R E S E A R C H A R T I C L E
Do lower environmental, social, and governance (ESG) rated
companies have higher systemic impact? Empirical evidence
from Europe and the United States
Karoline Bax1,2
|
Giovanni Bonaccolto3
|
Sandra Paterlini1
1Department of Economics and Management,
University of Trento, Trento, Italy
2Center for Digital Transformation, TUM
Campus Heilbronn, TUM School of
Management, Technical University of Munich,
Heilbronn, Germany
3School of Economics and Law, “Kore”
University of Enna, Enna, Italy
Correspondence
Karoline Bax, Center for Digitial
Transformation, TUM Campus Heilbronn,
TUM School of Management, Technical
University of Munich, Heilbronn, Germany
Email: karoline.bax@tum.de
Abstract
In recent years, companies have increasingly been characterized by environmental,
social, and governance (ESG) scores, and investors and academics have raised ques-
tions concerning financial performance and investment risks. Now, as the European
Banking Authority has acknowledged that ESG risks can potentially impact the eco-
nomic and financial system, the debate on systemic risk has gained traction. Under-
standing the relationship between ESG merit and systemic risk is of utmost
importance for the stability of the economic and financial system, still, research is lim-
ited. Relying on real-world European and United Stated data, we quantify systemic
risk by means of QL-CoVaR. Empirical analyses of the entire period from 2007 to
2021 show that companies with high ESG scores tend to exhibit low QL-CoVaR
values indicating a positive effect of ESG scores. Such evidence is confirmed by
clustering the individual companies into ESG portfolios and focusing on COVID-19.
Additional insights using the individual pillars are also provided.
K E Y W O R D S
CoVaR, ESG, sustainability, financial stability, systemic risks
1
|
INTRODUCTION
Company characteristics are the key driver in determining perfor-
mance and risk profiles. Besides financial information, non-financial
information has recently gained relevant attention as investors also
look for sustainability in their investment decisions. Companies are
increasingly characterized by their non-financial information, more
specifically by environmental, social, and governance (ESG) scores and
the debate about the impact of ESG information on company perfor-
mance and risk is still open.
Generally, ESG scores are designed by different rating providers
(e.g., Bloomberg and Reuters) and aim to assess the ESG performance
of companies by considering several criteria, measurements, and
quantitative and qualitative methods; see, among others, Bhatta-
charya and Sharma (2019), and Berg and Lange (2020). ESG scores
typically range between 0 and 100. More specifically, assets with
higher ESG scores indicate a more responsible ESG behavior. More-
over, ESG scores are typically associated with a rating class (i.e., A,B,C,
D) using thresholds or quartiles of the ESG score values.
This complementary non-financial information can have the
potential to increase the accuracy of performance forecasts and risk
assessments (Achim & Borlea, 2015). Technically, quantifying these
aspects should help to reach one of the main objectives of the
European Commission's 2018 Action Plan, which is “managing
financial risks stemming from climate change, resource depletion,
environmental degradation, and social issues” (European Banking
Received: 27 June 2022
Revised: 12 November 2022
Accepted: 17 November 2022
DOI: 10.1002/csr.2427
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any
medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2022 The Authors. Corporate Social Responsibility and Environmental Management published by ERP Environment and John Wiley & Sons Ltd.
Corp Soc Responsib Environ Manag. 2022;1–15.
wileyonlinelibrary.com/journal/csr
1
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

Authority, 2021, p. 15). To do so, it is crucial to understand how these
factors translate into financial risks that may affect the whole financial
system
(European
Banking
Authority,
2021).
In
particular,
the
European Banking Authority (EBA) states that ESG risks can material-
ize when ESG factors have a negative impact on financial performance
or solvency (European Banking Authority, 2020). Moreover, according
to a recent report, “ESG risks can also impact the financial system
and economy as a whole, with potential systemic consequences”
(European Banking Authority, 2021, p. 33). This effect could be
prominent in macroeconomic factors (i.e., economic growth, labor pro-
ductivity, or sovereign debt) and hence impact different institutions
through the whole economy (European Banking Authority, 2021).
Thus, higher ESG scores could be connected to more prudent and sus-
tainable actions leading to a reduction in the overall risk. Additionally,
Cerqueti et al. (2021) argued that high ESG-ranked funds could com-
prise an intrinsic property of lowering systemic risk due to less overlap
with other funds, lower contagion, and a long-term investment
approach. Furthermore, companies with higher ESG scores are less
exposed to the risk of future litigation (Cerqueti et al., 2021).
While this is an essential topic for the stability of the financial sys-
tem and the economy as a whole, research is still limited and often
provides ambiguous results. Boubaker et al. (2020) were among the
first to find that better corporate, social, and responsibility (CSR) prac-
tices, measured by all qualitative dimensions (except corporate gover-
nance) of the MSCI ESG index, lead to lower financial distress and
default risk, with positive effects on financial stability and more crisis-
resilient economies. Later, Eratalay and Cortés ´Angel (2022) proposed
a VAR-MGARCH model, performing a principal component analysis
(Billio et al., 2012) to obtain systemic risk indicators. They found that
companies with higher ESG scores contribute 7.3% less to systemic
risk. Focusing on the COVID-19 period and the individual pillars,
Eratalay and Cortés ´Angel (2022) found no significant effects for the
E-Pillar. In contrast, the S- and G-Pillar had a statistically significant
impact (positive and negative, respectively) on systemic risk. Similarly,
building on network analysis, Cerqueti et al. (2021) showed that ESG
funds, defined as interconnected components of a unified system,
may be less vulnerable to systemic shocks and more resilient to
contagion under lower-volatility regimes. Furthermore, active disclo-
sure is a key factor in effectively reducing the systemic risk for
Chinese listed companies (Waner, 2021).
When considering the banking sector, Murè et al. (2021) added
that higher ESG scores are connected to a lower probability of sanc-
tions for Italian banks. Ducassy (2013) argued that lowering environ-
mental costs and risks can aid in promoting financial stability during
crisis periods. Moreover, Chiaramonte et al. (2021) showed that
higher ESG and individual pillar scores are associated with lower
default risk for European banks during times of crisis. These findings
are in line with Aevoae et al. (2022), who used the ΔCoVaR to show
that higher ESG Combined Scores are connected to a reduction of
system-wide distress using a sample of publicly listed banks. In addi-
tion, higher G-Pillar scores imply lower bank interconnectedness,
lower systemic risk, and higher financial stability (Aevoae et al., 2022).
Furthermore, the S-Pillar also seems to play an important role. Indeed
Scholtens and Van't Klooster (2019) argued that higher sustainability
scores could reduce the systemic risk contribution of banks. Similarly,
Lupu et al. (2022) found evidence that the overall ESG score, as well
as the individual pillar score, has a notable impact on the financial sta-
bility of banks in Europe, stressing that this influence is nonlinear.
Additionally, the duration of ESG disclosure also plays a role, as Chiar-
amonte et al. (2021) showed that it can positively affect stability for
European banks. On the other hand, the study of Anginer et al. (2018)
highlighted that shareholder-friendly corporate governance is linked
to higher systemic risk in the US banking sector.
Several approaches have been proposed to estimate systemic risk
in the literature. Among them, the CoVaR and ΔCoVaR introduced by
Adrian and Brunnermeier (2016) are some of the most widely used.
Recently, Bonaccolto, Caporin, and Paterlini (2019) proposed the so-
called QL-CoVaR, which extends the CoVaR by employing an estima-
tion process that captures the state in which the response and the
conditioning variables are jointly in distress. An accurate estimation of
the magnitude of the distress degree in financial connections is of
utmost importance during economic or financial crises when the cor-
relation among companies increases and the risk of contagion can
impact the stability of the whole system (Bonaccolto, Caporin, &
Paterlini, 2019). This is achieved by linking the left tails of the com-
pany's return and systemic return distributions. Thus, the QL-CoVaR
better captures the degree of distress a stressed company exerts on
the market. In contrast, the CoVaR's coefficients reflect the stressed
state of the response variable only. As shown by Bonaccolto, Caporin,
and Paterlini (2019), the QL-CoVaR model provides improvements in
terms of predictive accuracy and is more informative than the CoVaR
during tail events.
In this paper, relying on a large sample of European (EU) and
American (US) companies, we estimate the systemic risk by adopting
the ΔQL-CoVaR and analyze the relationship between ESG merit and
systemic risk, with a particular focus on crisis periods. Empirical
analyses on the entire period from 2007 to 2021 show that A-rated
companies tend to exhibit more limited ΔQL-CoVaR values than lower-
rated companies. We also confirm these findings with a portfolio
analysis, in which we cluster the individual companies into four ESG
indexes during the COVID-19 pandemic, which turns out to be the
most relevant event, in terms of systemic impact, from 2007 to 2021.
The paper is structured as follows. Section 2 introduces the QL-
CoVaR. Section 3 describes the data and the empirical setup. Then,
Section 4 reports the empirical results obtained from the ESG analysis,
while Section 5 focuses on the individual E-, S-, and G-Pillars analyses.
Finally, Section 6 concludes the paper.
2
|
QUANTILE-LOCATED CONDITIONAL
VALUE-AT-RISK
Let yt and xj,t be, respectively, the returns of the economic system and
of company j observed at time t, for t = 1, …, T and j = 1, …, N. We
also define Mt as the vector of a set of control variables observed at
time t. Following Adrian and Brunnermeier (2016), we estimate the
2
BAX ET AL.
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

