ESG Investment in the Global Economy (SpringerBriefs in Economics) 9811629927, 9789811629921

Citation preview

SPRINGER BRIEFS IN ECONOMICS KOBE UNIVERSITY SOCIAL SCIENCE RESEARCH SERIES

Tadahiro Nakajima · Shigeyuki Hamori · Xie He · Guizhou Liu · Wenting Zhang · Yulian Zhang · Tiantian Liu

ESG Investment in the Global Economy

SpringerBriefs in Economics Kobe University Social Science Research Series

Series Editors Yunfang Hu, Kobe University Graduate School of Economics, Kobe, Japan Shigeyuki Hamori, Kobe University Graduate School of Economics, Kobe, Japan Editorial Board Masahiro Enomoto, Kobe University RIEB, Kobe, Japan Yoshihide Fujioka, Kobe University Graduate School of Economics, Kobe, Japan Yuka Kaneko, Kobe University Graduate School of International Cooperation Studies, Kobe, Japan Kazumi Suzuki, Kobe University Graduate School of Business Administration, Kobe, Japan Kenji Yamamoto, Kobe University Graduate School of Law, Kobe, Japan

The Kobe University Social Science Research Series has been established as a subseries of the SpringerBrief in Economics Series, but in fact this exciting interdisciplinary collection encompasses scholarly research not only in the economics but also in law, political science, business and management, accounting, international relations, and other subdisciplines within the social sciences. As a national university with a special strength in the social sciences, Kobe University actively promotes interdisciplinary research. This series is not limited only to research emerging from Kobe University’s faculties of social sciences but also welcomes cross-disciplinary research that integrates studies in the arts and sciences. Kobe University, founded in 1902, is the second oldest national higher education institution for commerce in Japan and is now a preeminent institution for social science research and education in the country. Currently, the social sciences section includes four faculties — Law, Economics, Business Administration, and International Cooperation Studies — and the Research Institute for Economics and Business Administration (RIEB). There are some 230-plus researchers who belong to these faculties and conduct joint research through the Center for Social Systems Innovation and the Organization for Advanced and Integrated Research, Kobe University. This book series comprises academic works by researchers in the social sciences at Kobe University as well as their collaborators at affiliated institutions, Kobe University alumni and their colleagues, and renowned scholars from around the world who have worked with academic staff at Kobe University. Although traditionally the research of Japanese scholars has been publicized mainly in the Japanese language, Kobe University strives to promote publication and dissemination of works in English in order to further contribute to the global academic community.

More information about this subseries at http://www.springer.com/series/15423

Tadahiro Nakajima Shigeyuki Hamori Xie He Guizhou Liu Wenting Zhang Yulian Zhang Tiantian Liu •

•

•

• •

•

ESG Investment in the Global Economy

123

Tadahiro Nakajima The Kansai Electric Power Company, Incorporated Osaka, Japan

Shigeyuki Hamori Graduate School of Economics Kobe University Kobe, Japan

Xie He Graduate School of Economics Kobe University Kobe, Japan

Guizhou Liu Beijing Dajia Internet Information Technology Co., Ltd. Beijing, China

Wenting Zhang Graduate School of Economics Kobe University Kobe, Japan

Yulian Zhang Graduate School of Economics Kobe University Kobe, Japan

Tiantian Liu Graduate School of Economics Kobe University Kobe, Japan

ISSN 2191-5504 ISSN 2191-5512 (electronic) SpringerBriefs in Economics ISSN 2520-1697 ISSN 2520-1700 (electronic) Kobe University Social Science Research Series ISBN 978-981-16-2992-1 ISBN 978-981-16-2990-7 (eBook) https://doi.org/10.1007/978-981-16-2990-7 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

1 ESG Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tadahiro Nakajima 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Activities and Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Corporate Development and Expansion of Its Stakeholders . 1.5 Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Market Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Anomalies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 Limits of the MPT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9 Guide to Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

..... . . . . . . . . . .

2 Does ESG Index Have Strong Conditional Correlations with Sustainability Related Stock Indices? . . . . . . . . . . . . . . . . Wenting Zhang, Tadahiro Nakajima, and Shigeyuki Hamori 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Empirical Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Data and Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . 2.4 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 AR-EGARCH Specification . . . . . . . . . . . . . . . . . . 2.4.2 A-DCC Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 AR (1) Model for the Estimated DCC with Dummy Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

. . . . . . . . . .

1 2 4 7 10 11 13 15 17 18

....

21

. . . . . .

. . . . . .

21 24 26 29 29 31

.... .... ....

33 34 35

. . . . . . . . . .

. . . . . . . . . .

. . . . . .

. . . . . . . . . .

. . . . . .

v

vi

3 Measuring Tail Dependencies Between ESG and Renewable Energy Stocks: A Copula Approach . . . . . . . . . . . . . . . . . . . Xie He, Guizhou Liu, and Shigeyuki Hamori 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Empirical Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Copula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Time-Varying Copula . . . . . . . . . . . . . . . . . . . . . 3.2.3 Marginal Density . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Data and Summary Statistics . . . . . . . . . . . . . . . . . . . . . 3.4 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Portfolio Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Contents

......

37

. . . . . . . . . .

. . . . . . . . . .

37 38 38 39 41 42 44 46 51 52

..

53

.. ..

53 55

..

55

. . . . . . .

. . . . . . .

56 57 60 61 64 69 70

.....

71

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

4 Which Factors Will Affect the ESG Index in the USA and Europe: Stock, Crude Oil, or Gold? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tiantian Liu, Tadahiro Nakajima, and Shigeyuki Hamori 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Empirical Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Measures of the Directional Spillover Effects in the Time Domain . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Measures of the Directional Spillover Effects in the Frequency Domain . . . . . . . . . . . . . . . . . . . . . . 4.3 Data and Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Analysis of Full-Sample Spillover Effects . . . . . . . . . . 4.4.2 Analysis of Time-Varying Spillover Effects . . . . . . . . . 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 How Does the Environmental, Social, and Governance Index Impacts the Financial Market and Macro-Economy? . . . . . . . Yulian Zhang, Tadahiro Nakajima, and Shigeyuki Hamori 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Empirical Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 DY 12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 BK 18 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Data and Summary Statistics . . . . . . . . . . . . . . . . . . . . . . 5.4 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Full Sample Analysis . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Rolling-Window Analysis . . . . . . . . . . . . . . . . . . . 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . . .

. 71 . 73 . 73 . 74 . 76 . 79 . 79 . 89 . 91 . 92 . 100

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Chapter 1

ESG Investment Tadahiro Nakajima

1.1

Introduction

ESG integrates the three factors of environment, social, and governance. A company’s commitment to ESG issues represents its sustainability; however, it is not directly reflected in its financial information. In traditional investment, we use indicators based on financial information such as price book-value ratio (PBR), price-earnings ratio (PER), return on equity (ROE), and expected future cash flows, as one of the investment criteria. On the other hand, in ESG investment, we must sufficiently reflect the company’s efforts on ESG issues, which comprise non-financial information, in our investment decisions. Recently, we have widely accepted the philosophy that a company’s ESG efforts are essential for its long-term growth; therefore, many companies proactively disseminate information on their various approaches to ESG issues through their annual reports, integrated reports, corporate social responsibility reports, environmental reports, sustainability reports, and their websites. The following are examples of ESG issues: Environment: climate change, greenhouse gas emissions, air pollution, resource efficiency, biodiversity, toxic emissions and waste, clean technology Social: human rights, labor management, health and safety, human diversity, human capital development, relationship with the local community, product liability Governance: Corporate governance, compliance, corruption, board diversity, ownership, executive pay, tax transparency, risk management. Since ESG investment is expected to play a social role, it can be defined as a socially responsible investment (SRI). In other words, ESG investment is aimed at both financial and social returns. This social return is a decisive difference from traditional investments.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. Nakajima et al., ESG Investment in the Global Economy, Kobe University Social Science Research Series https://doi.org/10.1007/978-981-16-2990-7_1

1

2

1 ESG Investment

To focus on investment performance, such as profitability improvement and risk reduction by ESG investment, this book discusses the background of ESG investment and present a few empirical analyses of ESG investment cases after introducing the methods adopted.

1.2

History

Since the emergence of SRI in the early twentieth century, ethical investment with a focus on social issues has been gradually spread by investors who value social returns. Then, in 2000, the institutional positioning of ESG investment became clearer. In 2006, the Principles for Responsible Investment (PRI) were launched at the New York Stock Exchange (NYSE), based on the notion that the performance of investment portfolios can be affected by ESG issues. Since then, ESG investment has become the focus of attention in terms of financial returns, and institutional investors have been expanding their participation in ESG investments. A more detailed history of ESG investment is introduced in the next paragraph. During the 1920s, a mutual fund which excluded stocks of companies involved in certain industries (e.g., alcohol, gambling, tobacco) from its portfolio as “sin stock,” was established in the United States. This was just an ethical investment in the sense of addressing social issues. However, this investment method can be interpreted as “Negative/exclusionary screening,” one of the ESG investment methods described below. Prior to this, the Quaker applied social standards to their investment standards in seventeenth century England. However, it was not a public offering for funds from general investors. During the 1960s and 1970s, changes in the values of civil society became visible in the US, such as the African American Civil Rights Movement and the opposition to the Vietnam War. In this context, economic activities have also placed importance on social issues, and SRI and shareholder proposals that prioritize social ethics have come to the forefront. It was around this time that the investment funds that excluded stocks of companies involved in the Vietnam War from their portfolio and those that consisted of stocks of excellent companies in environmental protection activities and/or companies with respect to human rights emerged. Since then, social movements have had an increasing impact on economic activity. The non-investment movement in the Republic of South Africa, which became popular in the US during the 1970s and 1980s, might have contributed to the end of the apartheid. On the contrary, in Europe, the philosophy of SRI, which emphasizes following social morals for investment activities, has gradually spread to charities, educational institutions, and individuals, since church funds have traditionally been managed on the basis of religious ethics. However, the main purpose of SRI for investors was not to obtain financial returns but to contribute to reducing external costs and increasing external profits, motivated by religious ethics and social movements. In other words, it can be regarded as a social movement by shareholders.

1.2 History

3

In the 1990s, the globalization of the economy accelerated due to the market economy triggered by the disappearance of the Soviet Union, the rise of huge multinational companies, and the rapid growth of emerging countries. In 1992, Agenda 21, an action plan that countries and relevant international organizations execute to achieve “sustainable development” in the twenty-first century, was adopted at the Earth Summit held by the United Nations (UN) in Rio de Janeiro, Brazil. In 1994, John Brett Elkington, a world authority on corporate responsibility and sustainable development, proposed a “triple bottom line” that evaluates the performance of corporate activities on three axes: economic, environmental, and social. Around this time, the concept of corporate social responsibility (CSR) became generalized worldwide, and as a result, boycott campaigns were carried out to prompt global companies to correct social problems (e.g., child labor, forced labor, long working hours, low wage labor, sexual harassment) occurring in overseas subcontractor factories. As a result, many investors think that a corporation’s CSR assessment, which is non-financial information, should be incorporated into their investment decisions to secure long-term profits. However, it was argued that SRI may breach the fiduciary duty because of the potential loss of profitability, and it has been intensely debated. In managing pension funds, while the Employee Retirement Income Security Act (ERISA) stipulates that investment returns should be maximized and the risk of significant large losses should be minimized, the US Department of Labor expressed the view that considering the other factors, profitability would not be regarded as a fiduciary duty breach in 1998. This so-called Calvert Letter, which can be interpreted as allowing trustees to consider ESG factors as long as these do not negatively affect portfolio performance in terms of returns, risk, liquidity, and diversification, paved the way for ESG investment in corporate pension funds in the US. In the United Kingdom (UK), the Pension Act 1995 and other related regulations require pension funds to include their investment policies in their annual reports. Moreover, the revised Act, which came into effect in 2000, has mandated that trustees should document and disclose not only their portfolio composition, its risks, and their expected returns, but also their investment policies, including the factor, whether to consider social, environmental, and ethical matters when buying, holding, or selling securities and the exercise of voting rights attached to investments, if any. This regulation has allowed ESG investment in the management of pension funds, although it does not proactively encourage ESG investment. Since then, the introduction of a system similar to that in the UK in other European countries has led to the expansion of ESG investments. In 2006, the UN advocated the PRI, which directly referred to ESG investment, to the financial industry, and the launching ceremony was held at the NYSE, attended by the 7th UN Secretary-General Kofi Annan. The PRI are voluntary principles (i.e., not legally binding), but they are also guidelines for ESG investment for institutional investors not to breach fiduciary duty. The PRI consist of six steps:

4

1 ESG Investment

1. We will incorporate ESG issues into investment analysis and decision-making processes. 2. We will be active owners and incorporate ESG issues into our ownership policies and practices. 3. We will seek appropriate disclosure on ESG issues by the entities in which we invest. 4. We will promote acceptance and implementation of the Principles within the investment industry. 5. We will work together to enhance our effectiveness in implementing the Principles. 6. We will each report on our activities and progress towards implementing the Principles. Moreover, the PRI state that the performance of investment portfolios can be affected by ESG issues and that institutional investors are obligated to pursue the most desired long-term interests by their beneficiaries. In other words, it can be inferred that investors should incorporate the effects of ESG issues on long-term corporate value into their investment strategies. This indicates that the ESG investment systematized by the PRI can be regarded as an investment method that pursues profits. In this respect, ESG investment is distinctly different from the previous SRI, whose main purpose is social movement. The framework of ESG investment converted the companies’ response to ESG issues in becoming an internal economy in the form of securing long-term profits. The PRI have continued to grow consistently since their inception in 2006. From March 30 2006 to March 30, 2020, the number of signatories increased from 63 to 3038, and the assets under management increased from 6.5 trillion USD to 103.4 trillion USD.

1.3

Activities and Strategies

The Global Sustainable Investment Alliance (GSIA) consists of seven ESG investment associations in the US, Canada, Europe, the UK, the Netherlands, Australia, and Japan, and provides various data on ESG investment through the “Global Sustainable Investment Review” issued once every two years. In this section, all datasets on sustainable investment are cited from the GSIA (2019). Tables 1.1, 1.2, and 1.3 show the ESG investment by region in 2018, the transition of ESG investment by region from 2016 to 2018, and the ratio of ESG investment to total investment by region in 2016 and 2018, respectively. Europe, which has led the world’s ESG investment, was the top leader in 2018, accounting for 45.9% of the total. From 2016 to 2018, the investment grew by 11.4%, but this was only due to the increase in total investment in Europe. The ratio of ESG investment to total investment was already saturated, falling below 50% in 2018. ESG investment in the US and Canada has grown steadily over the last few years. As a result, in 2018, the investment in North America was 13,694 billion US

1.3 Activities and Strategies

5

Table 1.1 Sustainable investment assets (2018) Region

Amount [in billions of US dollars]

Composition ratio [%]

Europe United States Japan Canada Australia/New Zealand Total

14,075 11,995 2180 1699 734 30,683

45.9 39.1 7.1 5.5 2.4 100

Table 1.2 Growth of sustainable investment assets (2016 to 2018) Region

2016

2018

Growth [%]

Europe [in billions of euros] United States [in billions of US dollars] Japan [in billions of Japanese yen] Canada [in billions of Canadian dollars] Australia/New Zealand [in billions of Australian dollars]

11,045 8723 57,056 1505 707

12,306 11,995 231,952 2132 1033

11.4 37.5 306.5 41.7 46.1

Table 1.3 Ratio of sustainable investment assets to total investment assets (2016 and 2018)

Region

2016 [%]

2018 [%]

Europe United States Japan Canada Australia/New Zealand

52.6 21.6 3.4 37.8 50.6

48.8 25.7 18.3 50.6 63.2

dollars, which is now very close to that of Europe, accounting for 44.6% of that of the whole world. Furthermore, ESG investment in North America can be expected to expand further, because the ratio of ESG investment to total investment in the US was only 25.7%, while in Canada, it reached 50.6%. Japan was behind other major countries in ESG investments. However, the Government Pension Investment Fund (GPIF), the world’s largest pension fund, signed the PRI in 2015. Since 2017, when GPIF started ESG investment, Japan’s ESG investment has been in full swing. Although ESG investment in Japan has grown rapidly over 300% in the past two years, the amount was less than 20% of that of the whole country in 2018. Therefore, future deployment is expected, while Japan’s ESG investment was only 7.1% of the world’s in 2018. ESG investments in Australia and New Zealand have grown steadily. However, even when the ratio of ESG investment to the total investment in this region exceeded 60% in 2018, the global share was 2.4%, which was not large.

6

1 ESG Investment

As described above, ESG investments are generally expanding, although there are regional variations. The profitability of ESG investments may have begun to be recognized globally. The ESG investment strategy originate from the policy to exclude investments in companies and industries that do not meet religious and ethical standards. Currently, financial institutions are actively developing and managing various ESG funds. Depending on their portfolio selection and management, GSIA (2019) classifies sustainable investing into the following seven categories: I. Negative/exclusionary screening This is defined as the exclusion of a fund or portfolio of certain sectors, companies, or practices based on specific ESG criteria. For example, stocks of companies related to weapons, nuclear power, child labor, alcohol, tobacco, and gambling are excluded from the portfolio. II. Positive/best-in-class screening This is defined as investment in sectors, companies, or projects selected for their positive ESG performance relative to industry peers. Although companies with positive ESG performance can be expected to have excellent financial performance over the medium to long term, there is growing concern that the number of investment candidate companies will be smaller than that under negative/exclusionary screening. III. Norms-based screening This is defined as the screening of investments against minimum standards of business practice based on international norms, such as those issued by the Organisation for Economic Co-operation and Development (OECD), International Labour Organization (ILO), UN, and United Nations Children’s Fund (UNICEF). The United Nations Global Compact (UNGC), officially launched in 2000, is a representative of international standards. It has a wider investment scope than the positive/best-in-class screening. It is mainly used in Europe. IV. ESG integration This is defined as the systematic and explicit inclusion of investment managers of environmental, social, and governance factors in financial analysis. This strategy is rapidly expanding through institutional investors who manage long-term investment funds. V. Sustainability themed investing This is defined as investment in themes or assets specifically related to sustainability (e.g., clean energy, green technology, or sustainable agriculture). Green bonds, transition bonds, and solar funds have also been developed. VI. Impact/community investing This is defined as targeted investments aimed at solving social or environmental problems, including community investing, where capital is specifically directed to traditionally underserved individuals or communities,

1.3 Activities and Strategies

7

as well as financing provided to businesses with a clear social or environmental purpose. Many small businesses are included as investment targets. Venture capitalists often adopt this strategy. VII. Corporate engagement and shareholder action This is defined as the use of shareholder power to influence corporate behavior, including through direct corporate engagement (i.e., communicating with senior management and/or boards of companies), filing or co-filing shareholder proposals, and proxy voting guided by comprehensive ESG guidelines. In recent years, there have been many actions for climate change measures of investee companies and the optimization of their executive compensation. This is also known as shareholder activism. Table 1.4 shows the investment for each strategy in 2018. Investment by negative/exclusionary screening, which is the origin of SRI, is the largest, but it is almost the same level as that of ESG integration, which proactively analyzes ESG indicators and aims to obtain excess returns. Investment by corporate engagement and shareholder action, often used in combination with other investment strategies, is medium. This indicates that the discipline and monitoring of management by institutional investors is still important. The number of the remaining four investment strategies is not very large.

