowers during theloan origination process. To facilitate the measurement of – and to highlight the extent of – potentially predatory practices
within a deal or market segment, we have developed quantitative-based metrics.
Outsized rates
This metric provides a measure of a lender’s overall deviation from the population for the interest rates extended toborrowers. We developed a benchmark for prediction of spread at origination (SATO) given input characteristics at eachpoint in time, based on a linear regression model to obtain coefficients of the various inputs across loan-level data from2010-2021. Each lender’s distribution is then compared to the population through a density comparison.
Early pay default risk
A mortgage loan is considered to have substantial early payment default risk (EPDR) if it displays delinquency above athreshold period within a relatively short timeframe following loan origination. While this can occur even with the involved
parties acting responsibly during loan origination, prevalence of early delinquency situations in recently originated loans
for a specific lender’s production highlights a potential inclination towards predatory behavior.
Irresponsible lending
Irresponsible lending, a predatory behavior on the part of the lender, is defined by the consistent offering of loans toborrowers with high-risk credentials. This can result in the borrower facing default risks, falling behind on existingcommitments and experiencing financial hardship. This behavior expressed in regard to loans with poor credit is identified
by looking at the consistent extension of credit to high-risk borrowers relative to other lenders within either a deal or

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3market segment, in combination with default rates. We note that lending to risky borrowers is not predatory – this is ofcourse required for healthily functioning markets – but is an issue to be highlighted if it occurs consistently and over along period.
We focus on the 12 months averaged pool-level versions of the metrics for this analysis.

ESG metric distributions
Figures 1 and 2 below show the distribution of pool-level metrics across all three agencies: GNMA, FNMA and FHLMC.
Distributions are shown via violin plots demonstrating both summary statistics (first quartile, median, third quartile) and thekernel density of the values for each of the metrics.

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Figure 1. Distributions of responsible lending scores
Figure 1a. Outsized rates (OR)The distributions of early pay default risk seen below clearly visualize the different collateral credit quality and lending
practices for FNMA and FHLMC vs GNMA.
Figure 1b. Early pay default risk (EPDR)

Figure 1c. Risky lending (RL)

Source: Yield Book

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Figure 2. Distributions of responsible servicing scores
Figure 2a. Loan churn (LC)

Figure 2b. Relative default rate (RDR)

On the delinquency policy charts below, we see again the different servicing practices of government-sponsoredenterprises vs GNMA. For this metric, higher values indicate more responsible practices.
Figure 2c. Delinquency policy (DP)

Source: Yield Book

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Total return analysis
The charts in Figures 3 and 4 show the moving time window of two months’ total returns on the investment for theuniverse of FNMA mortgage pools.
The results in Tables 1 and 2 show total return for two-month periods assuming various risk-free rates. The investment
universe is segmented into two sub-cohorts (low and high) for each ESG metric. The sub-cohorts are split at the weighted
average value of the pool-level metrics (weighted by the current balance of the pool).
On the responsible lending side, the charts and more detailed data in Table 1 show that investing in cohorts with lower
EDPR scores or lower risky lending scores on average produce higher total returns. Since the higher outsized rate scoreis indicative of higher loan rates, investing in those cohorts predicably produces higher returns.
Figure 3: Total return graphs by ESG RL cohorts (with 0.1 risk-free rate)
Figure 3a: Total return graph outsized rates (OR)

Figure 3b: Total return graph early pay default risk (EPDR)

Figure 3c: Total return graph risky lending (RL)

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Table 1: Total return by ESG RL cohorts

Risk freerate (%)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
1.0
1.0
1.0
1.0
1.0
1.0
1.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
3.0
3.0
3.0
3.0
3.0
3.0
3.0
4.0
4.0
4.0
4.0
4.0
4.0
4.0
5.0
5.0
5.0
5.0
5.0
5.0
5.0

