Measuring Risk in Environmental Finance

doi: 10.1111/j.1467-6419.2007.00526.x

MEASURING RISK IN ENVIRONMENTALFINANCE

Suhejla Hoti

Department of Treasury and Finance, Western Australia

Michael McAleer

School of Economics and Commerce, University of Western Australia

Laurent L. Pauwels

Graduate Institute of International Studies, Geneva, and Hong KongMonetary Authority

Abstract. Environmental sustainability indices, such as the Dow Jones Sustain-ability Indexes and the Ethibel Sustainability Index, quantify the developmentand promotion of sustainable social, ethical and environmental values in thecommunity. Moreover, such indices provide a benchmark for managing sustain-ability portfolios, and developing financial products and services that are linkedto sustainable economic, environmental, social and ethical criteria. This paperreviews the existing data and risk indices in environmental finance. The mainpurpose of the paper is to analyse existing sustainability and ethical indices inenvironmental finance, and evaluate empirical environmental risk by estimatingconditional volatility clustering that is inherent in these indices. Financialvolatility models are estimated to analyse the underlying conditional volatilityor time-varying risk that is inherent in alternative environmental sustainabilityindices. Volatility clustering is observed for most series, but some extremeobservations are also evident. The log- and second-moment conditions suggestthat valid inferences can be drawn for purposes of sensible empirical analysis.

Keywords. Sustainability indices; Social and ethical indices; Socially responsibleinvestment; Environmental finance and risk; Conditional volatility clustering;GARCH; GJR

1. Introduction

Environmental finance and risk are important concepts for countries in evaluatingtheir potential for economic and social sustainability. The definition of environmentalrisk can be quite broad as it is used in a variety of contexts. For example,environmental risk is defined as a catastrophe, pesticide risk or the relativesustainability of the environment to social and economic activities. Some definitions

Journal of Economic Surveys (2007) Vol. 21, No. 5, pp. 970–998C© 2007 The Authors. Journal compilation C© 2007 Blackwell Publishing Ltd, 9600 Garsington Road,Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

MEASURING RISK IN ENVIRONMENTAL FINANCE 971

of environmental risk include a small number of indicators, while others incorporatea large number of components.

The scientific community has attempted to measure environmental risk throughthe form of indices, which are the focus of this paper. Furthermore, we pay specialattention to indices that are relevant to economics and the social sciences. Thecontent of an index will vary according to the definitions of environmental risk andthe context in which the index is established. Indices that are consistent over timeare not easily available, generally being cross-sectional and/or available on an annualbasis. In the literature, the main purpose in constructing these indices is to producepolicy reports and increase information on sustainability.

Many indices have been developed by governmental, non-governmental and inter-governmental agencies, as well as by private consulting firms. These indices tendto be specific to regions, countries and issues. Many different, and sometimesinconsistent, measures of the environment are available, but only a few enterpriseshave attempted to create an aggregate index measuring overall environmentalsustainability or risk over time and across countries.

This paper critically reviews 10 sustainability and ethical indices, and providesa dynamic risk evaluation of five of these indices. The plan of the remainder ofthe paper is as follows. Section 2 defines environmental sustainability and risk.Section 3 describes 16 of these sustainability indices, including screening methodsfor inclusion or exclusion of firms in an index. Section 4 presents a brief review ofsome financial econometric models that are used to evaluate the indices, Section 5discusses the data, and Section 6 summarises the empirical results. Some concludingremarks are given in Section 7.

2. Defining Environmental Sustainability and Risks

Defining environmental finance and risk for the social sciences and economics isa serious challenge. The primary difficulty arises from choosing the appropriateelements of environmental risk that are relevant for social and economic purposes.For example, the risks incurred with pesticides may be relevant to the natural ormedical sciences, but they may not be as important in the social sciences.

The environmental issues relevant to economics are directly associated withsustainability. Environmental sustainability is defined in the Environmental Sus-tainability Index (World Economic Forum, 2001, p. 9) report as ‘the abilityto produce high levels of performance on . . . these dimensions [environmentalsystems, reducing environmental stresses, reducing human vulnerability, social andinstitutional capacity and global stewardship] in a lasting manner’.

Environmental risk and environmental sustainability will be used interchangeablyin this paper. An identified risk to the environment for a region or country affectssustainability such that, the lower is the risk to the environment, the greater isits sustainability. It is difficult to determine what a ‘desirable path to sustainability’actually represents in scientific terms. Sustainability may be relative to other regionsor countries. Risk may be more easily evaluated for some issues, such as naturalresources, where near depletion might be regarded as having a ‘high risk’.

Journal of Economic Surveys (2007) Vol. 21, No. 5, pp. 970–998

C© 2007 The Authors. Journal compilation C© 2007 Blackwell Publishing Ltd

972 HOTI, MCALEER AND PAUWELS

The Environmental Risk Analysis Program at Cornell University defines environ-mental risk as clustered in five areas, namely consumption of energy (fuels), watershortages, disasters, global warming, poverty and population growth. Furthermore,they define that resources are used in a sustainable manner ‘when they are used at arate and in ways such that they are not depleted or permanently damaged’ (CornellUniversity, Department of Communication, 2005). The goal of indicators is to quan-tify observed phenomena to understand diverse and complex situations. Indices areusually the result of aggregated data, and they can be aggregated into more generalindices.

The primary purpose of sustainability and ethical indices is to provide assetmanagers and financial traders with a benchmark. Such a benchmark enables anassessment of sustainability portfolios and the development of financial services forcorporate sustainability and socially responsible investment (SRI). These processesare linked to sustainable economic, social, ethical and environmental criteria and thepolicies of market firms.

In this paper, we analyse empirically the conditional volatility or time-varyingrisk associated with investing in leading sustainability-driven firms. The issues tobe examined include a consideration of the volatility inherent in the returns to thesustainability and ethical indices, and differences in such returns. As the conceptof environmental risk has had several different interpretations in the economics andenvironmental literature, we use a modification of the definition given in Hoti et al.(2005), as follows:

Environmental risk is the time-varying conditional, stochastic or realised volatil-ity of shocks to the returns on a variety of environmental sustainabilityindices.

The definition can easily be extended to include ethical indices related to environ-mental finance.