conditional τ-th and θ-th quantiles of xj,t and yt, denoted as Qτ(xj,t) and
Q jð Þ
θ yt
ð
Þ, respectively, using the following quantile regression models
(Koenker & Bassett, 1978):
Qτ xj,t


¼ α jð Þ
τ þβ jð Þ
τ M0
t1,
ð1Þ
Q jð Þ
θ
yt
ð
Þ ¼ δ jð Þ
θ þλ jð Þ
θ xj,t þγ jð Þ
θ M0
t1,
ð2Þ
where τ,θ  (0, 1).
In the CoVaR framework, θ and τ take low values, typically within
the interval (0,0.05], to focus on left-tail relationships between yt and
xj,t. After estimating the parameters α jð Þ
τ , β jð Þ
τ , δ jð Þ
θ , λ jð Þ
θ and γ jð Þ
θ in Equa-
tions (1) and (2), as well as bQτ xj,τ


¼ bα jð Þ
τ þbβ
jð Þ
τ Mt1
0, Adrian and Brun-
nermeier (2016) computed the Conditional Value-at-Risk (CoVaR) of
the economic system conditional on the VaR of company j as follows:
CoVaR jð Þ
t,θ,τ ¼bδ
jð Þ
θ þbλ
jð Þ
θ bQτ xj,t


þbγ jð Þ
θ M0
t1:
ð3Þ
Setting xj,t ¼ bQτ xj,t


allows us to estimate the VaR of the
financial system conditional on institution i being in distress as
described by Adrian and Brunnermeier (2016). Likewise, the CoVaR of
yt conditional on the median of company j is obtained by replacing τ
with 1∕2:
CoVaR jð Þ
t,θ,1=2 ¼bδ
jð Þ
θ þbλ
jð Þ
θ bQ1=2 xj,t


þbγ jð Þ
θ M0
t1:
ð4Þ
From the difference of the conditional quantiles defined in Equa-
tions (3) and (4), we then compute the ΔCoVaR introduced by Adrian
and Brunnermeier (2016):
ΔCoVaR jð Þ
t,θ,τ ¼ CoVaR jð Þ
t,θ,τ CoVaR jð Þ
t,θ,1=2
¼bλ
jð Þ
θ
bQτ xj,t


 bQ1=2 xj,t


h
i
,
ð5Þ
which quantifies the marginal contribution of company j to the overall
systemic risk. Note that the parameters and coefficients in Equa-
tions (2)–(5) are functions of θ only, neglecting then the role of τ,
which reflects the stressed state of xj,t. In our study, we employ a gen-
eralization of the CoVaR, which builds on the quantile-on-quantile
approach introduced by Sim and Zhou (2015) to measure the effects
that the quantiles of oil price shocks have on the quantiles of the US
stock return. After that, the quantile-on-quantile method was adopted
by Bonaccolto, Caporin, and Panzica (2019) to estimate financial net-
works and introduced into the CoVaR framework by Bonaccolto,
Caporin, and Paterlini (2019), who proposed the so-called Quantile-
Located CoVaR (QL-CoVaR) model. The QL-CoVaR model builds on
an estimation process depending on both θ and τ, capturing then the
state in which yt and xj,t are jointly in distress.
Taking into account the effects of both θ and τ, the model defined
in Equation (2) is rewritten as:
Q jð Þ
θ,τ yt
ð
Þ ¼ δ jð Þ
θ,τ þλ jð Þ
θ,τxj,t þγ jð Þ
θ,τMt1
0:
ð6Þ
Note that the parameters in (6) have both θ and τ as subscripts,
as they depend on the quantile levels of both yt and xj,t, being esti-
mated from the following optimization problem:
arg min
δ jð Þ
θ,τ,λ jð Þ
θ,τ,γ jð Þ
θ,τ
X
T
t¼2
ρθ yt δ jð Þ
θ,τ λ jð Þ
θ,τxj,t γ jð Þ
θ,τMt1
0
h
i
K
bF xj,t


τ
h
 
!
,
ð7Þ
where
ρθ e
ð Þ ¼ e θ 1 e < 0
f
g


is
the
asymmetric
loss
function
characterizing
the
quantile
regression
method
introduced
by
Koenker and Bassett (1978), 1 
f g is an indicator function, which
takes the value of one if the condition in {} is true, and the
value of zero otherwise, whereas K () is a kernel function, with
bandwidth h, that captures the impact of xj,t in the neighborhood
of its τth quantile.
Following Sim and Zhou (2015), we employ a Gaussian kernel K ()
to weight the impact of xj,t in the neighborhood of its τ-th quantile, and
use the following specification of bF xj,t


:
bF xj,t


¼ T1 X
T
p¼1
1 xj,p < xj,t
f
g:
ð8Þ
We then compute the QL-CoVaR at the (θ,τ)-th level as follows:
QL-CoVaR jð Þ
t,θ,τ ¼bδ
jð Þ
θ,τ þbλ
jð Þ
θ,τ bQτ xj,t


