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SPRING
2021
Institute
RESEARCH INSIGHTS
EDHEC
EDHEC Research Insights 1
Contents
Climate change risk and
Introduction
corporate bonds������������������������ 2
Gianfranco Gianfrate
A holistic goals-based investing
I
am delighted to introduce the latest EDHEC-Risk Institute special issue
framework for analysing efficient of the EDHEC Research Insights supplement to Investment & Pensions
Europe, which aims to provide European institutional investors with an
retirement investment decisions
academic research perspective on the most relevant issues in the industry
in the presence of long-term care today.
risk���������������������������������������������� 5 We first look at the relationship between exposure to climate change and
a firm’s credit risk. Companies with a high carbon footprint are more likely
Jean-Michel Maeso, Lionel Martellini, to default, hence the exposure to climate risks affects the creditworthiness
Vincent Milhau, Anil Suri, Nevenka of loans and bonds issued by corporates.
In research supported by Bank of America, we then present a goals-based
Vrdoljak
investing framework for analysing retirement investment decisions in the
Measuring and managing ESG presence of long-term care risk. This is a flexible framework developed to
provide personalised advice on retirement investment decisions in the
risks in sovereign bond presence of life event risk.
portfolios��������������������������������� 11 As part of the Amundi ETF, Indexing and Smart Beta Investment
Lou-Salomé Vallée Strategies research chair at EDHEC-Risk Institute, we explore the impact of
ESG factors on the risk and return of sovereign bonds from an investor
From climate change to asset perspective, in particular investigating how to measure and manage ESG
risks in sovereign bond portfolios and their implications for sovereign bond
prices���������������������������������������� 18
portfolio strategies.
Riccardo Rebonato We consider the impact of climate change, and of the seriousness of our
abatement effort, on asset prices. How will investors fare under different
Introducing ESG with ETFs and in scenarios of climate change abatement and climate outcomes?
factor investing������������������������ 21 The results of the annual EDHEC European ETF, Smart Beta and Factor
Véronique Le Sourd, Lionel Martellini Investing Survey, which EDHEC-Risk Institute has been running since 2006
with the support of Amundi ETF, Indexing & Smart Beta, show a slowdown
Diversification and insurance: in the use of smart beta and factor investing strategies, and a growing
which should come first?���������� 25 interest in the integration of an SRI/ESG component into investment.
We then ask the following question as part of the EDHEC-Risk Institute/
Nicole Beevers, Hannes Du Plessis, FirstRand research chair on Designing and Implementing Welfare-Improv-
Lionel Martellini, Vincent Milhau ing Investment Solutions for Institutions and Individuals: if diversification
and insurance (ie, dynamic hedging) are not mutually exclusive techniques,
Benefits of selection and allocation is there an optimal order for them to be performed? Our results show that it
decisions in the French non-listed matters whether insurance or diversification comes first.
Finally, as part of the Swiss Life Asset Managers France research chair
real estate investment fund on Real Estate in Modern Investment Solutions, we examine the risk and
market �������������������������������������������� 32 return characteristics of French non-listed real estate funds to assess
whether traditional investment management techniques can be applied to
Béatrice Guedj, Lionel Martellini,
this growing universe of investment vehicles. We find supporting evidence
Shahyar Safaee that investors would indeed benefit from the implementation of selection
and allocation decisions.
We hope that the articles in the supplement will prove useful, informa-
tive and insightful. We wish you an enjoyable read and extend our warmest
© EDHEC-Risk Institute 2021. Research thanks to IPE for their collaboration on the supplement.
Insights is distributed with Investment &
Pensions Europe. No part of this publication Lionel Martellini, Professor of Finance, EDHEC Business School,
may be reproduced in any form without the Director, EDHEC-Risk Institute
prior permission of the publishers. Printed by
Pensord, Tram Road, Pontllanfraith, Black-
wood, Gwent NP12 2YA, UK.
The articles in this supplement have been
written by researchers at EDHEC-Risk Institute.
IPE’s association with the supplement should not
be taken as an endorsement of its contents. All
errors and omissions are the responsibility of
EDHEC-Risk Institute.
SPRING 2021
2 EDHEC Research Insights
Climate change risk
and corporate bonds
Gianfranco Gianfrate, Professor of Finance, EDHEC Business
School, Sustainable Finance Lead Expert, EDHEC-Risk Institute
Is there a relationship between exposure worthiness widely used by rating agencies absolute level but also in terms of carbon
to climate change and a firm’s credit risk? and investors. Several papers have intensity. The latter measure, obtained by
The distance to default, a widely used analysed the influence of sustainability scaling total emissions by firm revenue,
market-based measure of corporate factors either on a firm’s value or on the captures the operational configuration of
default risk, is actually negatively cost of its debt, while this study focuses companies and therefore their ability to
associated with the amount of a firm’s on the default probability in terms of switch to less polluting technology.
carbon emissions and carbon intensity. distance to default. Credit risk is defined as the risk that a
Therefore, companies with a high carbon Using a panel least squares regression, borrower will not be able to meet its
footprint are more likely to default, hence it is observed that there is a significant financial obligations on time. The Basel
the exposure to climate risks affects the and negative relationship between Committee defines it as the risk that a
creditworthiness of loans and bonds distance to default and the natural borrower will default on debt by failing to
issued by corporates. logarithm of CO2 emissions, ceteris make the required payments. Among the
paribus. We find that this result is robust approaches used in practice to estimate
also when carbon intensity, which is the the probability of corporate default, the
A
s climate change and global ratio between carbon emissions and sales, structural approach – which calculates the
warming are being addressed by is used. Several robustness checks are default probability on the basis of the
tougher regulations, new emerging performed and the results confirm a firm’s capital structure – is widely used.
technologies, and shifts in consumer significant and negative relationship In particular, the distance to default is
behaviours, global investors are increas- between distance to default and CO2 the number of standard deviations that
ingly treating climate risks as a key aspect footprint. the firm’s asset value is away from the
when pricing financial assets and deciding In order to investigate causality default and can be defined as:
on the allocation of their investment between climate risk exposure and
V σ2
portfolios. Recent estimates are shedding creditworthiness, we investigate the ln 0 + µ − t
light on the broader indirect impact of impact of the 2015 Paris Agreement as an F 2
DD = t
(1)
climate change on the value of assets held exogenous policy shock. After the Paris σ T
by banks and financial companies. Agreement, high-emitting companies where
Battiston et al (2017) find that, while significantly shorten their distance to V0 is the firm’s asset value at time 0,
direct exposures to the fossil fuel sector default in comparison with low emitters. m, sT are the firm’s value drift rate and
are small, the combined exposures to This finding supports the view that volatility,
climate policy-relevant sectors are large, financial markets are increasingly pricing Ft is the book value of the firm’s liabilities
heterogeneous, and amplified by large the climate risk exposure of listed to be paid by time t.