There has been virtually no empirical analysis of such sustainability indices,as reported by Jha and Murthy (2003), who argue that economists have shownlittle interest to date in analysing sustainability indices. Hoti et al. (2004) seem tohave presented the first empirical analysis of sustainability indices, especially asit relates to the time-varying conditional volatility of such indices. The techniquesused in this paper are derived essentially from financial econometrics, and these willprovide insight regarding the volatility in the underlying environmental sustainabilityindices.

Recent empirical applications of univariate and multivariate models of time-varying risk include, for example, McAleer et al. (2007b) and Hoti and McAleer(2004, 2005), who modelled the time-varying riskiness in country risk ratings,and Marinova and McAleer (2003), who analysed the volatility inherent inecological patents. Although Marinova and McAleer (2006) did not considermodelling the time-varying risk associated with anti-pollution technology strengthindicators, the techniques used in this paper could also be used for such ananalysis.




3. Sustainability Indices

3.1 Screening Methods

Two types of screening method are used for purposes of selecting firms for inclusionin a sustainability index, namely positive and negative screening. Those companiesthat are selected by positive screening are pro-active in adopting and moving towardsbest practice on sustainability issues. The specific criteria that are used to evaluate thepositive actions and contributions to ethically, socially and environmentally soundeconomic activities vary across the different indices.

On the other hand, negative (or exclusionary) screening seeks to excludecompanies that are involved in specific activities, such as gambling or the buildingof weapons. The exclusionary criteria are aimed at avoiding the inclusion ofcompanies that are involved in potentially controversial business activities, suchas environmental degradation, questionable ethics or socially unfriendly practices.

An index may contain positively or negatively screened companies, or both.The screening method for each of the indices surveyed is indicated in Table 1as + and/or −. Furthermore, Table 1 summarises some key information on all thesustainability indices analysed in this paper. Table 2 illustrates some of the broadcriteria used for positive and negative screening. The maintenance of most of theindices accommodates potential mergers, firms ceasing to trade, bankruptcy, or theconsequences of a firm not respecting the social screens imposed.

3.2 Sustainability and Ethical Indices

3.2.1 Dow Jones Sustainability Indexes (DJSI)

The Dow Jones Sustainability Indexes (DJSI) (2003a, b) were started in 1999, andreport on the financial performance of leading sustainability-driven firms. Thesesustainability indices were created by the Dow Jones Indexes, STOXX Limited andthe SAM group, and comprise companies from 60 industry groups and 18 marketsectors.

The DJSI are calculated and reviewed in a similar manner to the Dow JonesGlobal Indexes, and are sub-categorised into two sub-indices, namely (1) DJSIWorld and (2) DJSI STOXX, which is a pan-European index. DJSI STOXX isalso sub-divided into the DJSI EURO STOXX, which accounts solely for Euro-zonecountries. DJSI World is constructed by selecting the leading 10% of sustainabilityfirms in the Dow Jones Global Index. The two regional indices, DJSI STOXX andDJSI EURO STOXX, were first published on 15 October 2001. They include 179components and record the financial performance of the leading 20% of Europeansustainability companies chosen from the Dow Jones STOXX 600. The compositeDJSI World is available in four specialised subset indices, which exclude companiesthat generate revenue from (1) tobacco, (2) gambling, (3) armaments or firearms and(4) alcohol, in addition to the three previously mentioned items. Moreover, when acompany has been selected to join the DJSI World or DJSI STOXX, its sustainability




Tabl

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13

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MEASURING RISK IN ENVIRONMENTAL FINANCE 975Ta

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Euro

zone

120

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(EU

)C

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civil

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Eth

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Eth

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Euro

150

11

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Table 2. Positive and Negative Screening Criteria.

Negative criteria (exclusionary) Positive criteria (inclusionary)

Alcohol EnvironmentTobacco DiversityNuclear power Animal welfareArmaments and firearms Human rightsGambling Labour relationsMilitary contractsHuman rightsAdult entertainmentDiscrimination (gender, minorities or ethnicity)Child or forced labourInterfere in union’s activityChemicals damaging the environment or healthGenetic manipulationAnimal testing

performance is monitored on the basis of all the criteria for which it wasselected.

Hoti et al. (2004) conducted a detailed empirical analysis of the DJSI, andevaluated the time-varying risk inherent in the shocks to returns on the sustainabilityindices using various models of conditional volatility. They also compared theempirical performance of the environmental risk models with well-known financialindices, such as the S&P500 and the Dow Jones Industrial Average.

3.2.2 Ethibel Sustainability Indices (ESI)

The Ethibel Sustainability Index (ESI) is composed of four regional indices,namely ESI Global, ESI Americas, ESI Europe and ESI Asia-Pacific. The indicesare calculated and maintained by Standard & Poor’s (S&P), following S&Pmethodology. The ESI regional indices are designed in such a way as to approximatethe sector weights of the S&P Global 1200, such that each regional sub-componentaccounts for a share of ESI Global. Specifically, Asia-Pacific accounts for 11% inESI Global, the Americas 57% and Europe 32%.

The ESI Global tracks 162 companies in 19 different countries. In the samemanner as the DJSI, the ESI are calculated as both price and returns indices in USDand EUR, yielding a total of 16 indices (further information about the ESI and theregional ESI can be found at www.ethibel.org).

The selection of companies is based on a set of positive criteria whichexamine the best-in-sector and best-in-region companies. The screening and researchmethodology concentrates on two elements of corporate social responsibility, namely(1) sustainable development, guiding the research on a specific company over




environmental, internal and external social, economic and ethical aspects and policyand (2) stakeholder involvement, which translates to dialogue between the ESI andthe stakeholders during the research.

3.2.3 Calvert Social Index

The Calvert Social Index benchmarks the performance of US-based sociallyresponsible companies. Featuring a selection of the leading 1000 companies onthe New York Stock Exchange and NASDAQ-AMEX, the Calvert Social Indexis a market capitalisation-weighted index of around 638 companies. The index ismaintained and reconstituted by the Calvert Corporation on an annual basis, andwas created on 28 April 2004. Quarterly reviews are conducted by the CalvertCorporation to ensure that the index is modified in response to changes in socialcriteria, mergers and the economic sector weighting.