þbγ jð Þ
θ,τMt1
0,
ð9Þ
where bQτ xj,t


¼ bα jð Þ
τ þbβ
jð Þ
τ M0
t1 is obtained from Equation (1).
We also estimate the QL-CoVaR model with 1∕2 in place
of τ, and obtain the ΔQL-CoVaR: ΔQL-CoVAR jð Þ
t,θ,τ ¼ QL-CoVaR jð Þ
t,θ,τ
QL-CoVaR jð Þ
t,θ,1=2.
Following
Bonaccolto,
Caporin,
and
Paterlini
(2019), we decompose the ΔQL-CoVaR as follows:
ΔQL-CoVAR jð Þ
t,θ,τ ¼bδ
jð Þ
θ,τ bδ
jð Þ
θ,1=2 þbλ
jð Þ
θ,τ bQτ xj,t


 bQ1=2 xj,t


h
i
þ bλ
jð Þ
θ,τ bλ
jð Þ
θ,1=2


bQ1=2 xj,t


þ bγ jð Þ
θ,τ bγ jð Þ
θ,1=2


Mt1
0:
ð10Þ
We stress here the role of bλ
jð Þ
θ,τ, which quantifies the sensitivity of
yt to xj,t when both are in distress (see Equation 6). As a result, when
keeping
the
other
components
in
Equation
(10)
constant,
ΔQL-CoVAR jð Þ
t,θ,τ would react more readily to the shocks of those com-
panies with greater bλ
jð Þ
θ,τ
values. However, bλ
jð Þ
θ,τ
is multiplied by
bQτ xj,t


 bQ1=2 xj,t


h
i
in Equation (10), and these two components are
estimated from two different models; the ones defined in Equation (6)
and (7), respectively. More specifically,
bQτ xj,t


 bQ1=2 xj,t


h
i
quantifies
the specific idiosyncratic risk of company j, when it moves from its
own median to its own VaR and, therefore, does not depend on yt.
BAX ET AL.
3
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

As a result, risky companies with greater values of
bQτ xj,t


 bQ1=2 xj,t


h
i
would have a moderate or non-relevant systemic impact if bλ
jð Þ
θ,τ
approaches zero.
The component bλ
jð Þ
θ,τ bQτ xj,t


 bQ1=2 xj,t


h
i
given in Equation (10)
resembles the ΔCoVaR specification defined in Equation (5). How-
ever, bλ
jð Þ
θ,τ ≠bλ
jð Þ
θ due to the quantile-located effects.1 In our study, we
estimate the standard errors of the coefficients given in Equation (10)
by employing a bootstrap method (Efron, 1979): the xy-pair approach
of Kocherginsky (2003), which provides accurate results without any
distributional assumption.
3
|
DATA DESCRIPTION AND EMPIRICAL
SET-UP
Firstly, we consider the daily logarithmic return, daily market capitaliza-
tion, yearly environmental, social, and governance (ESG) and individual E-,
S-, and G-Pillar data of 294 EU companies which are the constituents of
the EURO STOXX 600 index and 323 US companies which are the con-
stituents of the Standard & Poor’ (S&P) 500 index whose observations
are continuously available from January 2, 2007 to May 3, 2022. The
return of each of these firms represents the variable xj,t from Equation (1)
to Equation (10).2 We employ the daily return of the S&P 500 index as
the response variable yt when focusing on the US firms, and the daily
return of the EURO STOXX 600 index when considering the EU compa-
nies, spanning the time interval from January 2, 2007 to May 3, 2022.
We extract the time series and ESG data from Refinitiv, the finan-
cial and risk business unit of Thomson Reuters. Their rank-based ESG
scores are based on publicly reported data (Refinitiv, 2021). ESG data
are only sparsely available in the early 2000s from our data provider,
thus we chose to start in 2007, allowing us to take into account differ-
ent states of the economy. More specifically, we are looking at the
2007–2009 sub-prime crisis, the EU sovereign debt crisis beginning in
late 2009, and the more recent COVID-19 pandemic and endemic,
which made fundamental changes to the work life of many employees
and employers (Microsoft, 2022).
Both data sets are similar in size, however, the US data exhibits
stronger average kurtosis as shown in Table 1. Additionally, we find
that the ESG scores of both data sets improve over time as companies
are under pressure to improve their ESG performance and continue to
disclose more information (see Table 2
and Figure 1) (Sahin
et al., 2022).
In this research, we aim to shed light on a possible relationship
between the ESG profile of a given firm and its systemic relevance
considering the EU market and US market separately. For this pur-
pose, we compute the average ESG score (ESG) of each company in
our data set from 2007 to 2022. We then cluster these companies
into four different classes, from A to D, using the thresholds defined
by Refinitiv. Specifically, class A includes those companies with
ESG > 75. The companies of class B have 50 < ESG ≤75, whereas we
require 25 < ESG ≤50 for class C. Finally, class D has the companies
with the lowest average scores: ESG ≤25. We provide the definition
of these classes and the number of assets in Table 3.
In addition to xj,t and yt, that we defined above, ΔQL-CoVAR jð Þ
t,θ,τ
depends on the vector of control variables Mt1, the composition of
which is described below. Starting from the EU market, we considered
the lagged observations of the following variables: (i) the return of the
EURO STOXX index (i.e., the lagged value of the response variable);
(ii) the return of the EURO STOXX 50 Volatility Index (VSTOXX)3;
(iii) the return of the New Composite Indicator of Systemic Stress
(CISS) Index provided by the European Central Bank.4 The additional
variables from (iv) to (ix) are the European Fama–French research fac-
tors: (iv) the excess market return (MKT-RF); (v) Small Minus Big, the
average return on the small stock portfolios minus the average return
on the big stock portfolios (SMB); (vi) High Minus Low, the average
return on the value portfolios minus the average return on the growth
portfolios (HML); (vii) Robust Minus Weak, the average return on the
robust operating profitability portfolios minus the average return on
the weak operating profitability portfolios (RMW); (viii) Conservative
Minus Aggressive, the average return on the conservative investment
portfolios minus the average return on the aggressive investment
portfolios (CMA); and (ix) the risk-free rate (RF).5 From the principal
component analysis on the control variables described above, we find
that the first two components explain 89.61% of their overall variabil-
ity. We then use these two components as entries of the Mt1 vector.
As highlighted by Bonaccolto, Caporin, and Paterlini (2019), this
choice allows us to exploit the near totality of the information con-
veyed by these control variables, with advantages in terms of compu-
tational costs.
TABLE 1
The table reports the
descriptive statistics for the constituents
of the EURO STOXX 600 and the S&P
500, respectively
Data set
j
T
bμ
bσ
d
kurt
d
skew
freq.
EURO STOXX 600
294
4002
0.02
0.04
16.89
0.37
Daily
S&P 500
323
4001
0.06
0.05
19.48
0.36
Daily
Note: Columns 1–8 report the data set, the number of constituents (j), the number of observations (T),
the average annualized mean (bμ), the average annualized standard deviation (bσ), the average kurtosis
(d
kurt), the average skewness ( d
skew) of the asset returns, and the sampling frequency (freq.).
1In our empirical analysis, we found that bλ
jð Þ
θ,τ estimated for the QL-CoVaR is typically greater
than the CoVaR bλ
jð Þ
θ coefficient, highlighting the fact that the co-movement between the
overall system and the individual companies becomes more relevant when capturing their
joint distress. These additional results are available upon request.
2Date of retrieval of the ESG data were April 28, 2022 and September 6, 2022 of the E, S,
and G Pillar data.
3This time series is available at https://www.stoxx.com.
4Further information about the CISS Index can be found online at https://sdw.ecb.europa.eu.
5The data on the variables from (iv) to (ix) were downloaded from the Kenneth R. French
library at https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
4
BAX ET AL.
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