1.4

Corporate Development and Expansion of Its Stakeholders

The brief history of a joint-stock company, a typical corporate form in the modern economy, is described. The earliest joint-stock company may depend on its definition. There was already a limited liability structure, such as Commenda and Magna Societas in the Middle Ages. However, the Dutch East India Company, established in 1602, is recognized as the origin because it has the following characteristics: (1) limited liability of all investors, (2) establishment of a board of directors, (3) tradable securitization of capital, and (4) permanent enterprise. It is no

Table 1.4 Sustainable investment assets by strategy (2018) Strategy

Amount [billions of US dollars]

Negative/exclusionary screening ESG integration Corporate engagement and shareholder action Norms-based screening Positive/best-in-class-screening Sustainability themed investing Impact/community investing

19,771 17,544 9835 4679 1842 1018 444

8

1 ESG Investment

exaggeration to say that company value was assessed only by the expected financial return to investors and that the company’s most important mission was to operate it with shareholders as the only stakeholder. In the eighteenth century, industrial capitalism was introduced as a result of the Industrial Revolution in Britain, and joint-stock companies became the center of economic activity. In 1862, the Companies Act was enacted, and the joint-stock company system was completed in the UK. After that, the system in the US and Japan was completed by the Corporation Laws of New Jersey, and the Japanese Commercial Code was implemented in 1875 and 1899, respectively. In this way, the joint-stock company system spread from Europe to the world, but it developed drastically in the US. At the end of the nineteenth century, many giant companies were established in the US, which had developed more economically than the UK. As companies expanded, specialized managers, not investors, came to control companies. Berle and Means (1932) report that the top 200 non-financial companies held 58% of the total assets of all companies in 1929, and 44% of companies were controlled by managers under the separation of ownership and control. They argue that joint-stock companies should be managed not only for the benefit of their shareholders and/or managers but also for the benefit of society (e.g., fair wages, rational public service, business continuity), in the context of social regulation of large companies. In the 1980s, the US government promoted the deregulation of economic activities. Each corporation had to streamline its organization and/or strengthen its international competitiveness in the short term. As a result, the number of corporate M&As increased rapidly. Since then, the philosophy that “maximizing shareholder value” as the only corporate purpose has become dominant. Then, institutional investors played the same major role in corporate governance as outside directors by exercising the voting rights of investee companies and restraining the management team. However, the definition of “shareholder value” has changed over time because it evolves with investor philosophy. Similarly, the scope of stakeholders has changed. SRI has received attention as a means of eliminating companies not engaged in societal welfare because interest in the conflict between social and economic activities has increased since the 1970s. We expanded investment for not only the internal economy of companies but also the external economy, such as the environment, human rights, and national security. As a result, the issues that originally pertained to the external economy have been internalized and reflected in “shareholder value.” As interest in CSR is growing globally in addition to many companies actively expanding overseas, various international human rights groups and media have increased the scrutiny of companies’ behavior in communities with different cultures and their impact on local economies at different levels. Furthermore, they focus on investors as well, and the expansion of ESG investment continues to accelerate. A company finds it difficult to survive if it is not properly governed in harmony with the economy, society, and environment. The larger and more global a corporation, the more multiplexed and diversified its stakeholders become. It can be said that the expansion of ESG investments and of stakeholders

1.4 Corporate Development and Expansion of Its Stakeholders

9

are two sides of the same coin. The globalization of corporate activities and financial transactions is expected to continue accelerating and societal demands on companies to continue diversifying. Therefore, for a company to sustain and grow, the scope of stakeholders should not be limited to the current ones, such as shareholders, creditors, managers, employees, and customers, but must expand indefinitely in various axes (e.g., time axis, sales channel axis, manufacturing process axis, region axis, religion axis, and culture axis). Then, not only the ethical and social roles played by stakeholders of a company but also the mechanism built ensures that these roles are added to traditional shareholders, whose production value is less than the debt value when estimating a corporate value. Until now, the main entities that have utilized ESG indicators have been institutional investors that manage funds and listed and/or large companies that require money. It is one of the most difficult challenges in ESG investment to appropriately index corporate ESG efforts concerning investment returns. Investigating the causality to financial performance, the time lag before affecting financial performance, and the magnitude of the impact on financial performance, we should comprehensively evaluate various ESG efforts. The ESG efforts of expanded stakeholders will also be evaluated as the ESG efforts of the company. Naturally, large companies produce goods and services in a process that fits the philosophy of ESG investors. However, their supply chain includes micro-, small-, and medium-sized enterprises, which are parts suppliers and subcontractors. For large companies, which are at the center of economic activity, to tackle ESG issues, the cooperation of all companies, including their supply chain members, is indispensable. In other words, these smaller companies must follow the rules set by large companies to address ESG issues and must be monitored by large companies. Therefore, large companies as well as companies of various forms and sizes are eventually going to utilize ESG indicators as business companies. However, the current main players in ESG investments are institutional investors represented by pensions. Since they are intermediaries between individual investors and companies, it is necessary to educate individual investors about ESG issues to increase ESG investment. Immediately after the advent of SRI, there was concern that it might cause loss of investment returns for social purposes. However, given the increase in ESG investment since the launch of the PRI, it can be concluded that ESG investment returns are socially recognized as not being significantly lower than market average returns at its worst. Individual investors often buy mutual funds to avoid “the securities selection costs,” “inadequate diversification caused by their insufficient resource,” and “irrational investment behavior.” In such cases, ESG indices may become important in the near future. Moreover, since individual investors are also final consumers, as their awareness of ESG issues advances, their opinions will be provided to companies through various consumer markets. Ultimately, ESG indicators will not only be intended for large corporations and large institutional investors, but also for all entities such as individuals, firms, investees, investors, and those whose business activity can affect society at large.

10

1.5

1 ESG Investment

Future

Shareholders, who are the providers of risk assets, are the stakeholders most interested in management as compared to other stakeholders, because the economic returns they receive depends on corporate value. Therefore, management teams must be fully aware of the conflicts of interest and potential risks to shareholders. However, since large corporations whose ownership is widely dispersed are, in effect, public entities, it is difficult to maximize corporate value without considering the welfare of stakeholders other than shareholders. The larger and more complex a corporation is, the wider the scope of its stakeholders. Furthermore, their CSR efforts vary beyond standardization. It will become increasingly difficult to evaluate their approaches to ESG issues. Therefore, to explicitly and reasonably resolve conflicts of interest between stakeholders, it is particularly important to create a system in which information on all stakeholders is comprehensively, constantly, and smoothly reflected in the stock price. Through an efficient interest market, companies are evaluated based on public information, lender companies’ survey, and the information provided by credit rating agencies. Through the consumer goods market, companies are evaluated based on the quality and prices of goods and the behavior and reputation of suppliers. Through the labor market, companies are evaluated based on their working environment and salary, even though employees rarely affect corporate governance directly. Similarly, through the raw material market, companies are evaluated by their suppliers. As described above, companies are exposed to the evaluation of not only shareholders in the stock market but also various stakeholders in various markets. Although the range of stakeholders has expanded so far, stock prices have been formed almost efficiently by smoothly transmitting information related to corporate value through various efforts such as timely disclosure and appropriate indexing of company information, establishing fair trading markets, and digital information networking. Moreover, two-way communication between companies and stakeholders is an important element for effective CSR activities. From the perspective of ESG efforts, each stakeholder should have already evaluated companies through their directly related markets. However, since the history of ESG investment is not long, it is still uncertain whether a mechanism has been established in which each stakeholder’s information is efficiently reflected in the stock prices of the company. In other words, two questions remain regarding whether the total utility of stakeholders may not have been maximized and whether there is room for improving investment performance from the perspective of ESG issues. The ESG efforts of many companies that have complex relationships with various stakeholders are diverse. Moreover, various ESG evaluation standards are available. Therefore, there is room for verification as to whether ESG information circulates smoothly, is reasonably interpreted by stakeholders, and is linked to their behaviors. However, in the distant future, ESG information, which affects corporate value, will be properly reflected in the stock price. As a result, even in classifying trades

1.5 Future

11

that pursue economic returns, ESG investment might become defined as a real investment, and traditional investment based on financial information might become speculative. If ESG information is efficiently reflected in stock prices, we can expect not only that ESG investment will increase but also that companies’ efforts for ESG will become more efficient. In other words, by accurately analyzing ESG information during this transition period, excess returns from ESG investments may be expected.

1.6

Market Efficiency

Fama (1970) asserts that a market in which prices always “fully reflect” available information is called “efficient” and proposes the efficient market hypothesis (EMH). Since stock prices are always fair under the EMH, investors should never be able to beat the average annual returns that all investors can achieve using their best efforts. In other words, if the EMH is accepted in the current real stock market, excess returns cannot be obtained even when using ESG information. However, it is interesting to reflect ESG information, some efficiency types, and skepticism about EMH to verify whether ESG information is efficiently reflected in stock markets. Market efficiency is classified into three categories depending on the scope of the information contained in the stock market.

Weak-form efficiency This type of efficiency implies that all information about past prices is reflected in the current prices. This means that future prices are independent of today’s prices and all past ones. Under a weak-form efficient stock market, no one can predict future stock prices because the prices follow a random pattern. In other words, no one can beat the average returns by technically analyzing stock prices. Semi-strong-form efficiency This type of efficiency indicates that all obvious public information is reflected in the current prices. Under a semi-strong form efficient stock market, no one can beat the average returns even if they fundamentally analyze the stock prices using all kinds of public information, such as the financial statements and news releases of a company and the stock price forecasts by financial analysts. Naturally, technical analysis was also invalid. Strong-form efficiency This type of efficiency states that all company information, whether public or private, is reflected in the stock price. Under a strong form of an efficient stock market, even insider trading would be ineffective. Many market players deny the strong form of EMH in actual markets, so insider trading is prohibited.

12

1 ESG Investment

The above are three types of traditional market efficiency categorized in relation to the nature of the information. Moreover, Fama (1991) proposes a new classification of market efficiency. Return predictability This return predictability not only refers to the forecast power of past returns but also covers the more general return predictability based on other economic variables such as dividend yield and interest rates, considering asset-pricing models and the anomalies and seasonality in returns and excessive volatility in prices. Event studies This is just a title change from “semi-strong-form,” which implies the price adjustment of public announcements to “event studies” Private information This is also just a title change from “strong-form” to” private information,” which is more descriptive. Many studies have tested the EMH by utilizing actual stock market data. Acceptance of the EMH means denial of active portfolio management. Since the EMH was advocated in 1970, even in the business world, passive investment and index funds had spread, because many people admitted that it was difficult to exceed the market average return in their asset management. Ellis (1998), an American investment consultant and the author of “Winning the Loser’s Game,” says that since professional money management has become a loser’s game in which one wins by avoiding mistakes rather than by positive achievement, even professional money managers cannot appear to beat the market. Malkiel (2003), an American economist and the author of “A Random Walk Down Wall Street,” promotes the EMH and the random walk of stock prices. The ESG investment strategy is based on the philosophy that companies with excellent ESG ratings are more profitable and more valuable in the medium to long term than other companies. Is it possible for ESG investment to beat the average market returns? If each company accurately discloses ESG efforts as public information, the acceptance of semi-strong-form EMH denies the success of ESG investment in profitability. However, in the current situation, since ESG information is a relatively new concept, the rating method is not yet completely established compared with traditional financial information and other quantitative information. Moreover, even if there are appropriate ESG ratings, it is difficult to say that they are fully utilized in ESG investments. Therefore, if a company that excels in ESG efforts is better than other companies in terms of long-term financial performance, the prompt and appropriate use of ESG ratings that accurately evaluates companies’ ESG efforts must help ESG investors gain average market returns.

1.7 Anomalies

1.7

13

Anomalies

The modern portfolio theory (MPT), proposed by Markowitz (1952), claims that we can construct efficient portfolios that minimizes risks and maximizes returns by combining various securities and considering them as one security. As a theory to reinforce MPT, Sharpe (1964), Lintner (1965), and Mossin (1966) advocate the capital asset pricing model (CAPM). This model is an equilibrium model that expresses the relationship between risk and returns and reflects the risk reduction effect of diversified investments. This model is expressed as follows:   E ðRi Þ ¼ Rf þ bi E ðRM Þ  Rf

ð1:1Þ

where E ðRi Þ is the expected return of asset i, Rf is the return of the risk-free asset, E ðRM Þ is the expected return of the market, and bi is the sensitivity of EðRi Þ  Rf , known as the risk premium to E ðRM Þ  Rf , known as the market premium. The expected return of an individual asset is determined by the return of risk-free assets and the sensitivity of the individual asset’s risk premium to the market premium. Many empirical analyses have verified the CAPM. Although many of the early studies supported CAPM, an increasing number of studies have been denying CAPM to explain the difference in returns between individual stocks. Since companies addressing ESG issues have relatively low environmental, social, and governance risks, it is meaningful to analyze the ESG investment risk premium. It would be interesting to verify the possibility of beating the market through ESG investment. It is important to pursue the existence of anomalies in ESG investments. The following is a summary of typical anomalies observed in the stock market that cannot be explained by EMH and/or CAPM.

Small firm effect Banz (1981) reports that the average return on smaller capitalization firm stocks is higher than that predicted by the CAPM.

Value effect Basu (1977) argues that portfolios consisting of stocks of lower-PER firms earn higher returns than portfolios consisting of stocks of higher PER firms. Although value stock is defined as the stock traded at a lower price than the company’s intrinsic value, we come across the challenge of identifying what the intrinsic value is and how to calculate it. Therefore, PER and PBR were adopted as the value stock indices. Rosenberg et al. (1985) and Lakonishok et al. (1994) report this value effect using dividend yield, net asset price ratio, cash flow yield, and sales yield.

14

1 ESG Investment

Return reversal effect De Bondt and Thaler (1985) discover a negative correlation between the last returns for three to five years and the subsequent returns. In other words, portfolios with lower (higher) historical returns tend to earn more (less) than those with higher (lower) historical returns.

Momentum effect While return reversal effects are observed, Jagadeesh and Titman (1993) find a positive correlation between recent past returns for three to twelve months and subsequent returns. In other words, recent past winners are going to outperform recent losers. Rowenhorst (1998) reports the same effect in European markets as well.

Revision effect Stickel (1991) demonstrates that stock prices continue to drift in the direction of the revision for about six months after revision by investigating the relationship between changing expectations of earnings and changing security prices. Stocks revised upward tend to show abnormal returns ex post facto.

Surprise effect Bernard and Thomas (1989) argue that stock prices continue to drift in the direction of deviation from the forecast of company performance for a while after the announcement of financial results. When a company announces much better (worse) results than advance forecasts, its stock shows positive (negative) abnormal returns for a while after that.

Accrual effect Sloan (1996) attributes the negative association between accounting accruals and subsequent stock returns to the difference in persistence between accruals and cash flow and demonstrates an annual average of more than 10% abnormal return by taking a long position in the bottom 10% accounting accruals portfolio and taking a short position in the top 10% portfolio.

Capital and shareholder return policy effect Petit (1972) finds that positive (negative) changes in dividend payments induce positive (negative) abnormal returns. Loughran and Ritter (1995) confirm that when

1.7 Anomalies

15

a company discloses information on public offerings, its stock price reacts negatively. Ikenberry et al. (1995) found abnormal returns after public stock repurchase announcements. Daniel and Titman (2006) and Pontiff and Woodgate (2005) observe a negative correlation between net equity finance (i.e., equity finance minus repurchase) and subsequent returns. Investment returns cannot be increased without considering additional risks under the EMH and CAPM. However, many instances of markets being beaten have been observed in the actual stock market. Anomaly is an important concept in discussing the effectiveness of ESG investment because investors cannot accurately evaluate the intrinsic value of a company, public information about a company, or various company activities.

1.8

Limits of the MPT

The MPT proposed by Markowitz (1952) is the basis of the modern equity investment theory. The MPT recommends that investors should not manage the performance of each security individually, but the risk and return of all securities held clarifies the appropriate components of the portfolio. Moreover, Markowitz (1952) explains the benefit of diversified investment by stating that investors can reduce the volatility of their portfolio returns when they select stocks whose price fluctuations are negatively correlated with each other. Until then, the importance of diversified investment had been intuitively recognized, as typified by the saying “Do not put all the eggs in one basket.” However, Markowitz (1952) advocates the effects of diversified investment and concludes that an investor can construct an efficient portfolio that earns the highest expected return under a certain standard deviation. Although there were practical issues such as having to solve complex optimization problems, having to treat many input parameters, and needing to identify the utility function of investors, Markowitz’s (1952) MPT was reinforced by Sharpe’s (1964) CAPM and Fama’s (1970) EMH concept; thus, it has been generally accepted. The CAPM concept expresses that the return of an individual security is determined by the return of risk-free assets and the sensitivity of the security risk premium to the market risk premium, which means that we cannot increase the returns without taking additional risks, no matter how much we analyze the prices of a security, and regardless of the investment strategy we adopt. In other words, the MPT implies the rationality of market portfolio investment and contributes to the spread of financial products linked to the stock market index. However, as described in the previous section, in the real stock market, we can observe anomalies that MPT cannot fully explain. Although the MPT can roughly explain the market, there are limits to the range that can be explained. One of the reasons for this is the existence of irrational market participants. Their trades diverge stock prices from their intrinsic values, and anomalies are observed. Reasonable investors with perfect information gathering ability and excellent information analysis skills should be able to beat the average market returns by

16

1 ESG Investment

exploiting the mispricing caused by irrational market participants. Some kinds of irrational investor behaviors have been revealed by behavioral finance theory, which applies psychology to finance theory. De Bondt and Thaler (1985), Lakonishok et al. (1994), and Jagadeesh and Titman (1993) explain the causes of the return reversal effect, the value effect, and the momentum effect, respectively, using behavioral finance theory. Behavioral finance plays a certain role by providing a supplementary explanation of phenomena that are difficult to explain by traditional finance theory. Another reason for the limits of the MPT is the existence of a risk premium not formulated by any random theory. It is recognized both academically and commercially that the multi-factor model, such as the arbitrage pricing theory by Ross (1976) and intertemporal CAPM by Merton (1973), is more convincing in the real stock market than the single-factor model by Sharpe (1964) and Lintner (1965). Fama and French (1993) design a three-factor model that describes stock returns using three variables. The three factors are the return of the market portfolio minus the risk-free return, the return of the small market capitalization portfolio minus the return of the big market capitalization portfolio, and the return of the high PBR portfolio minus the return of the low PBR portfolio. Fama and French (1996) argue that typical anomalies can be explained using this three-factor model. Many studies explain anomalies theoretically, while others conclude that stock market anomalies are nothing more than a coincident phenomenon caused by data samples. In other words, their argument is that, out of several empirical analyses of anomalies, only the empirical results consistent with the hypothesis proposed by each researcher are reported. However, we cannot completely deny the existence of stock market anomalies. In contrast, we consider them interesting. ESG investment is expanding globally based on the hypothesis that excellent ESG companies have excellent financial performance over a medium to long term period. However, ESG investors who accept this hypothesis may be irrational; thus, it is meaningful to test the hypothesis by analyzing ESG funds and/or indicators. Even if this hypothesis is accepted, in the case that ESG indicators accept the semi-strong EMH, ESG investment that utilizes public information of companies cannot beat the market; thus, the investment strategy can be said to be in vain. Just as efforts by investors who are skeptical about the EMH make markets more efficient, efforts by investors who believe that ESG investment should be able to beat the average market returns may bring ESG investment closer to nonsensical investment. Conversely, when the stock market is inefficient for ESG information, the ESG value effect allows ESG investments to earn returns above average market returns because the company’s ESG efforts enhance its intrinsic value. ESG investment has been systematized since the twenty-first century; therefore, the method of evaluating ESG information may not be established due to its short history. Therefore, it is questionable whether the ESG value is correctly evaluated by the stock market. Even if the stock market can accurately recognize the ESG information, if it takes some time to reflect it, ESG investment can beat the market. As companies have become larger, more globalized, and more complex, the factors that companies should consider have become more diverse. In a primitive

1.8 Limits of the MPT

17

company, investors should focus on maximizing their profits. However, the factors that companies should consider are increasing, such as agency problems due to separation of ownership and management in large companies, the overseas situation for global companies, global environmental problems that get worse year by year, complicated supply chains, and appropriate corporate governance. ESG investment requires comprehensive corporate evaluation, including such new factors, which may increase in the future. It might be possible to formulate expected stock returns using a multi-factor model that adopts ESG information.