The firms are selected upon social auditing in four areas, namely (1) productsafety and benefits (excluding companies that produce pornography, gambling,tobacco, alcohol, firearms or military weapons); (2) environment (compliance withgovernment regulations and reduction of impact on environment); (3) workplace-related issues, such as fair treatment of employees; and (4) integrity, featuring non-discrimination and good community relations.

3.2.4 KLD Indices

KLD Research and Analytics has created several indices that are intended to providea benchmark for investors who wish to invest in socially responsible equities andsecurities. KLD has developed the Corporate Social Ratings Monitor (SOCRATES),which is a social research database comprising over 4000 companies in the USmarket. The research classifies companies according to social and environmentalcriteria, according to which they may be excluded or included in the indices. Indices2.2.5 to 2.2.10 are part of the KLD index family.

3.2.5 Domini 400 Social Index

Started in May 1990, the Domini 400 Social (DS 400) Index comprises a selectionof 400 US companies. It consists of approximately 250 companies drawn from theS&P500, 100 companies that are not included in the S&P500 but which are intendedto represent a broader range of industries, and around 50 companies that representstrong social characteristics.

The Domini 400 Social Index was created to evaluate whether social screeningbore any inherent financial costs, and to benchmark equities that had been sociallyscreened. The social screening was undertaken using exclusionary criteria, suchas companies deriving 2% or more of sales of some products (alcohol, tobacco,military weapons, gaming, and electric utilities linked to nuclear power plants),and qualitative criteria which evaluate the track records of firms regarding theenvironment, diversity, employee relations and products.




The selection of firms to enter the index features three requirements, namely (1)strong social commitment from the firm; (2) large market capitalisation; and (3) firmsin industries in which the Domini 400 Social Index is under-represented relative tothe S&P500. The weight KLD gives to each of these requirements is variable.

3.2.6 Select Social Index

The Select Social Index is a large capitalisation index consisting of 300 to 350 firms.The selection process features positive social and environmental factors, while under-weighting companies with negative factors. The companies are screened sociallyand environmentally from the Russell 1000 and the S&P500 indices. The rating istransformed into a score, and is then normalised. There is no exclusionary screening,except for tobacco, and no capitalisation weighting. The KLD Select Social Indexselects firms from a broad spectrum of industries (except for tobacco), and weightsthem according to their relative social and environmental performance.

3.2.7 Large Cap Social Index

The Large Cap Social (LCS) index is a socially screened subset of the Russell 1000index. LCS features a market capitalisation weighting method, in a similar mannerto the Domini Social 400 Index, and has 718 companies. The index represents about92% of the available market capitalisation in the USA, and screens broadly forenvironmental and social factors.

3.2.8 Broad Market Social Index

The KLD Broad Market Social (BMS) Index consists of all the KLD-screenedcompanies within the Russell 3000 index. The screening method consists ofexclusionary (negative) and positive criteria. Firms that are involved in alcohol,tobacco, firearms, gambling, nuclear power and military contracting are excludedfrom the BMS Index. The index comprises about 2351 companies, which are alsoevaluated using, among other factors, employee diversity, product, innovation andcommunity involvement.

3.2.9 NASDAQ Social Index

The NASDAQ Social (NS) index is a benchmark for socially screened securitieson the NASDAQ stock market. The NS index is a market value weighted indexof the approximately 180 largest US corporations in the technology, finance andtelecommunications sectors. This index is intended as a socially and environmentallyscreened version of the well-known NASDAQ financial index. Corporate socialperformance is evaluated, for example, by a company’s environmental stewardship,employee relations, non-US operations, and (lack of) involvement in the tobaccoindustry.




3.2.10 Catholic Values 400 Index

The Catholic Values 400 (CV400) Index is a market capitalisation weighted indexthat is screened according to the KLD social and environmental criteria and KLD’sCatholic values screening criteria. In calculating the CV400 index, the selectionprocess draws 250 companies from the S&P500, 100 mid-cap companies to providea good industry representation, and 50 small-cap companies that are particularly pro-active socially. The CV400 index excludes companies that are involved in alcohol,firearms, gambling, military, nuclear power, tobacco and abortion.

3.2.11 Advance Sustainable Performance Index

The Advance Sustainable Performance Index (ASPI) Eurozone was launched inJuly 2001 by VIGEO, and is the first of the ASPI family, tracking the top 120sustainability performers chosen from the Dow Jones EURO STOXX benchmark.Calculation of the ASPI is based on the same methodology as the DJ STOXX indices.Relative weights of any of the companies are not permitted to exceed 10% of thetotal free float market capitalisation of the ASPI Eurozone. The ASPI Eurozone iscalculated in both Euro and USD, and is available in price and total returns.

The ASPI uses VIGEO’s rating system, with companies selected using positiverather than negative screening. Selection criteria are based on the social, environmen-tal and financial performance of firms. Five specific areas are considered, namely (1)community and international civil society; (2) corporate governance; (3) customersand suppliers; (4) health, safety and the environment; and (5) human resources andinternational labour standards. Companies receive scores in the five areas on a 0to 4 scale. If a company scores 0 in any one area, it is excluded; otherwise, thefinal score is a geometric average that determines the firm’s overall ranking. Theinclusion or exclusion of firms is based on annual and quarterly reviews.

3.2.12 Ethical Index

The Ethical Index Management System, created by E. Capital Partners, comprises afamily of equity and bond indices. The family of indices is intended to accommodatedifferent investment strategies, such as banks or fund managers. There are two mainequity indices, namely the Ethical Index Euro and the Ethical Index Global. TheEthical Index Euro includes 150 large-cap European companies of the EuropeanMonetary Union (EMU), 11 stock markets, plus the UK, Switzerland, Norway,Sweden and Denmark. The Ethical Index Global comprises 300 large-cap firms in24 countries, including the EMU zone.

There are four bond-based Ethical Indices, namely the Ethical Index EuroGovernment Bond, Corporate Bond, Agency and Supranational Bond and the EthicalIndex Global Government Bond. All of these bond indices must have a minimumrating, must be Baa3 (Moody’s) or BBB- (S&P), and have passed the E. CapitalPartners ethical positive and negative screens for inclusion in the index. The positivescreens are based on social and environmental performance, and the negative criteria




include armaments, nuclear energy, alcohol, tobacco, gambling, pornography andcontraceptives.

