Methodology

Sustainable Sovereign Risk
Assessment Methodology
Sovereign ESG revisited

ftserussell.com

An LSEG BusinessFebruary 2023

Contents

1. ESG integration into the sovereign risk analysis ...................3
1.1. The scoring framework ................................................................................ 3
1.2. The relative and systematic quantitative framework ................................ 4
1.2.1. From raw data to indicators ...............................................4
1.2.2. Aggregation – from indicators to pillars .................................7
1.2.3. From pillars to an aggregated ESG risk score ........................92. By-products of 2SRM .............................................................. 10
2.1. Income-adjusted scores ............................................................................ 10
2.2. Momentum scores ...................................................................................... 11

Appendix: Partial Least Squares (PLS) regression and
Variable Importance in Projection (VIP) score .......................... 12

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Section 1

ESG integration into the sovereign risk analysis
The Sustainable Sovereign Risk Assessment Methodology (2SRM) is based on its predecessor, theSovereign Risk Monitor (SRM).1 It responds to increasing market maturity, customers’ needs and the WorldBank’s recommendations 2 and delivers a renewed and strengthened methodology.
2SRM is a quantitative, relative and systematic approach, based on 36 indicators for 151 countries, divided
into three pillars of sustainable sovereign risk assessment.
Beyond Ratings calculates a score on a quarterly basis for each indicator, starting from 1999 until the end of
the latest quarter. Each of the 36 indicators is the outcome of numerous adjustments – systematic to a large
extent – based on public, private and proprietary data.
All indicators are combined at (i) a risk theme level and (ii) a pillar level to obtain an aggregated score, which
is derived from advanced statistical and econometric techniques discussed hereafter.
Finally, the scores are aggregated from each pillar to obtain an aggregated ESG score.

1.1

The scoring framework

2SRM relies on the quantitative assessment of Environmental (E), Social (S) and Governance (G) pillars
which characterise sovereign creditworthiness. Each pillar is structured around sub-pillars, which consist of
several risk themes that include several indicators (see Figure 1).
The Environmental Pillar, for example, is represented by three sub-pillars: Energy, Climate and Natural
Capital. The Energy sub-pillar is consequently made up of three risk themes: Energy Policy, Low-Carbon
Energy and Energy Independence. The Energy Policy risk theme is composed of two indicators: Electricity
Access and Energy Consumption. The quantity of indicators varies from one risk theme to another, but on
average, they range between two and four.

1
2

For more details, refer to the Sovereign Risk Monitor Methodology.
Refer to the aforementioned paper, Demystifying Sovereign ESG.

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Figure 1. The 2SRM framework

Source: Beyond Ratings.

1.2

The relative and systematic quantitative framework

1.2.1. From raw data to indicators
Figure 2 illustrates the general framework through which we transform raw data into indicators.
Geographical coverage
Before starting the statistical transformation of the raw data, we check that we have sufficient raw data for agiven indicator and a given period for the statistical transformations to be robust enough. Thanks to a flagging
process, we check for geographical coverage gaps. We first identify a flagging ratio of 25%, which is definedas the minimum threshold of geographical coverage for any indicator, for a given period. When a given
indicator’s coverage does not surpass this flagging ratio, it is discarded as an indicator, meaning that the raw
data is then not taken into account for this period. It is important to note that this 25% flagging ratio applies totwo different groups of sovereigns depending on the level of development.3

We consider the International Monetary Fund’s dynamic country classification, classifying countries as (i) Advanced Economies or (ii)
Emerging Markets and Developing Economies. See section 3.2.2. and footnote 18 for more details.
3

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Time conversion
Raw datasets are received in an annual format. As 2SRM provides scores on a quarterly basis, annual values
will be attributed to the fourth quarter of a given year, which is followed by a linear interpolation between every
current fourth quarter and the fourth quarter of the previous year. When the data gap is larger than the
difference between two consecutive fourth quarters, the linear interpolation will only be performed for the first,
second and third quarter of the given year and the fourth of the previous year. Remaining gaps will not be
filled at this stage.4
Figure 2. From raw data to indicators

Raw data
Coverage
Time conversion
Winsorisation
Standardisation
Normalisation
Dilation
Smoothing
Indicators

Source: Beyond Ratings.