indirect exposures via financial counter- companies – especially because of growing For the empirical analysis, we calculate
parties. Thus, the exposure to climate risk commitment among institutional the one-year probability of default for our
could potentially pose systemic threats to investors (Dyck et al [2019]; Krueger et al sample in the period from 2008 to 2018.
global financial stability. [2020]). In particular, the value of asset V is
While the relationship between climate All the above-mentioned results not calculated for each trading day, approxi-
risk exposure and share prices is receiving only underline the importance of carbon mating it as the sum of the market value
growing attention from scholars and awareness as a business strategy for of equity and book value of liabilities for
investors, the impact on corporate bonds polluting firms, but also show the key role the same date. Using the obtained series
and loans appears relatively underex- it plays with respect to those lenders that of asset value estimates, log asset returns
plored. We contribute to filling this gap in are exposed to their clients’ default and are calculated and their volatility is then
the literature by investigating whether a reputational risk. However, whether computed. The newly calculated volatility
firm’s exposure to climate risks, measured investors consider the level of CO2 of asset value sV is introduced to the
as its level of CO2 emissions and carbon emissions in their corporate fixed income inverted Black-Scholes formula to obtain
intensity, is associated with Merton’s investments remains underexplored. a new series of market values of assets,
distance to default, a measure of credit- Carbon footprint can be measured at and a new value for sV is then computed.
SPRING 2021
EDHEC Research Insights 3
This process is repeated iteratively until opportunities and shocks. The ratio
the difference between two adjacent asset between retained earnings and total assets
1. Average distance to default
value estimates (calculated as the sum of is used. per quintile
squared differences) is lower than a small l Industry and country effects. Each
measure, chosen arbitrarily as 10–5. Once sector and country has different structural
the asset values are obtained, the next characteristics and cyclical sensitivities
step is the calculation of distance to that could impact firms’ creditworthiness.
default and corresponding probability of The baseline tests examine the
default. The missing value is m, which is relationship between a firm’s carbon
calculated as the natural logarithm of the footprint and its distance to default using
expected returns obtained using the the following specification:
Capital Asset Pricing Model.
Our sample consists of the companies DD = α + β X it + γ ′Yit + ∆ + εit (2)
included in the Bloomberg Barclays Agg
Corporate index. Out of the index where the dependent variable is the
constituents, only companies that issued distance to default of firm i in year t, Xit is
investment-grade fixed-rate corporate the carbon footprint measured either as
bonds are included: the final sample the amount of CO2 emissions or as carbon Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5
comprises 458 companies observed from intensity obtained as CO2 emissions scaled
December 2006 to December 2017. by firm revenue, Yit are a set of firm-level,
For the calculation of annual distance industry and country controls in year t, R-squared of 30.9%. It can be noted that
to default, daily data for market value of and D are year fixed effects. the natural logarithm of carbon emissions
equity, index returns and risk-free returns An initial investigation of the data is has a highly significant negative relation-
are employed. For liabilities, book values obtained by partitioning the sample by ship with distance to default. Therefore,
are used, meaning only annual observa- CO2 emission levels. The pooled data are we can expect companies that produce
tions were available in most cases. All data divided into quintiles, each containing more CO2 emissions to face higher risks in
are collected from Thomson Reuters about 453 observations. Quintile 1 terms of activity disruptions or payments
DataStream, and expressed in US dollars. contains the top 20% of companies with of fines and, hence, a smaller distance to
All data on emissions are from Asset4. the lowest level of carbon emissions, and default. Emissions are a part of non-
To test the relationship between the fifth quintile contains the bottom 20%. financial data that is clearly considered by
distance to default and climate risk Figure 1 demonstrates that the average investors when making decisions. In
exposure, we quantify the carbon annual distance to default decreases as the terms of economic significance, an
footprint – measured as the amount of level of carbon emissions increases. It can increase by 1% in carbon emissions
CO2 emitted – and carbon intensity, be observed that the negative correlation reduces the firm’s distance to default by
measured as the ratio between CO2 between CO2 emissions and Merton’s about 28.6% on average, all other variables
emissions and firm revenue. distance to default is consistent and is remaining constant. The fact that the
The control variables are identified in approximately linear. adjusted R-squared can be improved leads
the existing literature as corporate As a further step, panel least squares to the idea that other non-financial
characteristics that appear to influence regressions are run between distance to variables should be considered in the
the distance to default. In particular, default and the natural logarithm of investment analysis.
these are: emissions. These regressions allow us to All the control variables used are
l Firm size, measured as the natural establish if the relationship analysed in indicators of a company’s high probabil-
logarithm of total assets. Larger firms are the descriptive statistics section is ity of bankruptcy from a financial point
expected to have a lower probability of significant. of view. The relationship between the
default than smaller firms. First, a regression is run with only one distance to default and the debt ratio is
l Firm profitability, which provides explanatory variable, the natural loga- negative and significant: the lower the
important information on the probability rithm of total emissions. Even though the debt ratio, the higher the likehood that a
of a firm going bankrupt. Less profitable adjusted R-squared of the first regression firm can survive in the future, and so an
firms are assumed to be more likely to be is very low (0.026), the independent increase in that ratio tends to be
acquired or go bankrupt. We use the variable appears to be significantly and associated with a decrease in the distance
operating margin as the metric to account negatively correlated with Merton’s to default. The operating margin gives an
for profitability. distance to default. indication of a company’s profitability
l Financial leverage, which is associated A second regression is then performed and, therefore, it is appropriate to
with the probability of a firm going mantaining Merton’s distance to default positively link it with distance to default,
bankrupt. Firms with lower equity are as a dependent variable and the natural based on the following observation: the
said to face more difficulties during logarithm of total emissions as an higher a company’s profitability, the
periods of liquidity shortage, when it independent variable, but also including lower the probability of default. Indeed,
becomes tougher to renew debt. all the control variables described in the operating margin appears to be signifi-
l Asset value volatility: firms with higher section above. cantly and positively related to distance
asset volatility are expected to be more From figure 2, it can be observed that to default. The retained earnings to total
vulnerable than others. all the variables used are significant at assets ratio helps to measure the extent
l Short-term liquidity needs, namely the 10%, 5% and 1% significance levels, with to which a company relies on leverage.
ratio between working capital and total the exeption of retained earnings/total The lower this ratio, the higher its
assets. assets and working capital/total assets. leverage, which again increases the risk
l Retained earnings as an equity buffer to This second regression has a good of bankruptcy where the firm cannot
deal with potential unexpected growth explanatory power, with an adjusted timely fulfil its debt obligations. How-
SPRING 20214 EDHEC Research Insights
An additional robustness check is
2. Results of the multivariate analysis with pooled cross sections carried out, as shown in model 5 in figure
OLS of the calculated Merton distance to default, 2008–18 2. Instead of using total disclosed
emissions, only direct emissions are used.