3.2.13 Jantzi Social Index (JSI)

Michael Jantzi Research Associates Inc. (MJRA) launched the Jantzi Social Index(JSI) in January 2000. Consisting of the top 60 Canadian companies that are screenedon a social and environmental basis, the JSI relies on a market capitalisation weightedcommon stock index that is modelled on both S&P500 and TSX60. The intentionof JSI is to act as a benchmark for the performance of socially screened portfoliosin Canada.

MJRA developed a combination of exclusionary and qualitative social screens.The screening aims to exclude companies that have a significant involvement innuclear power, tobacco products, and weapons-related contracts. Furthermore, itaims to exclude companies that have poor relationships with aboriginal commu-nities, fraudulent business records, poor employee records, poor environmental-performance records, significant operational problems outside Canada, and thatmanufacture unsafe products.

The JSI also includes, through positive screening, companies that have developedgood relationships with the communities in which they operate, encourage diversity,have strong relationships with employees, have progressive environmental records,and have excellent corporate governance records.

3.2.14 FTSE4Good

Launched in July 2001, the FTSE4Good index series is composed of fivebenchmark indices and their tradable equivalents. FTSE4Good Global, FTSE4GoodEurope, FTSE4Good UK and FTSE4Good US are the five benchmark indices,whereas FTSE4Good Global100, FTSE4Good Europe50, FTSE4Good UK50 andFTSE4Good US100 are the five tradable indices. The FTSE4Good indices arederived from the screened FTSE indices universe, specifically the FTSE-All ShareIndex (UK) and the FTSE All-World Developed Index (Global). All eligible firmsfrom either universe are included in the benchmarks. The companies are rankedaccording to the free-float and liquidity-adjusted market capitalisation, and the 100or 50 largest of the benchmark indices are included in the respective tradable indices.

The screening methodology of the FTSE4Good index family includes both positiveand negative criteria. Eligibility of a company depends on three broad standards,namely positive relationships with the stakeholders, environmental sustainability,and the support of human rights. The latter two standards were added in May 2002and March 2003, respectively. The FTSE4Good exclusionary criteria are intendedto exclude companies that are involved in tobacco, nuclear power and weapons,uranium extraction or processing, and the production of armaments. Companies aresubjected to constant monitoring in order to remain in the indices.




3.2.15 Natur Aktien Index (NAI)

The Natur Aktien Index (NAI), provided by the German based company Securvita,is composed of 25 stocks which are listed on recognised stock exchanges in the EU,North America and Japan. These stocks are diversified by country and by industry,and consist of 75% of companies with an annual turnover in excess of US$100million, and 25% of companies with an annual turnover of less than US$100 millionbut which are active in the development of innovative ecological products. The indexis not based on market capitalisation or a free float of the stocks, so that all 25 stocksare weighted equally.

The selection criteria to enter the NAI are based on ethical, social and ecologicalperformance of the companies included, as well as on some exclusionary criteria.The index seeks to include companies that (1) produce services and products thatare intended to contribute to sustainable solutions for fundamental ecological andsocial problems; and companies that lead in (2) product design, (3) technicalmanagement of its production and sales processes and (4) social managementof its production and distribution processes to improve social, ecological andethical aspects. If two of these positive criteria are satisfied, the company willbe included in the index. The companies undergo exclusionary screening, whichmust be passed in order to be included in the NAI (such as those listed inTable 2).

3.2.16 Humanix Ethical Index

The Humanix Ethical Index family, provided by the Swedish based companyHumanix Holding AB, was created in 2001 and it is composed of four indices,namely Humanix 175 Europe, Humanix 175 USA, Humanix 50 Sweden andHumanix 200 Global. All indices are capitalisation-weighted indices, and rep-resent the 175, 50 and 200 largest enterprises in their respective geographicalareas.

Humanix 175 Europe is composed of EU and EFTA countries. Humanix 50Sweden is essentially derived from companies listed on the Stockholm StockExchange. The Humanix 200 Global Index comprises all geographical regionscovered by the other sub-indices, together with Australia, Canada, Hong Kong,Singapore and Japan. Five exclusionary screening criteria are applied to companiesconsidered for the Humanix Ethical Index family, namely environment, human rightsand, if 3% or less of total turnover is derived from alcoholic beverages, tobaccoproducts and arms.

3.2.17 Other Indices

Some other indices that are not examined explicitly in this paper include, amongothers, the Global Ethical Standard, the Triodos Global Sustainability Universe andthe Axia Ethical Index. These indices have not been included in the discussionpresented here due to the lack of available information.




4. Modelling Time-varying Risk

Several models and methods from the financial econometrics literature will be usedto evaluate the time-varying risk or conditional volatility in the returns of each of theindices presented in Section 2. This section presents some of the techniques that areused to evaluate conditional time-varying empirical risk, namely Bollerslev’s (1986)symmetric generalised autoregressive conditional heteroscedasticity (GARCH) tech-nique, and its extension to accommodate asymmetric shocks, Glosten, Jagannathanand Runkles (1992) (GJR) model. These two models are the most widely used in thefinancial volatility literature. Hoti et al. (2004) were the first to apply these modelsto estimate time-varying environmental risk. McAleer (2005) provides a criticalcomparison of a variety of univariate and multivariate, conditional and stochastic,financial volatility models.

The GARCH model is an extension of the specification based on Engle’s (1982)symmetric autoregressive conditional heteroscedasticity (ARCH) model. In order todistinguish between the impact of negative and positive shocks on leverage throughchanges in the debt–equity ratio, Glosten et al. (1992) developed the GJR model,which is an asymmetric GARCH model. The use of diagnostic checks for thepresence of conditional volatility (or risk) is statistically equivalent to testing thesignificance of the parameter estimates in the conditional volatility model.