Total return for the tw o m onths period
Cohorttype
Jan-21
Feb-21
Mar-21
Apr-21
May-21
Jun-21
Jul-21
Aug-21
Sep-21
Oct-21
Nov-21
Dec-21Avg
ALL-0.73%-1.40%
0.02%
0.65%-0.11%
0.64%
0.48%-0.59%-0.64%-0.01%-0.15%-2.06%-0.33%EPDR HIGH-2.17%-2.80%
0.34%
1.47%
0.36%
1.03%
0.64%
-1.02%-0.99%
0.03%
0.04%-2.22%-0.44%EPDR LOW-0.58%-1.15%-0.06%
0.41%-0.29%
0.47%
0.39%-0.34%-0.42%-0.04%-0.30%-1.92%-0.32%RL HIGH-0.98%-1.62%
0.09%
0.82%-0.02%
0.70%
0.50%-0.69%-0.75%-0.01%-0.12%-2.09%-0.35%RL LOW-0.03%-0.76%-0.20%
0.19%-0.37%
0.49%
0.40%-0.27%-0.34%-0.01%-0.25%-1.98%-0.26%OR HIGH
0.13%-0.31%-0.23%-0.22%-0.69%
0.09%
0.26%
0.01%
-0.16%
0.01%-0.34%-1.90%-0.28%OR LOW-1.02%-1.74%
0.09%
0.89%
0.03%
0.77%
0.53%
-0.71%-0.75%-0.02%-0.10%-2.11%-0.34%
ALL-0.72%-1.39%
0.02%
0.66%-0.11%
0.65%
0.49%-0.58%-0.64%-0.01%-0.15%-2.06%-0.32%EPDR HIGH-2.17%-2.80%
0.34%
1.48%
0.36%
1.03%
0.65%
-1.01%-0.99%
0.03%
0.04%-2.21%-0.44%EPDR LOW-0.57%-1.15%-0.05%
0.42%-0.28%
0.48%
0.40%-0.33%-0.41%-0.03%-0.30%-1.92%-0.31%RL HIGH-0.97%-1.61%
0.10%
0.82%-0.02%
0.70%
0.51%-0.69%-0.74%-0.01%-0.12%-2.08%-0.34%RL LOW-0.02%-0.76%-0.19%
0.20%-0.36%
0.50%
0.41%-0.27%-0.33%
0.00%-0.24%-1.97%-0.25%OR HIGH
0.14%-0.30%-0.22%-0.21%-0.68%
0.10%
0.27%
0.02%
-0.15%
0.01%-0.34%-1.90%-0.27%OR LOW-1.01%-1.73%
0.10%
0.89%
0.04%
0.78%
0.53%
-0.71%-0.74%-0.01%-0.10%-2.10%-0.34%
ALL-0.71%-1.39%
0.03%
0.67%-0.10%
0.66%
0.49%-0.58%-0.63%
0.00%-0.14%-2.05%-0.31%EPDR HIGH-2.17%-2.80%
0.34%
1.48%
0.36%
1.03%
0.65%
-1.01%-0.98%
0.04%
0.04%-2.21%-0.44%EPDR LOW-0.56%-1.15%-0.04%
0.43%-0.27%
0.49%
0.41%-0.33%-0.40%-0.03%-0.29%-1.91%-0.30%RL HIGH-0.96%-1.61%
0.11%
0.83%-0.01%
0.71%
0.52%-0.68%-0.73%
0.00%-0.11%-2.08%-0.34%RL LOW-0.01%-0.75%-0.18%
0.21%-0.35%
0.50%
0.42%-0.26%-0.33%
0.00%-0.24%-1.97%-0.25%OR HIGH
0.15%-0.30%-0.21%-0.19%-0.67%
0.11%
0.28%
0.03%
-0.14%
0.02%-0.33%-1.89%-0.26%OR LOW-1.00%-1.73%
0.10%
0.90%
0.04%
0.78%
0.54%
-0.70%-0.74%-0.01%-0.09%-2.10%-0.33%
ALL-0.70%-1.38%
0.04%
0.67%-0.09%
0.66%
0.50%-0.57%-0.62%
0.01%-0.14%-2.05%-0.31%EPDR HIGH-2.17%-2.80%
0.35%
1.48%