Winsorisation
To minimise the impact of potential outliers, all indicators have been corrected for extreme values that are
higher than the 97.5th percentile or lower than the 2.5th percentile of the distribution (see Figure 3).

4 The Implied Temperature Rise (ITR) indicator will, however, be back backward filled for the whole time series of every country as it is aforward-looking indicator which is not recorded historically.

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Figure 3. Standard distribution curve: Winsorisation process

Source: Beyond Ratings.

Standardisation
Most indicators are transformed into z-scores5 for each country, by year and quarter. This allows us to assess
the relative risk linked to the initial data and corrects for data scaling.
There are four indicators or families of indicators that are not winsorised and standardised – the World
Governance Indicators produced by the World Bank, the Red List Index provided by the Sustainable
Development Goals Database, the Physical Risk indicators and the Implied Temperature Rise indicator
developed by Beyond Ratings – as they have already gone through a standardisation process during
production.
Normalisation
The z-scores are transformed into continuous scores on an interval, ranging from 0 to 100 6 in accordance withthe cumulated distribution of a standard normal distribution – where 0 represents the worst score and 100 thebest score.
Two different cases provide the general framework for these additional adjustments:(i) When the optimum is a maximum, the higher the value for the data, the higher the value of thecorresponding z-score, and the higher the indicator (see Figure 4 on the left-hand side).
(ii) When the optimum is a minimum, the lower the value for the data, the lower the value of thecorresponding z-score, and the higher the indicator (see Figure 4 on the right-hand side).

5

For a raw datum denoted 𝑋𝑡,𝑖 with 𝑡 the date and 𝑖 the country, 𝑧˗𝑠𝑐𝑜𝑟𝑒𝑋𝑡,𝑖 =̅̅̅𝑡
𝑋𝑡,𝑖 −𝑋
𝜎𝑋𝑡

with ̅̅̅
𝑋𝑡 = 𝑛−1 ∑𝑛𝑗=1 𝑋𝑡 and 𝜎𝑋𝑡 =2√(𝑛 − 1)−1 ∑𝑛𝑗=1(𝑋𝑗 − ̅̅̅𝑋𝑡 ) .
6

The cumulated distribution of a standard normal distribution provides a value between 0 and 1 for a given z-score. This value is thenmultiplied by 100 for the needs of the model.

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Dilation
In order to maximise the discriminating power between sovereigns, a linear dilation is performed on all scoresto ensure they range from 0 to 1007. This third phase allows us to calculate scores (i.e., indicators).
The general framework detailed above does not apply at all to the Physical Risks indicators as the data
already ranges from 0 to 100. However, the scale will be inverted such that higher values correspond to ahigher ranking.
Smoothing
Finally, for every quarterly value for a given indicator and country, we will apply the smoothing effect.
Following an exponential rule, we apply different weights to values in T, T-1, T-2 and T-3, with T being
assigned the heavier weight and T-3 the lowest. This method allows us to account for fluctuations over thelast four quarters and to smooth potential one-off effects or erratic data.
Figure 4. Standard Normal Cumulative Distribution Function (x axis: z-scores; y axis: scores)
Optimum: minimum

Optimum: maximum

Source: Beyond Ratings.

1.2.2. Aggregation – from indicators to pillars
Figure 5 illustrates the systematic approach to assigning a score to a pillar based on its underlying indicators.
The chart shows how the six indicators in the Governance pillar are aggregated to provide the Governance
Pillar score, representing an approach that allows us to derive a score in the form of a weighted average.
Accounting for the level of development
The relevance of each of indicator in predicting the probability of default will depend on the level of economic
development for every country. As a result, we split Advanced Economies (AEs) and Emerging Markets andDeveloping Economies (EMDEs), as defined by the International Monetary Fund’s dynamic country
classification,8 to derive an indicators’ weight for each group of countries. In other words, indicators such as
Poverty will be assigned a weight of 24.8% for AEs and 33.1% for EMEs.

7

The linear dilation formula is the following: 𝑋̂𝑡,𝑖 =

𝑋𝑡,𝑖 −min 𝑋𝑡
𝑋

max 𝑋𝑡 −min 𝑋𝑡
𝑋

.

𝑋

8

The main criteria used by the IMF to classify the world into advanced economies and emerging market and developing economies are (i)per capita income level, (ii) export diversification (excludes resource-based economies, which hold high GDP per capita due to oil or gas
rents) and (iii) degree of integration into the global financial system. This classification is updated once a year. Further information on
https://www.imf.org/external/pubs/ft/weo/2020/02/weodata/groups.htm.