1.9

Guide to Empirical Analysis

From the perspective of the history and scale of ESG investment, Europe and North America are promising regions for conducting empirical analyses of ESG markets. We have little difficulty accessing ESG data because the European and North American markets have various types of ESG funds with high liquidity, widely used ESG indicators, and abundant historical data. Moreover, since there is a wealth of previous literature, by changing the sample period, we can consider changes in the investment environment and obtain policy implications for the future. Additionally, by verifying the universality of the empirical analysis results in the European and North American markets, it may be possible to examine the Asia– Pacific and South American markets. However, we might encounter data availability issues. As one suggestion of empirical studies, there is a test for the following hypothesis that an excellent ESG company has better long-term financial performance than the others. This means that the causality test from ESG factors affects corporate financial performance. As an example, we should examine whether each ESG factor, such as a company’s score on non-nuclear power, the number of female directors, tax transparency, and its comprehensive ESG score, Granger-causes its financial performance, such as market capitalization, bond interest, and credit default swap. Moreover, we should measure the resulting impact, if any. However, when pursuing completeness, this is estimated to become a challenging study because of the diversity of ESG issues, corporate ESG efforts, and corporate performance evaluation methods. For pre-processing, it is also important to develop a scoring method for each ESG approach. As another suggestion, to reveal the profit advantage of ESG investment, it is unavoidable to test the EMH. First, the EMH and market anomalies are controversial, even in the general stock market. It is hard to believe that the market correctly and quickly reflects ESG information due to the diversity of ESG issues and the shorter history of using ESG information than other public information. On the other hand, by adopting the multi-factor model, it might be possible to formulate the relationship between a company’s ESG information and the expected rate of return of its stock. This could be an interesting study.

18

1 ESG Investment

As a final suggestion, we encourage empirical analyses aimed at the wide use of ESG indices, which are a calculation of the portfolio risk with ESG funds as components, measurement of the connectedness between the portfolio’s constituent securities including ESG funds, and examination of the spillover effect between ESG funds and various indices. For example, it is important to calculate the estimated shortfall of a portfolio consisting of fuel futures, wholesale electricity futures, carbon emission allowance futures, and the funds excluding stocks of nuclear companies. Of course, it is interesting to measure portfolio connectedness. Moreover, it is also meaningful to clarify the spillover effects between ESG funds, GDP, consumer price indexes, and the financial stress index. The remainder of this paper is organized as follows. Chapter 2 uses the asymmetric dynamic conditional correlations model to evaluate the conditional correlations between the world ESG index, the new energy global index, the green bond index, and the sustainability index. Chapter 3 adopts the copula approach to estimate the tail dependencies between the world ESG index, the new energy global index, the US-listed clean energy index, and the European renewable energy index and then compares the portfolio performance based on the adjusted risk-return, standard deviation, value-at-risk, and conditional value-at-risk. Chapter 4 investigates the return and volatility spillover effects from the equity market, crude oil market, and gold market to the ESG index in both the US and Europe. Chapter 5 measures the connectedness between the ESG index, financial stress index, macroeconomic index, interest rates, and crude oil prices in the US market.

References Banz RW (1981) The relationship between return and market value of common stocks. J Financ Econ 9(1):3–18. https://doi.org/10.1016/0304-405X(81)90018-0 Basu S (1977) Investment performance of common stocks in relation to their price-earnings ratios: a test of the efficient market hypothesis. J Finance 32(3):663–682. https://doi.org/10.2307/ 2326304 Berle AA, Means GC (1932) The modern corporation and private property. Routledge, London Bernard VL, Thomas JK (1989) Post-earnings-announcement drift: delayed price response or risk premium? J Account Res 27:1–36. https://doi.org/10.2307/2491062 Daniel K, Titman S (2006) Market reactions to tangible and intangible information. J Finance 61 (4):1605–1643. https://doi.org/10.1111/j.1540-6261.2006.00884.x De Bondt WFM, Thaler R (1985) Does the stock market overreact? J Finance 40(3):793–805. https://doi.org/10.1111/j.1540-6261.1985.tb05004.x Ellis CD (1998) Winning the loser’s game: timeless strategies for successful investing. McGraw Hill, New York Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25 (2):383–417. https://doi.org/10.2307/2325486 Fama EF (1991) Efficient capital markets: II. J Finance 46(5):1575–1617. https://doi.org/10.1111/ j.1540-6261.1991.tb04636.x Fama EF, French KR (1993) Common risk factors in the returns on stocks and bonds. J Financ Econ 33(1):3–56. https://doi.org/10.1016/0304-405X(93)90023-5

References

19

Fama EF, French KR (1996) Multifactor explanations of asset pricing anomalies. J Finance 51 (1):55–84. https://doi.org/10.1111/j.1540-6261.1996.tb05202.x Global Sustainable Investment Alliance (2019) Global sustainable investment review 2018. http:// www.gsi-alliance.org/wp-content/uploads/2019/03/GSIR_Review2018.3.28.pdf. Accessed 1 Jun 2020 Ikenberry D, Lakonishokb J, Vermaelen T (1995) Market underreaction to open market share repurchases. J Financ Econ 39(2–3):181–208. https://doi.org/10.1016/0304-405X(95)00826-Z Jagadeesh N, Titman S (1993) Returns to buying winners and selling losers: implications for stock market efficiency. J Finance 48(1):65–91. https://doi.org/10.1111/j.1540-6261.1993.tb04702.x Lakonishok J, Shleifer A, Vishny RW (1994) Contrarian investment, extrapolation, and risk. J Finance 49(5):1541–1578. https://doi.org/10.1111/j.1540-6261.1994.tb04772.x Lintner J (1965) Security prices, risk, and maximal gains from diversification. J Finance 20 (4):587–615. https://doi.org/10.2307/2977249 Loughran T, Ritter JR (1995) The new issues puzzle. J Finance 50(1):23–51. https://doi.org/10. 1111/j.1540-6261.1995.tb05166.x Malkiel BG (2003) A random walk down wall street: The time-tested strategy for successful investing. W. W. Norton & Company, New York Markowitz H (1952) Portfolio selection. J Finance 7(1):77–91. https://doi.org/10.2307/2975974 Merton RC (1973) An intertemporal capital asset pricing model. Econometrica 41(5):867–887. https://doi.org/10.2307/1913811 Mossin J (1966) Equilibrium in a capital asset market. Econometrica 34(4):768–783. https://doi. org/10.2307/1910098 Petit RR (1972) Dividend announcements, security performance, and capital market efficiency. J Finance 27(5):993–1007. https://doi.org/10.2307/2978844 Pontiff J, Woodgate A (2005) Share issuance and cross-sectional returns. J Finance 63(2):921–945. https://doi.org/10.1111/j.1540-6261.2008.01335.x Rosenberg B, Reid K, Lanstein R (1985) Persuasive evidence of market inefficiency. J Portf Manag 11(3):9–16. https://doi.org/10.3905/jpm.1985.409007 Ross SA (1976) The arbitrage theory of capital asset pricing. J Econ Theory 13(3):341–360. https://doi.org/10.1016/0022-0531(76)90046-6 Rowenhorst KG (1998) International momentum strategies. J Finance 53(1):267–284. https://doi. org/10.1111/0022-1082.95722 Sharpe WF (1964) Capital asset prices: a theory of market equilibrium under conditions of risk. J Finance 19(3):425–442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x Sloan RG (1996) Do stock prices fully reflect information in accruals and cash flows about future earnings? Account 71(3):289–315 Stickel SE (1991) Common stock returns surrounding earnings forecast revisions: more puzzling evidence. Account 66(2):402–416

Chapter 2

Does ESG Index Have Strong Conditional Correlations with Sustainability Related Stock Indices? Wenting Zhang, Tadahiro Nakajima, and Shigeyuki Hamori

2.1

Introduction

Environment, Social, and Governance (ESG) is a set of principles that socially responsible investors use to screen prospective investments for business operations. Environmental standards understand how a corporation works as a steward of nature. Social criteria analyze how a corporation handles relationships with workers, vendors, clients, and the societies in which it works. In recent years, ESG has become an increasingly common way for investors to determine the companies they may like to invest in. Additionally, several mutual funds, investment companies, and robo-advisors are now selling products that use the ESG criterion. This can also help investors avoid businesses that, because of their environmental or other policies, may pose a higher financial risk. In fact, the indices of corporate social responsibility (CSR) and socially responsible investment (SRI) are similar to ESG. CSR is a form of self-regulating international private businesses aimed at contributing to the social objectives of a philanthropic, activist, or charitable nature by participating in or encouraging voluntary or ethically focused activities. As Gillan et al. (2021) stated, ESG relates to the incorporation of environmental, social, and governance issues by businesses and investors, into their business model. However, CSR has historically been applied to the practices of companies in order to be more socially conscious and better corporate citizens. SRI allows investors to choose stocks or funds based on a set of criteria, such as positive or negative screening, the degree of shareholder participation, or the impact of the investment. However, compared with CSR and SRI, ESG has gained increasing attention as investors have understood the materiality of the risks that lie outside the traditional financial statement analysis. In particular, lower capital constraints and costs, and stock price movements are correlated with the ESG index (Amel-Zadeh and Serafeim 2018). This study investigates the dynamic conditional correlations between ESG Index and the renewable energy index, green bond, and sustainability index worldwide © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. Nakajima et al., ESG Investment in the Global Economy, Kobe University Social Science Research Series https://doi.org/10.1007/978-981-16-2990-7_2

21

22

2 Does ESG Index Have Strong Conditional Correlations …

employing the asymmetric dynamic conditional correlation (A-DCC) model. We aim to demonstrate the ability to substitute assets for investment portfolios and create index portfolios that perform better than existing assets. Many institutional investors are now concentrating on investing in renewable energy. Compared with other investment methods, these investment portfolios employ the renewable energy index, green bond, and sustainability index as the benchmark. This makes them more valuable but with lower potential risks. ESG indexes are familiar to many global investors that are interested in choosing securities with high sustainability standards. Compared with the near-total collapse of oil and gas, renewable energy stocks have held up remarkably well even during extreme events. This indicates that they are less volatile than normal fossil fuels. Ferrer et al. (2018) investigated the correlations between renewable energy stocks and crude oil prices. By observing volatility plots, they found that renewable energy stocks are relatively stable. Green bonds can provide yields, ratings, and return characteristics similar to other fixed income investments. They also fund projects that have tangible and measurable impacts on addressing environmental challenges. The sustainability index benchmarks the performance of the entire industry and allows us to assess the progress being made in achieving strategic sustainability goals. This is also a management tool that can contribute to continuously improving the sustainability performance of our business. To the best of our knowledge, our study is among the first to assess the dynamic conditional correlations between ESG Index and renewable energy stock, green bonds, and sustainability indexes by applying the A-DCC model. Some studies have investigated the function of the ESG index in portfolio investments. Giese et al. (2019) examined how asset owners can achieve ESG integration through index-based allocation of portfolios to replicate ESG indexes. They found that global and regional versions of the MSCI ESG Leaders Indexes (as proxies for regional allocations) over a seven-year study span resulted in substantial variations in their respective ESG profiles and results. However, all the main risk measures were clearly reduced in all the instances. Additionally, Auer and Schuhmacher (2016) used an ESG company rating dataset and the latest statistical methods to analyze the performance of social (irresponsible) investment in the Asia–Pacific region, the United States, and Europe. They concluded that investors who focus on the ethical utility of their portfolio choices can apply ESG-based investment methods, but still achieve similar performance to the broader market. Ashwin Kumar et al. (2016) introduced a new mathematical analysis; the results show that companies that combine ESG factors have lower stock performance volatility than their peers in the same industry. Further, each industry is influenced by different ESG factors. Additionally, companies that concentrate on ESG can achieve higher returns. An increasing number of researchers are focusing on the link between renewable energy stocks and the ESG index. Liu and Hamori (2020) employed the constant and time-varying copula model to examine the dependence structure of the ESG index and four different renewable energy indices, as well as the possible output of employing different ratios of the ESG index in the portfolio. They applied criteria

2.1 Introduction

23

such as risk-adjusted return and standard deviation to show that the ESG index can adequately lower the potential conditional value at risk (CVaR) and maintain a high return. Furthermore, Sultana et al. (2018) pointed out that investing in ESG indexes will gradually incentivize companies to enhance their business in a more environmentally friendly manner, and assist in the formulation of regulations. Kaminker and Stewart (2019) indicated that in many Organisation for Economic Co-operation and Development (OECD) countries, the current situation is marked by low-interest rates and sluggish economic growth, in which institutional investors are looking for assets such as renewable energy, which provide stable returns and are less relevant to other asset options. We investigate the asymmetric behavior of time-varying connectedness among the MSCI World ESG Leaders Index (ESG Index) and WilderHill New Energy Global Innovation Index (NEX: renewable energy stock) the S&P Green Bond Index Total Return (Green Bond index), and Dow Jones Sustainability World Composite Index (Sustainability Index) using the A-DCC model exploited by Cappiello et al. (2006). The A-DCC model is an extended version on the basis of the original DCC model by Engle (2002), which was used by Gjika and Hovath (2013), Wang and Moore (2012), and Hou and Li (2016). Toyoshima et al. (2012) also used this method to investigate the correlations between treasuries and swap markets. The A-DCC allows for conditional asymmetries in covariance and correlation dynamics; therefore, this method is always used to analyze stock markets. Kenourgios et al. (2011) used the A-DCC model to examine financial correlations in a multivariate time-varying asymmetric framework, while concentrating on four emerging equity markets. Hwang et al. (2013) also used the A-DCC model to evaluate the dynamic conditional correlations of the daily stock returns of 10 emerging economies during the US financial crisis. To investigate the asymmetric dynamic conditional correlation between the ESG index and renewable energy stock, green bond, and sustainability index, we used three steps to conduct the analysis. First, we use the univariate autoregressive exponential generalized autoregressive conditional heteroskedasticity (AR(k)EGARCH (p, q)) model to fit the four variables. Second, after deriving the conditional variance in the first step, we use the A-DCC model to analyze the dynamic conditional correlations. Third, we adopt the AR (1) model to fit the conditional correlations derived in step 2 with two dummy variables to examine the impact of the 2014 decrease in crude oil prices and the 2020 COVID-19 pandemic crisis. The main contributions of our research can be summarized as follows: First, only the correlation between ESG Index and NEX is significant at a 1% level of the asymmetric term (g1); this indicates that there is a stronger transferring effect between ESG Index and NEX when extreme events cause fluctuations in the stock market. Second, the ESG Index and sustainability index exhibit a relatively stable trend with high levels of correlations ranging from 0.9 to 0.98. Additionally, the DCC for the pairs of the ESG Index and Green Bond declined significantly around 2014, which may have been influenced by a decrease in crude oil prices. Third, we find that n1 is significant at a 5% level in the ESG Index-Green Bond case; this indicates that the 2014 decrease in crude oil prices significantly influences the

2 Does ESG Index Have Strong Conditional Correlations …

24

synchronization between the ESG index and Green Bond. Additionally, n2 is significant at a 1% level in the ESG Index-Sustainability Index case. This indicates that the 2020 COVID-19 pandemic significantly influences the dynamic conditional correlation between ESG Index and sustainability. The rest of this paper proceeds as follows. Section 2.2 describes the empirical techniques used in the study. Section 2.3 explains the data and summary statistics. Section 2.4 reports the empirical results of our study. Section 2.5 concludes the study.

2.2

Empirical Techniques

This study investigates the dynamic conditional correlation between the ESG index and clean energy stock, green bond, and sustainability index using the A-DCC model. Specifically, the overall model analysis process is separated into three steps. In the first step, we apply the AR(k)-EGARCH (p, q) developed by Nelson (1991) to estimate the conditional variance for the return of ESG Index, NEX, Green Bond, and Sustainability Index. Precisely, we can denote the return of the four variable spread changes by Dxt ; Eq. (2.1) shows the conditional mean and variance: Dxt ¼ u0 þ

k X

ui Dxti þ et ; et  GEDðvÞ

ð2:1Þ

i¼1 q p X X     ðai jzti j þ ci zti Þ þ bi log r2ti ; log r2t ¼ x þ i¼1

ð2:2Þ

i¼1

where zt ¼ et =rt . Further, we apply Schwarz’ s Bayesian information criterion (SBIC) by Schwarz (1978) to select the optimal lag lengths of AR (k)-EGARCH (p, q). Furthermore, regarding the density function et , we employ the generalized error distribution (GED), whose additional parameter v, which indicates the thickness of tails, is as follows: f ð xÞ ¼

  v v  v   exp   ; k 2½ðv þ 1Þ=v Cð1=vÞk sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2ð2=vÞ Cð1=vÞ k¼ Cð3=vÞ

ð2:3Þ

ð2:4Þ

We employ EGARCH since the logarithmic form of EGARCH can ensure the non-negativity of the conditional variance without the need to restrict the coefficients of the model. Moreover, we can obtain the asymmetric effect of positive and negative shocks by containing the term zti . Specifically, if ci > 0, zti will be

2.2 Empirical Techniques

25

positive, andP vice versa, then, the persistence of shocks to the conditional variance is given by pi¼1 bi . The EGARCH model enables cyclical activity in volatility because negative coefficients are not precluded. In the second step, by obtaining the conditional variance from Eq. 2.2, we can use the A-DCC model exploited by Cappiello et al. (2006) to analyze the dynamic conditional correlations between ESG Index and the other three variables. First, the conditional covariance matrix can be defined as follows: h 0i Ht ¼ E et et ¼ Dt Pt Dt

ð2:5Þ

where the diagonal matrix Dt is the conditional standard deviation calculated from Eq. 2.2. Next, zt ¼ et =rt , the standardized residuals, are used to evaluate the parameters of the A-DCC model. Additionally, the asymmetric generalized dynamic conditional correlation (AG-DCC) model is denoted by   Qt ¼ Q  A0 QA  B0 QB  G0 NG P þ A0 zt1 z0t1 A þ B0 Qt1 B þ G0 gt1 g0t1 G ð2:6Þ   where Q ¼ E zt z0t and N ¼ E gt g0t are the unconditional correlation matrices of zt and gt , respectively. Precisely, gt is defined as follows: gt ¼ I ½zt \0  zt

ð2:7Þ

where I ½ is a k  1 vector indicator function that only takes the value of 1 if the argument is true and 0 otherwise. Additionally, “” function expresses the Hadamard product. The A-DCC (1, 1) model is considered a special case of the AG-DCC (1, 1) model if the matrices A, B, and G are replaced by scalars (a1, b1, and g1). We then calculate the correlation matrix as follows: Pt ¼ Q1 Qt Q1 t t

ð2:8Þ

where Qt is a diagonal matrix with the square root of the ith diagonal element of Qt on its ith diagonal position. In the final step, we use the AR (1) model to model the conditional correlations derived from the second step, similar to Tamakoshi and Hamori (2013) and Toyoshima et al. (2012). We set two crisis dummy variables (Crisis1t and Crisis2t ) that represent the 2014 decrease in crude oil prices and the 2020 COVID-19 pandemic crisis, respectively. These two crises are used to test whether each crisis influences the dynamics of the estimated conditional correlations between the ESG index and the other three stock indices. Specifically, that is,