Dependent variable: Merton’s distance to default Since this level of detail is available only
1 2 3 4 5 for a limited number of companies, the
(Fixed effects) sample is reduced to 120 with 1,320 year
Emissions observations. Therefore, a panel least
Emissions (ln) –0.186*** –0.244*** –0.241*** squares regression is run using the same
(0.056) (0.049) (0.045) control variables and replacing the natural
Carbon intensity –0.171*** –0.216*** logarithm of total emissions with the
(0.057) (0.052) natural logarithm of direct emissions.
Firm characteristics The level of direct emissions continues
Debt ratio –0.174*** –0.184*** –0.196*** to be significantly and negatively related
(0.047) (0.047) (0.042) to Merton’s distance to default but only at
Operating margin 0.430*** 0.591*** 0.409*** a 10% significance level. These weaker
(0.165) (0.176) (0.149) results could be due to the smaller size of
Retained earnings ratio –0.067 –0.079 –0.011 the sample. In addition, in this case
(0.070) (0.070) (0.063) retained earnings/total assets is signfi-
Size –0.386*** –0.462*** –0.259*** cantly and positively related to distance to
(0.074) (0.073) (0.068) default, and working capital/total assets is
Volatility –24.579*** –24.523*** –21.084***
significantly but negatively related.
(0.877) (0.878) (1.023)
Surprisingly, the size variable is not
Working capital ratio 0.021 0.033 0.004
significant.
Model 5 in figure 2 shows a regression
(0.056) (0.056) (0.051)
with time fixed effects.The adjusted
Constant 10.160*** 23.126*** 7.407*** 20.820*** 20.094**
R-squared improves and our previous
(0.912) (1.443) (0.224) (1.365) (1.316)
results hold: the relationship between the
Industry controls Yes Yes Yes Yes Yes
natural logarithm of emissions and
Country controls Yes Yes Yes Yes Yes
distance to default continues to be
Observations 2,222 2,222 2,222 2,222 2,222
negative and significant.
Adjusted R² 0.026 0.309 0.025 0.306 0.441
The Paris Agreement and the increased
F statistic 20.82*** 111.14*** 20.13*** 110.026*** 93.11**
attention of investors to climate change
(df = 2; 2,219) (df = 8; 2,213) (df = 2; 2,219) (df = 8; 2,213) (df = 8; 2,213)
issues imposes risks on companies with
Source: Capassso et al (2020)
high CO2 emissions. Rigorous enforce-
** = Statistically significant at 5%;
ment of existing environmental laws and
*** = Statistically significant at 1%.
the introduction of stricter criminal and
civil penalties for polluters are expected
for the future. This could result in a spike
in costs and in impacts on issuers’
ever, in our model, the relationship is default. In line with this, the association creditworthiness.
negative rather than positive and is not observed is positive but not significant. This paper investigated whether a
significant. The results are probably In order to evaluate the robustness of firm’s CO2 emissions affect Merton’s
biased by the presence of the debt ratio, the results, two more panel least square distance to default. The results show that
which is another indicator of leverage. regressions are run. This time, instead of a higher level of emissions actually leads
Larger companies can be expected to be using the natural logarithm of carbon to a lower distance to default. Descriptive
evaluated by the market as safer than emissions, carbon intensity is employed. statistics already reveal the influence of
smaller companies; surprisingly the Carbon intensity is the ratio between the CO2 emissions on the probability of
association found is negative, suggesting level of emissions and total sales. This ratio default. The sample is divided into
that the market considers bigger compa- is particularly used in the energy sector, quintiles (and deciles) according to each
nies riskier. Volatility is another funda- where carbon emissions are compared firm’s level of emissions: we show that
mental indicator of creditworthiness. against the megajoule of energy produced. companies in the first decile or quintile
Merton’s structural credit risk model Given the many different industries (less polluting firms) have a higher
(1974) was the first to indicate that involved in the sample analysed, here distance to default compared to the most
reduced firm value volatility also leads to carbon emissions are divided by sales. As polluting firms. We find strong evidence
lower risk premiums, and in the regres- before, first only carbon intensity is used as that emissions are negatively associated
sion this relationship is indeed significant an independent variable, and then all the with distance to default. These findings
and negative. Lower volatility increases control variables are added. are confirmed using both the natural
the value of the assets and leads to a rise The results are not different from the logarithm of emissions and carbon
in the distance to default. Finally, previous analysis. Carbon intensity is intensity. The baseline results hold, even
working capital to total assets indicates a significantly and negatively associated excluding the energy and extractive
company’s ability to pay back creditors in with Merton’s distance to default. In industries. In unreported results, we
the short term. Those with a healthy and addition, retained earnings/total assets additionally find that the carbon footprint
positive working capital should not have and working capital/total assets are once decreases the distance to default following
problems paying their bills, and should again not significant, and size remains regulatory shocks such as the Paris
therefore have a larger distance to negatively related to distance to default. Agreement, which reveal policymakers’
SPRING 2021EDHEC Research Insights 5
intention to implement stricter climate financial risk disclosures for use by References
policies. companies in providing information to Battiston, S., A. Mandel, I. Monasterolo, F. Schütze and
Given the outlook of increasing global investors, lenders, insurers, and other G. Visentin (2017). A climate stress test of the financial
temperatures, it is important to assess stakeholders. Our findings prove that the system. Nature Climate Change 7(4): 283–288.
the impact of this on the macro-economy work and recommendations of the task Capasso, G., G. Gianfrate and M. Spinelli (2020). Climate
and financial markets. Rising tempera- force are justified as the amount of change and credit risk. Journal of Cleaner Production 266:
tures may disrupt financial markets and carbon emitted by companies provides 1–10.
the banking system. Our results show investors with relevant information. Dyck, I.J., K. Lins, L. Roth and H. Wagner (2019).
that firm creditworthiness is already However, transparency is only the first Do Institutional Investors Drive Corporate Social
affected by exposure to climate risks. step. As carbon risks appear more Responsibility? International Evidence. Journal of Financial
Policymakers should carefully consider pervasive and material for the global Economics 131(3): 693–714.
the impact of climate change risks on the financial system than previously thought, Krueger, P., Z. Sautner and L. Starks (2020). The
stability of both lending intermediaries the compelling issue for investors and Importance of Climate Risks for Institutional Investors. The
and corporate bond markets. The Task financial regulators is how to manage or Review of Financial Studies 33(3): 1067–1111.