Consider the stationary AR(1)–GARCH(1, 1) model of rt, the return on asustainability or ethical index (as measured in log differences):

rt = φ1 + φ2rt−1 + εt |φ2| < 1 (1)

where the shocks to returns, εt , are given by

εt = ηt√

ht ηt ∼ iid(0, 1)

ht = ω + αε2t−1 + βht−1

(2)

in which ω > 0, α ≥ 0 and β ≥ 0 are sufficient conditions to ensure a strictlypositive conditional variance ht > 0. The ARCH (or α) effect captures the short-run persistence of shocks, and the GARCH (or β) effect indicates the contributionof shocks to long-run persistence, α + β. In GARCH models with normal errors,the parameters are typically obtained by the maximum likelihood estimation (MLE)method. In the absence of normality of the standardised residuals, η t , the parametersare obtained by the quasi-maximum likelihood estimation (QMLE) method (forfurther details see, for example, Li et al., 2002).

The GARCH model treats the effects of positive and negative shocks in asymmetric manner in terms of the effects of shocks on the conditional variance,ht. The GJR(1, 1) model, on the other hand, accommodates the asymmetric effectsof shocks by allowing negative shocks to have a greater impact on volatility andgreater leverage than positive shocks of a similar magnitude. The asymmetric GJR(1,1) model is given as follows:

ht = ω + [α+ γ I (ηt−1)]ε2t−1 + βht−1 (3)




where ω > 0, α ≥ 0, α + γ ≥ 0 and β ≥ 0 are sufficient conditions for ht > 0,and I (η t ) is an indicator variable defined by

I (ηt ) ={

1 εt < 00 εt ≥ 0

as η t has the same sign as εt . The indicator variable distinguishes between positiveand negative shocks, where the asymmetric effect (γ ≥ 0) measures the contributionof shocks to both short-run persistence (α + γ /2) and long-run persistence (α +β + γ /2).

Several important theoretical results are relevant for the GARCH model. Lingand McAleer (2002a) established the necessary and sufficient conditions for strictstationarity and ergodicity, as well as for the existence of all moments, for theunivariate GARCH(p, q) model, while Ling and McAleer (2003) demonstrated thatthe QMLE for GARCH(p, q) is consistent if the second moment is finite, E(ε2

t ) <

∞, and asymptotically normal if the fourth moment is finite, E(ε4t ) < ∞. The

necessary and sufficient condition for the existence of the second moment of εt forthe GARCH(1, 1) model is α + β < 1.

Another important result is that the log-moment condition for the QMLE ofGARCH(1, 1), which is a weak sufficient condition for the QMLE to be consistentand asymptotically normal, is given by E[log (αη2

t + β)] < 0. These results werederived in Elie and Jeantheau (1995) and Jeantheau (1998) for consistency, and inBoussama (2000) for asymptotic normality. In practice, it is more straightforward toverify the second moment condition than the weaker log-moment condition, as thelatter is a function of unknown parameters and the mean of a random variable.

The GJR model has also had some important theoretical developments. In the caseof symmetry of η t , the regularity condition for the existence of the second momentof GJR(1, 1) is α + β + γ /2 < 1 (see Ling and McAleer, 2002b). Moreover, theweak log-moment condition for GJR(1, 1), E(ln {[α + γ I (η t )]η

2t + β}) < 0, is

sufficient for the consistency and asymptotic normality of the QMLE (see McAleeret al., 2007a).

5. Data

The data were obtained from Datastream and Reuters. Although some of theindices have been launched only recently, the companies producing the indiceshave occasionally requested that the index managing firm (S&P500, for example)reconstruct the index to go further into the past.

The benchmark FTSE4Good indices are used rather than the tradable indices. Itis worth noting that the tradable indices and their equivalent benchmarks are almostidentical in their correlations. Moreover, no empirical analysis is undertaken forthe FTSE4Good Japan index as it was launched only very recently, so that thereare insufficient observations for obtaining any reliable estimates of time-varyingconditional volatility.

In general, the indices include a wide variety of measures of environmentalelements to examine various aspects of environmental issues and human dimensions.




This may limit empirical analysis when searching for relationships among diverseenvironmental and social elements. Such problems arise from the fact that anunderstanding of sustainability is increasingly complex and requires more accuratedata. However, indices are based on simple aggregation procedures, while specialisedindices are limited as they are concerned with only a limited aspect of the broadconcept of sustainability.

GDP, for example, is a powerful measure, but is limited to the output of a marketeconomy. Its narrow measure ignores several important aspects, such as the state ofecosystems, and environmental and social costs (arising from pollution and resourcedepletion). Despite the increasing information contained in reports and measures ofenvironmental factors, of both an ecosystem and human nature, few studies haveattempted to incorporate these environmental issues comprehensively into an indexwhich might be as powerful and informative as GDP.

It is worth noting that measurement errors are also present in the construction ofthe various indices. For instance, ESI uses a number of ‘proxies’ in its construction(World Economic Forum, 2002, p. 6). When ESI is used in cross-sectional analysis,the results may suffer from endogeneity and measurement error problems. Dataproblems seem to be a major hindrance to the measurement of environmental riskand sustainability. The ESI report indicates that a number of crucial environmentalfactors had been omitted, while others were measured imprecisely. Measurementerrors in the construction of the index are a serious problem when it is used inempirical analysis.

All the sustainable and ethical indices to be analysed empirically are price indices,and are given in Figure 1. The sustainability indices have different trending patterns,as follows: (1) the Ethibel Global, Ethibel Americas and Ethibel Asia-Pacific indicesgenerally having positive trends; (2) the Ethibel Europe has a weaker positive trend;(3) the ASPI Eurozone has a break in trend, with a positive trend followed bya negative trend; (4) the Calvert Social index, Ethical Index Euro and EthicalIndex Global generally have negative trends; and (5) the FTSE4Good (Europe),FTSE4Good (UK), FTSE4Good (USA) and FTSE4Good (Global) indices havebreaks in trend, with positive trends followed by negative trends. Each sustainabilityindex has a significant clustering in the index returns, which are given as the logdifferences of the corresponding index.

The starting and ending dates for all series, together with the currency denomi-nations and sources of the data, are given in Table 3. The Humanix Ethical indices,KLD indices, NAI and JSI are not analysed empirically due to the lack of dataavailability. Furthermore, we do not analyse the conditional volatility in the DJSI asthese have already been analysed in Hoti et al. (2004).