Likewise, when using the US data, we employ the first two com-
ponents of the lagged values of the following variables: (i) the return
of the S&P 500 index (i.e., the lagged value of the response variable);
(ii) the return of the VIX index6; (iii) the change in the Financial Stress
Index (FSI) provided by the Office of Financial Research of the US
Department of the Treasury.7 The additional six variables are the
same Fama–French research factors listed above for the EU data but
are now obtained from the US market.8
In our empirical analysis, we set θ = τ = 0.05, so that we focus on
the left tails of the conditional distributions of yt and xj,t. Finally, we
set the bandwidth value h equal to 0.15. As explained by Bonaccolto,
Caporin, and Paterlini (2019), this is a good compromise between the
bias and the variance of the estimates.
4
|
EMPIRICAL RESULTS FOR ESG
INDEXES
In this section, we analyze the results obtained from the estimation of
ΔQL-CoVAR jð Þ
t,θ,τ defined in Equation (10). ΔQL-CoVAR jð Þ
t,θ,τ depends on
different components. Among them, we highlight the central role of
bλ
jð Þ
θ,τ, which quantifies the sensitivity of yt to the stressed state of xj,t.
We first analyze in Figure 2 the boxplots of bλ
jð Þ
θ,τ estimated for each
firm included in our data set. We cluster the EU (Figure 2a) and US
(Figure 2b) companies into classes A, B, C, and D, that we build
according to their ESG values (see Table 3). Moreover, for each box-
plot, we contrast bλ
jð Þ
θ,τ with bλ
jð Þ
θ,1=2 to assess the quantile-located effects.
In general, bλ
jð Þ
θ,τ and bλ
jð Þ
θ,1=2 take positive values (see Figure 2), highlight-
ing the fact that there is a positive relationship between the return of
a given company and the quantile of the overall system. Moreover,
the greater bλ
jð Þ
θ,τ and bλ
jð Þ
θ,1=2 are, the greater is the response of yt in its
θ-th quantile. Thus, we would observe a larger negative impact on
systemic risk and financial stability. Additionally, it is notable that the
bλ
jð Þ
θ,τ tend to be larger for the US sample compared with the EU sample
indicating higher sensitivity.
For each class, bλ
jð Þ
θ,τ is greater than bλ
jð Þ
θ,1=2, highlighting the fact that
the relationships between the overall system and each firm become
more relevant when they are both in distress. Focusing on the EU
TABLE 2
The table reports the
descriptive statistics for the ESG data of
the constituents of the EURO STOXX
600 and the S&P 500, respectively
Data set
j
T
bμ
bσ
d
kurt
d
skew
freq.
EURO STOXX 600
294
16
62.35
76.53
3.09
0.54
yearly
S&P 500
323
16
54.84
92.28
2.92
0.42
yearly
Note: Columns 1–8 report the data set, the number of constituents (j), the number of observations (T),
the average mean (bμ), the average standard deviation (bσ), the average kurtosis (d
kurt), the average
skewness ( d
skew) of the asset returns, and the sampling frequency (freq.).
FIGURE 1
Boxplots of ESG scores over time (y-axis. We report the boxplots for the individual pillar scores in Appendix A. (a) EU companies
(b) US companies
TABLE 3
Composition of ESG classes A, B, C, and D
ESG class
Condition
EU companies
US companies
A
(ESG > 75)
71
33
B
(50 < ESG ≤75)
158
165
C
(25 < ESG ≤50)
59
117
D
(ESG ≤25)
6
8
Note: From left to right, this table reports the definition of classes A, B, C,
and D, the condition required for each class, and the number of EU and
US companies within each of them.
6This time series has been downloaded from Thomson Reuters.
7Further information about the FSI can be found online at https:www.financialresearch.gov.
8These time series were downloaded from https://mba.tuck.dartmouth.edu/pages/faculty/
ken.french/data_library.html.
BAX ET AL.
5
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

companies (Figure 2a), bλ
jð Þ
θ,τ takes, on average, greater values for
class D, followed by classes A, B, and C, with medians of 0.301, 0.282,
0.256, and 0.219, respectively. The third quartile of bλ
jð Þ
θ,τ follows the
same order across the four classes: 0.389 for class D, 0.322 for
class A, 0.299 for class B, and 0.280 for class C. In contrast, the
median of bλ
jð Þ
θ,τ decreases from class A to class D in Figure 2b, where
we focus on the US market: 0.356 for class A, 0.322 for class B, 0.296
for class C, and 0.279 for class D. However, we again find a U-shape
behavior in the third quartile of bλ
jð Þ
θ,τ, which takes higher values for the
extreme A and D classes. Thus, as indicated by the greater bλ
jð Þ
θ,τ, the
economic and financial system is more sensitive to shocks of compa-
nies belonging to the extreme ESG classes A and D. Classes A and D
might be attracting more attention from investors due to the
positive and negative screening investment policies, respectively
(Amel-Zadeh & Serafeim, 2018). In fact, best in class and worst in
class are two investment criteria commonly used to decide which
assets to focus on.
Thus, the boxplots in Figure 2 suggest substantial preliminary evi-
dence: the VaR of both the EU and US markets tends to be more sen-
sitive to the stressed state of companies that stand out from the
crowd for more extreme ESG scores (such as the ones belonging to
the A and D classes). This result is more notable for the D-rated com-
panies in the EU market. Moreover, bλ
jð Þ
θ,τ is almost always statistically
significant at a 5% level, with a p-value greater than 0.05 for only
three EU companies belonging to class C.
We now focus on the overall ΔQL-CoVAR jð Þ
t,θ,τ time series to ana-
lyze the systemic impact of the conditioning EU and US companies.
We remind the reader that more negative values of ΔQL-CoVAR jð Þ
t,θ,τ
point out a greater systemic impact. Again, we evaluate the effects of
the ESG classification. For this purpose, we compute, for each day
t (with t = 1, …, T), the mean of ΔQL-CoVAR jð Þ
t,θ,τ estimated for those
companies belonging to class k, denoted as ΔQL-CoVAR
k
ð Þ
t,θ,τ, with
k  A, B, C, D
f
g. As a result, we obtain four-time series displayed in
Figure 3. First, we highlight the strong impact of important tail events,
such as the 2007–2009 sub-prime crisis and the EU sovereign debt
crisis beginning in late 2009. However, the greatest impact is caused
by the outbreak of the more recent COVID-19 pandemic. Specifically,
the most extreme (negative) peak in the EU market was observed on
March 13, 2020. The day before, on March 12, 2020, the European
Central Bank (ECB) president Christine Lagarde had stated that “we
are not here to close spreads, this is not the function or the mission of
the ECB.” This statement triggered the fears of investors, who inter-
preted it as refuting the “whatever it takes” policy advocated by the
previous ECB president Mario Draghi (Moessner & de Haan, 2022). In
contrast, the most extreme peak in the US market was detected on
March 17, 2020, when Wall Street witnessed one of its worst days in
history, given the threat of a possible global recession in the
near term.
Second, we highlight (with the red points in Figure 3) the impact
of the D-rated companies for the EU and US markets. Starting from
the EU market (see Figure 3a), on average, the D-rated companies
FIGURE 2
Boxplots of bλ
jð Þ
θ,τ (STRESSED, cyan color) and bλ
jð Þ
θ,1=2 (MEDIAN, red color) estimated for the EU and US companies clustered by ESG
classes. (a) EU companies (b) US companies
FIGURE 3
Average of ΔQL-CoVAR jð Þ
t,θ,τ for the EU and US
companies clustered by ESG classes: A (blue line), B (black line), C
(green line), and D (red line and red points) (a) EU companies (b) US
companies
6
BAX ET AL.
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