Force on Climate-related Financial neutralise such risks once they have been Merton, R. (1974). On the pricing of corporate debt: the risk
Disclosures (TCFD) has developed identified and quantified. structure of interest rates. The Journal of Finance 28(2):
voluntary, consistent climate-related 449–470.
A holistic goals-based
investing framework
for analysing efficient
retirement investment
decisions in the presence
of long-term care risk
Jean-Michel Maeso, Senior Quantitative The annuity puzzle
A major crisis is threatening the sustain-
Researcher, EDHEC-Risk Institute; Lionel ability of pension systems across the
Martellini, Professor of Finance, EDHEC globe. The first pillar of pension systems,
which is made up of public social security
Business School, Director, EDHEC-Risk Institute; benefits and aims to provide a universal
Vincent Milhau, Research Director, EDHEC-Risk core of pension coverage to address basic
consumption needs in retirement, is
Institute; Anil Suri, Head of Investment Analytics, strongly impacted by rising demographic
Merrill Lynch Global Wealth Management imbalances. Life expectancy at age 65 in
OECD countries is expected to grow by
Group, Bank of America; Nevenka Vrdoljak, 4.2 years for women and 4.6 years for men
Director of Retirement Strategies, Merrill between 2020 and 2065. As a result, the
number of individuals aged 65 and over
Lynch Wealth Management, Bank of America for every 100 individuals aged between 20
and 64 rose from 13.9 in 1950 to 27.9 in
2015, and is expected to grow to 58.6 by
2075.1 In parallel a massive shift from
1 Figures cited here are from the OECD report, Pensions at a Glance 2017. defined benefit pension schemes to
SPRING 20216 EDHEC Research Insights
defined contribution pension schemes is changing locations to lower or higher cost will be measured with a number of key
taking place across the world, implying a cities or countries, decisions about indicators, which can be broadly sorted
transfer of retirement risks from corpora- retirement dates, and also, perhaps most into figures of merit (to be maximised) and
tions to individuals. notably, long-term care needs driven by figures of risk (to be minimised). In terms
As an almost universal rule, pillar I and health-related issues in the later stage of of figures of merit, we first report the
pillar II pension arrangements deliver retirement. These uncertainties require median discounted income shortfall, which
replacement income that is inferior to the changes to retirement plans on a regular is defined for a given scenario as the
needs of individuals in retirement, and basis, annually or when life events occur, discounted sum of the differences between
the resulting inadequacy risk is sometimes which is simply not possible with actual withdrawals and target withdrawals
severe. According to the aforementioned annuities. (which are defined as 3%, 4% or 5% of initial
OECD report, an individual earning the In Maeso et al (2020) we propose a wealth subject to a 2% cost-of-living
average income in the US can expect to comprehensive simulation framework adjustment (COLA), plus cost of long-term
enjoy a mere 49.1% replacement rate upon that contains notably: care needs, if and when they are incurred).
retirement, a number that falls to 29.0% in l A market simulation engine, incorpo- By definition, this quantity is equal to zero
the UK. With the need to supplement rating Monte Carlo simulations coupled at best, when the individual has enough
public and private retirement benefits via with flexible long-term Capital Market wealth to finance all target withdrawals. As
voluntary contributions, the so-called Assumptions (CMAs); a related indicator we also report the
third pillar of pension systems, individuals l A product simulation engine, incorpo- median discounted percentage of lifetime
are becoming increasingly responsible for rating scenarios for stocks and bonds, income (PLI) achieved, which is defined as
their own retirement savings and invest- balanced funds and target date funds, as the median value across all scenarios of the
ment decisions. This global trend poses well as a rather exhaustive range of ratio between the sum of the individual’s
substantial challenges, as people often annuity products; discounted actual withdrawals and the sum
lack the expertise required to make such l A client simulation engine, incorporat- of the discounted target withdrawals until
complex financial decisions. ing mortality risk scenarios, as well as death. We finally report the median
In principle, annuity products, target levels of replacement income cash discounted bequest value, which is
designed as contracts by which the flows, including random shocks to cash unbounded. In terms of figures of risk, we
beneficiary pays a premium today in flows due to life events such as long-term first report a short-term risk indicator
exchange for receiving lifetime income, care needs; and defined as the median (over the scenarios)
can be used to generate a target level of l A comprehensive goals-based retire- maximum (over time) annual loss (MAL)
replacement income throughout ment investing solution evaluation on the liquid portion of the portfolio
retirement. system, which defines and develops (invested in the balanced fund). We also
In practice, however, the demand for metrics that can be used to determine the report several long-term risk indicators,
such products is extremely low, despite relative value and trade-off of various including:
their risk-free nature in a retirement options with a focus on assessing client- l The shortfall probability, defined as the
investment context. Using the RAND centric outcomes. We report in this paper percentage of scenarios where the
Health and Retirement Study dataset for some of our main findings in a simple individual outlives her assets;
the cohort aged 65–75 in 1998, Pash- setting with two assets. l The median and extreme shortfall
chenko (2013), for example, reports that durations, defined respectively as the
only 5% of individuals receive income from Framework overview median and the 95% percentile of the
annuities, with a peak at 12.2% among the We consider the framework developed in number of years when the actual with-
highest income quintile and a low at 0.4% Maeso et al (2020) applied to a 65-year- drawal is lower than the target with-
for the lowest quintile. Common explana- old woman who is already retired (and drawal; and
tions of this so-called ‘annuity puzzle’ are assumed to have just retired) in a l The extreme discounted shortfall
related to the fact that annuities involve two-asset base case universe, where defined as the 5% percentile of the
counterparty risk and high levels of fees, retirement wealth is allocated to a discounted differences between actual and
and also that they do not contribute to 50%/50% stock/bond balanced fund and a target withdrawals (not including the
bequest objectives. One additional key single premium immediate annuity (SPIA) bequest).
drawback of annuity products is their with a 2% cost-of-living adjustment and a An optimisation exercise requires the
severe lack of flexibility. Indeed, annuiti- death benefit.2 In terms of retirement identification of a proper optimisation
sation is an almost irreversible decision, needs, we test withdrawal rates of 3%, 4% objective (and possibly some constraints).