2 Does ESG Index Have Strong Conditional Correlations …

26

^ t ¼ d0 þ d1 DCC ^ t1 þ n1 Crisis1t þ n2 Crisis2t þ vt DCC

ð2:9Þ

^ t is the dynamic conditional correlation, and vt is white noise. The where DCC dummy variables are defined precisely by:

Crisis1t ðDecline in crude oil pricesÞ ¼

1ðt ¼ 2014=12=09; . . .; 2020=11=12Þ 0ðt ¼ 2010=11=02; . . .; 2014=12=08Þ ð2:10Þ

Crisis2t ðCOVID-19 pandemic crisisÞ ¼

1ðt ¼ 2020=01=23; . . .; 2020=11=12Þ 0ðt ¼ 2010=11=02; . . .; 2020=01=22Þ ð2:11Þ

2.3

Data and Summary Statistics

We use the daily data of ESG Index, NEX, Green Bond, and Sustainability Index from October 29, 2010, to November 12, 2020, excluding uncommon business days. Furthermore, we unified the currency units of all the variables into US dollars to remove the effect of the exchange rate on the results of the analysis. Table 2.1 shows the abbreviations and sources of the variables used in this study. The MSCI World ESG Leaders Index is a capitalization-weighted index that provides information to companies with ESG. This index is structured by assembling several regional indexes namely MSCI Pacific ESG Leaders Index, MSCI Europe & Middle East ESG Leaders Index, MSCI Canada ESG Leaders Index, and MSCI USA ESG Leaders Index. Therefore, this index is highly representative for global use. The WilderHill New Energy Global Innovation Index, a updated dollar weighted index of publicly traded companies that are active in renewable and low-carbon energy; it has been the first and best-known index for this theme since 2006. Furthermore, the S&P Green Bond Index Total Return is composed of a universe of “green” climate-labeled global bonds. It is a bond whose proceeds are used to fund projects that are environmentally friendly. In conclusion, based on their sustainability and environmental activities, the Dow Jones Sustainability

Table 2.1 Description of the variables used in this study Variables

Data

Source

ESG Index NEX Green Bond Sustainability Index

MSCI World ESG Leaders Index WilderHill New Energy Global Innovation Index S&P Green Bond Index Total Return Dow Jones Sustainability World Composite Index

Bloomberg Bloomberg Bloomberg Bloomberg

2.3 Data and Summary Statistics

27

World Composite Index is a global index consisting of the top 10% of the main 2500 S&P global Broad Market Index stocks. Furthermore, the criteria for selecting these stock indices includes climate change strategies, energy consumption, and corporate governance. Figure 2.1 shows the raw data series of the four variables in our study. Evidently, around 2016 and 2020, the four indices have approximately varying degrees of decrease. Meanwhile, to calculate the return of these four indices, we use the first logarithmic difference method. Figure 2.2 shows the plots. The returns of the four variables fluctuate significantly for some periods and sometimes spike downwards and upwards, while continue to be stable for a long time. Therefore, we can employ the AR (k)-EGARCH (p, q) model to calculate the conditional variance of these four variables.

Fig. 2.1 Time-variations of raw data series. Notes ESG Index refers to the MSCI World ESG Leaders Index, NEX refers to WilderHill New Energy Global Innovation Index, Green Bond refers to S&P Green Bond Index Total Return and Sustainability Index refers to the Dow Jones Sustainability World Composite Index

28

2 Does ESG Index Have Strong Conditional Correlations …

Fig. 2.2 Time-variations of return series. Notes ESG Index Return refers to the first logarithmic difference of MSCI World ESG Leaders Index; NEX Return refers to the first logarithmic difference of WilderHill New Energy Global Innovation Index; Green Bond Return refers to the first logarithmic difference of S&P Green Bond Index Total Return; Sustainability Index Return refers to the first logarithmic difference of the Dow Jones Sustainability World Composite Index

Table 2.2 shows the descriptive statistics for the return series of the four indices. Evidently, NEX has the largest maximum return value and Green Bond has the smallest return value. We then find that NEX has the largest standard deviation value. Furthermore, on the grounds of the skewness value, the four variables’ skewness value indicates that they are left-skewed and the positive value of kurtosis indicates that the four variables will realize more peaked and fat tails. Furthermore, we use the Jarque–Bera test developed by Jarque and Bera (1987) to examine whether the return series of the four variables are normally distributed. The Jarque– Bera tests statistics reject normality for each variable at the 1% significance level. The stationarity of variables in this study is checked using the Augmented Dickey–

2.3 Data and Summary Statistics

29

Table 2.2 Summary statistics Descriptive Statistics for Return ESG Index

NEX

Green Bond

Sustainability Index

Mean 0.00028 0.00026 0.00009 0.00022 Median 0.00064 0.00087 0.00015 0.00053 Maximum 0.08627 0.09396 0.02573 0.07694 Minimum −0.10271 −0.12541 −0.03079 −0.10605 Std.Deviation 0.00949 0.01230 0.00399 0.01003 Skewness −1.12256 −0.84940 −0.32510 −0.98494 Kurtosis 17.79447 10.68641 6.06402 12.52056 Jarque–Bera 34,255.0 12,470.0 3962.7 17,108.0 P-value 0.000 0.000 0.000 0.000 ADF −32.0083*** −29.6152*** −34.1122*** −32.5145*** Note ADF: Augmented Dickey and Fuller Unit Root Test (1979); *** denotes rejection of the null hypothesis at the 1% significance level

Fuller (ADF) unit roots test exploited by Said and Dickey (1984). The results of the ADF test show the rejection of the null hypothesis at the 1% level, meaning that all the variables do not have unit roots.

2.4 2.4.1

Empirical Results AR-EGARCH Specification

In our analysis, first, we use the univariate AR (k)-EGARCH (p, q) model to fit each series of returns on the ESG Index, NEX, Green Bond, and Sustainability Index. Table 2.3 shows the results of the AR (k)-EGARCH (p, q) model. In our study, we selected AR (1)-EGARCH (1, 1) for all the variables based on the Schwarz Bayesian information criterion (SBIC). Evidently, all the parameters except the GED parameters of NEX and Green Bond, which are significant at the 5% level, are significant at the 1% level. Parameter b1 is used to estimate the degree of volatility persistence whose values all exceed 0.95, and are significant at the 1% significance level. Additionally, we performed the Ljung–Box test to examine the autocorrelation. Qð20Þ is a test statistic for the null hypothesis indicates that there is no autocorrelation up to order 20 forstandard residuals; Q2 ð20Þ is for standard residuals squared. All the variables have p-values larger than 0.05, which indicates that there is no autocorrelation up to order 20 for bothstandard residuals and standard residuals squared.

0.0004 0.0797*** −0.2888*** −0.1447*** 0.1946*** 0.9714*** 1.3351*** 28.096 0.107 20.739

0.0001 0.0172 0.0483 0.0215 0.0291 0.0049 0.0488

0.0005 0.2076*** −0.1837*** −0.0626*** 0.1868*** 0.9802*** 1.4383** 26.068 0.164 29.107

0.0002 0.0218 0.0289 0.0124 0.0214 0.0031 0.0563

NEX AR (1)-EGARCH (1,1) Estimate SE 0.0001 −0.0065*** −0.0838*** 0.0011*** 0.1345*** 0.9927*** 1.4095** 21.359 0.376 29.000

0.0001 0.0200 0.0031 0.0104 0.0055 0.0004 0.0490

Green Bond AR (1)-EGARCH (1,1) Estimate SE

0.0004 0.0885*** −0.2240*** −0.1224*** 0.1605*** 0.9772*** 1.3546*** 29.259 0.083 15.729

0.0002 0.0195 0.0173 0.0133 0.0222 0.0019 0.0510

Sustainability Index AR (1)-EGARCH (1,1) Estimate SE

Q2 ð20Þ p-value 0.413 0.086 0.088 0.733 Note “***” and “**” represent statistical significance at the 1% and 5% levels, respectively. Q(20) and Q2(20) are the Ljung–Box statistics with 20th lags for the standard residuals and standard residuals squared, respectively

u0 u1 x a1 c1 b1 GED parameter Qð20Þ p-value

ESG Index AR (1)-EGARCH (1,1) Estimate SE

Table 2.3 Analysis results of the AR-EGARCH model

30 2 Does ESG Index Have Strong Conditional Correlations …

2.4 Empirical Results

2.4.2

31

A-DCC Model

We then analyze the A-DCC model exploited by Cappiello et al. (2006). Because this study aims to investigate the dynamic conditional correlations between the ESG index and the other three indices, we present the empirical results in Table 2.4. Evidently, the results of the parameters of standardized residuals (a1 ) and those of innovations in the DCC matrix (b1 ) are statistically significant at the 1% level, except for a1 of the ESG Index-Sustainability Index, which is statistically significant at the 5% level. Additionally, regarding the estimate of the parameter of the asymmetric term (g1 ), only the correlation between ESG Index and NEX is significant at the 1% level, which indicates that there is a stronger transferring effect between ESG Index and NEX when extreme events cause fluctuations in the stock market. Figure 2.3a, b, and c show the estimates of the DCC between each pair of ESG Index and the other three indices. Generally, all the pairs fluctuated around 2020 when the COVID-19 pandemic broke out. Further, the conditional correlations were not absolutely stable over the sample period for all the cases. As shown in Fig. 2.3a, the levels of correlations range from 0.55 to 0.85; the conditional correlation between ESG Index and NEX exhibits an unstable trend. However, in Fig. 2.3b, the conditional correlation between ESG Index and Green Bond ranges from 0.15 to 0.3, which is very weak compared with the other two cases. Furthermore, the DCC for the pairs of ESG Index and Green Bond evidently decrease significantly around 2014. This is when the decrease in crude oil prices broke out in 2014, which indicates that the crisis weakened the correlation between ESG Index and Green Bond. In Fig. 2.3c, one interesting finding is that compared to other cases, the pair of ESG Index and Sustainability Index exhibits a relatively stable trend with high levels of correlations ranging from 0.9 to 0.98. Additionally, another interesting finding is that from the numerical display on the vertical axis, the dynamic conditional correlation between ESG Index and sustainability, whose highest value can exceed 0.98, is the strongest. This shows the high connectedness between ESG Index and sustainability index. The detection of certain systemic breaks for DCC prompts us to use dummy variables to examine the impact of the 2014 decrease in crude oil prices and the 2020 COVID-19 pandemic crisis. Therefore, we apply AR (1) models with dummy variables to explain this in the next sub-section.

Table 2.4 A-DCC estimates between ESG Index and other variables

Coefficient a1 b1 g1 Note “***” and

ESG Index-NEX

ESG Index-Green Bond

Estimate

SE

Estimate

0.0148*** 0.9597*** 0.0361*** “**” represent

0.0051 0.0017*** 0.0047 0.0179** 0.0084 0.0144 0.9971*** 0.0116 0.9533*** 0.0217 0.0049 0.0000 0.0047 0.0261 0.0220 statistical significance at the 1% and 5% levels, respectively

SE

ESG Index-Sustainability Index Estimate SE

32

2 Does ESG Index Have Strong Conditional Correlations …

(a)

(b)

(c) Fig. 2.3 Dynamic conditional correlations between ESG Index and three others indices. a is the plot of DCC between ESG Index and NEX; b is the plot of DCC between ESG Index and Green Bond; c is the plot of DCC between ESG Index and Sustainability Index

2.4 Empirical Results

2.4.3

33

AR (1) Model for the Estimated DCC with Dummy Variables

We then apply the AR (1) model with two dummy variables representing the 2014 decrease in crude oil prices and 2020 COVID-19 pandemic crisis, to analyze the evolution of the evaluated dynamic conditional correlations. Table 2.5 exhibits the results of the AR (1) model. Evidently, the constant parameter (d0 ) and the coefficient of AR (1) terms (d1 ) are statistically significant at the 1% level. Furthermore, regarding the dummy variables, it is clear that n1 is significant at the 5% level in the ESG Index-Green Bond case, which is consistent with the conclusion obtained from Fig. 2.3b, that the dynamic conditional correlation between ESG Index and Green Bond dropped significantly around the 2014 decrease in crude oil prices. Additionally, n2 is significant at the 1% level in the ESG Index-sustainability index case. This result indicates that the 2020 COVID-19 pandemic crisis has a profound impact on the synchronization between the ESG Index and the sustainability index. However, in the ESG Index-NEX case, both n1 and n2 are all insignificant at the 10% level. Furthermore, we derive another interesting finding that states that n1 is negative in all cases, and n2 is positive in all the cases. Therefore, from Fig. 2.3, it is obvious that the dynamic conditional correlations in the three pairs have various degrees of downtrend around 2014 and an uptrend around 2020. The negative coefficients perhaps indicate that the arbitrary transactions between ESG Index and the other three indices do not occur on a sufficient scale during the decrease in the crude oil price period.

Table 2.5 AR (1) models with dummy variables for DCC coefficients

Coefficient

ESG Index-NEX

ESG Index-Green Bond

Estimate

Estimate

SE

SE

ESG Index-Sustainability Index Estimate SE

0.7824*** 0.0461 0.5623*** 0.1262 0.9421*** 0.0349 d0 0.9639*** 0.0055 0.9034*** 0.0085 0.7251*** 0.0136 d1 −0.006 0.0493 −0.2932** 0.1618 −0.018 0.0465 n1 0.0791 0.0618 0.1261 0.2507 0.2814*** 0.0811 n2 Note “***” and “**” represent statistical significance at the 1% and 5% levels, respectively. n1 refers to the coefficient of the 2014 decrease in the crude oil price dummy variable, and n2 refers to the coefficient of the 2020 COVID-19 pandemic crisis dummy variable

2 Does ESG Index Have Strong Conditional Correlations …

34

2.5

Conclusion

This study uses the A-DCC and AR models while employing the financial crisis dummy exploited by Cappiello et al. (2006) and Yiu et al. (2010) to investigate the dynamic conditional correlations between the ESG Index and renewable energy index, green bond, and sustainability index. This analysis reveals some interesting findings between ESG Index and the other three indices, which are exhibited as follows: First, regarding the A-DCC model, only the correlation between ESG Index and NEX is significant at the 1% level of the asymmetric term (g1 ), which indicates that there is a stronger transferring effect between ESG Index and NEX when extreme events cause fluctuations in the stock market. Second, from the dynamic conditional correlation plots, we can derive the finding that the pair of ESG Index and Sustainability Index exhibits a relatively stable trend with high levels of correlations ranging from 0.9 to 0.98, which shows the existence of a high correlation between ESG Index and the sustainability index. Additionally, the DCC for the pairs of ESG Index and Green Bond decreased significantly around 2014. This is when the decrease in crude oil prices broke out in 2014, which indicates that the crisis weakened the correlation between ESG Index and Green Bond. Moreover, around 2020, the dynamic conditional correlation between ESG Index and the other three indices fluctuates significantly, which indicates that the influence of the 2020 COVID-19 pandemic is profound. Third, by employing the AR model to estimate the dynamic conditional correlation with dummy variables, we find that n1 is significant at the 5% level in the ESG Index-Green Bond case, which is consistent with the decreasing trend around the 2014 decrease in crude oil prices in the ESG Index-Green Bond dynamic conditional correlation. Additionally, n2 is significant at the 1% level in the ESG Index-Sustainability Index case, which indicates that the 2020 COVID-19 pandemic significantly influences the dynamic conditional correlation between ESG Index and sustainability. Furthermore, n1 is negative in all cases, and n2 is positive in all cases. The results of our analysis indicate that ESG Index is not only used to facilitate the diversity of capital investment and fund managers, but can also be applied to help fund managers build risk-adjusted outperformance in a more integrated and active manner. As Verheyden et al. (2016) points out, one such integrated strategy, often referred to as “ESG Quant,” considers a variety of non-financial variables to turn the investment universe more actively into businesses that are ideally placed for long-term outperformance.

References

35

References Amel-Zadeh A, Serafeim G (2018) Why and how investors use ESG information: evidence from a global survey. Financial Anal J 74(3):87–103 Ashwin Kumar NC, Smith C, Badis L, Wang N, Ambrosy P, Tavares R (2016) ESG factors and risk-adjusted performance: a new quantitative model. J Sustain Finance Invest 6(4):292–300 Auer BR, Schuhmacher F (2016) Do socially (ir)responsible investments pay? New evidence from international ESG data. Q Rev Econ Finance 59:51–62 Cappiello L, Engle R, Sheppard K (2006) Asymmetric dynamics in the correlations of global equity and bond returns. J Financ Econom 4:557–572 Engle R (2002) Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 20:339–550 Ferrer R, Shahzad SJH, López R, Jareño F (2018) Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Econ 76:1–20 Giese G, Lee LE, Melas D, Nagy Z, Nishikawa L (2019) Performance and risk analysis of index-based ESG portfolios. J Index Invest 9(4):46–57 Gillan ST, Koch A, Starks LT (2021) Firms and social responsibility: a review of ESG and CSR research in corporate finance. J Corp Finance (In Press). https://doi.org/10.1016/j.jcorpfin. 2021.101889 Gjika D, Hovath R (2013) Stock market comovements in Central Europe: evidence from the asymmetric DCC model. Econ Model 33:55–64 Hou Y, Li, S (2016) Information transmission between U.S. and China index futures markets: An asymmetric DCC GARCH approach. Econ Model 52(B):884–897 Hwang E, Min HG, Kim H (2013) Determinants of stock market comovements among US and emerging economies during the US financial crisis. Econ Model 35:338–348 Jarque CM, Bera AK (1987) Test for normality of observations and regression residuals. Int Stat Rev 55(2):163–172 Kaminker C, Stewart F (2019) The role of institutional investors in financing clean energy. OECD working papers on finance, insurance and private pensions, 2012, No. 23, OECD Publishing. Available online www.oecd.org/daf/fin/wp. Accessed on 1 Sept 2019 Kenourgios D, Samitas A, Paltalidis N (2011) Financial crises and stock market contagion in a multivariate time-varying asymmetric framework. J Int Financial Mark Inst Money 21(1):92–106 Liu G, Hamori S (2020) Can one reinforce investments in renewable energy stock indices with the ESG index? Energies 13(5):1179 Nelson D (1991) Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59:347–370 Said SE, Dickey DA (1984) Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika 71(3):599–607 Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464 Sultana S, Zulkifli N, Zainal D (2018) Environmental, social and governance (ESG) and investment decision in Bangladesh. Sustainability 10(6):1831 Tamakoshi G, Hamori S (2013) An asymmetric DCC analysis of correlations among bank CDS indices. Appl Financial Econ 23(6):475–481 Toyoshima Y, Tamakoshi G, Hamori S (2012) Asymmetric dynamics in correlations of treasury and swap markets: evidence from the US market. J Int Financial Mark Inst Money 22(2):381–394 Verheyden T, Eccles RG, Feiner A (2016) ESG for all? The impact of ESG screening on return, risk, and diversification. J Appl Corp Finance 28(2):47–55 Wang P, Moore T (2012) The integration of the credit default swap markets during the US subprime crisis: Dynamic correlation analysis. J Int Financial Mark Inst Money 22:1–15 Yiu MS, Ho WA, Choi DF (2010) Dynamic correlation analysis of financial contagion in Asian markets in global financial turmoil. Appl Financial Econ 20:345–354

Chapter 3

Measuring Tail Dependencies Between ESG and Renewable Energy Stocks: A Copula Approach Xie He, Guizhou Liu, and Shigeyuki Hamori

3.1

Introduction

Energy security issues, technological innovation, fossil fuel depletion, and high and volatile prices of petroleum-based fuels have resulted in a worldwide consensus that renewable energy is a viable energy alternative. Therefore, the renewable energy sector has experienced remarkable growth in the global economy during the last decade (Ferrer et al. 2018; Liu and Hamori 2020). Meanwhile, investment in renewable energy companies, which represents sustainable and responsible investment (SRI), has also attracted a lot of attention from investors and researchers. There is extensive financial empirical research on renewable energy stocks (Bai et al. 2019; Ferrer et al. 2018; Inchauspe et al. 2015; Liu and Hamori 2020; Reboredo 2015). On the other hand, investment in stocks of companies that have the highest environmental, social, and governance (ESG) rated performance can be a potential reinforcement investment in renewable energy stocks because they both include SRI. Considering this background, the major purpose of this study is to measure the tail dependence between stock returns of companies with the highest ESG-rated performance and renewable energy companies, and examine whether renewable energy stock investors can effectively increase their portfolio performance by constructing portfolios with the best ESG companies. To achieve this, we chose the MSCI World ESG Leaders Index (ESG) and the following three renewable energy indices as our analysis objects: the Wilder Hill Clean Energy Index (ECO), European Renewable Energy Total Return Index (ERIX), and Wilder Hill New Energy Global Innovation Index (NEX). As an alternative to correlation in the modeling of financial risks, tail dependence describes the amount of dependence of large loss (gain) events between different assets, which is crucial to extreme risk management. The concept of tail dependence can be embedded within the Copula Theory. Copulas allow researchers to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. Nakajima et al., ESG Investment in the Global Economy, Kobe University Social Science Research Series https://doi.org/10.1007/978-981-16-2990-7_3

37

3 Measuring Tail Dependencies Between ESG …

38

construct flexible multivariate distributions exhibiting rich patterns of tail behavior, ranging from tail independence to tail dependence, and different kinds of asymmetry (Rodriguez 2007). Hence, copula is a mature method that is extensively used in financial studies (Chollete et al. 2011; Mensi et al. 2017; Nguyen and Bhatti 2012; Reboredo 2015; Rodriguez 2007). Thus, the copula-based model was chosen to measure tail dependence between the ESG index and renewable energy stock indices in this research. The plan for this contribution is as follows: Sect. 3.2 provides a basic introduction to empirical techniques, including copulas, tail dependence, and marginal density models. Section 3.3 introduces the data and reports summary statistics. The empirical results are presented in Sect. 3.4. Section 3.5 introduces portfolio performance measurements and comparisons among portfolios. Finally, Sect. 3.6 concludes the paper.