continuously have the greatest systemic impact from January 2, 2017,
to May 3, 2022. Indeed, ΔQL-CoVAR
D
ð Þ
t,θ,τ < ΔQL-CoVAR
k
ð Þ
t,θ,τ for t = 1,
…, T and k ≠D. The A-rated companies continuously record, on aver-
age, the second greatest systemic impact for t = 1, …, T. Therefore,
the companies with extreme ESG classes (i.e., classes D and A) turn
out to be, on average, more systemically relevant, consistent with the
U-shape behavior of the boxplots given in Figure 2a. As for the US
market (see Figure 3b), the D-rated companies record, on average, the
greatest systemic impact on 2819 out of 4000 days. The C-rated com-
panies have, on average, the greatest systemic impact on the remain-
ing 1180 days. The latter result might appear quite surprising, given
the moderate values of bλ
jð Þ
θ,τ for class C in Figure 2b. However, in addi-
tion to bλ
jð Þ
θ,τ, ΔQL-CoVAR jð Þ
t,θ,τ depends on other relevant components.
For instance, we refer to bQτ xj,t


 bQ1=2 xj,t


: the risk of a given com-
pany when it moves from its median to its extreme τth quantile,
regardless of the dynamics of yt. Similar to the previous exercise, for
each day t, we compute the mean of bQτ xj,t


 bQ1=2 xj,t


estimated for
those companies belonging to class k, with k ¼ A, B, C, D
f
g.9
The full-sample analysis discussed so far provides important find-
ings. However, it relies on time-invariant coefficients estimated from
a relatively long interval spanning 16 years, characterized by a contin-
uous alternation of stable and turbulent phases of the financial
markets. Therefore, the effects of an important tail event would be
smoothed by other events (possibly different in nature) along this long
time interval and would not be properly captured using these full-
sample coefficients. For this reason, we enrich our study with an addi-
tional exercise based on a shorter time interval. Among the different
and relevant events we can consider from 2007 to 2022, we focus on
the COVID-19 pandemic, as it has the greatest impact in terms of
ΔQL-CoVAR jð Þ
t,θ,τ on both the EU and US markets. Specifically, we
re-estimate the QL-CoVaR model using the data from March 3, 2020,
to July 31, 2020 and evaluate the contribution of each ESG class by
computing the ΔQL-CoVAR
k
ð Þ
t,θ,τ
time series, that we display in
Figure 4. In contrast to the previous full-sample analysis, where we
clustered the EU and US companies of our data set according to ESG,
we now directly use the ESG scores of the year 2020 to be consistent
with the sub-sample from which we obtain the new estimates. The
systemic impact of class D in Figure 4 is clearer compared with
Figure 3. Interestingly, the D-rated companies have, on average, the
greatest impact on only 32 out of 109 days in Figure 4a. In contrast,
the A-rated companies provide the lowest ΔQL-CoVAR jð Þ
t,θ,τ on 71 days.
As a result, it might seem that the latter have a greater systemic rele-
vance during the time interval March 3, 2020–July 31, 2020. How-
ever, if we focus on the most relevant peaks, as well as on other
bearish phases, class D has the greatest impact, with relevant values
of the difference ΔQL-CoVAR
D
ð Þ
t,θ,τ ΔQL-CoVAR
k
ð Þ
t,θ,τ
(with k ≠D).
Moving to the US data, the D-rated companies have, on average, the
greatest systemic impact on 89 out of 109 days.
So far, we have evaluated the link between ESG and systemic rel-
evance by estimating the QL-CoVaR model for each company in our
data set. In a second step, we aggregated the resulting estimates by
ESG class. As a robustness analysis, we can also proceed differently,
building on portfolio analysis. In a first step, we aggregate the individ-
ual companies into four indexes, according to their ESG scores
observed in the year 2020, for both the EU and US markets. For
instance, the constituents of Index A are the companies belonging to
class A (i.e., the ones with ESG > 75). The return of Index A is com-
puted as the weighted mean of the returns yielded by its constituents.
The weight of each company is updated with a daily frequency and is
calculated as its market capitalization observed on day t divided by
the sum of the market capitalization of the other A-rated companies
calculated on day t, for each day from March 3, 2020, to July
31, 2020. Likewise, we obtain the returns of Indexes B, C, and D. In a
second step, we then estimate ΔQL-CoVAR k
ð Þ
t,θ,τ: the ΔQL-CoVaR that
we obtain by replacing xj,t (i.e., the return of company j with the return
of Index k, with k  A, B, C, D
f
g. We display the results in Figure 5.
We see that Index D has the greatest systemic impact during the out-
break of the COVID-19 pandemic in both the EU and US markets. It is
interesting to see that the mean of ΔQL-CoVAR D
ð Þ
t,θ,τ in the bottom-
right panel of Figure 5a (depicted with the plotted-red line) is lower
than the mean of ΔQL-CoVAR B
ð Þ
t,θ,τ and ΔQL-CoVAR C
ð Þ
t,θ,τ in the same fig-
ure. However, ΔQL-CoVAR D
ð Þ
t,θ,τ reaches the minimum value of 0.065
on March 13, 2020. ΔQL-CoVAR C
ð Þ
t,θ,τ also exhibits a relevant peak, but
of a more moderate value of 0.052 on the same day. In contrast,
ΔQL-CoVAR B
ð Þ
t,θ,τ does not show any particular extreme value on March
FIGURE 4
Average of ΔQL-CoVAR jð Þ
t,θ,τ for the EU and US
companies clustered by ESG classes: A (blue line), B (black line), C
(green line), and D (red line and red points). The underlying QL-CoVaR
model is estimated using the data observed from March 3, 2020, to
July 31, 2020 (a) EU companies (b) US companies
9Similar results are obtained using the median instead of the mean as we observe a change of
category for less than 12% of the companies.
BAX ET AL.
7
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