unless one is willing to bear the costs of and 5% of the initial wealth, and a 2% This raises two main questions: the
extremely high surrender charges, which annual cost-of-living adjustment. integration of potentially conflicting goals
can amount to several percentage points We systematically report the results and aggregation of risk and return
of the invested capital. This lack of obtained in both the absence and presence dimensions for a given goal. Meaningful
flexibility is a major shortcoming in the of life events to check for the impact of goals include expected lifetime income
presence of life event uncertainties such long-term care needs on the optimal needs, unexpected lifetime income needs
as marriage and children, changing jobs, demand for annuities. We assume that if (long-term care), a bequest and capital
and when the individual experiences preservation. The first challenge is to
long-term care needs, she will need aggregate these four goals, which can
2 The death benefit is defined as follows: if the total additional retirement income to secure a conflict with each other in the framework
income paid by the annuity to the individual until her semi-private room at a cost of $90,155 per developed above. Expected and unex-
death is lower than the premium she paid, then her heirs year, and an annual cost increase of pected lifetime income needs can
will receive the difference between the premium paid 3.10%.3 naturally be aggregated so that when the
and the income collected. The performance and risks associated probability of a shortfall or expected
3 These figures are borrowed from the Genworth Cost of with any given allocation between the two shortfall is reported, it includes both the
Care Survey 2019. available assets (SPIA and balanced fund) expected and unexpected components as
SPRING 2021EDHEC Research Insights 7
part of the target. The bequest objective Base case analysis percentage of her replacement income
must then be aggregated with the total We now define and analyse a base case needs that she can secure with certainty
(expected plus unexpected) income situation, where we assume that the (and in the absence of a life event) by
objective. We propose an integrated individual (a 65-year-old female) is investing her assets in an SPIA-COLA
approach where we treat the bequest as a endowed with a $500,000 initial wealth annuity. These funding ratios for initial
final income cash flow, which is equivalent level at retirement date. As indicated target withdrawals of 3%, 4% and 5% are
to treating it as a residual quantity. before, we assume that she has access to a respectively 3.79/3 = 126.3%, 3.79/4 =
Formally we define the discounted simple investment universe that contains 94.8% and 3.79/5 = 75.8%. These results
surplus on a given scenario as the a balanced fund (BF) with an annually suggest that someone with an aggressive
discounted bequest plus the sum of rebalanced 50%/50% stock/bond mix, and target withdrawal rate (5%) is initially
discounted income shortfalls (note that an immediate annuity with a 2% cost-of- underfunded (by a bit less than 25%, with
this quantity can be positive or negative, living adjustment and a death benefit a funding ratio at 75.8%) while someone
and it is a deficit and not a surplus when it (SPIA-COLA). In this base case analysis, with a conservative target withdrawal rate
is negative). We then use the average we systematically test three levels of (3%) is initially overfunded (by a bit more
(across scenarios) discounted surplus (AS) initial target withdrawal rates, namely 3%, than 25%, with a funding ratio at 126.3%).
as a performance indicator in the 4% and 5%, and we let target withdrawals Figure 2 displays selected charts
optimisation objective, and the 5% VaR, or grow by 2% per year to account for representing the various indicators
5% percentile (VS) as a risk indicator in expected growth in the cost of living.4 introduced in the previous section as a
the optimisation objective. Defining l as a Reading in figure 1 that the initial pay-out function of the initial percentage alloca-
risk-aversion parameter that characterises rate of the SPIA-COLA annuity for a tion to the SPIA-COLA, with values
the risk appetite of the individual, we can 65-year-old female is 3.79%, we can define ranging from 0% to 100%, with a grid step
finally write the objective function as: the individual funding ratio as the of 1%. We observe that for a given initial
arg max AS ( w1 ,..., wn ) + l VS ( w1 ,..., wn )
w1 ,..., wn
1. Payout rate for the SPIA with a 2% COLA and Capital Market
where w1, ..., wn represents the percentage Assumptions
of initial wealth invested in each asset,
encompassing financial liquid assets, Age Male Female
annuities and insurance. In the analysis 60 3.49% 3.33%
that follows, as indicated before, we 61 3.60% 3.42%
consider only two assets (n = 2), namely 62 3.72% 3.52%
the balanced fund and an SPIA, and we 63 3.83% 3.62%
take five values for l (l = 0.5, 1, 2, 4, 6), 64 3.91% 3.69%
which we interpret as defining the 65 4.02% 3.79%
aggressive, moderately aggressive, 66 4.14% 3.90%
moderate, moderately conservative and 67 4.26% 4.01%
conservative investor, respectively. We 68 4.39% 4.12%
also report results for two limit cases, 69 4.53% 4.24%
namely l = 0, which captures a pure focus 70 4.61% 4.37%
on performance, and l = 1,000, which 71 4.75% 4.48%
represents a strong focus on risk. 72 4.91% 4.63%
Overall the main inputs of our 73 5.07% 4.78%
framework are: 74 5.16% 4.87%
l Age
75 5.32% 5.02%
l Sex
76 5.49% 5.17%
l Initial wealth
77 5.66% 5.33%
l Initial target withdrawal rate
78 5.85% 5.49%
l Withdrawal COLA-indexation
79 6.00% 5.66%
l Universe
80 6.09% 5.77%
l Number of Monte Carlo simulations
l Grid weight step Asset classes Arithmetic return (%) Volatility (%) Fees (%) Geometric return
l Optimisation problem: AS +l × VS after fees (%)
where: US equity 9.90 18.18 0.50 8.25
• AS is the average discounted surplus US fixed income 3.89 5.17 0.45 3.76
which aggregates discounted income Cash 2.83 1.69 0.18 2.82
shortfall (a series of zero or negative Correlation US equity (%) US fixed income (%) Cash (%)
values) and bequest surplus (a zero or
US equity 1 -0.09 0.26
positive terminal value);
US fixed income –0.09 1 0.19
• VS is the 5% VaR of the discounted
Cash 0.26 0.19 1
surplus distribution;
This figure gives the initial payout rate (as a percentage) of a single premium immediate annuity indexed by a
• l is the risk-aversion parameter that
2% COLA (SPIA-COLA) as a function of the individual’s sex and age. Quotes are obtained as of 10 July 2020 from
characterises the risk appetite of the
CANNEX, a data provider that compiles information and calculations about a variety of financial products, including
individual.
annuity products, and makes that information available to financial service providers through a central exchange. It
also reports the capital market assumptions used for the Monte Carlo market simulations.
4 The initial withdrawal rate is expressed as a percentage
of the initial wealth.