3.2 3.2.1

Empirical Techniques Copula

Copulas are “functions that join or couple multivariate distribution functions to their one-dimensional marginal distribution functions.” Sklar’s theorem is a building block of the theory of copulas. Theorem 2.1.1 (Sklar 1959): Let F be a d-dimensional joint c.d.f. with margins
 F1 ; F2 ; . . .; Fd Let Aj denote the range of Fj , Aj :¼Fj R ðj ¼ 1; 2; . . .; d Þ: Then, d

there exists a d-copula C such that for all x in R ; Fðx1 ; x2 ; . . .xd Þ ¼ CðF1 ðx1 Þ; F2 ðx2 Þ; . . .; Fd ðxd ÞÞ:

ð3:1Þ

If F1 ; F2 ; . . .; Fd are all continuous, then C is uniquely determined on A1  A2      Ad . In reverse, if C is a d-copula and F1 ; F2 ; . . .; Fd are distribution functions, the function F defined above is a d-dimensional joint c.d.f. with margins F1 ; F2 ; . . .; Fd . Moreover, if we assume that Fj is differentiable, and C and F are d times differentiable. Then, deriving both sides of (3.1) to obtain the joint p. d. f., we obtain f ðx1 ; x2 ; . . .xd Þ ¼

d @ n CðF1 ðx1 Þ; F2 ðx2 Þ; . . .; Fd ðxd ÞÞ Y  fi ðxi Þ: @x1    @xd i¼1

ð3:2Þ

That is, the density of F is expressed as the product of the copula density and univariate marginal densities. In this sense, we say that the copula contains all the information about the dependence structure.

3.2 Empirical Techniques

39

Copulas have certain properties that are useful for studying dependence. One of the most important properties is that it allows researchers to measure asymptotic tail dependence, which plays a vital role in risk management. Let (X, Y) be a vector of continuous random variables with marginal distribution functions FX and FY . The upper tail dependence of (X, Y) is kU ¼ lim PrðFX [ ujFY [ uÞ ¼ lim u!1

u!1

1  2u þ Cðu; uÞ : 1u

ð3:3Þ

Cðu; uÞ : u

ð3:4Þ

Similarly, the lower tail dependence of (X, Y) is kL ¼ lim PrðFX  ujFY  uÞ ¼ lim u!0 þ

u!0 þ

Tail dependence is important because it measures the asymptotic likelihood that two variables simultaneously increase or decrease. To capture different patterns of tail dependence, we used three copulas that have been previously studied in existing literature: Clayton, rotated Gumbel, and student’s t copulas. The definitions and properties of these copulas are summarized in Table 3.1.

3.2.2

Time-Varying Copula

In order to further investigate the time-varying dependence features, we exploit the generalized autoregressive score (GAS) model on the student’s t copula. The GAS model, proposed by Creal et al. (2013), has been widely considered as a useful tool for capturing dynamic feature of parameters in copula function. We denote dt as a dynamic copula parameter. Like the generalized autoregressive conditional heteroskedasticity (GARCH) model, another term is used to describe the movement 1=2 of the autoregressive parameter. The score of likelihood It st is used as the information set. The implicit form of dt is expressed as ft ¼ hðdt Þ. In the student t copula, dt ¼ ð1  expfft gÞ=ð1 þ expfft gÞ is used to ensure that the correlation parameter lies between − 1 and 1. ft ¼ hðdt Þ $ dt ¼ h1 ðft Þ 1=2

where, ft þ 1 ¼ x þ bft þ aIt st 

st .

@ log cðU1t ; U2t ; dt Þ; @d

  It  Et1 st s0t ¼ I ðdt Þ:

ð3:5Þ

1 2p

1q

Name Clayton Rotated Gumbel Student’s t

Parameter space c 2 ð0; þ 1Þ c 2 ð1; þ 1Þ q; m 2 ð1; 1Þ  ð2; 1Þ

Notes The empirical lower tail dependence of the Clayton copula and rotated Gumbel copula are calculated by ^kL ¼ 21=^c and ^kL ¼ 2  21=^c . Both tail  qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  ^ ^; ^mÞ ¼ 2  Fstudent  ð^m þ 1Þ q^q^1 dependences in the student’s t copula were calculated in gT ðq þ 1; m þ 1 . See Patton (2013) for details on the copula functions.

1

Copula family
1=c c C ðu1 ; u2 Þ ¼ uc 1 þ u2  1 n o 1=c C ðu1 ; u2 Þ ¼ u1 þ u2  1 þ exp ½ð lnð1  u1 ÞÞc þ ð lnð1  u2 Þc  n ov þv 2 tv1 ðu1 Þ tv1 Rðu2 Þ 2 þ y2 p1ffiffiffiffiffiffiffiffi 1 þ x 2qxy C ðu1 ; u2 Þ ¼ R dydx vð1q2 Þ 2

Table 3.1 Copula functions

40 3 Measuring Tail Dependencies Between ESG …

3.2 Empirical Techniques

3.2.3

41

Marginal Density

GARCH models was proposed by Engle (1982), it has been widely considered as an important tool for modelling and forecasting volatilities of financial time series that exhibiting time-varying volatility and volatility clustering. In this research, the marginal model is built on the standard autoregressive average-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model, in which both parametric and nonparametric models are applied to the standard residual. The ARMA-GARCH (1, 1) model can be defined as: Yt ¼ /0;y þ

p X i¼1

/i;y Yti þ

pffiffiffiffi et ¼ ht zt ; zt  iidDð0; 1Þ

q X

hj;y etj þ et ;

j¼1

ht ¼ x þ bht1 þ ae2t1

ð3:6Þ

ð3:7Þ

where et is the residual, zt is the standard residual, has a specific distribution D, ht is the conditional variance, and /0;y ; and x are constant terms. To ensure the positivity of the variance, x [ 0; a; b 0; and a þ b\1. Following Patton, we exploit both parametric and nonparametric models for D. As the parametric model for D, we choose the skewed t distribution, which is a simple and flexible distribution, proposed by Hansen (1994). The density function of the skewed t distribution is: 8 
bz þ a2 ðg þ 1Þ=2 > 1 > ; z\  ab < bc 1 þ g2 1k

skewed  tðzjg; kÞ ¼  2 ðg þ 1Þ=2 > 1 bz þ a > ; z  ab : bc 1 þ g2 1þk

ð3:8Þ

where g 2 ð2; 1Þ, and k 2 ð1; 1Þ. The values of a, b, and c are given by Cðg þ2 1Þ 2 2 2 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi a ¼ 4kc g2 . g1, b ¼ 1 þ 2k  a , and c ¼ pðg2ÞCðg2Þ For the nonparametric estimate of D, we used the empirical distribution function (EDF): ^ ðzÞ  F

T 1 X 1f^zt  zg: T þ 1 t¼1

ð3:9Þ

42

3.3

3 Measuring Tail Dependencies Between ESG …

Data and Summary Statistics

In this research, we choose the MSCI World ESG Leaders Index to represent the stock prices of companies with the highest ESG-rated performance in the global market. On the other hand, following the variable selection of Liu and Hamori (2020), we choose three renewable energy stock indices, the Wilder Hill Clean Energy Index, European Renewable Energy Total Return Index, and Wilder Hill New Energy Global Innovation Index, as the stock prices of renewable energy companies in the U.S., European, and global markets, respectively. All underlying data are the daily nominal indexes, which were converted into U.S. dollars at the official rate from September 28, 2007, to October 30, 2020, as taken from Bloomberg. The variations in the four indices are shown in Fig. 3.1. The basic information of the indices is summarized as follows.

Fig. 3.1 Daily prices of four indices from September 28, 2007, to October 30, 2020

3.3 Data and Summary Statistics

43

MSCI World ESG Leader Index (ESG) The MSCI World ESG Leaders Index is a capitalization-weighted index that offers a representation of the leading global companies in terms of ESG criteria. It is constituted by aggregating many regional indices including MSCI USA ESG Leaders Index, MSCI Canada ESG Leaders Index, MSCI Europe & Middle East ESG Leaders Index, and MSCI Pacific ESG Leaders Index. Constituent selection was based on data from the MSCI ESG Research. Wilder Hill Clean Energy Index (ECO) The Wilder Hill Clean Energy Index, calculated by the New York Stock Exchange (NYSE), mainly includes U.S.-listed companies focusing on the technologies for utilizing greener, renewable sources of energy. These technologies include renewable energy production (21% weight), energy storage (20% weight), energy conversion (21% weight), greener utilities (13% weight), cleaner fuels (5% weight), and power delivery and conservation (20% weight). European Renewable Energy Total Return Index (ERIX) The European Renewable Energy Total Return Index tracks the performance of the largest European renewable energy companies that are active in either or several of the following six investment clusters: solar, water, wind, biofuels, geothermal, and marine. Each component has a minimum weight of 5%. Wilder Hill New Energy Global Innovation Index (NEX) The Wilder Hill New Energy Global Innovation Index is composed of companies worldwide whose innovative technologies focuse on renewables—solar (27.0% weight), wind (20.0% weight), Biofuels and Biomass (6.0% weight), and others (5.0% weight), energy efficiency (16.0% weight), energy storage (15.0% weight), and energy conversion (11.0% weight). The daily returns of the indices were calculated as the changes in the logarithm of the daily closing prices. Table 3.2 summarizes the descriptive statistics of the daily returns for the four indices. Among the four indices, only the ESG index has a positive mean return. The worst case is ECO, which has a negative 0.0184% mean return. Meanwhile, the ECO has the highest standard deviation, which also indicates the highest volatility. All returns are left-skewed, implying that investing in these assets may have many small gains and a few extreme losses. In terms of kurtosis, all returns are leptokurtic, suggesting that the distributions of all returns are more peaked and have fat tails. The results of the Jarque–Bera test show almost zero p-values, thereby rejecting the null hypothesis of normally distributed returns.

3 Measuring Tail Dependencies Between ESG …

44

Table 3.2 Descriptive statistics of returns of four indices Mean

Std

Min

Max

Skew

Kurt

JB

ESG 1.03E−04 0.0114 − 0.1027 0.0863 − 0.7221 14.4956 0.00 ECO − 1.84E−04 0.0221 − 0.1624 0.1452 − 0.5365 9.2450 0.00 NEX − 4.42E−05 0.0154 − 0.1254 0.1207 − 0.6541 12.0374 0.00 ERIX − 1.18E−04 0.0212 − 0.1697 0.1582 − 0.4507 10.9085 0.00 Notes The table summarizes the descriptive statistics of daily returns ranging from September 28, 2007, to October 30, 2020, including the mean (Mean), standard deviation (Std), minimum (Min), maximum (Max), skewness (Skew), kurtosis (Kurt), and the p-value of the Jarque and Bera (1987) test (JB). ESG, ECO, NEX, and ERIX refer to the MSCI World ESG Leaders Index, Wilder Hill Clean Energy Index, Wilder Hill New Energy Global Innovation Index, and European Renewable Energy Total Return Index, respectively

3.4

Empirical Results

Table 3.3 reports the results of the marginal distributions. The standard residuals of ESG, ECO, NEX, and ERIX are abstracted from the ARMA(0,1)-GARCH(1,1), ARMA(2,0)-GARCH(1,1), ARMA(1,0)-GARCH(1,1), and ARMA(2,0)-GARCH (1,1) based on the Akaike information criteria (AIC). Almost all coefficients in the variance equation of the standard GARCH model for the four returns are at a 1% significance level. The Ljung-Box test is given, and the insignificant results document the non-autoregressive feature (up to 25 lags) of standardized residuals and squared ones. In addition, Table 3.3 reports the estimated results of skew t distribution and results of Goodness-of-Fit (GoF) tests, including the Kolmogorov– Smirnov (KS) and Cramer-von Mises (CvM) tests. The results of the KS and CvM tests prove that the skewed t distribution is well-specified for all standardized residuals. In this research, in addition to the skewed t distribution, we also used the empirical distribution function to describe the marginal distribution of the standardized residuals. The nonparametric estimate for standardized residuals with parametric models for the conditional means and variances makes the model semiparametric. Hence, we also have two types of copula models (parametric and semiparametric). In this research, the ESG index and renewable energy stock indices are divided into three groups (portfolios): “ESG and ECO”, “ESG and NEX”, and “ESG and ERIX”. We estimated three constant copula models including Clayton, rotated Gumbel, and student’s t for these three groups, and the estimated results are presented in Table 3.4. In addition, we also reported tail dependences between the ESG index and renewable energy stock indices under different copulas. According to the results, the tail dependence between ESG and NEX is highest under the three copula models, followed by ESG and ECO. The tail dependence between the ESG and ERIX was the weakest. Table 3.4 also reports the p-values from two GoF tests for the three copula models. The results reveal that Clayton copulas for all groups are rejected (only the

3.4 Empirical Results

45

Table 3.3 Marginal distributions ESG ARMA model 0.053*** /0 – /1 – /2 0.087*** h1 GARCH model x 0.013*** a 0.136*** b 0.859*** Skewed t distribution k 6.423 N − 0.1523 Ljung-box test Qð25Þ 22.019 p value 0.635 22.828 Q2 ð25Þ

ECO

NEX

ERIX

0.018 0.055** 0.023 –

0.039* 0.205*** – –

0.055* 0.028 0.035 –

0.050*** 0.084*** 0.904***

0.012*** 0.091*** 0.906***

0.052*** 0.080*** 0.907***

11.100 − 0.169

8.538 − 0.111

7.107 − 0.101

17.941 0.845 33.005

20.242 0.734 29.565

14.268 0.957 7.890

p value 0.588 0.131 0.241 0.999 Goodness-of-fit test KS 0.408 0.854 0.729 0.154 CvM 0.507 0.720 0.458 0.167 Notes The table provides the estimated results of the ARMA-GARCH-skewed t model for each return, and the results of the test of Ljung and Box (1978) (Q, Q2 ) for independence (using 25 lags) and Goodness-of-Fit (GoF) tests, including the Kolmogorov–Smirnov (KS) and Cramer-von Mises (CvM) test of the skewed t distribution. ***, **, and * represent significance levels of 1%, 5%, and 10%, respectively. The null hypothesis of the GoF test (based on 1000 simulations) is that the data follow the specified skewed t distribution, while the alternative hypothesis is that the data do not follow the specified skewed t distribution

group of ESG and ECO passes the CvM test in the parametric case), while rotated Gumbel and student’s t copula both pass the GoF tests in the parametric case. The estimated results of the time-varying student’s t copula and p-values from two GoF tests are reported in Table 3.5. The results of the GoF test reveal that the ESG-ECO and ESG-ERIX groups pass the GoF tests in the parametric case, while the ESG-NEX group is rejected in both cases. Time-varying tail dependence between ESG and the three renewable energy stock indices are also presented in Figs. 3.2, 3.3, 3.4. In Figs. 3.2, 3.3, 3.4, although we can see that the tail dependence between ESG and renewable energy stocks has been volatile, the trend of strengthening can also be found during some extreme events such as the 2008 Global Financial Crisis, 2011 European Debt Crisis, 2018 China-U.S. Trade War, and 2019 COVID-19 pandemic.