13, 2020. ΔQL-CoVAR A
ð Þ
t,θ,τ continuously has a stable trend from March
3, 2020 to July 31, 2020 in the top-left panel of Figure 5a. Moving to
the US market, ΔQL-CoVAR A
ð Þ
t,θ,τ and ΔQL-CoVAR C
ð Þ
t,θ,τ have a similar
behavior, whereas ΔQL-CoVAR B
ð Þ
t,θ,τ is more stable (see Figure 5b).
5
|
RESULTS FOR INDIVIDUAL E-, S-, AND
G-PILLARS
So far, we have analyzed the systemic impact of the EU and US com-
panies in our data set clustered by ESG scores. The ESG score of a
given company is computed from three different components: the E-,
S-, and G-Pillars. Hence, it is interesting to further develop our
analysis by disentangling these three different pillars. Similar to
Figure 1, we also find an increase in individual E-, S-, and G-Pillar
scores throughout time as presented in Figure A1. Additionally, we
point out that about a third to a half of the companies tend to change
classes (A, B, C, or D) with a tendency toward higher levels (for
instance, one asset might be in ESG class C and equally in individual
E-, S-, or G-Pillar class B), therefore, the results might differ when
using the individual pillar scores.
Madison and Schiehll (2021) showed that the individual pillars
might have a different sensitivity to materiality issues. Moreover,
Lupu et al. (2022) found that the impact of the individual pillars on
systemic risk varies to a certain degree. Finally, according to the
European Banking Authority (2021), especially the E-Pillar could
FIGURE 5
Trend of ΔQL-CoVAR k
ð Þ
t,θ,τ, with k  {A,B,C,D} clustered by ESGscore. The underlying QL-CoVaR model is estimated using the data
observed from March 3, 2020 to July 31, 2020 (a) EU companies (b) US companies
8
BAX ET AL.
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

significantly affect the overall financial system due to the scale and
complexity of the environmental risks. It is worth taking a closer look
at this additional analysis, the results of which are reported and dis-
cussed below.
Following the same order adopted in Section 4, we start from the
analysis of the bλ
jð Þ
θ,τ coefficients. Again, we cluster these coefficients
according to the average scores of the individual EU and US compa-
nies, which are now computed for the three different pillars, denoted
as E-Pillar, S-Pillar, and G-Pillar, respectively. We display the results in
Figure 6. Similar to the estimates depicted in Figure 2, bλ
jð Þ
θ,τ tends to be
greater than bλ
jð Þ
θ,1=2. Therefore, we again have evidence that the rela-
tionships between the overall system and the conditioning companies
become more relevant when both are in distress. The U-shaped
behavior previously found in Figure 2 is again present when using the
G-Pillar on the EU data in Figure 6e. In contrast, in the other cases,
the median of bλ
jð Þ
θ,τ tends to decrease from higher to lower classes. Let
us remember that the bλ
jð Þ
θ,τ coefficient quantifies the sensitivity of the
ΔQL-CoVaR to
bQτ xj,t


 bQ1=2 xj,t


; that is, the specific risk of
company j when it moves from its median to its stressed state (see
Equation 10). As a result, on average, the systemic risk would react
more readily to the idiosyncratic risks or shocks of higher E-, S-, and
G-Pillar classes for a given value of bQτ xj,t


 bQ1=2 xj,t


. Nevertheless,
as discussed in Section 4, moderate values of bλ
jð Þ
θ,τ could be offset by
large values of bQτ xj,t


 bQ1=2 xj,t


in determining the magnitude of
the overall ΔQL-CoVaR measure. Focusing on the E-Pillar, we find
larger variability in the bλ
jð Þ
θ,τ values of D-rated EU companies and A-
rated US companies. Moving to the S-Pillar, D-rated companies show
the lowest variability. It is also interesting to observe that the variabil-
ity in the bλ
jð Þ
θ,τ values of both the EU and US companies is similar across
the G-Pillar classes; see Figure 6e,f.
Similar to Figure 3, Figure 7 shows the time series of the
cross-sectional mean of ΔQL-CoVAR jð Þ
t,θ,τ estimated for the EU and US
companies. Using the individual E-, S-, and G-Pillars still allows us to
identify the impact of important tail events, such as the sub-prime
crisis, the EU sovereign debt crisis, and the COVID-19 outbreak. In
Section 4, we saw that, on average, the D-rated companies have the
FIGURE 6
Boxplots of bλ
jð Þ
θ,τ (STRESSED, cyan color) and bλ
jð Þ
θ,1=2 (MEDIAN, red color) estimated for the EU and US companies clustered by the
individual pillar classes(a) EU companies—clustered by E-Pillar classes (b) US companies—clustered by E-Pillar classes(c) EU companies clustered
by S-Pillar classes (d) US companies clustered by S-Pillar classes (e) EU companies clustered by G-Pillar classes (f) US companies clustered by
G-Pillar classes
BAX ET AL.
9
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

greatest impact, followed by the A-rated companies (see Figure 3).
Now, the D-rated companies are the most critical contributors to
systemic risk in the US market when employing the S- and G-Pillars
(Figure 7d,f). However, the A-rated companies have a lower
relevance in Figure 7d,f. As for the EU market, the systemic rele-
vance of the A- and D-rated companies is evident when adopting
the E- and S-Pillars, mainly during the tail events mentioned above
(Figure 7a,c).
The results discussed above build on the estimates obtained
from the full-sample data. Now, we present the results based on the
estimates derived from the COVID-19 period. We find that, during
the COVID-19 pandemic, the results obtained from the overall ESG
scores are similar to those using the E-, S-, and G-Pillar scores on the
EU data, and for the S-Pillar score on the US data as presented in
Figure 8. However, we find notable differences in the US data when
clustering the companies according to the E- and G-Pillar scores
(Figure 8b,f). In Figure 8b, in which we focus on the E-Pillar score,
C-rated companies show the highest systemic impact during the
COVID-19 pandemic. In contrast, when considering the G-Pillar
(Figure 8f), companies in the highest (A) rating class show the highest
systemic impact. We highlight the fact that this class includes the
companies that are generally top performers on the topics of Man-
agement, Shareholders, and CSR.10 Thus, better governance does
not necessarily imply less systemic risk. It is evident that the
COVID-19 pandemic has impacted employees and employers and
thus affected Management and CSR. Employees have reconsidered
how they define the role of work in their lives and have raised their
expectations toward their employer (Microsoft, 2022). These include
the request for more flexibility and well-being. Additionally, 43% of
the employees, especially Gen Z and millennials, are significantly
more likely to change employers this year (Microsoft, 2022). As com-
panies are already competing in the war for talent, possibly the
threat of top performers leaving to work at a different company is
more prominent in already responsible (i.e., high G-Pillar score)
FIGURE 7
Average of ΔQL-CoVAR jð Þ
t,θ,τ for the EU and US companies clustered by individual pillar classes: A (blue line), B (black line), C (green
line), and D (red line and red points). (a) EU companies—clustered by E-Pillar classes. (b) US companies—clustered by E-Pillar classes. (c) EU
companies clustered by S-Pillar classes. (d) US companies clustered by S-Pillar classes. (e) EU companies clustered by G-Pillar classes. (f) US
companies clustered by G-Pillar classes
10The G-Pillar score includes these three main categories Refinitiv (2021).
10
BAX ET AL.
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