0.37%
1.04%
0.65%
-1.01%-0.98%
0.04%
0.05%-2.21%-0.43%EPDR LOW-0.55%-1.14%-0.02%
0.44%-0.27%
0.49%
0.42%-0.32%-0.39%-0.02%-0.28%-1.91%-0.30%RL HIGH-0.95%-1.61%
0.12%
0.83%-0.01%
0.71%
0.52%-0.68%-0.73%
0.00%-0.11%-2.07%-0.33%RL LOW
0.01%-0.75%-0.17%
0.21%-0.35%
0.51%
0.42%-0.25%-0.32%
0.01%-0.23%-1.96%-0.24%OR HIGH
0.17%-0.29%-0.20%-0.18%-0.66%
0.12%
0.29%
0.04%
-0.13%
0.03%-0.32%-1.89%-0.25%OR LOW-0.99%-1.73%
0.11%
0.91%
0.05%
0.79%
0.54%
-0.70%-0.73%
0.00%-0.09%-2.09%-0.33%
ALL-0.68%-1.38%
0.05%
0.68%-0.09%
0.67%
0.50%-0.56%-0.62%
0.01%-0.13%-2.04%-0.30%EPDR HIGH-2.17%-2.80%
0.35%
1.48%
0.37%
1.04%
0.65%
-1.00%-0.98%
0.04%
0.05%-2.20%-0.43%EPDR LOW-0.54%-1.14%-0.01%
0.45%-0.26%
0.50%
0.43%-0.31%-0.38%-0.01%-0.28%-1.90%-0.29%RL HIGH-0.94%-1.60%
0.13%
0.84%
0.00%
0.72%
0.53%
-0.67%-0.72%
0.01%-0.10%-2.07%-0.32%RL LOW
0.02%-0.75%-0.16%
0.22%-0.34%
0.52%
0.43%-0.25%-0.31%
0.01%-0.23%-1.96%-0.23%OR HIGH
0.18%-0.29%-0.18%-0.17%-0.65%
0.13%
0.30%
0.05%
-0.12%
0.04%-0.32%-1.88%-0.24%OR LOW-0.98%-1.72%
0.12%
0.91%
0.05%
0.79%
0.55%
-0.69%-0.72%
0.01%-0.08%-2.09%-0.32%
ALL-0.67%-1.38%
0.06%
0.69%-0.08%
0.67%
0.51%-0.56%-0.61%
0.02%-0.13%-2.04%-0.29%EPDR HIGH-2.16%-2.80%
0.35%
1.48%
0.37%
1.04%
0.66%
-1.00%-0.97%
0.04%
0.05%-2.20%-0.43%EPDR LOW-0.52%-1.13%
0.00%
0.45%-0.25%
0.51%
0.43%-0.30%-0.38%
0.00%-0.27%-1.89%-0.28%RL HIGH-0.93%-1.60%
0.14%
0.85%
0.01%
0.73%
0.53%
-0.66%-0.71%
0.02%-0.09%-2.06%-0.32%RL LOW
0.03%-0.74%-0.15%
0.23%-0.33%
0.52%
0.44%-0.24%-0.31%
0.02%-0.22%-1.95%-0.22%OR HIGH
0.19%-0.28%-0.17%-0.16%-0.64%
0.14%
0.31%
0.06%
-0.12%
0.04%-0.31%-1.87%-0.23%OR LOW-0.97%-1.72%
0.13%
0.92%
0.06%
0.80%
0.55%
-0.68%-0.72%
0.01%-0.08%-2.08%-0.32%On the responsible servicing side, the charts and more detailed data in Table 2 show that investing in cohorts with lower
loan churn scores also, on average, produce higher total returns.
Due to the agencies’ disclosure content, there is no sufficient data available to complete a similar analysis for relative
default rate scores and delinquency policy scores.