3 Measuring Tail Dependencies Between ESG …

46 Table 3.4 Estimation of constant copulas ESG-ECO Parametric

Semi

ESG-NEX Parametric

Semi

ESG-ERIX Parametric

Semi

1.5122 0.0621 0.6323

2.0025 0.0743 0.7074

2.0324 0.0641 0.7110

1.0442 0.0402 0.5149

1.0696 0.0436 0.5231

log f 1020.08 KSC 0.02 0.05 CvMC Rotated Gumbel copula ^c 1.9606 S.E. 0.0372 0.5759 ^sL

1027.97 0.00 0.00

1398.21 0.01 0.02

1419.14 0.00 0.00

657.41 0.01 0.01

668.69 0.00 0.00

1.9655 0.0368 0.5771

2.3446 0.0432 0.6560

2.3582 0.0415 0.6583

1.6813 0.0251 0.4898

1.6908 0.0316 0.4932

log f 1159.68 0.56 KSC 0.41 CvMC Student’s t copula ^ q 0.7081 S.E. 0.0097 1 0.1418 ^m

1162.45 0.01 0.00

1635.83 0.16 0.17

1647.75 0.00 0.00

769.36 0.11 0.08

776.31 0.00 0.00

0.7076 0.0103 0.1458

0.7962 0.0076 0.1656

0.7970 0.0074 0.1671

0.6186 0.0113 0.1264

0.6207 0.0116 0.1296

Clayton copula ^c 1.4942 S.E. 0.0620 0.6288 ^sL

S.E. 0.0190 0.0217 0.0193 0.0215 0.0211 0.0215 ^; ^mÞ 0.2743 0.2800 0.4010 0.4039 0.1815 0.1881 gT ðq log f 1159.20 1154.82 1675.83 1674.73 804.92 804.99 0.53 0.01 0.20 0.37 0.13 0.26 KSC 0.48 0.00 0.33 0.00 0.29 0.00 CvMC Notes In this table, the estimated coefficients and standard errors obtained in the simulation with 100 bootstraps are reported. “Parametric” and “Semi” represent the parametric and semi-parametric models, respectively. The parameter boundaries of the Clayton copula, rotated Gumbel copula, and student’s t copula are set as (0, ∞), (1, ∞), and (−1, 1)  (2, ∞). Log likelihood (log f) values were reported as positive signs. P-values of GoF tests, including the Kolomogorov-Smirnov ðKSC ) and Cramer-von Mises (CvMC ) tests on constant copula models of the standardized residuals are also reported in Table. P-values less than 0.05 are in bold

3.5

Portfolio Performance

In this section, we compare the portfolio performance between ESG and different renewable energy stock indices based on several traditional performance standards: adjusted risk return (Sharp ratio), standard deviation, value-at-risk (VaR), and conditional value-at-risk (CVaR, also known as expected shortfall). For portfolio weights, we consider three types of weighting strategies for the three portfolios: static weighting, diversified risk-parity weighting, and optimal portfolio weighting. In this research, xi1;t is the weight in the ESG under the i-th

3.5 Portfolio Performance

47

Table 3.5 Estimation of time-varying student’s t copula

^ x S.E. ^a S.E. ^ b

ESG-ECO Parametric

Semi

ESG-NEX Parametric

Parametric

ESG-ERIX Semi Parametric

0.1028 0.0306 0.1289 0.0230 0.9423

0.1017 0.0396 0.1280 0.0184 0.9427

0.0476 0.0157 0.0853 0.0134 0.9783

0.0480 0.0207 0.0796 0.0134 0.9775

0.0130 0.0089 0.0485 0.0106 0.9908

0.0130 0.0082 0.0485 0.0102 0.9908

S.E. 0.0171 0.0222 0.0071 0.0094 0.0061 0.0055 0.1233 0.1246 0.1259 0.1352 0.1153 0.1153 ^m1 S.E. 0.0236 0.0217 0.0223 0.0225 0.0201 0.0173 log f 1203.69 1197.49 1746.50 1743.79 844.09 843.01 0.38 0.03 0.00 0.00 0.17 0.00 KSR 0.27 0.00 0.00 0.00 0.14 0.00 CvMR Notes In this table, the estimated coefficients and standard errors obtained in the simulation with 100 bootstraps are reported. “Parametric” and “Semi” represent the parametric and semiparametric models, respectively. Log likelihood values (log f) were reported as positive signs. The p-values of the KSR and CvMR methods test on the time-varying copula model of the Rosenblatt transform of the standardized residuals are also reported in Table. P-values less than 0.05 are in bold

Fig. 3.2 Time-varying tail dependence between ESG and ECO based on student’s t copula (GAS) model

Fig. 3.3 Time-varying tail dependence between ESG and NEX based on student’s t copula (GAS) model

3 Measuring Tail Dependencies Between ESG …

48

Fig. 3.4 Time-varying tail dependence between ESG and ERIX based on student’s t copula (GAS) model

  type of weighting strategies and xi2;t ¼ 1  xi1;t as the weight in the renewable energy stock index at time t. The assumption made is there is no transaction cost, allowing portfolio weights change over time and will not affect the returns. The specific information of the three weighting strategies is summarized as follows: (1) Static Weighting Under this strategy, a constant weight x11;t is allocated to the ESG index. (2) Diversified Risk-Parity Weighting Under this strategy, assets with less volatility will be allocated a larger weight. Specifically, the weight in the ESG index can be calculated as
 x21;t ¼ ^h22;t = ^h11;t þ ^h22;t , where ^h11;t and ^h22;t are the conditional volatilities of the ESG index and renewable energy stock index, respectively. (3) Optimal Portfolio Weighting Under this strategy, the weight in the ESG index can be calculated as:
 ¼ ð^h22;t  ^h12;t Þ= ^h11;t þ ^h22;t  2  ^h12;t , if x31;t \0; x31;t :¼ 0, and if [ 1; x3 :¼ 1, where ^h12;t is conditional covariance between the ESG index

x31;t x31;t

1;t

and renewable energy stock index. In this research, time-varying VaR and CVaR measures were obtained by using simulation. Specifically, for time t from 1 to T (total sample size), S(=5000) observations are generated from the dynamic student’s t copula, form the portfolio return, and use the empirical distribution of simulated portfolio returns to estimate the time-varying VaR and CVaR measures (the confidence level is set as 99%). To explore whether performance will be reinforced after putting the ESG index into the portfolios with renewable energy stocks, we compared the difference between portfolio performance, including the ESG index, and investing in renewable energy stocks without ESG (x1;t ¼ 0; x2;t ¼ 1). RRcom ; SDcom ; VaRcom ; CVaRcom refer to the amount of adjusted risk return (the Sharp ratio), standard deviation, VaR, and CVaR (expected shortfall) that are improved (or reduced) by putting the ESG index into the portfolio, compared to investing merely in the

3.5 Portfolio Performance

49

renewable energy stocks. Specifically, RRcom ; SDcom ; VaRcom ; EScom can be calculated as: RRcom ¼ ðRRw2  RRw1 Þ  100%

ð3:10Þ

SDcom ¼ ðSDw2  SDw1 Þ=SDw1  100%

ð3:11Þ

VaRcom ¼ 1=T

T
X

 VaRw2;t  VaRw1;t =VaRw1;t  100%

ð3:12Þ

 CVaRw2;t  CVaRw1;t =CVaRw1;t  100%

ð3:13Þ

t¼1

CVaRcom ¼ 1=T

T
X t¼1

where RRw1 and SDw1 are adjusted risk return (Sharp ratio) and standard deviation of merely investing in renewable energy stocks without ESG; RRw2 and SDw2 are adjusted risk return (Sharp ratio) and standard deviation of portfolios including ESG. VaRw1;t , CVaRw1;t are time-varying VaR and CVaR of merely investing in renewable energy stocks without ESG in time t. VaRw2;t and CVaRw2;t are time-varying VaR and CVaR of portfolios including ESG. Based on Table 3.6, our calculations show that the risk-adjusted (Sharp ratio) of portfolios is improved by 13.24% by the ESG index under different strategies on average compared to merely investing in renewable energy stocks without ESG. Meanwhile, the standard deviation, VaR, and CVaR of portfolios including the ESG index are reduced by 50.94%, 33.30%, and 32.16%, respectively. Thus, we

Table 3.6 Portfolio performance comparison Strategy Static

Measure

ESG-ECO (%)

ESG-NEX (%)

ESG-ERIX (%)

RRcom 10.80 2.94 5.33 − 37.97 − 42.79 − 34.96 SDcom − 28.72 − 17.22 − 27.28 VaRcom − 28.15 − 15.76 − 27.02 CVaRcom 24.75 4.18 10.95 Diversified risk-parity RRcom − 52.59 − 53.27 − 51.18 SDcom − 43.08 − 21.37 − 41.72 VaRcom − 41.82 − 19.63 − 41.39 CVaRcom 35.91 8.92 15.36 Optimal portfolio RRcom − 56.02 − 70.61 − 59.05 SDcom − 48.04 − 25.02 − 47.26 VaRcom − 46.15 − 22.55 − 47.00 CVaRcom Notes In this table, four measurements of the performance of comparison between each portfolio and benchmark are reported. In the static strategy, only the results of equally weighted example (50–50%) are reported. In each strategy, we report the best results in each row of RRcom , SDcom , VaRcom , and CVaRcom in bold font

3 Measuring Tail Dependencies Between ESG …

50

(a)

and (b)

Fig. 3.5 Time-varying VaR between ESG and ECO

can conclude that renewable energy stock investors can effectively increase their risk-adjusted returns (Sharp ratio) and reduce standard deviation, VaR, and CVaR by constructing portfolios with ESG. We can also conclude that the portfolios of ESG and ECO have the best performance in improving risk-adjusted returns (Sharp ratio) and reducing VaR and CVaR (except CVaR under optimal portfolio weighting). In addition, the time-varying VaRs of the four different portfolios are shown in Figs. 3.5, 3.6, 3.7. In Figs. 3.5, 3.6, 3.7, (a) displays the time-varying VaR under the static weighting strategy, while (b) shows the time-varying VaR under diversified risk-parity weighting and optimal portfolio weighting. The benchmark (black line) refers to investing in renewable energy stocks without ESG (x1;t ¼ 0; x2;t ¼ 1). From the figures, we can see that all portfolios suffered extreme losses during extreme events such as the 2008 Global Financial Crisis, 2011 European Debt

(a) Fig. 3.6 Time-varying VaR between ESG and NEX portfolio

and (b)

3.5 Portfolio Performance

(a)

51

and (b)

Fig. 3.7 Time-varying VaR between ESG and ERIX portfolio

Crisis, and 2019 COVID-19 pandemic. However, the figures also reveal that balancing renewable energy stocks with the ESG index can effectively reduce extreme losses.

3.6

Conclusion

This study analyzed the tail dependences between the ESG index and three renewable energy stock indices, the Wilder Hill Clean Energy Index, European Renewable Energy Total Return Index, and Wilder Hill New Energy Global Innovation Index, from September 28, 2007, to October 30, 2020, by using several copula-based models. The following findings were obtained: First, under the constant copulas, the tail dependence between ESG and NEX is the highest under the three copulas, followed by ESG and ECO. The tail dependence between the ESG and ERIX was the weakest. Second, under the time-varying student’s t copula, although dynamic tail dependence between ESG and renewable energy stocks has been volatile, the strengthening trend can also be found during some extreme events. In addition, we examined whether performance will be reinforced after putting the ESG index into the portfolios with renewable energy stocks and found that ESG can effectively benefit portfolios in improving risk-adjusted returns and lowering the standard deviation, VaR, and CVaR. Meanwhile, we also found that balancing renewable energy stocks with the ESG index can also be helpful in effectively reducing extreme losses during financial turmoil.

52

3 Measuring Tail Dependencies Between ESG …

References Bai L, Liu Y, Wang Q, Chen C (2019) Improving portfolio performance of renewable energy stocks using robust portfolio approach: evidence from China. Phys A Stat Mech Appl 533. https://doi.org/10.1016/j.physa.2019.122059. http://www.ncbi.nlm.nih.gov/pubmed/122059 Chollete L, de la Peña V, Lu CC (2011) International diversification: a copula approach. J Bank Finance 35(2):403–417. https://doi.org/10.1016/j.jbankfin.2010.08.020 Creal D, Koopman SJ, Lucas A (2013) Generalized autoregressive score models with applications. J Appl Economet 28(5):777–795. https://doi.org/10.1002/jae.1279 Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4):987–1007 Ferrer R, Shahzad SJH, López R, Jareño F (2018) Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Economics 76:1–20 Hansen BE (1994) Autoregressive conditional density estimation. Int Econ Rev 35(3):705–730 Inchauspe J, Ripple RD, Trück S (2015) The dynamics of returns on renewable energy companies: a state-space approach. Energy Econ 48:325–335. https://doi.org/10.1016/j.eneco.2014.11.013 Jarque CM, Bera AK (1987) A test for normality of observations and regression residuals. Int Stat Rev/revue Internationale De Statistique 55(2):163. https://doi.org/10.2307/1403192 Liu T, Hamori S (2020) Spillovers to renewable energy stocks in the US and Europe: are they different? Energies 13(12):3162. https://doi.org/10.3390/en13123162 Ljung GM, Box GEP (1978) On a measure of lack of fit in time series models. Biometrika 65 (2):297–303. https://doi.org/10.1093/biomet/65.2.297 Mensi W, Tiwari A, Bouri E, Roubaud D, Al-Yahyaee KH (2017) Energy Econ 66:122–139. https://doi.org/10.1016/j.eneco.2017.06.007 Nguyen CC, Bhatti MI (2012) Copula model dependency between oil prices and stock markets: evidence from China and Vietnam. J Int Finan Markets Inst Money 22(4):758–773. https://doi. org/10.1016/j.intfin.2012.03.004 Reboredo JC (2015) Is there dependence and systemic risk between oil and renewable energy stock prices? Energy Econ 48:32–45. https://doi.org/10.1016/j.eneco.2014.12.009 Rodriguez JC (2007) Measuring financial contagion: a Copula approach. J Empir Financ 14 (3):401–423. https://doi.org/10.1016/j.jempfin.2006.07.002 Patton A (2013) Copula methods for forecasting multivariate time series. In: Elliott G, Timmermann A (eds), Handbook of Economic Forecasting, vol. 2, Elsevier, pp 899–960. https://doi.org/10.1016/B978-0-444-62731-5.00016-6 Sklar M (1959) Fonctions de repartition an dimensions et leurs marges. Publ. Inst. Statist. Univ. Paris 8: 229–231.

Chapter 4

Which Factors Will Affect the ESG Index in the USA and Europe: Stock, Crude Oil, or Gold? Tiantian Liu, Tadahiro Nakajima, and Shigeyuki Hamori

4.1

Introduction

With the growing environmental pollution, climate change, ecological imbalances, environmental problems, and sustainable social development are of great concern. Investors have realized the significance of sustainability and shift toward sustainable investments in the last ten years. Sustainable investment refers to a new type of investment that incorporates sustainability into capital markets, which incorporates environmental, social, and corporate governance (ESG) factors into investment activities. What is the ESG? ESG refers to the three factors (environmental, social, and governance) needed to evaluate the sustainability of a company. Environmental criteria include energy use, waste, and pollution caused by a company (e.g., carbon emissions); social criteria focus on the relationship of the company’s business (e.g., human rights), and governance criteria include how the companies are governed and the making of effective decisions (e.g., management structure). ESG criteria provide important information for investors to evaluate whether it is worth investing in the companies. A number of studies on ESG investment have concentrated on the comparative analysis of ESG and general market indices (Jain et al. 2019; Miralles‐Quiros et al. 2017; López et al. 2007). However, few studies have considered the linkage between ESG indices and other assets. Understanding the information concerning the ESG index and other assets can help investors design optimal portfolio strategies, hedging practices, and risk management. Overall, the stock market transmits important information on financial markets. Crude oil is considered as a strategy resource and gold is used as a safe haven asset, both of which have significant economic impacts on financial markets. Hence, overall, we selected the stock index, crude oil, and gold to explore their relationship with the ESG index. According to the Global Sustainable Investment Alliance (GSIA), the two largest regions based on the value of their sustainable investing assets in 2018 were Europe © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. Nakajima et al., ESG Investment in the Global Economy, Kobe University Social Science Research Series https://doi.org/10.1007/978-981-16-2990-7_4

53

54

4 Which Factors Will Affect the ESG Index …

and the United States of America (USA). To explore the difference in ESG investment in these two regions, we perform a comparative analysis of the spillover effects of the ESG index in the USA and Europe. Therefore, the intention of this study is to explore the spillover effects between gold, stock, crude oil, and the ESG index in the USA and Europe. This study measures the return and volatility spillover effects between three assets and the ESG index with the Diebold and Yilmaz approach and the Barunik and Krehlik methodology. In the time domain, the Diebold and Yilmaz approach is applied to obtain the return and volatility spillover effects in one investment horizon. One of the greatest advantages of the Diebold and Yilmaz approach is that it allows us to identify the direction of spillover effects across different assets and calculate the value of the spillover effects in the system. In addition, considering that investors with different investment horizons may have different investment decisions and portfolios, we further explore the spillover effects between three assets and the ESG index in the frequency domain based on the Barunik and Krehlik methodology. Finally, the rolling window is applied to depict the time-varying return and volatility spillover effects between gold, stock, crude oil, and the ESG index, which is particularly helpful for observing the variation of the spillover effects during the sample period. The Diebold and Yilmaz approach, and the Barunik and Krehlik methodology have been widely used to analyze the spillover effects across different assets in recent literature (Zhang et al. 2020a, b; He et al. 2020). Our analysis has several contributions to the literature. First, this analysis examines the return and volatility spillover effects of stock, crude oil, and gold on the ESG index in the USA and Europe. Furthermore, this study decomposes the return and volatility spillover effects into three different frequency bands (high frequency/short term, medium frequency/medium term, and low frequency/long term). Second, we capture the time-varying spillover effects over the sample period using the rolling window method. Third, we compare the spillover effects received by the ESG index in the USA and Europe to explore whether the return and volatility spillover effects of the ESG index are different in these two regions. Our analysis of the return and volatility spillover effects between stock, crude oil, gold, and the ESG index will provide helpful information for investors and portfolio managers in the diversification of investment in the ESG index during financial turmoil. The rest of this study is organized as follows. Section 4.2 presents the empirical techniques. Section 4.3 reports the data and summary statistics. Section 4.4 discusses our empirical results and findings. Section 4.5 concludes the study.

4.2 Empirical Techniques

4.2

55

Empirical Techniques

In this study, we apply the Diebold and Yilmaz approach, and the Barunik and Krehlik methodology to assess the directional return and volatility spillover effects in the time and frequency domains across the ESG index, gold, crude oil, and stock in the USA and Europe.

4.2.1

Measures of the Directional Spillover Effects in the Time Domain

This study measures the directional spillover effects in the time domain using the Diebold and Yilmaz approach proposed by Diebold and Yilmaz (2009, 2012, 2014), based on generalized variance decompositions from vector autoregression (VAR) models. Following Diebold and Yilmaz’s (2014) spillover effect measures, we assume a VAR(p) with n variables as yt ¼

p X

Ui yti þ et ;

ð4:1Þ

i¼1

where yt represents an N  1 vector of the observed variables, U is the N  N coefficient matrix, and the error vector et i:i:d  ð0; RÞ. By transforming a stationary and invertible VAR model, a moving average representation can be shown as: yt ¼ WðLÞet ;

ð4:2Þ

where WðLÞ represents an N  N coefficient matrix of infinite-lag polynomials. The concept of the Diebold and Yilmaz approach is based on the generalized variance decompositions proposed by Koop et al. (1996), and Pesaran and Shin (1998), assessing how much of the H-step-ahead forecasting error variance of the j-th variable is due to shocks to the k-th variable in the system. The following equation represents the definition of the H-step-ahead generalized forecast error variance decomposition (GFEVD): 2 PH  r1 ðWh RÞjk kk h¼0 hH ;  PH  jk ¼ 0 h¼0 Wh RWh jj

ð4:3Þ

where hjk(H) is the variance contribution of variable j to variable k in the system at the selected forecast horizon H.

4 Which Factors Will Affect the ESG Index …

56

As the own- and cross-variable variance contributions are seen in the matrix’s main diagonal and the off-diagonal components, the sum of the variance shares is not necessarily equal to 1. Thus, the sum of the rows is employed to normalize each entry in the variance decomposition matrix as follows: hH ~hH ¼ P jk jk N k¼1

hH jk

;

ð4:4Þ

where ~hH jk stands for the pairwise spillover from the k-th variable to the j-th variable at horizon H in the time domain. The total spillover effects (from spillover) from all variables to the k-th variable at horizon H can be calculated as follows:

SH k

4.2.2



PN ~H j ¼ 1 hkj j 6¼ k ; ¼ 100  N

ð4:5Þ

Measures of the Directional Spillover Effects in the Frequency Domain

Křehlík and Baruník (2018) applied spectral decompositions of the variance to obtain the spillover effects in the frequency domain, expanding on the Diebold and Yilmaz approach. P The frequency response function Wðeix Þ = h eixh Wh ; obtained from the pffiffiffiffiffiffiffi Fourier transformation of the coefficient Wh , with i = 1: The following function express the generalized causation spectrum over frequencies x 2 (−p, p):

ðf ðxÞÞjk 

 2   ix r1 ð W ð e ÞR Þ  kk jk  ðWðeix ÞRW0 ðe þ ix ÞÞjj

;

ð4:6Þ

where ðf ðxÞÞjk denotes the portion of j-th variable’s spectrum at a given frequency x caused by shocks in the k-th variable. By weighting the function ðf ðxÞÞjk by the j-th variable frequency share of the variance, we can compute the generalized decomposition of the variance under frequency x.