SPRING 20218 EDHEC Research Insights
2. Reporting for the two-asset universe comprising an SPIA-COLA
target withdrawal rate, short-term risk
and a balanced fund
decreases as a function of the allocation to ITW = 3% – no LE ITW = 3% – with LE ITW = 3% – no LE ITW = 3% – with LE
ITW = 4% – no LE ITW = 4% – with LE ITW = 4% – no LE ITW = 4% – with LE
annuities, as expected. We also note that ITW = 5% – no LE ITW = 5% – with LE ITW = 5% – no LE ITW = 5% – with LE
for initial target withdrawal rates of 4%
and 5%, the shortfall probability increases Short-term risk (%) = f (SPIA-COLA weight) Shortfall probability (%) = f (SPIA-COLA weight)
with the allocation to the SPIA. This is 3 100
because investing a large fraction of the 0 80
portfolio in annuities does not generate -3
the amount of upside potential needed to 60
-6
finance a higher target level of consump- 40
tion in retirement. On the other hand, in -9
the case of a 3% withdrawal rate, the -12 20
individual is sufficiently funded (with a
-15 0
funding ratio at 126.3%) to be able to meet 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
target levels of withdrawals without
substantial upside potential, that is Median bequest value ($) = f (SPIA-COLA weight) Median percentage of LI (%) = f (SPIA-COLA weight)
without a significant investment in the 500,000 100
balanced fund. Similarly, we find that the 400,000
extreme shortfall durations increase with
the allocation to the SPIA-COLA, except 300,000
in the case where the initial target 80
200,000
withdrawal rate is equal to 3%. The
median percentage of lifetime income 100,000
decreases as the allocation to annuities
0 60
increases when the initial withdrawal 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
rates are 4% or 5% and when life events are
not taken into account. When accounting Median discounted shortfall ($) = f (SPIA-COLA weight) Extreme discounted shortfall ($) = f (SPIA-COLA weight)
for life events and when the initial 0 0
withdrawal rates are 4% or 5% respectively, -50,000 -200,000
the median percentage of lifetime income
-100,000
first decreases as the allocation to -400,000
annuities increases, then it reaches a -150,000
-600,000
minimum for annuity allocations of 92% -200,000
and 84% respectively, and finally starts to -250,000 -800,000
slightly increase for even higher annuity
allocations. We also note that the median -300,000
0% 20% 40% 60% 80% 100%
-1,000,000
0% 20% 40% 60% 80% 100%
discounted bequest is a decreasing
function of the allocation to annuities, as Median shortfall duration (years) = f (SPIA-COLA weight) Extreme shortfall duration (years) = f (SPIA-COLA weight)
expected. In the same spirit, we find that 30 50
in most cases (except for a 3% initial
25
withdrawal rate case in the presence of 40
life events), the extreme discounted 20
30
shortfall, which is measured as the fifth 15
percentile of the shortfall, decreases in 10
20
absolute value when the SPIA allocation 10
5
increases, again in line with the intuition
that the SPIA is the natural safe asset in a 0 0
0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
retirement context. On the other hand,
the median discounted shortfall is not a
AS ($) = f (SPIA-COLA weight) VS ($) = f (SPIA-COLA weight)
monotonous function of the allocation to 600,000 200,000
annuities. We confirm in particular that 500,000 100,000
the presence of long-term care needs has 400,000 0
-100,000
a strong impact on the distribution of 300,000
-200,000
discounted shortfall with a median value 200,000
-300,000
100,000
that is significantly lower (more negative), -400,000
0
especially in the case of a 3% withdrawal -100,000
-500,000
-600,000
rate, which is the situation where the -200,000 -700,000
unexpected component of replacement -300,000 -800,000
income needs is particularly sizable with 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
respect to the expected component.
This figure graphs the main statistics described in the Framework overview section for a 65-year-old female with
Turning to the two key ingredients in
initial wealth of $500,000 and a two-asset universe made up of a single premium immediate annuity with a 2%
the optimisation problem, namely the
COLA and a 50%/50% stock/bond balanced fund. We consider three initial target withdrawal rates: 3%, 4% and 5%.
average discounted surplus AS and the
We apply a grid weight step of 1% to compute the 101 corresponding strategies. We report the results in both the
value-at-risk of the discounted surplus VS,
absence and presence of life events.
we confirm from figure 2 the presence of a
typical risk-return trade-off. On the one
SPRING 2021EDHEC Research Insights 9
hand, an increase in the allocation to
annuities leads to a decrease in AS, which 3. Optimal strategies for the two-asset universe comprising an
is not desirable since AS is a measure of SPIA-COLA and a balanced fund
merit. We also confirm that the surplus
decreases with the target withdrawal rate, Initial target Optimal allocation = (% SPIA, % BF)
as expected since it is more difficult to withdrawal
maintain a surplus starting from a given Risk aversion level 0 0.5 1 2 4 6 1,000
wealth level (here $500,000) when 3% Without life events (0, 1) (0, 1) (0, 1) (0.62, 0.38) (0.78, 0.22) (0.88, 0.12) (0.92, 0.08)
replacement income needs are higher. We With life events (0, 1) (0, 1) (0, 1) (0.04, 0.96) (0.18, 0.82) (0.18, 0.82) (0.22, 0.78)
also confirm, as expected, that the 4% Without life events (0, 1) (0, 1) (0, 1) (1, 0) (1, 0) (1, 0) (1, 0)
presence of the life event (solid lines) With life events (0, 1) (0, 1) (0, 1) (0.11, 0.89) (0.19, 0.81) (0.19, 0.81) (0.30, 0.70)
leads to a smaller surplus compared to a 5% Without life events (0, 1) (0, 1) (0, 1) (1, 0) (1, 0) (1, 0) (1, 0)
situation without the life event (dotted With life events (0, 1) (0, 1) (0, 1) (0.18, 0.82) (0.33, 0.67) (0.43, 0.57) (0.49, 0.51)
lines), and this is true for all values of the
This figure displays the optimal allocation in single premium immediate annuities (SPIA) and a balanced fund (BF)
initial withdrawal rate. On the other hand,
for different levels of risk aversion and initial target withdrawal rates. The target withdrawal rates are indexed to a
an increase in the allocation to annuities
2% COLA. We report the results in both the absence and presence of life events.
tends to lead to a higher (less negative)
value for VS, which is desirable since VS is
a measure of risk. This is actually always
the case in the absence of the life event conservative individual (l = 4) decreases cally, we test two additional initial wealth
(dotted lines), for all values of the initial from 78% to 18% when long-term care values, namely $250,000 and $1m. For
withdrawal rate. When life events are needs are accounted for. This impact is given values of the initial target with-
introduced, the monotonic relationship even more dramatic for the conservative drawal rate and the risk aversion param-
between VS and the allocation to annui- investor (l = 6), for whom we find that the eter, in the absence of life events, the
ties no longer holds. In particular, the introduction of long-term care needs optimal allocation is independent of initial
fifth percentile of the discounted surplus reduces the demand for annuities from wealth since the replacement income
distribution ceases to increase beyond a 88% to 18%. Overall, these results suggest needs of the individual represents the
certain allocation to annuities. that the costly reversibility of annuitisa- same deterministic percentage of her
In other words, an allocation to tion decisions can help explain the initial wealth. However, when life events
annuities beyond the 22%, 30% and 49% annuity puzzle for individuals facing life are taken into account, their relative costs
levels in the 3%, 4% and 5% withdrawal rate event uncertainty. have a stronger impact on individuals with
cases, respectively, not only implies a a lower initial wealth. The charts in figure
decrease in performance (measured by Robustness check with respect to 4 confirm this intuition. In particular, we
AS) but also an increase in risk. Intui- initial wealth find that the location and shape of the risk
tively, this is because a minimum amount In this section we conduct the following indicator (VS) are strongly impacted by
of upside potential, which is generated by robustness check for a base case with a 4% changes in the individual’s initial wealth,
a non-zero allocation to the balanced initial target withdrawal rate: we test for with a maximum value corresponding to
fund, is needed in the 5% worst scenarios the impact of changes in initial wealth on SPIA-COLA allocations of 1%, 30% and 67%
to ensure that replacement income needs risk and return indicators and on the for initial wealth levels of $250,000,
(including both the expected and unex- optimal demand for annuities. Specifi- $500,000 and $1m, respectively.
pected components) are met.