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Figure 4: Total return graph by ESG RS cohorts loan churn (LC) (with 0.1 risk-free rate)

Table 2: Total return by RS ESG cohorts
Total return for the tw o m onths period
Rist freerate (%)

Cohorttype

Jan-21

Feb-21

Mar-21

Apr-21

May-21

Jun-21

Jul-21

Aug-21

Sep-21

Oct-21

Nov-21

Dec-21Avg

0.1 ALL-0.73%-1.40%

0.02%

0.65%

-0.11%

0.64%

0.48%

-0.59%-0.64%-0.01%-0.15%-2.06%-0.33%

0.1 LC HIGH

-0.73%-1.40%

0.02%

0.65%

-0.12%

0.63%

0.45%

-0.60%-0.56%-0.01%-0.09%-2.02%-0.32%

0.1 LC LOW-0.58%-1.34%

0.04%

0.68%

-0.02%

0.81%

0.63%

-0.53%-0.68%-0.01%-0.16%-2.06%-0.27%

1.0 ALL-0.72%-1.39%

0.02%

0.66%

-0.11%

0.65%

0.49%

-0.58%-0.64%-0.01%-0.15%-2.06%-0.32%

1.0 LC HIGH

-0.72%-1.39%

0.02%

0.66%

-0.11%

0.64%

0.46%

-0.60%-0.55%-0.01%-0.08%-2.02%-0.31%

1.0 LC LOW-0.57%-1.34%

0.04%

0.69%

-0.01%

0.81%

0.63%

-0.53%-0.68%-0.01%-0.16%-2.06%-0.26%

2.0 ALL-0.71%-1.39%

0.03%

0.67%

-0.10%

0.66%

0.49%

-0.58%-0.63%

0.00%-0.14%-2.05%-0.31%

2.0 LC HIGH

-0.71%-1.39%

0.03%

0.66%

-0.11%

0.64%

0.46%

-0.59%-0.55%

0.00%-0.08%-2.01%-0.30%

2.0 LC LOW-0.56%-1.34%

0.05%

0.69%

-0.01%

0.82%

0.63%

-0.52%-0.67%

0.00%-0.15%-2.05%-0.26%

3.0 ALL-0.70%-1.38%

0.04%

0.67%

-0.09%

0.66%

0.50%

-0.57%-0.62%

0.01%-0.14%-2.05%-0.31%

3.0 LC HIGH

-0.70%-1.39%

0.04%

0.67%

-0.10%

0.65%

0.47%

-0.58%-0.54%

0.01%-0.07%-2.01%-0.30%

3.0 LC LOW-0.55%-1.34%

0.06%

0.70%

-0.01%

0.82%

0.64%

-0.52%-0.67%

0.01%-0.14%-2.05%-0.25%

4.0 ALL-0.68%-1.38%

0.05%

0.68%

-0.09%

0.67%

0.50%

-0.56%-0.62%

0.01%-0.13%-2.04%-0.30%

4.0 LC HIGH

-0.69%-1.38%

0.05%

0.68%

-0.09%

0.66%

0.48%

-0.58%-0.53%

0.01%-0.07%-2.00%-0.29%

4.0 LC LOW-0.55%-1.33%

0.06%

0.70%

0.00%

0.83%

0.64%

-0.51%-0.66%

0.01%-0.14%-2.04%-0.25%

5.0 ALL-0.67%-1.38%

0.06%

0.69%

-0.08%

0.67%

0.51%

-0.56%-0.61%

0.02%-0.13%-2.04%-0.29%

5.0 LC HIGH

-0.68%-1.38%

0.06%

0.69%

-0.09%

0.66%

0.48%

-0.57%-0.52%

0.02%-0.06%-1.99%-0.28%

5.0 LC LOW-0.54%-1.33%

0.07%

0.71%

0.00%

0.83%

0.65%

-0.51%-0.66%

0.02%-0.13%-2.04%-0.24%

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9S-curves and WALA ramps
In the charts that follow, we present side-by-side graphs for various combinations of agency and RL/RS metrics. Each row
of graphs has an S-curve on the left and a WALA ramp on the right.
The S-curves graph prepayment rates based on interest rate incentive experienced for 30-year FRM MBS cohorts for2021-2022 factor dates.
The WALA ramps plot prepayment seasoning for 30-year FRM MBS cohorts for 2021-2022 factor dates.
For each ESG metric, we have created two loan-level cohorts: high and low. The high cohort contains all loans with ESG
scores above the balance weighted average of the ESG score. The low cohort is the complementary set. We could not
produce graphs for all metrics because certain metrics highlight rare events and lack sufficient differentiation to create
cohorts of adequate size – for example, EPDR events for FNMA loans.
Figure 5: Responsible lending metrics
For outsized rates, higher score cohorts (the part of the loan universe with the higher rates) predictably prepay faster,
especially in GNMA space.
Figure 5a: Outsized rates (OR) FNMA