4.2 Empirical Techniques

57

The weighting function is: Cj ðxÞ ¼

ðWðeix ÞRW0 ðe þ ix ÞÞjj R p 1 ix ÞRW0 ðe þ ix ÞÞ dk jj 2p p ðWðe

ð4:7Þ

Given a frequency band d = (a, b): a, b 2 ðp; pÞ, for a < b, the generalized variance decompositions on frequency band d are written as hjk ðd Þ ¼

1 Zb Cj ðxÞðf ðxÞÞjk dx; 2p a

ð4:8Þ

Similarly, the generalized variance decomposition is scaled under the frequency band d = (a, b): a, b 2 ðp; pÞ, a < b. ~hjk ðd Þ ¼ Phjk ðd Þ ; k hjk ð1Þ

ð4:9Þ

~hjk ðd Þ represents the pairwise spillover effects received by the j-th variable from the k-th variable with a given frequency band d. The total spillover effects (from spillover) from all other variables to the k-th variable in the given frequency band d is shown as:

SFk

4.3

PN ~ j ¼ 1 hkj ðd Þ j 6¼ k :  ðd Þ ¼ 100  N

ð4:10Þ

Data and Summary Statistics

The data employed in this study consist of the daily data of the ESG index, gold, the overall stock index, and crude oil in the USA and Europe between July 1, 2009, and November 1, 2020. The data are retrieved from Bloomberg, and all the index series are in US dollars, totaling 2785 daily observations. The variables are presented in Table 4.1. The MSCI USA ESG Leaders index and the MSCI Europe ESG Leaders index are chosen as the proxy indices of ESG investment in the USA and Europe, the two important indices in the MSCI ESG Leaders index series. The MSCI ESG Leaders index is a capitalization-weighted index that represents the performance of companies with high ESG performance. The index series with relatively low tracking errors in the underlying equity market aims to provide information for investors who seek exposure to companies with diversified sustainability profiles. The MSCI USA ESG Leaders index consists of large and mid-cap companies in the USA

4 Which Factors Will Affect the ESG Index …

58 Table 4.1 Variables in the model

Variable USA USA ESG S&P 500 WTI USA gold Europe Europe ESG STOXX 600 Brent Europe gold

Data MSCI USA ESG leaders index Standard and poor 500 index West Texas intermediate crude oil futures COMEX gold price MSCI Europe ESG leaders index STOXX Europe 600 index Brent crude oil futures London Bullion market association gold price

market, while the MSCI Europe ESG Leaders index contains large and mid-cap companies in 15 developed market countries1 in Europe. The S&P 500 index and the STOXX Europe 600 index as common stock indices are used to represent the whole stock market performance in the USA and Europe, respectively. The S&P 500 index is a capitalization-weighted index that measures the performance of 500 large companies in the USA. The STOXX Europe 600 represents the European stock market, including large, mid, and small capitalization companies across 17 European countries. West Texas Intermediate (WTI) crude oil and Brent crude oil are considered as the main representatives of the international benchmark for crude oil prices and are widely used in economic and financial research. The WTI crude oil futures are traded on the New York Mercantile Exchange (NYMEX), which represents the crude oil market in the USA. Brent crude oil futures represent the European crude oil market, trade on the Intercontinental Exchange (ICE). The gold prices used in the USA and Europe are the New York (COMEX) gold price and the London Bullion Market Association (LBMA) gold price. The New York (COMEX) gold is traded on the NYMEX as the American gold benchmark price. The ICE Benchmark Administration (IBA) provides an auction platform for the LBMA gold price and reflects the gold price benchmark for Europe. Figures 4.1 and 4.2 show the price variations of the variables during the sample period. As shown in Figs. 4.1 and 4.2, the prices of all variables decreased in March 2020 as a result of the COVID-19 pandemic. We compute the first order lagged differences in logarithm prices as the returns of variables. Then, the volatilities are obtained using fitted autoregressivegeneralized autoregressive conditional heteroscedasticity (AR-GARCH) model. Table 4.2 describes the statistical features of the ESG index, stock market index, crude oil, and gold in terms of return and volatility in the USA and Europe. As shown in Table 4.2, the USA ESG and S&P 500 have the highest mean returns. 1

15 countries include: Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the UK.

4.3 Data and Summary Statistics

Fig. 4.1 Variable price series in the USA

Fig. 4.2 Variable price series in Europe

59

4 Which Factors Will Affect the ESG Index …

60

Table 4.2 Descriptive statistics of the return and volatility series in the USA and Europe Mean

Std. dev

Skewness

Kurtosis

ADF

PP

Panel A: return series in the USA USA ESG 0.0005 0.0111 − 0.8513 19.3022 − 36.2931*** − 61.1700*** S&P 500 0.0005 0.0112 − 0.8683 18.6403 − 36.2955*** − 61.1710*** WTI − 0.0000 0.0266 0.2195 32.0223 − 37.7532*** − 52.7200*** USA gold 0.0003 0.0105 − 0.5631 9.2907 − 36.1369*** − 53.9740*** Panel B: volatility series in the USA − 3.3931*** USA ESG 0.0001 0.0003 11.0337 157.7670 − 6.6148*** *** S&P 500 0.0001 0.0003 11.1268 162.7314 − 6.8850 − 8.4874*** WTI 0.0007 0.0018 8.4079 83.6427 − 5.6303*** − 6.3631*** *** USA gold 0.0001 0.0001 2.8350 13.4119 − 3.0840 − 5.7916*** Panel C: return series in Europe Europe ESG 0.0001 0.0127 − 0.6887 11.9464 − 37.1464*** − 52.0080*** STOXX 600 0.0001 0.0130 − 0.6218 9.5698 − 37.0685*** − 51.5810*** Brent − 0.0001 0.0223 − 0.3752 17.5432 − 35.4647*** − 52.7900*** Europe gold 0.0003 0.0103 − 0.4680 8.8623 − 38.0087*** − 53.5590*** Panel D: volatility series in Europe − 7.1488*** Europe ESG 0.0002 0.0002 4.8806 36.8195 − 5.0239*** *** STOXX 600 0.0002 0.0002 5.1473 41.4451 − 4.7731 − 7.3067*** *** Brent 0.0005 0.0008 6.3077 50.7390 − 5.3947 − 6.0303*** *** Europe gold 0.0001 0.0001 2.4766 10.7988 − 3.3931 − 6.3636*** Note ADF denotes the Augmented Dickey and Fuller Unit Root Test; PP denotes Phillips and Perron unit root test; *, **, and *** denote rejection of the null hypothesis at the 10%, 5% and 1% significance levels, respectively

Apart from the WTI crude oil return, all returns of the indices are left skewed, and the volatilities to all indices are right skewed. The kurtosis coefficients are higher than three for all returns and volatilities, suggesting that the probability distributions of all indices’ returns and volatilities are leptokurtic. The Augmented Dickey-Fuller (ADF) unit root test results and the Phillips and Perron (PP) unit root test results show that all variables reject the null hypothesis (at the 1% significance level) of a unit root that is present in the return and volatilities series in the USA and Europe.

4.4

Empirical Results

This section shows the results from the Diebold and Yilmaz approach, and the Barunik and Krehlik methodology described in Sect. 4.2. First, four types of four-variable VAR models are estimated, including the returns and volatilities of the ESG index, gold, crude oil, and stock index in the USA and Europe for the full sample period. The Schwarz criterion (SC) is used to choose the lag length of the

4.4 Empirical Results

61

VAR models. Following the Diebold and Yilmaz’s approach and Liu and Hamori (2020), a 100-days ahead forecasting horizon (H) for variance decomposition is used to measure the spillover effects transmitted from gold, crude oil, and stock returns and volatilities to the ESG index returns and volatilities in the USA and Europe. Second, based on the Barunik and Krehlik methodology, we obtain the spillover effects in three frequency bands: “Frequency S”: 1–5 days period; “Frequency M”: 5–21 days period; “Frequency L”: period longer than 21 days. The choice of frequency bands was the same as that in Liu and Hamori (2020). Finally, considering that the changing trends in spillover effects are much more informative, the rolling windows method allows us to capture the time-varying spillover effects.

4.4.1

Analysis of Full-Sample Spillover Effects

Tables 4.3 and 4.4 display the results of the return spillover effects transmitted from the stock market, crude oil, and gold to the ESG index in the USA and Europe, using the Diebold and Yilmaz approach, and the Barunik and Krehlik methodology, respectively. The jk-th entry in the connectedness table is the measured spillover effects from market k to market j. The last column “From” exhibits the total spillover effects received by the ESG index from the other three markets. As clearly seen from Table 4.3, the “From” column of return spillover effects of the USA ESG index attain a value of 13.13%, which is higher than the “From” return spillover effects (12.30%) of the Europe ESG index. This result suggests that, compared to the impact of stock, crude oil, and gold returns in Europe on the European ESG index, gold, crude oil, and stock returns in the USA have more influence on the USA ESG index returns. In both the USA and Europe, stock contributes the most spillover effects on the ESG index, followed by crude oil and gold. The stock market has a significant impact on the ESG index returns in both the USA and Europe, which can be explained by the construction of the ESG index. The ESG index is a capitalization-weighted index that comprises large and mid-cap companies with high ESG ratings, which can be considered as a part of the overall stock market. Thus, it is not difficult to understand why the variation in the overall stock market plays an important role in the ESG index. Our results are also supported by previous

Table 4.3 Diebold and Yilmaz approach: return spillover results USA USA ESG Europe Europe ESG

USA ESG 47.50

S&P 500 47.04

WTI 5.30

USA gold 0.15

From 13.13

Europe ESG 50.79

STOXX 600 40.88

Brent 6.50

Europe gold 1.83

From 12.30

62

4 Which Factors Will Affect the ESG Index …

Table 4.4 Barunik and Krehlik methodology: return spillover results USA Frequency S 1–5 days USA ESG USA ESG 40.80 Frequency M 5–21 days USA ESG USA ESG 4.95 Frequency L > 21 days USA ESG USA ESG 1.74 Europe Frequency S 1–5 days Europe ESG Europe ESG 40.93 Frequency M 5–21 days Europe ESG Europe ESG 7.26 Frequency L > 21 days Europe ESG Europe ESG 2.60

S&P 500 40.45

WTI 4.43

USA gold 0.11

From 11.25

S&P 500 4.88

WTI 0.64

USA gold 0.03

From 1.39

S&P 500 1.71

WTI 0.23

USA gold 0.01

From 0.49

STOXX 600 32.64

Brent 4.88

Europe gold 1.62

From 9.79

STOXX 600 6.06

Brent 1.19

Europe gold 0.16

From 1.85

STOXX 600 2.18

Brent 0.43

Europe gold 0.05

From 0.67

literature (Mensi et al. 2017; Balcilar et al. 2017); for example, Balcilar et al. (2017) find stock markets affect sustainable indices significantly by analyzing dynamic correlations between the stock market and sustainable indices from a number of regions. The ESG index returns in the USA and Europe receive higher spillover effects from crude oil returns than gold returns, consistent with the conclusion of Mensi et al. (2017), who find crude oil has a higher impact on the sustainability stock index than gold. The results of the Barunik and Krehlik methodology are presented in Table 4.4, which show the return spillover effects from gold, crude oil, and stock to the ESG index in the USA and Europe in three different frequency bands. Table 4.4 shows that the majority of return spillover effects from stock, crude oil, and gold on the ESG index appear in the short term, rather than the long term. This result suggests that the return spillover effects received by the ESG index decrease with an increase in investment horizons. This also implies that most information and return shocks are transmitted within one week. Based on the Barunik and Krehlik methodology, several studies find similar results: the return spillover effects across financial assets have a higher transmission degree in the short term (Liu et al. 2020; Tiwari et al. 2018; Ferrer et al. 2018). Table 4.5 summarizes the volatility spillover effects of gold, crude oil, and stock on the ESG index in the USA and Europe. The volatility spillover effects received

4.4 Empirical Results

63

Table 4.5 Diebold and Yilmaz approach: volatility spillover results USA USA ESG Europe Europe ESG

USA ESG 45.81

S&P 500 46.29

WTI 6.53

USA gold 1.37

From 13.55

Europe ESG 44.17

STOXX 600 42.05

Brent 10.55

Europe gold 3.23

From 13.96

Table 4.6 Barunik and Krehlik methodology: volatility spillover results USA Frequency S 1–5 days USA ESG USA ESG 1.02 Frequency M 5–21 days USA ESG USA ESG 6.40 Frequency L > 21 days USA ESG USA ESG 38.39 Europe Frequency S 1–5 days Europe ESG Europe ESG 1.23 Frequency M 5–21 days Europe ESG Europe ESG 3.75 Frequency L > 21 days Europe ESG Europe ESG 39.20

S&P 500 1.03

WTI 0.58

USA gold 0.07

From 0.42

S&P 500 6.46

WTI 1.27

USA gold 0.07

From 1.95

S&P 500 38.80

WTI 4.68

USA gold 1.23

From 11.18

STOXX 600 0.93

Brent 0.46

Europe gold 0.06

From 0.36

STOXX 600 2.94

Brent 1.57

Europe gold 0.24

From 1.19

STOXX 600 38.18

Brent 8.52

Europe gold 2.92

From 12.41

by the USA ESG index (13.55%) from all other markets are slightly lower than the volatility spillover received by the European ESG index (13.96%). Similar to the results of the return spillover effects of the Diebold and Yilmaz approach, stock contributes the most volatility spillover effects to the ESG index; gold contributes the least volatility spillover effects to the ESG index in both regions. It is evident from the results of the Barunik and Krehlik methodology, as displayed in Table 4.6, that the volatility effects from gold, crude oil, and stock on the ESG index of the USA and Europe in the long term are higher than the volatility effects in the short term. The volatility spillover effects increase with a decrease in fluctuation frequency, which is in agreement with the results reported in the previous research (Liu et al. 2020; Tiwari et al. 2018; Ferrer et al. 2018), suggesting

64

4 Which Factors Will Affect the ESG Index …

that the transmitted shocks affect the ESG index volatility over the long term. To gain greater investment gains, investors with long-term investment horizons should pay more attention to volatility spillover effects across assets in the long term.

4.4.2

Analysis of Time-Varying Spillover Effects

The static spillover effects from the other markets to the ESG index over the sample period are characterized by analysis of the full-sample spillover effects. However, this analysis overlooks the variation in spillover effects over time in the sample period. A 500-day rolling window employed to estimate the time-varying spillover effects in the time and frequency domains. Figures 4.3 and 4.4 present the time-varying total (From) return spillover effects from all other markets (gold, crude oil, and stock) to the ESG index in the USA and Europe, respectively. The trend in total return spillover effects of the European ESG index changes more rapidly than the USA ESG index is observed in Figs. 4.3 and 4.4, indicating that the European ESG index is more vulnerable to the effects of the other markets (gold, crude oil, and stock) in Europe. It is clear that there is a sudden increase in the time-varying total return spillover effects of the ESG index in the USA and Europe in 2020, which may be caused by the crude oil price crash and COVID-19 in 2020. Figures 4.3 and 4.4 depict the time-varying return spillover effects in different frequency bands: the long-dashed (red) line is the time-varying spillover effect in the short term, the dotted (green) line is the medium term, and the two-dash (blue) line is the long term. Most time-varying total return spillover effects are also concentrated in the short term. Moreover, the time-varying spillover effects only in

Fig. 4.3 Time-varying return spillover effects in the USA

4.4 Empirical Results

65

Fig. 4.4 Time-varying return spillover effects in Europe

the short term show a similar change trend to the Diebold and Yilmaz approach results, while the time-varying spillover effects in the medium and long term present different trends. Figure 4.5 presents the directional (pairwise) return spillover effects from gold, crude oil, and stock to the ESG index in the USA and Europe. The time-varying spillover effects from crude oil (WTI crude oil and Brent crude oil) to the ESG index in the USA and Europe show a similar pattern. Moreover, the time-varying spillover effects from crude oil to the ESG index in Fig. 4.5 provide evidence that the crude oil crash in 2020 caused a sudden increase in total time-varying spillover effects in 2020. Among the three markets, the stock market is the main contributor to the total return spillover effects of the ESG index in the USA and Europe. The time-varying total (From) volatility spillover effects from gold, crude oil, and stock to the ESG index in the USA and Europe are illustrated in Figs. 4.6 and 4.7, respectively. Figure 4.8 presents the directional (pairwise) volatility spillover effects from gold, crude oil, and stock on the ESG index. When extreme events occur, the time-varying volatility spillover effects change more rapidly, in contrast to the time-varying return spillover effects that have relative smoothness trends. In other words, the time-varying volatility spillover effects between the other markets and the ESG index rise drastically during financial turmoil, which is consistent with previous literature (Ferrer et al. 2018; Li et al. 2016; Křehlík and Baruník 2017). This suggests the time-varying volatility spillover effects are more vulnerable to extreme events. In line with the results in Tables 4.4 and 4.6, most time-varying volatility spillover effects appear in the long term, suggesting that the speed of information transmitted from other markets to the ESG index volatilities is slower than that of the ESG index returns. Figures 4.6 and 4.7 present several peaks in the time-varying volatility spillover effects of the ESG index in both regions. For instance, the first spike of the

Fig. 4.5 Time-varying return spillover effects (pairwise) in the USA and Europe

66 4 Which Factors Will Affect the ESG Index …

4.4 Empirical Results

67

Fig. 4.6 Time-varying volatility spillover effects in the USA

Fig. 4.7 Time-varying volatility spillover effects in Europe

time-varying volatility spillover effects in 2015 in both regions was a result of the crude oil crash in 2014. This result is also confirmed in the directional (pairwise) volatility spillover effects of crude oil on the ESG index in the USA and Europe (Fig. 4.8). The second spike in 2020 in the USA and Europe was caused by a crude oil crash and COVID-19 in 2020. However, we find a sharp increase in the time-varying spillover effects of the European ESG index in 2016, but not in the USA ESG index. A plausible explanation for this was Brexit in 2016. That is, the UK voted to leave the EU in June 2016, which led to economic fluctuations in Europe.

Fig. 4.8 Time-varying volatility spillover effects (pairwise) in the USA and Europe

68 4 Which Factors Will Affect the ESG Index …

4.5 Conclusion

4.5

69

Conclusion

This study explores the return and volatility spillover effects of gold, crude oil, and stock on the ESG index in the USA and Europe by the Diebold and Yilmaz approach, and the Barunik and Krehlik methodology. Moreover, we also capture the time-varying spillover effects of the ESG index, which allows us to identify the intensity and direction of transmission of information and the spillover effects during financial turmoil. We obtain some important findings regarding the spillover effects of gold, crude oil, and stock on the ESG index. The findings are summarized as follows. First, the total return spillover effects from all other markets to the ESG index in the USA are higher than those of the ESG index in Europe, while the total volatility spillover effects of the European ESG index are higher than those of the USA ESG index. Second, among the three assets, the stock market contributes the most return and volatility spillover effects of the ESG index in both regions. The reason may be that the ESG index is a capitalization-weighted index that can be seen as a subset of the overall stock market. Thus, the stock market has a important impact on the ESG index in the USA and Europe. Additionally, gold transmits the least return and volatility spillover effects to the ESG index in both regions, indicating that gold provides greater diversification benefits than the stock and crude oil markets. Third, in both regions, the majority of the return spillover effects of the ESG index are concentrated in the short term, while the majority of volatility spillover effects appear in the long term. This result illustrates that shocks and information transmission from the stock market, crude oil, and gold to the ESG index returns are completed within one week, while most of the shocks and information on volatility are transmitted over the long term. Fourth, compared to the variation of the time-varying return spillover effects of the European ESG index, the time-varying return spillover effects of the ESG index in the USA are relatively steady over the sample periods. Fifth, the time-varying volatility spillover effects of the ESG index in the USA and Europe change more rapidly and dramatically than the time-varying return spillover effects of the ESG index during the period of extreme events in our sample, suggesting that the time-varying volatility spillover effects are more sensitive to extreme events. When extreme events occur, the volatility spillover from the stock market, crude oil, and gold to the ESG index in both regions increase rapidly. This finding suggests that during periods of financial turmoil, there is a gradual increase in connectedness across assets.