companies. Companies in lower categories might not be as severely
affected as they are not competing for top performers in the first
place.11
As a last analysis, we replace company j with the return of Index
k, with k  A, B, C, D
f
g. Now, in place of the mean ESG score, we use
the mean of the individual pillar scores. Thus, for each pillar analysis,
we have four indexes. Index A has low variability and impact in
Figure 5. Now, for the E-Pillar (Figure 9b, top-left) on US data and the
G-Pillar (Figure 9e,f, top-left) on EU and US data, we find larger vari-
ability for higher-rated indexes. It is also notable that the US Index B
shows negligible variability and systemic impact when adopting the
G-Pillar scores (Figure 9f, top-right). The fact that Index A shows a
high impact could result from the increased attention and possibly the
increased expectations and change in the workforce, as discussed
previously. Similar to the analysis based on the overall ESG scores, we
find, for all individual pillars in the EU market and the E- and S-Pillars
in the US market, large variability and systemic impact for the Index
D. Finally, we highlight the relevant peak observed for Index D built
from the S-Pillar scores in March 2020. This highlights the social
effects of the lockdown due to the outbreak of the COVID-19
pandemic, which were more severe for those companies characterized
by low S-Pillar scores.
Overall, when looking at the individual pillars and the ESG score,
we find a notable difference in the full sample as well as during the
COVID-19 pandemic. Additionally, we find evidence of differences
between the EU and US markets. In the EU market, especially the S-
and G-Pillar show higher volatility compared with the ESG score for
Index A during the COVID-19 pandemic. In contrast, we find some
notable differences between the pillar and overall ESG scores in the
US, especially when looking at the E- and G-Pillars during the COVID-
19 pandemic.
FIGURE 8
Average of ΔQL-CoVAR jð Þ
t,θ,τ for the EU and US companies clustered by the individual pillar classes: A (blue line), B (black line), C
(green line), and D (red line and red points). The underlying QL-CoVaR model is estimated using the data observed from March 3, 2020, to July
31, 2020 (a) EU companies—clustered by E-Pillar classes (b) US companies—clustered by E-Pillar classes (c) EU companies clustered by S-Pillar
classes (d) US companies clustered by S-Pillar classes (e) EU companies clustered by G-Pillar classes(f) US companies clustered by G-Pillar classes
11While aspects of employee relations are attributed to the S-Pillar (i.e., working conditions
or career development), the G-Pillar also covers some aspects related to employees
(i.e., compensation, committee structures, takeover defense.
BAX ET AL.
11
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

6
|
CONCLUSION
The aim of this research is to analyze the relationship between ESG
scores and systemic risk, the importance of which has been stressed
recently by the EBA and has gained further traction. We do so by esti-
mating the QL-CoVaR proposed by Bonaccolto, Caporin, and Paterlini
(2019). By relying on a large sample of EU and US companies over a
long time period, we are able to compare different economic states
within two different markets. We highlight here the main results. By
contrasting bλ
jð Þ
θ,τ with bλ
jð Þ
θ,1=2, we found that the relationships between
the overall system and the conditioning companies become more rele-
vant when focusing on a state in which they both are in distress. This
evidence is also supported by the comparison of bλ
jð Þ
θ,τ with bλ
jð Þ
θ defined
in Equation (5) for the CoVaR model (Adrian & Brunnermeier, 2016),
with bλ
jð Þ
θ,τ >bλ
jð Þ
θ .12 On the one hand, bλ
jð Þ
θ,τ tends to follow a U-shape
behavior across the ESG classes, suggesting greater sensitivity of the
VaR of the system to the stressed state of those companies belonging
to the extreme A and D classes. Due to investment policies of best
and worst in class, assets belonging to class A and D tend to receive
more attention from the market and this could explain their greater
sensitivities. Still, it could be reasonable to expect that class A assets
could be more resilient and have less systemic impact due also to the
overall
evaluation
they
record
with
respect
to
non-financial
FIGURE 9
Trend of ΔQL-CoVAR k
ð Þ
t,θ,τ, with k  {A,B,C,D} clustered by individual pillar. The underlying QL-CoVaR model is estimated using the
data observed from March 3, 2020, to July 31, 2020 (a) EU companies—clustered by E-Pillar classes (b) US companies—clustered by E-Pillar
classes (c) EU companies clustered by S-Pillar classes (d) US companies clustered by S-Pillar classes (e) EU companies clustered by G-Pillar classes
(f) US companies clustered by G-Pillar classes
12For the sake of space, we do not report here the estimates obtained from the estimation of
the CoVaR model (Adrian & Brunnermeier, 2016). However, these estimates are available
upon request.
12
BAX ET AL.
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