Figure 5b: Outsized rates (OR) GNMA FHA

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10Figure 5c: Outsized rates (OR) GNMA VA

The risky lending charts below show faster prepayment for GNMA high score cohorts. This effect is less pronounced for
FNMA cohorts, which reflects known, inherent different characteristics of FNMA and GNMA collateral.
Figure 5d: Risky lending (RL) FNMA

Figure 5e: Risky lending (RL) GNMA FHA

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Figure 5f: Risky lending (RL) GNMA VA

Figure 6: Responsible servicing metrics
In line with our expectations, the charts show that cohorts with higher loan churn and relative default rate scores prepay
faster than the corresponding lower score cohorts.
Figure 6a: Loan churn (LC) FNMA

Figure 6b: Loan churn (LC) GNMA FHA

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Figure 6b: Loan churn (LC) GNMA VA

Figure 6c: Relative default rate (RDR) GNMA FHA

Figure 6c: Relative default rate (RDR) GNMA VA

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Figure 6c: Delinquency policy (DP) FNMA

Figure 6c: Delinquency policy (DP) GNMA FHA

Figure 6c: Delinquency policy (DP) GNMA VA

For the delinquency policy metric, a higher score means that servicers do a better job of mitigating borrower delinquency
and avoiding foreclosures. Consequently, the seasoning charts show that high-score cohorts prepay more slowly thanlower-score cohorts for both GNMA and FNMA, with the effect being more pronounced for FNMA.

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Metric distribution and CPR regression by entity
Responsible lending metrics
We present individual 12-month weighted average metric scores from the selected top lenders as of December 2020
below. Due to broader underwriting guidelines, the distribution for GNMA lenders is more right-skewed than thecorresponding distributions for FMNA and FHLMC.
Figure 7: Histogram of RL ESG scores by lender

We plot three-month prepayment speeds (3m CPR) against each ESG metric by lender. In the analysis, we include loans
originated in 2020 by the selected top sellers. Three-month CPR is calculated as of December 2021. The size of eachcircle represents the outstanding balance of the lender.

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Figure 8: Correlation of CPR to RL ESG metrics FNMA and GNMA

For outsized rates, a high score indicates the loan was made by a lender who systematically originates loans with rates
higher than can be explained by the loan’s characteristics and the prevailing market rate. The results above align with our
hypothesis that loans with high outsized rates should prepay faster.
In the coming months, we want to extend this analysis to include a study of the involuntary vs. voluntary prepayment
speeds for ESG cohorts for all agencies.

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Responsible lending metrics
We show individual 12-month weighted average metric scores from the selected top servicers as of December 2021
below.
Figure 9: Histogram of RS ESG scores by lender

We plot three-month prepayment speeds (3m CPR) against each ESG metric by servicer. In the analysis, we include
loans originated in 2021 by the selected top servicers. 3m CPR is calculated as of December 2021. The size of eachcircle represents the outstanding balance of the servicer.
Figure 10: Correlation of CPR to RS ESG metrics FNMA and GNMA

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For loan churn, a higher score means the servicers are actively encouraging borrowers to refinance repeatedly. This will
naturally drive up the prepayment speed.

Conclusion
In summary, our findings indicate three critical outcomes:•••

Four out of the five ESG metrics used in the prepayment study demonstrated consistent behavior in terms of
relative prepayment speeds for high and low ESG cohorts. The fifth metric, delinquency policy, followed our
expectations for the seasoning curve and only slightly deviated from the other ESG cohorts at the higher incentive
rates end of the S-curve.
In the total return study, three out of the four ESG metrics showed higher average returns during the period underconsideration. The only easily explainable exception was the outsized rates metric.
The correlation of speeds vs ESG metrics study also demonstrated stable and consistent non-negative
correlations.

These results suggest that the use of Yield Book-developed ESG scoring metrics would not only enable responsible
investing but could also enhance the performance of MBS portfolio.