Figure 3 shows the optimal strategies
for the base case universe in both the
absence and presence of life events for 4. Reporting for the two-asset universe comprising an SPIA-COLA
various risk aversion levels. As expected and a balanced fund with different values of initial wealth
given the analysis of the risk-return
IW = $250k – no LE IW = $250k – with LE IW = $250k – no LE IW = $250k – with LE
trade-offs involved in an increase in the IW = $500k – no LE IW = $500k – with LE IW = $500k – no LE IW = $500k – with LE
allocation to annuities, we find that IW = $1m – no LE IW = $1m – with LE IW = $1m – no LE IW = $1m – with LE
investors with low risk aversion (l = 0,
AS ($) = f (SPIA-COLA weight) VS ($) = f (SPIA-COLA weight)
0.5, 1) will find it optimal not to purchase
800,000 0
annuities, and this is true for all initial 700,000 -100,000
withdrawal rates and in both the absence 600,000 -200,000
and presence of life events. As risk 500,000
-300,000
aversion increases, the optimal demand 400,000
-400,000
for annuities increases when risk aversion 300,000
-500,000
200,000
becomes higher than 1. One important -600,000
100,000
finding in this analysis, which is robust to 0 -700,000
changes in withdrawal rates, is that the -100,000 -800,000
presence of long-term care risk strongly 0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
reduces the optimal demand for annuities
for most individuals, at least those that This figure graphs the main statistics described for the first robustness check, ie, for a 65-year-old female with
are sufficiently risk-averse to show some three different levels of initial wealth ($250,000, $500,000 and $1m) and a two-asset universe made up of a single
appetite for annuities in the first place. premium immediate annuity with a 2% COLA and a 50%/50% stock/bond balanced fund. We consider an initial
Focusing for example on the case of a 3% target withdrawal rate of 4%. We apply a grid weight step of 1% to compute the 101 corresponding strategies. We
withdrawal rate, we find that the demand report the results in both the absence and presence of life events.
for annuities from the moderately
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Figure 5 shows the weight for the
optimal strategies in the base case 5. Optimal strategies for the two-asset universe comprising an
universe in the absence and presence of SPIA-COLA and a BF with different values of initial wealth
life events for these three different initial
wealth levels. We again find that the Initial wealth Optimal allocation = (% SPIA, % BF) – initial target withdrawal = 4%
introduction of long-term care needs has Risk aversion level 0 0.5 1 2 4 6 1,000
a strong impact on the demand for $250,000 Without life events (0, 1) (0, 1) (0, 1) (1, 0) (1, 0) (1, 0) (1, 0)
annuities for sufficiently risk-averse With life events (0, 1) (0, 1) (0, 1) (0, 1) (0.01, 0.99) (0.01, 0.99) (0.01, 0.99)
individuals (l = 6), an impact that $500,000 Without life events (0, 1) (0, 1) (0, 1) (1, 0) (1, 0) (1, 0) (1, 0)
decreases in the initial wealth level. For With life events (0, 1) (0, 1) (0, 1) (0.11, 0.89) (0.19, 0.81) (0.19, 0.81) (0.30, 0.70)
example, when the initial wealth is $1m Without life events (0, 1) (0, 1) (0, 1) (1, 0) (1, 0) (1, 0) (1, 0)
$250,000, the optimal demand for With life events (0, 1) (0, 1) (0, 1) (0.34, 0.66) (0.58, 0.42) (0.65, 0.35) (0.67, 0.33))
annuities decreases from 100% to 1% when This figure displays the optimal allocation in single premium immediate annuities (SPIA) and a balanced fund (BF)
the life event is introduced for the for different levels of risk aversion, an initial target withdrawal rate of 4% and three different levels of initial wealth:
moderately conservative investor (l = 4), $250,000, $500,000 and $1m. The target withdrawal rates are indexed to a 2% COLA. We report the results in both
while it decreases from 100% to 19% when the absence and presence of life events.
initial wealth is $500,000 (our base case
value) and only from 100% to 58% for
initial wealth of $1m. Overall these results
suggest that the impact of life events fund and an immediate annuity with a 2% References
should be stronger for individuals with COLA indexation. The analysis presented Maeso, J.M., and L. Martellini (2020). A Holistic Goals-
lower initial endowment, as expected. here can be extended in a number of Based Investing Framework for Analyzing Efficient
directions involving the use of alternative Retirement Investment Decisions in the Presence of Long-
Conclusion welfare functions or the introduction of Term Care Risk. Working Paper.
Maeso et al (2020) present a flexible additional assets such as target date funds Pashchenko, S. (2013). Accounting for Non-Annuitization.
framework developed to provide personal- or variable annuities. We refer the Journal of Public Economics 98: 53–67.
ised advice on retirement investment interested reader to Maeso et al (2020) for
decisions in the presence of life event risk. more details on these extensions.
This article shows an application of this
framework in a simple setting with two The research from which this article was
assets, a 50%/50% stock/bond balanced drawn was supported by Bank of America.
Measuring and managing
ESG risks in sovereign
bond portfolios
Lou-Salomé Vallée, PhD in Finance Student, EDHEC Business School
Sustainable investing in sovereign investor demands, fiduciary duty, climate ESG indicators into sovereign bond
bond markets change and the development of new investments is consistent with the relative
Over the past decade, sustainable and regulations and values. Sustainability in scarcity of available academic research on
responsible investing have gained the financial sector is becoming main- the subject, which has focused more on
momentum and continue to grow in stream and is reshaping global markets. ESG investing in equity markets.
popularity among investors, and it is Nevertheless, the integration of ESG In a recent paper (Martellini and Vallée
increasingly recognised that the financial factors into sovereign bond investment [2021]1), we explore the impact of ESG
system has a particularly important role analysis and investment decision making factors on the risk and return of sovereign
to play in the transition towards a is not systematic due to a lack of under- bonds from an investor perspective, in
low-carbon and climate-resilient econ- standing among investors of how to particular investigating how to measure
omy. The integration of sustainability integrate ESG issues into sovereign debt
considerations into the decision-making analysis and a lack of consistency in 1 Martellini, L., and L.-S. Vallée (2021). Measuring and
process for investments, as measured by defining and measuring material ESG Managing ESG Risks in Sovereign Bond Portfolios and
environmental, social and governance factors. The absence of a coherent Implications for Sovereign Debt Investing. EDHEC-Risk
(ESG) indicators, has been driven by investment framework for integrating Publication.