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Figure 5. From indicators to pillars, example

Source: Beyond Ratings.

The calibration process
The weights of each indicator for each pillar, i.e., intra-pillar weights, have been calibrated using the
econometric modelling framework called Partial Least Squares (PLS), with an added Variable Importance in
Projection (VIP) score (see Appendix 1 for further details).
This type of econometric modelling aims to be more robust than a simple linear model of the Ordinary Least
Squares (OLS) type.9 The PLS econometric model with an added VIP score allows us to take into accountpotential issues linked to collinearity between each indicator and to rank the information value contained ineach indicator within a pillar to estimate an aggregated measure of sovereign risk.
The aggregated sovereign risk measure 10 is the endogenous variable. It is calculated as the average of nonlinear numerical adjustment of the empirical default probabilities derived from the financial credit ratings of thethree main credit rating agencies.11 This aggregate measure of sovereign risk is therefore considered tocalibrate the intra- and inter-pillar12 weights of 2SRM.

9The OLS econometric modelling does not take into account the potential issues linked to collinearity between each indicator. Indeed, it
is obvious that some indicators are strongly correlated with others, e.g., a country’s general government overall balance is de facto
strongly correlated with the general government primary balance of this same country. Therefore, the coefficients estimated through OLS
are biased.
10
This aggregated sovereign risk measure is a good proxy of a default probability.
11
Standard & Poor’s, Moody’s Investors Service and Fitch Ratings, as publicly disclosed on their websites.
12
Intra-pillar means within each pillar (e.g., within the Social performance pillar of the Sustainability profile) while inter-pillar means
between each pillar (e.g., between the four pillars of the Economic and Financial profile).

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Once we have estimated the coefficients for each indicator within each pillar, we normalise the scores under asignificance constraint13 to obtain a weighting set with a 100% sum for each pillar.
The results derived from this advanced econometric framework have been calibrated on a data sample fromQ4 1999 to Q4 2020.14
Exceptions
The weights of some indicators cannot be exclusively decided using an econometric framework, but rather
through the support of multidisciplinary research. This is especially the case for Physical Risk, Transition Risk,
Air Pollution, Water Stress, Biodiversity and Food Security indicators.
Even if such indicators do not show a direct theoretical link to sovereign credit risk, they quantify the degree of
exposure and vulnerability of countries’ populations, infrastructure and ecosystems to environmental
degradation, which can help predict the severity of economic impact in the short, medium and long term.
In such cases, the indicators have been attributed in equal weights to represent their equal importance,
irrespective of their level of economic development.

1.2.3. From pillars to an aggregated ESG risk score
The E, S and G pillars are equally weighted to provide the ESG risk score. The reasoning for this approach isthat the use of econometric regressions to derive an objective measure of the relationship between
environmental indicators and sovereign credit risk assessment will be incomplete. Empirical econometricanalysis tends to reduce the importance of the Environmental pillar. As a result, it seems legitimate torelatively overweight these issues and not directly attribute the weights derived from econometric models.
Indeed, such issues are becoming more important to investors and are already starting to weigh on sovereignrisk in those areas of the world more exposed to physical and/or transition risks due to climate change.
Moreover, overall resource depletion ought to be accounted for as a set of weak signals, which are precursors
for potential second-round effects in geopolitical and economic terms.

13

In order to not underestimate too much the weight of some indicators in the modelling, we assign a minimum value (𝑀𝑖𝑛𝑖𝑚𝑢𝑚 𝑊𝑒𝑖𝑔ℎ𝑡 =1/2𝑁 with 𝑁 the number of indicators constituting the pillar) below which no weight can be. If some indicators are assigned that minimum
weight, all the other weights are once again normalised in order to obtain weightings set the sum of which is 100% for each pillar.
14
The calibration period runs from Q4 1999 to Q4 2020. The choice of this period was motivated by several constraints. First, we wanted
to have the most up-to-date data for some of the most lagging indicators (especially in the Environmental pillar). Second, we wanted tohave enough degrees of freedom for the econometric estimates. Besides, thanks to some out-of-sample estimates, we were able tohighlight the strong stability of the coefficients for the regressions over time and across countries.