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CORPORATE

About Yield Book
Yield Book is a trusted and authoritative source for fixed income analytics that enables market makers and institutional
investors to perform complex analysis of their portfolios, benchmarks, trading decisions, historical performance, and risk.
Yield Book products offer analytical insight into an extensive range of financial products in the fixed income space
including governments, agencies, corporates, high yield, emerging markets, mortgages, ABS, CMBS, CMOs, CLOs, andderivatives. The platform utilizes dedicated centralized servers that help ensure reliable, prompt data delivery. Yield Book
forms part of London Stock Exchange Group's Data and Analytics Division.
To learn more, contact us at sales@yieldbook.com or visit our website: solutions.yieldbook.com
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EMEA
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© 2023 London Stock Exchange Group plc and its applicable group undertakings (the “LSE Group”). The LSE Group includes (1) FTSE International
Limited (“FTSE”), (2) Frank Russell Company (“Russell”), (3) FTSE Global Debt Capital Markets Inc. and FTSE Global Debt Capital Markets Limited(together, “FTSE Canada”), (4) MTSNext Limited (“MTSNext”), (5) Mergent, Inc. (“Mergent”), (6) FTSE Fixed Income LLC (“FTSE FI”), (7) The Yield
Book Inc (“YB”) and (8) Beyond Ratings S.A.S. (“BR”). All rights reserved.
FTSE Russell® is a trading name of FTSE, Russell, FTSE Canada, MTSNext, Mergent, FTSE FI, YB and BR. “FTSE®”, “Russell®”, “FTSE Russell®”,“MTS®”, “FTSE4Good®”, “ICB®”, “Mergent®”, “The Yield Book®”, “Beyond Ratings®” and all other trademarks and service marks used herein (whether
registered or unregistered) are trademarks and/or service marks owned or licensed by the applicable member of the LSE Group or their respective
licensors and are owned, or used under licence, by FTSE, Russell, MTSNext, FTSE Canada, Mergent, FTSE FI, YB or BR. FTSE International Limited
is authorized and regulated by the Financial Conduct Authority as a benchmark administrator.
All information is provided for information purposes only. All information and data contained in this publication is obtained by the LSE Group, fromsources believed by it to be accurate and reliable. Because of the possibility of human and mechanical error as well as other factors, however, suchinformation and data is provided "as is" without warranty of any kind. No member of the LSE Group nor their respective directors, officers, employees,
partners or licensors make any claim, prediction, warranty or representation whatsoever, expressly or impliedly, either as to the accuracy, timeliness,completeness, merchantability of any information or of results to be obtained from the use of FTSE Russell products, including but not limited to indexes,
data and analytics, or the fitness or suitability of the FTSE Russell products for any particular purpose to which they might be put. Any representation ofhistorical data accessible through FTSE Russell products is provided for information purposes only and is not a reliable indicator of future performance.
No responsibility or liability can be accepted by any member of the LSE Group nor their respective directors, officers, employees, partners or licensors
for (a) any loss or damage in whole or in part caused by, resulting from, or relating to any error (negligent or otherwise) or other circumstance involved in
procuring, collecting, compiling, interpreting, analyzing, editing, transcribing, transmitting, communicating or delivering any such information or data orfrom use of this document or links to this document or (b) any direct, indirect, special, consequential or incidental damages whatsoever, even if any
member of the LSE Group is advised in advance of the possibility of such damages, resulting from the use of, or inability to use, such information.
No member of the LSE Group nor their respective directors, officers, employees, partners or licensors provide investment advice, and nothing contained
herein or accessible through FTSE Russell products, including statistical data and industry reports, should be taken as constituting financial orinvestment advice or a financial promotion.
Past performance is no guarantee of future results. Charts and graphs are provided for illustrative purposes only. Index returns shown may not represent
the results of the actual trading of investable assets. Certain returns shown may reflect back-tested performance. All performance presented prior to theindex inception date is back-tested performance. Back-tested performance is not actual performance but is hypothetical. The back-test calculations are
based on the same methodology that was in effect when the index was officially launched. However, back-tested data may reflect the application of theindex methodology with the benefit of hindsight, and the historic calculations of an index may change from month to month based on revisions to theunderlying economic data used in the calculation of the index.
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No part of this information may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical,
photocopying, recording or otherwise, without prior written permission of the applicable member of the LSE Group. Use and distribution of the LSEGroup data requires a licence from FTSE, Russell, FTSE Canada, MTSNext, Mergent, FTSE FI, YB, BR and/or their respective licensors.
TO THE EXTENT THAT ANY INDICATIVE PRICING INFORMATION IS PROVIDED HEREUNDER (THE “VALUATION”) AS PART OF THE DATA
AND/OR MODELS, SUCH VALUATION IS BEING PROVIDED AT CUSTOMER’S REQUEST FOR CUSTOMER’S INFORMATION PURPOSES ONLYAND IS NOT INTENDED AS AN OFFER OR SOLICITATION FOR PURCHASE OR SALE OF A SECURITY OR A CONTRACTUAL OBLIGATION(COLLECTIVELY, THE “FINANCIAL INSTRUMENTS”), INCLUDING THE FINANCIAL INSTRUMENTS SPECIFIED HEREIN. ANY VALUATION IS
ONLY AN ESTIMATE AS OF THE DATE PROVIDED AND THE ACTUAL MARKET PRICE OF THE FINANCIAL INSTRUMENTS CAN BE
DETERMINED ONLY WHEN AND IF EXECUTED IN THE MARKET; CONSEQUENTLY, ANY VALUATION MAY NOT REFLECT LEVELS AT WHICH:(A) ACTUAL TRANSACTIONS MAY OCCUR OR HAVE OCCURRED OR (B) COLLATERAL CALLS MAY BE MADE. THERE MAY BE NO OR MAY
NOT HAVE BEEN ANY SECONDARY TRADING MARKET FOR ANY SUCH FINANCIAL INSTRUMENTS. ANY VALUATION MAY INCORPORATEINFORMATION FROM THE MOST ACTIVE MARKETS TO WHICH A SOURCE HAS ACCESS AND, CONSEQUENTLY, MAY NOT REPRESENT AN