6. Empirical Results

As shown in Figure 2, which provides the sample and estimated GARCH(1, 1)volatilities, there is substantial volatility clustering in most of the series, but less sofor three ESI. The Ethibel Global, Ethibel Americas and Ethibel Europe series do nothave obvious volatility clustering, but do seem to have some extreme observations. In




800

900

1000

1100

1200

1300

2003M01 2003M07 2004M01 2004M07

Ethibel Global

-.12

-.08

-.04

.00

.04

.08

2003M01 2003M07 2004M01 2004M07

Ethibel Global

900

1000

1100

1200

1300

1400

1500

2003M01 2003M07 2004M01 2004M07

Ethibel Americas

-.10

-.05

.00

.05

.10

2003M01 2003M07 2004M01 2004M07

Ethibel Americas

700

800

900

1000

1100

1200

2003M01 2003M07 2004M01 2004M07

Ethibel Asia-Pacific

-.06

-.04

-.02

.00

.02

.04

2003M01 2003M07 2004M01 2004M07


600

700

800

900

1000

1100

1200

2003:01 2003:07 2004:01 2004:07

Ethibel Europe

-.16

-.12

-.08

-.04

.00

.04

.08

.12

.16

.20

2003M01 2003M07 2004M01 2004M07

Ethibel Europe

Figure 1. Daily Data for Sustainability Indices (Left) and Index Returns (Right).




200

400

600

800

92 93 94 95 96 97 98 99 00 01 02 03 04

ASPI Eurozone

-.06

-.04

-.02

.00

.02

.04

.06

.08

92 93 94 95 96 97 98 99 00 01 02 03 04

ASPI Eurozone

40

50

60

70

80

90

100

110

2000 2001 2002 2003 2004

Calvert Social Index

-.06

-.04

-.02

.00

.02

.04

.06

.08

2000 2001 2002 2003 2004


4000

5000

6000

7000

8000

02M01 02M07 03M01 03M07 04M01 04M07

Ethical Index Euro

-.06

-.04

-.02

.00

.02

.04

.06

.08

02M01 02M07 03M01 03M07 04M01 04M07

Ethical Index Euro

400

500

600

700

800

900

02M01 02M07 03M01 03M07 04M01 04M07

Ethical Index Global

.00

.02

.04

.06

02M01 02M07 03M01 03M07 04M01 04M07


Figure 1. Continued.




1000

2000

3000

4000

5000

6000

7000

97 98 99 00 01 02 03 04

FTSE4Good Europe ( )

-.08

-.06

-.04

-.02

.00

.02

.04

.06

.08

97 98 99 00 01 02 03 04


2500

3000

3500

4000

4500

5000

5500

6000

6500

97 98 99 00 01 02 03 04

FTSE4Good UK (£)

-.06

-.04

-.02

.00

.02

.04

.06

97 98 99 00 01 02 03 04

FTSE4Good UK (£)

2000

3000

4000

5000

6000

7000

8000

97 98 99 00 01 02 03 04

FTSE4Good USA (US$)

-.08

-.06

-.04

-.02

.00

.02

.04

.06

.08

97 98 99 00 01 02 03 04

FTSE4Good USA (US$)

2000

3000

4000

5000

6000

7000

8000

97 98 99 00 01 02 03 04

FTSE4Good Global (US$)

-.06

-.04

-.02

.00

.02

.04

.06

97 98 99 00 01 02 03 04






Table 3. Data Sources.

Index Data source Currency Starting date Ending date

Ethibel Reuters USD 30/09/2002 11/09/2004ASPI Eurozone Datastream USD 31/12/1991 11/09/2004Calvert Social Index Datastream USD 28/04/2000 11/09/2004Ethical Index Datastream USD 12/09/2001 11/09/2004FSTE4Good (Global) Datastream USD 01/07/1996 08/10/2004FSTE4Good (USA) USDFSTE4Good (UK) GBPFSTE4Good (Europe) Euro

comparison, all the other volatility series appear similar to typical financial volatilityseries, with clear clustering but no extreme observations or outliers.

Using the data on the daily indices, the conditional mean is modelled ineach case as a stationary AR(1) process, as given in equation (1). The uni-variate AR(1)–GARCH(1, 1) and AR(1)–GJR(1, 1) models are used to provideestimates of the symmetric and asymmetric conditional volatilities, respectively,associated with all the indices. The Berndt et al. (BHHH; 1974) algorithm inthe econometric software package EViews 5 is used to maximise the conditionallog-likelihood function. Using the RATS 6 econometric software package yieldedvirtually identical results. Several different sets of initial values have been usedin each case, but do not lead to substantial differences in the estimates. Tables4 and 5 report two sets of t-ratios associated with each parameter estimate,namely the asymptotic t-ratios and the Bollerslev and Wooldridge (1992) robustt-ratios.

In Table 4, the short-run contribution of shocks to the index returns, namely theARCH (or α) effect, is estimated to be insignificant for the Ethibel Asia-Pacific andEthibel Europe indices, although they are both of reasonable magnitudes, as in typicalfinancial returns data. However, the ARCH effect is both reasonable in magnitudeand also statistically significant for the other 10 data series, which indicates a strongpersistence of shocks in the short run. The contribution to the long-run persistence ofshocks, namely the GARCH (or β) effect, is statistically significant for all indices,except for the Ethibel Global index, for which it is also small in magnitude. It isinteresting to note that the magnitude of the GARCH effects is reasonably, though notexcessively, high in all cases, which indicates a strong contribution to the persistenceof shocks in the long run. Overall, the GARCH(1, 1) estimates indicate strongpositive and significant estimates for the ARCH and GARCH effects. Moreover, asthe empirical log-moment condition is satisfied in all cases, including the single casefor Ethibel Europe in which the empirical second moment condition is not satisfied,the QMLE of the parameters are consistent and asymptotically normal. This permitsvalid inferences to be drawn for purposes of sensible empirical analysis.

Table 5 reports the results of estimating the GJR(1, 1) model. The ARCH (or α)estimates are typically insignificant, except for the ASPI Eurozone index, and the




0

10000

20000

30000

40000

50000

60000

70000

2003M01 2003M07 2004M01 2004M07

Ethibel Global

.000

.001

.002

.003

.004

.005

.006

.007

.008

100 200 300 400 500

Ethibel Global

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

2003M01 2003M07 2004M01 2004M07

Ethibel Americas

.000

.001

.002

.003

.004

.005

.006

.007

.008

100 200 300 400 500

Ethibel Americas

0

10000

20000

30000

40000

50000

2003:01 2003:07 2004:01 2004:07


.0000

.0005

.0010

.0015

.0020

.0025

.0030

.0035

100 200 300 400 500


0

10000

20000

30000

40000

50000

60000

70000

2003:01 2003:07 2004:01 2004:07

Ethibel Europe

.000

.005

.010

.015

.020

.025

.030

100 200 300 400 500

Ethibel Europe

Figure 2. Sample Volatility (Left) and Estimated GARCH(1, 1) Volatility (Right).