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Section 2

By-products of 2SRM
2.1. Income-adjusted scores
Addressing the income bias
The integration of ESG criteria in sovereign credit risk analysis results in the Ingrained Income Bias. Highincome countries (i.e., AEs) tend to have higher ESG scores and are thus favoured in ESG assessments,
whereby low- and middle-income countries (i.e., EMDEs) are disadvantaged with lower ESG scores. Socialand Governance scores drive this bias due to the inherent correlation with economies’ level of development,
while Environmental scores are less concerned due to topic diversity and divergence in assessment
frameworks.
As 2SRM distinguishes between AEs and EMDEs, it allows the model to account for some of the income bias
ex ante. However, to correct for the persisting income bias, we use an ex post approach15 to generate
income-adjusted E, S and G scores.
Income-adjustment methodology
We use a simple econometric framework to construct income-adjusted sovereign ESG scores. To neutralise
the information related to income bias from 2SRM’s E, S and G scores, we use a univariate pooled ordinary
least square (POLS) regression for 149 economies, on a quarterly basis, between Q4 1999 and Q4 2020.
Empirical linkages between GNI per capita and ESG scores
We regress the Environmental, Social and Governance scores from 2SRM on the explanatory variable that is
the natural logarithm of the gross national income (GNI) per capita (at purchasing power parity in constant
USD) for each economy and quarter.
Environmental

SocialGovernance𝜶38.33*** (5.94)-69.67*** (6.30)-100.45*** (9.81)

𝜷1.90*** (0.69)

12.71*** (0.70)

16.12*** (1.11)

Observations

12,495

12,495

12,495R-squared

0.06

0.67

0.56

Source: Beyond Ratings.
Notes: The table presents the coefficient of the POLS 𝐸𝑖𝑡 = 𝛼𝐸 + 𝛽𝐸 ∗ 𝐿𝑁(𝐺𝑁𝐼 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖𝑡 ) + 𝑒𝑖𝑡 for Environmental Risk pillar, 𝑆𝑖𝑡 = 𝛼𝑆 +𝛽𝑆 ∗ 𝐿𝑁(𝐺𝑁𝐼 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖𝑡 ) + 𝑠𝑖𝑡 for Social Risk pillar and 𝐺𝑖𝑡 = 𝛼𝐺 + 𝛽𝐺 ∗ 𝐿𝑁(𝐺𝑁𝐼 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎𝑖𝑡 ) + 𝑔𝑖𝑡 for Governance Risk pillar. Standard
errors are in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% level of confidence, respectively.

15

For more details, refer to the Dealing with income bias in sovereign ESG scores - Sovereign ESG revisited.

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Beyond the GNI per capita
We then retrieve the residuals from these three regressions (which represent the share of the initial E, S, andG scores that is not explained by the income level) to calculate the income-adjusted sovereign E, S and G
scores.
To do these calculations, we transform these residuals into z-scores for each score and economy on aquarterly basis. Then, the z-scores are transformed into continuous scores based on an interval, ranging from0 to 100, in accordance with the cumulative distribution function of a standard normal distribution – where 0
represents the worst score and 100 the best score. Finally, we can aggregate the E, S, and G scores tocalculate overall income-adjusted sovereign ESG scores.