information, as captured by the ESG scores. In fact, the analysis of the
overall ΔQL-CoVaR measure points out a greater systemic impact of
the D-rated companies, due to their greater idiosyncratic risk. In con-
trast, the A-rated companies exhibit a lower idiosyncratic risk, leading
to less relevant systemic effects. Therefore, on the one hand, compa-
nies with greater ESG scores are more attractive for rational investors
and risk managers, who seek to reduce the risk of their financial port-
folios within a more stable and resilient financial system. On the other
hand, they offer important benefits for the entire society, stimulating
the growth of sustainable and environmentally-friendly investments.
It is reasonable to expect that companies with higher ESG scores
should help to foster the stability of the economic and financial sys-
tem, as they are more resilient and can absorb systemic shocks better.
Thus, they contribute to the health of the economic and financial sys-
tem. On the other hand, the fact that they are affected by best in class
investment policies might moderate such result as they are subject to
investors' attention.
Still, this evidence is clearer when focusing on the sub-sample
characterized by the outbreak of the COVID-19 pandemic. This result
is also confirmed, when clustering the individual firms into ESG
indexes and estimating the systemic impact of the resulting portfolios.
We, however, need to point out that results are not always consistent
when considering the single pillars, as they focus on specific aspects
of companies. For example, we find opposite evidence when consider-
ing the G-Pillar score for the US data, potentially implying that better
governance results in higher systemic risk impact. Further analysis to
disentangle further the impact of the single pillars rating is then
needed to provide further insights on which company characteristics
might impact the most its systemic relevance. Considering for exam-
ple also the 10 categories on which the E, S, and G scores are com-
puted (see Refinitiv, 2021) could allow to better detect which are the
key company characteristics to monitor to develop even more reliable
risk mitigation and management tools.
In our study, we draw some relevant findings for policymakers.
The key message is that improvements in terms of ESG perfor-
mance might lead to lower risk and more stable markets, while
instead, companies in the lower ESG classes might contribute the
most to systemic risk. Therefore, it is important to support disclo-
sure policies of non-financial information, as ESG class D stocks
typically are characterized by a lack of infrastructure and missing
information (see Sahin et al., 2022) as well other policies, such as
tax incentives, which stimulate companies to increase their efforts
and investments in ESG-related activities, this resulting in improved
ESG scores. By doing so, information on company characteristics,
both financial and non-financial, would increase, providing further
insights and knowledge, which we expect to lead to improved over-
all economic and financial stability, making the system less vulnera-
ble even in times of turmoil. By disentangling the effect of the
three pillars (E, S, and G) individually or even of the underlying pillar
categories, we could also better point out the relevant company
dimensions to be monitored and focus on the main drivers to
develop effective risk mitigation policies. High on the agenda is
investigating possible spillovers and interactions of the different
pillars and of the different companies that could “simultaneously
disrupt multiple parts of the financial system” as stressed by the
European Banking Authority (2021, p. 33). Further analysis could
include considering other sustainability measures and industry-
focused analysis. Additionally, it could be interesting to consider
the effect of different regulations set in place, that is, the Paris
Agreement in 2015, and possible causal relationships. Research is
still at an early stage and further investigations are needed, also
considering alternative data providers and modeling tools for cap-
turing systemic risk.
ACKNOWLEDGMENTS
We are grateful to two anonymous referees and the editor for their
feedback and comments that help us improving the paper. In addition,
we are grateful for the suggestions of conference participants, espe-
cially at the A.M.A.S.E.S 2022 and the 13th CEQURA 2022. Open
Access funding enabled and organized by Projekt DEAL.
ORCID
Karoline Bax
https://orcid.org/0000-0001-8368-0969
Giovanni Bonaccolto
https://orcid.org/0000-0002-8083-5993
Sandra Paterlini
https://orcid.org/0000-0003-4269-4496
REFERENCES
Achim, M. V., & Borlea, S. N. (2015). Developing of esg score to assess the
non-financial performances in romanian companies. Procedia Econom-
ics and Finance, 32, 1209–1224. https://doi.org/10.1016/S2212-5671
(15)01499-9
Adrian, T., & Brunnermeier, M. K. (2016). Covar. American Economic
Review, 106, 1705–1741.
Aevoae, G. M., Andries, A. M., Ongena, S., & Sprincean, N. (2022). Esg and
systemic risk. Swiss Finance Institute Research Paper.
Amel-Zadeh, A., & Serafeim, G. (2018). Why and how investors use esg
information: Evidence from a global survey. Financial Analysts Journal,
74, 87–103.
Anginer, D., Demirguc-Kunt, A., Huizinga, H., & Ma, K. (2018). Corporate
governance of banks and financial stability. Journal of Financial Eco-
nomics, 130, 327–346.
Berg, E., & Lange, K. W. (2020). Enhancing ESG-risk modelling-a study of
the dependence structure of sustainable investing. Master's thesis.
KTH Royal Institute of Technology http://hdl.handle.net/2445/
169669
Bhattacharya, S., & Sharma, D. (2019). Do environment, social and gover-
nance performance impact credit ratings: A study from India. Interna-
tional Journal of Ethics and Systems, 35, 466–484.
Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric
measures of connectedness and systemic risk in the finance and insur-
ance sectors. Journal of Financial Economics, 104, 535–559.
Bonaccolto, G., Caporin, M., & Panzica, R. (2019). Estimation and model-
based combination of causality networks among large us banks and
insurance companies. Journal of Empirical Finance, 54, 1–21.
Bonaccolto, G., Caporin, M., & Paterlini, S. (2019). Decomposing and back-
testing a flexible specification for CoVaR. Journal of Banking & Finance,
108, 105659.
Boubaker, S., Cellier, A., Manita, R., & Saeed, A. (2020). Does corporate
social responsibility reduce financial distress risk? Economic Modelling,
91, 835–851.
Cerqueti, R., Ciciretti, R., Dalò, A., & Nicolosi, M. (2021). ESG investing: A
chance to reduce systemic risk. Journal of Financial Stability, 54,
100887.
BAX ET AL.
13
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

Chiaramonte,
L.,
Dreassi,
A.,
Girardone,
C.,
&
Piserà,
S.
(2021).
Do ESG strategies enhance bank stability during financial turmoil?
Evidence from europe. The European Journal of Finance, 28, 1173–
1211.
Ducassy, I. (2013). Does corporate social responsibility pay off in times of
crisis? An alternate perspective on the relationship between financial
and corporate social performance. Corporate Social Responsibility and
Environmental Management, 20, 157–167.
Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The
Annals of Statistics, 7, 1–26.
Eratalay, M. H., & Cortés ´Angel, A. P. (2022). The impact of ESG ratings on
the systemic risk of European blue-chip firms. Journal of Risk and
Financial Management, 15, 153.
European Banking Authority. (2020). EBA Discussion paper on manage-
ment and supervision of ESG risks for credit institutions and
investment firms. Technical Report October. https://www.eba.europa.
eu/sites/default/documents/files/documen\_library/Publications/
Discussions/2021/Discussion\%20Paper\%20on\%20management\%
20and\%20supervision\%20of\%20ESG\%20risks\%20for\%20credit
\%20institutions\%20and\%20investment\%20firms/935496/2020-
11-02\%20\%20ESG\%20Discussion\%20Paper.pdf
European
Banking
Authority.
(2021).
EBA
Report
on
management
and supervision of ESG risks for credit institutions and investment
firms. Technical Report 18. https://www.eba.europa.eu/sites/default/
documents/files/document\_library/Publications/Reports/2021/
1015656/EBA\%20Report\%20on\%20ESG\%20risks\%
20management\%20and\%20supervision.pdf
Kocherginsky, M. (2003). Extensions of the Markov chain marginal bootstrap
[PhD Thesis]. University of Illinois Urbana-Champaign.
Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46,
33–50.
Lupu, I., Hurduzeu, G., & Lupu, R. (2022). How is the ESG reflected in
European financial stability? Sustainability, 14, 10287.
Madison, N., & Schiehll, E. (2021). The effect of financial materiality on
ESG performance assessment. Sustainability, 13, 3652.
Microsoft. (2022). Work trend index 2022. https://www.microsoft.com/
en-us/worklab/work-trend-index/great-expectations-making-hybrid-
work-work
Moessner, R., & de Haan, J. (2022). Effects of monetary policy announce-
ments on term premia in the euro area during the covid-19 pandemic.
Finance Research Letters, 44, 102055.
Murè, P., Spallone, M., Mango, F., Marzioni, S., & Bittucci, L. (2021). ESG
and reputation: The case of sanctioned Italian banks. Corporate Social
Responsibility and Environmental Management, 28, 265–277.
Refinitiv. (2021). Environmental, Social and Governance (ESG) scores from
Refinitiv. https://www.refinitiv.com/content/dam/marketing/en\_us/
documents/methodology/refinitivesgscoresmethodology.pdf
Sahin, Ö., Bax, K., Paterlini, S., & Czado, C. (2022). Environmental, social,
governance scores and the missing pillar - why does missing informa-
tion matter. Corporate Social Responsibility and Environmental Manage-
ment, 29, 1782–1798. https://doi.org/10.1002/csr.2326
Scholtens, B., & Van't Klooster, S. (2019). Sustainability and bank risk. Pal-
grave Communications, 5, 1–8.
Sim, N., & Zhou, H. (2015). Oil prices, us stock return, and the dependence
between their quantiles. Journal of Banking & Finance, 55, 1–8.
Waner, Z. (2021). Research on the relationship between social responsibil-
ity and systemic risk. Advances in Economics, Business and Management
Research, 182, 278–281.
How to cite this article: Bax, K., Bonaccolto, G., & Paterlini, S.
(2022). Do lower environmental, social, and governance (ESG)
rated companies have higher systemic impact? Empirical
evidence from Europe and the United States. Corporate Social
Responsibility and Environmental Management, 1–15. https://
doi.org/10.1002/csr.2427
14
BAX ET AL.
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License

APPENDIX A
FIGURE A1
Distribution of individual pillar scores.
BAX ET AL.
15
 15353966, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/csr.2427 by CochraneItalia, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License