0

40000

80000

120000

160000

200000

240000

280000

320000

92 93 94 95 96 97 98 99 00 01 02 03 04

ASPI Eurozone

92 93 94 95 96 97 98 99 00 01 02 03 04

ASPI Eurozone

0

200

400

600

800

1000

1200

2000 2001 2002 2003 2004


.000

.001

.002

.003

.004

.005

2000 2001 2002 2003 2004


0

500000

1000000

1500000

2000000

2500000

3000000

3500000

02M01 02M07 03M01 03M07 04M01 04M07

Ethical Index Euro

.000

.001

.002

.003

.004

.005

02M01 02M07 03M01 03M07 04M01 04M07

Ethical Index Euro

0

10000

20000

30000

40000

50000

02M01 02M07 03M01 03M07 04M01 04M07


.0000

.0004

.0008

.0012

.0016

.0020

.0024

.0028

.0032

02M01 02M07 03M01 03M07 04M01 04M07






0

1000000

2000000

3000000

4000000

5000000

96 97 98 99 00 01 02 03 04


.000

.001

.002

.003

.004

.005

.006

96 97 98 99 00 01 02 03 04


0

400000

800000

1200000

1600000

2000000

2400000

2800000

96 97 98 99 00 01 02 03 04

FTSE4Good UK (£)

.0000

.0005

.0010

.0015

.0020

.0025

.0030

.0035

96 97 98 99 00 01 02 03 04

FTSE4Good UK (£)

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

96 97 98 99 00 01 02 03 04

FTSE4Good USA (US$)

.000

.001

.002

.003

.004

.005

.006

.007

96 97 98 99 00 01 02 03 04

FTSE4Good USA (US$)

0

1000000

2000000

3000000

4000000

5000000

6000000

96 97 98 99 00 01 02 03 04


.0000

.0005

.0010

.0015

.0020

.0025

.0030

96 97 98 99 00 01 02 03 04






Table 4. AR(1)–GARCH(1, 1).

Log SecondIndex ω α β moment moment

Ethibel Global 0.00007 0.206 0.206 −1.155 0.41110.12 3.405 2.634

1.638 1.966 0.609

Ethibel Americas 0.00002 0.175 0.628 −0.272 0.8032.935 3.898 6.5832.282 1.909 5.097

Ethibel Asia-Pacific 0.000008 0.047 0.899 −0.059 0.9451.776 3.577 24.960.980 1.135 10.21

Ethibel Europe 0.00001 0.306 0.761 −0.065 1.0672.983 4.573 18.870.787 0.980 4.275

ASPI Eurozone 0.000001 0.061 0.933 −0.011 0.9934.193 11.52 168.62.946 5.677 83.14

Calvert Social Index 0.000001 0.068 0.926 −0.011 0.9941.745 5.406 72.031.959 4.689 65.82

Ethical Index Euro 0.000002 0.084 0.898 −0.026 0.9834.005 4.765 50.311.540 3.972 37.48

Ethical Index Global 0.000001 0.049 0.941 −0.013 0.9902.182 4.306 75.051.399 3.148 56.48

FTSE4Good (Europe) 0.000002 0.082 0.905 −0.020 0.9873.838 8.218 78.653.129 5.771 60.68

FTSE4Good (UK) 0.000002 0.080 0.908 −0.019 0.9873.795 8.634 85.003.123 5.521 60.58

FTSE4Good (USA) 0.000002 0.074 0.916 −0.017 0.9893.277 10.35 101.03.087 4.493 57.25

FTSE4Good (Global) 0.000001 0.074 0.915 −0.017 0.9893.242 9.177 94.853.180 4.858 58.80

Note: The three entries corresponding to each parameter are their estimates, their asymptotic t-ratiosand the Bollerslev and Wooldridge (1992) robust t-ratios.




Tabl

e5.

AR

(1)–

GJR

(1,

1).

Log

Sec

ond

Index

ωα

γβ

α+

γ/2

mom

ent

mom

ent

Eth

ibel

Glo

bal

0.0

0006

0.5

52

−0.4

29

0.1

49

0.3

37

−0.9

85

0.4

87

11.9

45.0

11

−3.8

05

3.1

63

2.4

32

1.3

87

−0.9

95

0.6

73

Eth

ibel

Am

eric

as0.0

0004

0.2

43

−0.1

84

0.5

50

0.1

51

−0.3

21

0.7

00

2.5

24

3.3

38

−2.8

06

3.6

28

3.7

78

1.5

34

−0.9

42

5.8

95

Eth

ibel

Asi

a-P

acif

ic0.0

0001

−0.0

16

0.0

92

0.8

90

0.0

30

−0.1

36

0.9

20

2.2

13

−0.7

61

3.1

18

21.5

51.5

43

−0.5

94

1.5

88

12.8

9

Eth

ibel

Euro

pe

0.0

003

0.2

22

−0.2

85

0.2

62

0.0

80

−1.0

88

0.3

42

7.9

24

9.7

41

−7.1

49

2.7

72

2.1

17

1.5

74

−2.1

78

0.8

18

AS

PI

Euro

zone

0.0

00001

0.0

30

0.0

49

0.9

35

0.0

55

−0.0

37

0.9

90

5.1

61

4.1

55

5.2

22

167.3

3.4

01

2.7

40

3.0

13

86.3

7

Cal

ver

tS

oci

alIn

dex

0.0

00001

−0.0

19

0.1

27

0.9

51

0.0

45

−0.0

70

0.9

96

2.5

20

−2.1

35

6.8

03

85.7

63.1

18

−1.3

12

4.6

14

93.6

7

Eth

ical

Index

Euro

0.0

00002

−0.0

16

0.1

58

0.9

25

0.0

63

−0.0

96

0.9

88

4.1

25

−1.3

29

5.0

56

68.5

82.2

92

−0.7

12

4.9

58

52.0

3




Tabl

e5.