2.2. Momentum scores
Understanding ESG trends
In order to better understand contemporary trends country by country and by pillar, we have built a simple andex post approach known as Momentum scoring. These Momentum scores allow for the differentiation ofcountries according to the recent evolutions of their respective E, S and G scores. Indeed, while the absolutelevels of the E, S and G scores provide instant information, recent developments make it possible (i) to qualify
the improvements or deteriorations and (ii) to anticipate future developments.
Momentum Scores Methodology
We use a simple approach based on a three-year average annual change (3Y AAC16) for each score of the E,
S and G pillars and for each country that we compare to a rolling distribution of these same 3Y AAC over 10
years. To do this, we calculate the 10-year moving average (10Y MA) and the 10-year moving standard
deviation (10Y MSTD) of the 3Y AAC. Then, we compare the 3Y AAC to its 10Y MA ± one or two 10Y MSTD.
Thus, Momentum scores can take five different values:
i. If 3𝑌 𝐴𝐴𝐶 ≥ [10𝑌 𝑀𝐴 (3𝑌 𝐴𝐴𝐶) + 2 ∗ 10𝑌 𝑀𝑆𝑇𝐷 (3𝑌 𝐴𝐴𝐶)] then 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚 𝑠𝑐𝑜𝑟𝑒 = 2;
ii. Else if 3𝑌 𝐴𝐴𝐶 ≥ [10𝑌 𝑀𝐴 (3𝑌 𝐴𝐴𝐶) + 10𝑌 𝑀𝑆𝑇𝐷 (3𝑌 𝐴𝐴𝐶)] then 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚 𝑠𝑐𝑜𝑟𝑒 = 1;iii. Else if 3𝑌 𝐴𝐴𝐶 ≤ [10𝑌 𝑀𝐴 (3𝑌 𝐴𝐴𝐶) − 2 ∗ 10𝑌 𝑀𝑆𝑇𝐷 (3𝑌 𝐴𝐴𝐶)] then 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚 𝑠𝑐𝑜𝑟𝑒 = −2;
iv. Else if 3𝑌 𝐴𝐴𝐶 ≤ [10𝑌 𝑀𝐴 (3𝑌 𝐴𝐴𝐶) − 10𝑌 𝑀𝑆𝑇𝐷 (3𝑌 𝐴𝐴𝐶)] then 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚 𝑠𝑐𝑜𝑟𝑒 = −1;
v. Else 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚 𝑠𝑐𝑜𝑟𝑒 = 0.

Finally, given the lag with which the raw data are available, we smooth these Momentum scores by using themedian over four quarters to give them more simultaneity over the recent period.
A Momentum score of 2 denotes that the E, S or G score represents a strong improvement, while a
Momentum score of -2 means a strong deterioration. For a Momentum score of 1 (or -1), the improvement (ordeterioration) is moderate whereas for a Momentum score of 0 there is no significant change to report.

16

For example, the three-year average annual change for the Governance score for a country 𝑖 at date 𝑡 is computed as follows:
3𝑌 𝐴𝐴𝐶 (𝐺𝑖𝑡 ) = (𝐺𝑖𝑡 − 𝐺𝑖𝑡−12 )⁄3. Since 2SRM scores are released quarterly, we compare the score in quarter 𝑡 with the same score inquarter 𝑡 − 12 to report on the three-year period.

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Appendix: Partial Least Squares (PLS) regression
and Variable Importance in Projection (VIP) score
The Sovereign Risk Monitor aims to produce a comprehensive and relevant assessment of sovereign risk. To
design such a framework, we have developed a statistical and econometric methodology capable of analysing
multiple indicators and extracting valuable sovereign risk-related information. Then, we outline the statisticaland econometric methodology and describe the key steps leading to the estimation of our two different
profiles.
Sovereign risk is often influenced by numerous indicators, covering topics as wide-ranging and as different as
economic performance, public finances, social features, etc. but also exposure to climate change or thequality of governance. Some indicators that make up these topics are uncorrelated, while others show astrong correlation. Therefore, extracting precise and specific sovereign risk-related information cannot be
undertaken by using simple regression techniques as the results would be biased. To circumvent this issue,
we use specific regression techniques to estimate the weight of each indicator in predicting an aggregatedsovereign risk measure. The model we use is as follows:𝑁

𝑌 = 𝛼+∑𝑗=1

𝛽𝑗 𝑋𝑗 + 𝜖

where:-

𝑌 is the aggregated sovereign risk measure with 𝑌 = (𝑌1 , … , 𝑌𝑛 )𝑡 , 𝑡 the number of quarters and 𝑛 thenumber of countries;
For 𝑗 = 1, … , 𝐽, 𝑋𝑗 is the 𝑗-th explicative indicator 𝑋 matrix and 𝐽 the number of indicators;
𝛽𝑗 is the 𝑗-th coefficient. As already stated, it cannot be estimated by a simple Ordinary Least Squares
regression as this estimator would be biased.