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CORPORATEESTIMATE OF THE VALUE OF THE PARTICULAR FINANCIAL INSTRUMENT IN THE MOST ACTIVE MARKET TO WHICH OTHERS MAY HAVEACCESS.
Any Valuation is only an estimate of LSE Group or third-party data providers as to the general value of the specified Financial Instruments, as of thedates indicated, and are subject to change at any time without notice. Each Valuation is only one view as to the estimated general value of a particularFinancial Instrument at a particular point in time.
Any Valuation may take into account a number of factors including, but not limited to, any one or more of the following: (a) general interest rate andmarket conditions; (b) macroeconomic and/or deal-specific credit fundamentals; (c) valuations of other financial instruments which may be comparable interms of rating, structure, maturity and/or covenant protection; (d) investor opinions about the respective deal parties; (e) size of the transaction; (f) cash
flow projections, which in turn are based on assumptions about certain parameters that include, but are not limited to, default, recovery, prepayment andreinvestment rates; (g) administrator reports, asset manager estimates, broker quotations and/or trustee reports, and (h) comparable trades, whereobservable. LSE Group’s view of these factors and assumptions may differ from other parties, and part of the valuation process may include the use of
proprietary models. Any Valuation is based upon information derived from sources believed to be reliable; however, LSE Group have not independently
verified such information. In addition, reports may be available only periodically and with a delay and accordingly, where any Valuation relies upon themost recently available information in such reports for a transaction, any Valuation may be based on information that may not be current as of thevaluation date.
LSE Group is not acting as your advisor, agent or fiduciary in providing any Valuation to you. To the extent permitted by law, LSE Group expressly
disclaim any responsibility for or liability (including, without limitation liability for any direct, punitive, incidental or consequential loss or damage, any actof negligence or breach of any warranty) relating to: (a) the accuracy of any models, market data input into such models or estimates used in deriving
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circumstances beyond LSE Group’s control, and (d) any uses to which such Valuation is put. You are responsible for your own independent verificationand should consult with your own auditors and other advisors with respect to any Valuation and before deciding the uses to which any Valuation may be
put. Specifically, LSE Group does not assert that any Valuation is appropriate for the purposes of valuing particular Financial Instruments in yourfinancial statements in accordance with the requirements of your local accounting framework (for example FASB Statement No. 157 “Fair Value
Measurements” under US GAAP or International Accounting Standard No. 39 “Financial Instruments: Recognition and Measurement”).
LSE Group and any third-party data provider may make a market in or engage in transactions in the Financial Instruments referred to herein. Any
Valuation may be affected by those parties’ own transactions and own quotations.

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