Con

tinu

ed.

Log

Sec

ond

Index

ωα

γβ

α+

γ/2

mom

ent

mom

ent

Eth

ical

Index

Glo

bal

0.0

000009

−0.0

02

0.0

90

0.9

51

0.0

43

−0.0

52

0.9

94

2.0

37

−0.2

38

4.5

59

86.1

91.6

47

−0.0

91

2.8

50

59.5

4

FT

SE

4G

ood

(Euro

pe)

0.0

00003

0.0

20

0.0

90

0.9

14

0.0

65

−0.0

68

0.9

79

5.2

70

1.6

71

6.3

56

79.0

73.6

12

1.2

60

4.0

08

64.4

1

FT

SE

4G

ood

(UK

)0.0

00002

0.0

07

0.0

96

0.9

31

0.0

55

−0.0

64

0.9

86

4.5

67

0.6

19

7.1

83

89.9

53.3

87

0.6

01

4.4

31

84.3

0

FT

SE

4G

ood

(US

A)

0.0

00003

−0.0

22

0.1

64

0.9

25

0.0

60

−0.1

03

0.9

85

6.3

77

−2.7

52

12.9

0111.3

4.0

65

−1.9

37

5.9

28

69.9

6

FT

SE

4G

ood

(Glo

bal

)0.0

00002

−0.0

12

0.1

37

0.9

27

0.0

57

−0.0

89

0.9

84

6.3

59

−1.4

22

10.0

7102.9

95

4.3

48

−0.9

58

5.3

31

73.1

69

No

te:

Th

eth

ree

entr

ies

corr

esp

on

din

gto

each

par

amet

erar

eth

eir

esti

mat

es,th

eir

asy

mp

toti

ct-

rati

os

and

the

Bo

ller

slev

and

Wo

old

rid

ge

(19

92

)ro

bust

t-ra

tio

s.




signs can vary, depending on the index. These results suggest that the effect of apositive shock on the index returns is not significant, and hence does not have alasting short-run effect. On the other hand, the GARCH estimates are all positive andare typically highly significant, except for the Ethibel Global and Europe indices,for which the GARCH effects are also small in magnitude. Overall, as in the caseof the GARCH(1, 1) estimates in Table 4, the magnitude of the GARCH effectsis reasonably, though not excessively, high in most cases, which indicates a strongcontribution to the persistence of shocks in the long run.

The GJR asymmetry parameter, γ , is typically estimated to be positive and highlysignificant, which suggests that negative shocks to index returns have a greaterimpact on volatility than do positive shocks of a similar magnitude. Thus, theleverage of negative shocks to index returns exceeds that of positive shocks. Theasymmetry parameter estimates for the Ethibel indices are highly variable, with theestimates for the Ethibel Global, Ethibel Americas and Ethibel Asia-Pacific beinginsignificant. Somewhat surprisingly, the Ethibel Europe index estimate of γ issignificant and negative, which is contrary to expectations. This may be the effect of apossible outlier towards the end of the series, as seen in Figures 1 and 2. These resultssuggest that the effect of a negative shock on the index returns is typically significant,and hence has a lasting short-run effect. The GARCH effect is insignificant for theEthibel Global and Ethibel Europe indices. Finally, the log-moment moment andthe empirical second condition are satisfied in all cases. Therefore, the QMLE ofthe parameters are consistent and asymptotically normal, which provides anotherrobust set of results.

The strong empirical evidence of the existence of conditional volatility in allseries is given in Figure 2, together with the sample volatility, which is defined asthe squared deviation from the mean of the respective series. It is clear that there isstrong evidence of volatility clustering, although the ESI show evidence of volatilityclustering. There is also the possibility of some extreme observations in the EthibelEurope index.

7. Concluding Remarks

Environmental sustainability indices, such as the DJSI and the ESI, quantify thedevelopment and promotion of sustainable social, ethical and environmental valuesin the community. Moreover, such indices provide a benchmark for managingsustainability portfolios, and developing financial products and services that arelinked to sustainable economic, environmental, social and ethical criteria.

SRI has become an important way for investors to diversify their portfolio, andalso to promote socially and environmentally sound management and economicactivities of firms. There exist several benchmarks for SRI, imposing exclusionaryor/and positive criteria for the selection of firms to the benchmark indices. Themethodology and criteria used for screening the companies are specific to eachindex family, though many of the issues considered are similar across the variousindices.




This paper analysed existing sustainability and ethical indices in environmentalfinance, and evaluated empirical environmental risk by examining conditionalvolatility clustering that is inherent in these indices. Alternative financial volatil-ity models were estimated to analyse the underlying conditional volatility (ortime-varying risk) that is inherent in five families of environmental sustainabilityindices.

The empirical results were mixed for the ESI family, which showed mixedevidence with respect to the effect of the short-run and long-run persistence of shocksto the index returns, as well as the asymmetric effect of negative relative to positiveshocks. For all the other indices, both the GARCH(1, 1) and GJR(1, 1) modelswere supported empirically. The ARCH effects for GJR(1, 1) were insignificant formost indices, except the ASPI Eurozone index, whereas these effects were significantwhen estimating GARCH(1, 1), except for the Ethibel Americas, Ethibel Asia-Pacificand Ethibel Europe indices. Overall, the time series performance of the sustainabilityand ethical indices was virtually identical to that of a typical financial time series.There was clear evidence of volatility clustering in the shocks to the sustainabilityindex returns, and these were modelled adequately using two univariate conditionalvolatility models. The results indicate that financial assets such as sustainability andethical indices provide asset managers and financial traders with a benchmark. Sucha benchmark enables an assessment of sustainability portfolios and the developmentof financial services for corporate sustainability and SRI.

Acknowledgements

The authors wish to thank two referees for very helpful comments and suggestions. Theviews expressed in this paper are those of the authors and do not necessarily reflect those ofthe Department of Treasury and Finance, Western Australia, and the Hong Kong MonetaryAuthority. The first two authors wish to acknowledge the financial support of the AustralianResearch Council.

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Documents

Measuring Risk in Environmental Finance