SPRING 202112 EDHEC Research Insights
and manage ESG risks in sovereign bond
portfolios and their implications for 1. Estimation results for developed and emerging countries of the
sovereign bond portfolio strategies. impact of E, S and G scores of sovereign bond yield spreads
Impact of ESG criteria on risk and Developed countries Emerging countries
return characteristics of sovereign Bond yield spreads Bond yield spreads
bonds Spread _ (i,T) Spread _ (i,T)
We first provide an assessment of the 1Y 5Y 10Y 1Y 5Y 10Y
materiality and impact of ESG scores2 Spread_(i,t–1) 0.713*** 0.686*** 0.661*** Spread_(i,t–1) 0.710*** 0.852*** 0.604***
taken individually on key risk and return (0.065) (0.066) (0.067) (0.073) (0.079) (0.090)
indicators of relevance to asset owners in Eco_(i,t–1) –0.003 –0.002 –0.003 Eco_(i,t–1) –0.003 –0.003 –0.005**
both developed and emerging markets.3 (0.003) (0.004) (0.003) (0.004) (0.003) (0.003)
Our main goal is to analyse whether Env_(i,t–1) –0.013** –0.025*** –0.023*** Env_(i,t–1) 0.001 0.002 0.002
cross-sectional differences in the risk and (0.005) (0.006) (0.004) (0.006) (0.005) (0.004)
return of sovereign bonds from various Soc_(i,t–1) 0.003 0.005* 0.003* Soc_(i,t–1) –0.007*** –0.004** –0.001
developed or emerging issuing countries (0.003) (0.004) (0.003) (0.002) (0.002) (0.001)
can be explained partly by cross-sectional Gov_(i,t–1) 0.013** 0.013* 0.009* Gov_(i,t–1) 0.004 0.004 0.002
differences in E, S or G scores. (0.005) (0.006) (0.005) (0.003) (0.002) (0.002)
We draw an important distinction Observations 190 190 190 Observations 150 150 150
between the perspective of long-term Countries 19 19 19 Countries 15 15 15
buy-and-hold investors, for whom Fixed effects Yes Yes Yes Fixed effects Yes Yes Yes
performance can be captured by bond yield R-sq 0.651 0.629 0.633 R-sq 0.676 0.602 0.419
spreads, and the perspective of shorter-
Standard deviation in parentheses. Level of significance: * 10%, ** 5%, *** 1%.
term investors, who will not hold the bond
until maturity, and as such cannot use
bond yield as a measure of expected
performance because of the uncertainty 2. Estimation results for developed and emerging countries of the
regarding the selling price of the sovereign
impact of E, S and G scores of sovereign bond returns
bonds held in their portfolios. In the latter
case, we will instead use average annual- Developed countries Emerging countries
ised return as a measure of performance. Bond returns Bond returns
In both cases, we conduct univariate and Ret _ (i,T) Ret _ (i,T)
multivariate regression analyses4 to explore 1Y 5Y 10Y 1Y 5Y 10Y
to what extent ESG dimensions influence Eco_(i,t–1) –4.22E-06 –0.045 –0.030 Eco_(i,t–1) –0.061 –0.052 –0.046
sovereign bond yield spreads in addition to (0.021) (0.033) (0.048) (0.041) (0.038) (0.049)
information already contained in the Env_(i,t–1) –0.110*** –0.082 –0.051 Env_(i,t–1) –0.012 –0.081 –0.125*
economic fundamentals, as suggested by (0.037) (0.058) (0.083) (0.061) (0.057) (0.075)
the literature on the determinants of Soc_(i,t–1) –0.017 –0.049 –0.078 Soc_(i,t–1) –0.082*** –0.047** –0.017
sovereign bond yield spreads. (0.0245) (0.038) (0.055) (0.023) (0.021) (0.028)
Regarding the impact of cross-sectional Gov_(i,t–1) –0.096** –0.139** –0.201** Gov_(i,t–1) 0.011 –0.022 –0.044
differences in each score (E, S and G) on (0.038) (0.060) (0.086) (0.035) (0.033) (0.044)
sovereign bond yield spreads, our b_0 2.683*** 3.378*** 3.822*** b_0 1.835*** 2.222*** 2.439***
estimation results allow us to extract two (0.370) (0.577) (0.827) (0.434) (0.403) (0.530)
key conclusions (see figure 1). First, we Observations 200 200 200 Observations 150 150 150
find that for developed countries, after Countries 20 20 20 Countries 15 15 15
controlling for economic5 scores and other Fixed effects Yes Yes Yes Fixed effects Yes Yes Yes
fixed effects, the E dimension has a R-sq 0.118 0.102 0.074 R-sq 0.144 0.112 0.056
significant and negative impact on bond
Standard deviation in parentheses. Level of significance: * 10%, ** 5%, *** 1%.
yield spread. These results mean that a
higher E score is associated with a lower
spread for one-year, five-year and 10-year
bond maturity, and this impact is more impact on bond yield spread, meaning
2 We use the Verisk Maplecroft database for ESG pronounced in the medium run. From an that a higher S score is associated with a
indicators. issuer standpoint, better E scores can lower spread for five-year and 10-year
3 Our sample comprises annual observations for 20 therefore lead to reduced borrowing costs, bond maturity, and this impact is more
developed countries, of which the US will be used as the everything else being equal. From the pronounced in the short run. Hence, from
reference country when a risk-free rate is needed, as well investor standpoint, this result suggests an investor standpoint, a lower yield is to
as 15 emerging countries from 2010 to 2020, resulting that a lower yield is to be expected when be expected when investing in countries
respectively in 200 observations for developed countries investing in countries with higher with higher social performance, suggest-
and 150 observations for emerging countries. environmental performance, which tells ing that a negative premium is associated
4 More information on the panel regression models and us that a negative premium is associated with this reduction in social risk.
estimation methods used are available in the paper. with this reduction in environmental risk. We then turn to the impact of cross-
5 We prefer to use the Verisk Maplecroft Economics On the other hand, for emerging coun- sectional differences in E, S and G on the
index rather than credit ratings, since credit rating tries, after controlling for economic scores performance characteristics of short-term
agencies might already incorporate ESG criteria into and other fixed effects, we find that the S sovereign bond returns (see figure 2). We
their analyses. dimension has a significant and negative find that for developed countries, after
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