These indicators can present strong correlations (e.g., between Social Risk and Governance Risk indicators),hence, to consider this specificity of the selected data, we use Partial Least Squares (PLS) regressions. Thateconometric framework, developed by Wold17 in the 1960s, enables the construction of predictive models in
the presence of many correlated independent variables. It finds orthogonal components – thus eliminating themulticollinearity issue – of the 𝑋 matrix that explain as much as possible the covariance between 𝑋 and 𝑌.
Then, this breakdown of 𝑋 is used in the regression to predict 𝑌.18 More precisely, the PLS regressions follow
several steps:(i) The PLS regressions produce a matrix 𝑊 such as 𝑇 = 𝑋𝑊, where 𝑇 is the factor score matrix and 𝑊
is estimated such as to minimise collinearity and maximise the covariance between the explanatory
and endogenous variables;(ii) We estimate the matrix 𝑄 so that 𝑌 = 𝑇𝑄 + 𝐸;(iii) We estimate the matrix 𝑃 so that 𝑋 = 𝑇𝑃 + 𝐸′;(iv) We compute 𝛽 = 𝑊𝑄.
Wold, H., 1966, “Estimation of principal components and related models by iterative least squares”, in P.R. Krishnaiaah (Ed.),
Multivariate analysis, pp.391-420.
18
Abdi, H., 2003, “Partial Least Squares (PLS) Regression”, The University of Texas at Dallas.
17

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To estimate the 𝑇 matrix, the standard algorithm for computing PLS components is used, i.e., Nonlinear
Iterative Partial Least Squares (NIPALS) algorithm. It uses all the matrices defined above to estimate 𝑊 andthen compute 𝑇.
The aim is not to predict directly 𝑌 but rather to find the optimal weights of each indicator in SRM. So, the 𝛽coefficient we find in the regressions is not used directly. Instead, the Variable Importance in Projection (VIP)
score is used. It represents the summary of the importance of each indicator in finding the components of the
𝑋 matrix19 during the first step of the PLS regressions. Formally, it is the weighted sum of squares of the PLS
weights (the 𝑊 matrix), which considers the explained variance of each dimension. It is used to select relevant
predictors according to their value. In the academic literature, the VIP score is statistically significant if it
based above a given threshold ranging from 0.8 to 1.20 However, as we do not want to exclude too many
indicators, we use the VIP scores directly to calculate the weights. This approach remains relevant because
VIP scores higher than 0.8 account for more than 80% of SRM indicators. The last 20% are rarely below 0.5.

Palermo, G., P. Piraino, and H.-D. Zucht, 2009, “Performance of PLS regression coefficients in selecting variables for each response ofa multivariate PLS for omics-type data”, Advances and Applications in Bioinformatics and Chemistry: AABC, 2, pp. 57–70.
20
Chong, I.G., and C.H. Jun, 2005, “Performance of some variable selection methods when multicollinearity is present”, Chemometrics andIntelligent Laboratory Systems 78, pp. 103–112.
19

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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 inwhole or in part caused by, resulting from, or relating to any error (negligent or otherwise) or other circumstance involved in procuring, collecting, compiling, interpreting, analysing,editing, transcribing, transmitting, communicating or delivering any such information or data or from 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
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No member of the LSE Group nor their respective directors, officers, employees, partners or licensors provide investment advice and nothing in this document should be taken as
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the advisability of investing in any asset or whether such investment creates any legal or compliance risks for the investor. A decision to invest in any such asset should not be madein reliance on any information herein. Indices cannot be invested in directly. Inclusion of an asset in an index is not a recommendation to buy, sell or hold that asset nor confirmationthat any particular investor may lawfully buy, sell or hold the asset or an index containing the asset. The general information contained in this publication should not be acted upon
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About FTSE Russell
FTSE Russell is a leading global index provider creating and managing awide range of indices, data and analytic solutions to meet client needs acrossasset classes, style and strategies. Covering 98% of the investable market,
FTSE Russell indices offer a true picture of global markets, combined withthe specialist knowledge gained from developing local benchmarks aroundthe world.
FTSE Russell index expertise and products are used extensively byinstitutional and retail investors globally. For over 30 years, leading asset
owners, asset managers, ETF providers and investment banks have chosen
FTSE Russell indices to benchmark their investment performance and create
investment funds, ETFs, structured products and index-based derivatives.
FTSE Russell indices also provide clients with tools for asset allocation,
investment strategy analysis and risk management.
A core set of universal principles guides FTSE Russell index design andmanagement: a transparent rules-based methodology is informed byindependent committees of leading market participants. FTSE Russell isfocused on index innovation and customer partnership applying the highest
industry standards and embracing the IOSCO Principles. FTSE Russell iswholly owned by London Stock Exchange Group.
For more information, visit ftserussell.com.

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