70

4 Which Factors Will Affect the ESG Index …

References Alliance GSI (2018) Global sustainable investment review, vol 3. www.gsi-alliance.org/wpcontent/uploads/2019/03/GSIR_Review2018 Balcilar M, Demirer R, Gupta R (2017) Do sustainable stocks offer diversification benefits for conventional portfolios? An empirical analysis of risk spillovers and dynamic correlations. Sustain 9:1799 Baruník J, Křehlík T (2018) Measuring the frequency dynamics of financial connectedness and systemic risk. J Financ Econ 16:271–296 Diebold FX, Yilmaz K (2009) Measuring financial asset return and volatility spillovers, with application to global equity markets. Econ J 119:158–171 Diebold FX, Yilmaz K (2012) Better to give than to receive: predictive directional measurement of volatility spillovers. Int J Forecast 28:57–66 Diebold FX, Yilmaz K (2014) On the network topology of variance decompositions: measuring the connectedness of financial firms. J Econ 182:119–134 Ferrer R, Shahzad SJH, López R, Jareño F (2018) Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Econ 76:1–20 He X, Takiguchi T, Nakajima T, Hamori S (2020) Spillover effects between energies, gold, and stock: the United States versus China. Energy Environ 31:1416–1447 Jain M, Sharma GD, Srivastava M (2019) Can sustainable investment yield better financial returns: a comparative study of ESG indices and MSCI indices. Risk 7:15 Koop G, Pesaran MH, Potter SM (1996) Impulse response analysis in nonlinear multivariate models. J Econ 74:119–147 Křehlík T, Baruník J (2017) Cyclical properties of supply-side and demand-side shocks in oil-based commodity markets. Energy Econ 65:208–218 López MV, Garcia A, Rodriguez L (2007) Sustainable development and corporate performance: a study based on the Dow Jones sustainability index. J Bus Ethics 75:285–300 Liu T, Hamori S (2020) Spillovers to renewable energy stocks in the US and Europe: are they different? Energies 13:3162 Liu T, He X, Nakajima T, Hamori S (2020) Influence of fluctuations in fossil fuel commodities on electricity markets: evidence from spot and futures markets in Europe. Energies 13:1900 Li L, Yin L, Zhou Y (2016) Exogenous shocks and the spillover effects between uncertainty and oil price. Energy Econ 54:224–234 Mensi W, Hammoudeh S, Al-Jarrah IMW, Sensoy A, Kang SH (2017) Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications. Energy Econ 67:454–475 Miralles-Quiros MDM, Miralles-Quiros JL, Arraiano IG (2017) Sustainable development, sustainability leadership and firm valuation: differences across Europe. Bus Strategy Environ 26:1014–1028 Pesaran HH, Shin Y (1998) Generalized impulse response analysis in linear multivariate models. Econ Lett 58:17–29 Tiwari AK, Cunado J, Gupta R, Wohar ME (2018) Volatility spillovers across global asset classes: evidence from time and frequency domains. Q Rev Econ Finance 70:194–202 Zhang Y, He X, Nakajima T, Hamori S (2020a) Oil, gas, or financial conditions-which one has a stronger link with growth? North Am J Econ Finance 54:101220 Zhang W, He X, Nakajima T, Hamori S (2020b) How does the spillover among natural gas, crude oil, and electricity utility stocks change over time? Evidence from North America and Europe. Energies 13:727

Chapter 5

How Does the Environmental, Social, and Governance Index Impacts the Financial Market and Macro-Economy? Yulian Zhang, Tadahiro Nakajima, and Shigeyuki Hamori

5.1

Introduction

The environmental, social, and governance (ESG) criteria are a series of standards for the company or investor who pursues long-term sustainable benefits. ESG investment refers to an investing behavior in that companies are concerned about environmental issues (climate change, greenhouse effect, environmental crisis, pollution, and renewable resources), social issues (improvement of the working environment, recruitment of diverse talents, a responsibility to local communities, human rights, and animal welfare, etc.), and governance issues (management structure, employee relationships, executizve compensation, transparency of management strategies, expansion of information disclosure contents, the value of shareholders’ opinions, etc.), and investments. Many companies focus on reasonable long-term profits. It is increasingly important to study ESG investment because both investors and companies are concerned about ESG concerning sustainability. In the early twenty-first century, major parts of the investment markets still accepted the historical assumption that ethical-oriented investments may reduce financial returns. It is well known that philanthropy is not a highly profitable business. Friedman and Freidman (1990) provide a widely accepted academic foundation that proves that the cost of ethical and responsible behaving will outweigh the benefits. However, these assumptions were challenged. Edmans (2011) studies the relationship between stock returns and employee satisfaction in America, and the stock returns of the 100 best companies to work are 2.1% higher than the industry benchmark in the long term. There are many studies on the correlations between ESG and investment returns. Goyal and Aggarwal (2014) showed that the ESG stock portfolios have exceeded blue chips and market portfolios from 2008 to 2013 in India. Jain et al. (2019) indicate that a sustainable index (such as various ESG indices) is a good substitute to the traditional conventional indices considering the diversification of risk and hedging. ESG standards are becoming increasingly popular for investors to evaluate the companies in which © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. Nakajima et al., ESG Investment in the Global Economy, Kobe University Social Science Research Series https://doi.org/10.1007/978-981-16-2990-7_5

71

72

5 How Does the Environmental, Social, and Governance Index

they want to invest. There is also an investigation on the renewable energy indices and the dependence structure of the ESG index using a copula model, providing a satisfactory result that the ESG index can lower the potential value-at-risk and maintain a high return (Liu and Hamori 2020). However, Torre et al. (2020) also provide a different opinion—the companies’ performance of Eurostoxx50 does not seem to be different from their efforts in the ESG commitments. The social and environmental values are moving up investors’ agendas (IPE European Institutional Asset Management Survey 2009). ESG can be a very important role concerning the sustainable development of society and companies. Before the concept of ESG appeared, there was another wording: SRI (socially responsible investing). SRI is an investment behavior that attempts to balance the social or environmental good and financial return to achieve the positive social revolutions the supporters believe. It became famous for “sustainable investing” or “responsible investing.” Generally, investors with a sense of social responsibility encourage corporate actions which can promote environmental management, consumer protection, gender or racial diversity, and human rights. The IR (impact investing) is a subset of SRI, committed to consciously creating social influence through investment. The origins of SRI may date back to the Christian denomination; it is a strong social and environmentally conscious ethical investment method—different from ordinary investment. The purpose of SRI or IR investment differs from that of ESG. The former uses funding and investment activities to express the value and mission of institutions, and the latter pursue improving investment performance (Caplan et al. 2013). Therefore, we consider the ESG index. However, much of the research to date has been descriptive in the relationship between ESG and stock return; the literature on spillover over among ESG and financial markets or macroeconomics are insufficient. Therefore, it is necessary to study the relevance of ESG to macroeconomics and financial markets so that the policymaker can consider some preferential policies for them, and investors can also consider if ESG is a good investment choice. We use the weekly data spanning from July 2009 to November 2020 (574 observations), ending on Friday. In this study, we aim to measure the connectedness among the MSCIESG, STLFSI, WEI, WGS1MO, and WTI in America, employing mixed approaches of Diebold and Yilmaz (2012, 2015) (hereinafter DY 12) and Baruník and Křehlík (2018) (hereinafter BK 18). To see the secular changes of the spillover index, we also employ the moving-window approach. The spillover index shows the spillover effect of crises or impacts in the system. The connectedness studies could help policymakers formulate accurate financial policies. We find the MSCIESG return has a huge impact on the financial market compared with the impact on GDP (gross domestic product), interest rate, and crude oil future price in the return spillover. Some suggested that the total return spillovers are almost focused on the short-term; this does not appear to be the case. In this study, the total spillover index focuses on the long-term, demonstrating that the shocks coming from the return system will have a long-term rather than short-term impact. Based on the results of the moving-window, most surprisingly, the total

5.1 Introduction

73

return spillover index in the long-term accounts for the most considerable weight during the COVID-19 pandemic in February 2020, indicating that extreme events or shocks could have a long-term influence on the return system. The rest of this paper is organized as follows. We explain the empirical techniques in Sect. 5.2, and then the data and summary statistics are displayed in Sect. 5.3. In Sect. 5.4 we show the empirical results, and we conclude the investigation in Sect. 5.5.

5.2 5.2.1

Empirical Techniques DY 12

We employ a generalized VAR (vector autoregression) process proposed by Diebold and Yilmaz (2012) to obtain the spillover index. We construct an N-variable (p) model: Yt ¼

p X

Ui Yti þ et ;

ð5:1Þ

i¼1

where Yt represents the N  1 observation variable vector at time t, Ui is the N  N coefficient matrix, and error term et is the i.i.d. series (independently and identically distributed), and et * W.N. (R), which we suppose the expectation of et is zero. If the root of jUðzÞj is outside the unit circle, the VAR framework can become the moving average (i.e., MA (1)): Zt ¼ WðLÞet ;

ð5:2Þ

WðLÞ is an N  N coefficient matrix of the infinite lag polynomials. Then, we employ variance decomposition to obtain the spillover index that we want to know. Multivariate variance decompositions require orthogonality of the error terms. We can use Cholesky factorization to ensure the error term orthogonality; however, the variance decomposition will rely on the ordering of variance. Koop et al. (1996) and Pesaran and Shin (1998) propose a new VAR model to ensure that the generalized forecast error variance decomposition (GFEVD) is independent of the ordering, followed by Diebold and Yilmaz (2012). According to Baruník and Křehlík (2018), the H-step-ahead GFEVD is represented as: hH pq

PH 2 r1 jj h¼0 ððWh RÞpq Þ ¼ PH ; 0 h¼0 ððWh RWh Þpp

ð5:3Þ

where hH pq displays the forecast error variance decomposition contribution from the qth to the pth variable at horizon h, where Wh is the N  N coefficient matrix of the moving average at lag h.

74

5 How Does the Environmental, Social, and Governance Index

To ensure that the sum of all variables GFEVD is 1, we divide every entry by the raw entries. hH ~hH ¼ P pq ; pq N H j¼1 hpq

ð5:4Þ

~hH is called the pairwise spillover from the jth to the ith variable at horizon H. pq PN ~H PN ~H Moreover, j¼1 hpq ¼ 1 and i;j¼1 hpq ¼ N. Then, the total spillover index is displayed as follows: PN PN ~H ~H n o1 0 i; j ¼ 1 hpq i; j ¼ 1 hpq ~ Tr hH pq i 6¼ j i 6¼ j H @ A; ¼ 100  1  S ¼ 100  PN ¼ 100  ~hH N N i;j¼1 pq ð5:5Þ where SH provides a simple summary of the average spillover of shocks among the system to the total forecast error variance, and Tr fg is the trace operator. Followed by Diebold and Yilmaz (2015), we also display the “total directional connectedness” of “to spillover” and “from spillover.” To Spillover

SH 

p

PN ~H q ¼ 1 hpq q 6¼ i  100; ¼ N

ð5:6Þ

SH  i means the total directional spillovers from variable p to all the other variables. From Spillover

SH p



PN ~H q ¼ 1 hqp q 6¼ p  100; ¼ N

ð5:7Þ

The SH i! represents the total directional spillovers from all the other variables to variable p.

5.2.2

BK 18

Diebold and Yilmaz (2012) provide an approach that can measure spillover over in the time domain. Followed by Diebold and Yilmaz (2012, 2014), Baruník and Křehlík (2018) employ the Fourier transform to get the connectedness changes in

5.2 Empirical Techniques

75

different frequency bands. It proposes frequency-dependent connectedness using the general spectral representation of variance decomposition. We apply Fourier transform to divide the three frequency dynamics, namely, the short-term, P mid-term, and long-term. The frequency response function Wðeix Þ ¼ h eiwh Wh , pffiffiffiffiffiffiffi where i ¼ 1. W; is the coefficient of the Fourier transform. We show the generalized causation spectrum over frequencies x 2 (−p, p) as

ðf ðxÞÞpq 

 2   ix r1 ð W ð e ÞR Þ  kk pq  ðWðeix ÞRW0 ðe þ ix ÞÞpp

;

ð5:8Þ

where Wðeix Þ is the Fourier transform of the impulse response W. ðf ðxÞÞpq shows the part of the spectrum of the qth variable to the pth variable due to shocks in the frequency band x. The denominator shows the spectrum of the pth variable at frequency x as a within-frequency causation. We can weigh ðf ðxÞÞpq by the frequency share of the variance of the pth variable. Additionally, we get a natural decomposition of GFEVD in frequency bands. The weighting function is displayed as follows: CpðxÞ ¼

ðWðeix ÞRW0 ðe þ ix ÞÞpp R ; 1 p ik ÞRW0 ðe þ ik ÞÞ dk pp 2p p ðWðe

ð5:9Þ

where Cj(x) indicates the power of the pth variable at a specified frequency band, totaling the frequencies to a constant value of 2p. We notice that the Fourier transform of the impulse response is, overall, a complex-valued quantity. We observe that the generalized causation spectrum is the squared modulus of the weighted complex numbers and produces a real quantity. Finally, let b = (c, d): c < d, c, d 2 ðp; pÞ. We show the GFEVD on frequency band b as 1 hpq ðbÞ ¼ 2p

Zd CpðxÞðf ðxÞÞpq dx;

ð5:10Þ

c

where hpq ðbÞ is also standardized like hH pq . Thus, the scaled GFEVD on frequency band b becomes ~hpq ðbÞ ¼ P hpq ðbÞ ; N k¼1 hpq ð1Þ

ð5:11Þ

where h~pq ðbÞ is the pairwise spillover of frequency band b from q to p. We can propose the total frequency connectedness band b and formula (5.5):

76

5 How Does the Environmental, Social, and Governance Index

F

S ðbÞ ¼ 100 

P ! P~ ~ hð bÞ hðbÞ Tr  P P~ ~ hð 1 Þ hð 1 Þ

ð5:12Þ

Tr fg is the trace operator. Similarly, the total directional spillover index of “to spillover” and “from spillover” can also be written as: To Spillovers on frequency band d PN ~ q ¼ 1 hqp ðbÞ q 6¼ p  100; SF p ðbÞ ¼ N

ð5:13Þ

SF p ðbÞ shows the pairwise spillovers transmitted to all other variables by variable p in frequency band b as well. From spillovers on frequency band d

SFp

PN ~ p ¼ 1 hpq ðbÞ q 6¼ p  100;  ð bÞ ¼ N

ð5:14Þ

As previously mentioned, the directional spillovers (from) measure the connectedness variable p received from other variables in frequency band b.

5.3

Data and Summary Statistics

We use weekly data in this study, which includes the MSCIESG, Financial Stress Index provided by the STLFSI, WEI, WGS1MO, and the WTI. These data span from July 2009 to November 2020 (574 observations), ending on Friday. The data sources are displayed in Table 5.1. The MSCIESG targets companies with the highest score in each sector of the parent index in terms of ESG. The selection range of the MSCIESG is part of the MSCI Global Investable Market Index, and the MSCIESG is a float-adjusted market value-weighted index. The MSCIESG data is downloaded from Bloomberg, and the STLFSI and WGS1MO are from the Federal Reserve Bank of St. Louis. The WEI is an index of ten actual economic activity indicators, and its scale is consistent with the GDP growth rate in the fourth quarter. The index also represents a series of common component concluding labor market, consumer behavior, and production, which can be downloaded from the Federal Reserve Bank of New York. The WTI can be found on—Investing.com. Figure 5.1 shows pictures of the raw data. Relating to the MSCIESG and WTI data, we calculate the logarithmic difference directly. We cannot take the difference between natural logarithms because the

5.3 Data and Summary Statistics

77

Table 5.1 Variables employed in the model Variable

Data

Data source

MSCIESG STLFSI

MSCI USA ESG leader index St. Louis Fed financial stress index

Bloomberg Economic research of federal reserve bank of St. Louis Federal reserve bank of New York Economic research of federal reserve bank of St. Louis Investing.com

WEI WGS1MO

Weekly economic index 1-month treasury constant maturity rate WTI West Texas intermediate crude oil future prices Note ESG refers to environmental, social, and governance. The price of crude oil is measured in USD per barrel

STLFSI fluctuates around zero. Then, we add 100 to every STLFSI data so that we can take the logarithmic difference. For the GDP growth rate proxy, we consider WEI directly as the return conservation. Furthermore, we calculate the first difference as the return data because WGS1MO shows the yields in percent per annum. The return data are shown in Fig. 5.2. We note both huge fluctuations in 2020. We consider a model that combines the ARMA (autoregressive moving average) model and the GARCH (generalized autoregressive conditional heteroskedasticity) model. We can obtain the conditional variance series as the volatility for every variable. Furthermore, the Akaike information criterion is employed to choose the order of the ARMA-GARCH model. We display the volatility series in Fig. 5.3. They show the same trend in 2020. The summary statistics for the return and volatility conservations are displayed in Table 5.2. For return data, the MSCIESG and WEI have positive mean values, while the others have negative mean values. The WEI has the lowest minimum and highest maximum values. Moreover, the WEI also showed the most volatile trend. Regarding skewness, only the STLFSI is right-skewed, while the other series are all left-skewed. Based on the kurtosis value, all return conservations are leptokurtic, which indicates that these distributions have heavier tails and higher peaks. According to Dickey and Fuller (1979), we develop the ADF (augmented Dickey– Fuller) test to examine if these variables have unit roots. We find that the WEI data has no unit root at the 5% significance level, and all other variables have no unit root at the 1% significance level from sub-table A of Table 5.2. For the volatility data from sub-table B in Table 5.2. All variables have a positive mean value. The WEI has the highest minimum, maximum, and standard deviation. Regarding skewness and kurtosis, we see that only STLFSI is right-skewed and all variables show leptokurtic like the return series. According to the ADF test, only MSCIESG volatility has no unit root at the 5% significance level, while other volatility series have no unit roots at the 1% significance level.

78

5 How Does the Environmental, Social, and Governance Index

Fig. 5.1 Time series of raw data. Note The figures represent the MSCI USA ESG leader index (MSCIESG), St. Louis Fed financial stress index (STLFSI), weekly economic index (WEI), 1-month treasury constant maturity rate (WGS1MO), and West Texas intermediate crude oil future prices (WTI)

5.4 Empirical Results

79

Fig. 5.1 (continued)

5.4 5.4.1

Empirical Results Full Sample Analysis

We display the return and volatility spillovers according to DY 12 and BK 18 in Tables 5.3 and 5.4, respectively. The forecast error variance decomposition contribution from variable j to i is shown as the ijth entry in every sub-table. According to DY 12, we can measure the intensity and direction of spillovers among the specified variables in the time domain, employing the forecast error variance decomposition. The total spillover indicates future uncertainty in the specified system. It is shown as the lower right corner in each sub-table, and it is the sum of all “from spillover” or “to spillover”. The lag length of the VAR models is 3 for the

80

5 How Does the Environmental, Social, and Governance Index

Fig. 5.2 Time series of return data. Note The figures represent the MSCI USA ESG leader index (MSCIESG), St. Louis Fed financial stress index (STLFSI), weekly economic index (WEI), 1-month treasury constant maturity rate (WGS1MO), and West Texas intermediate crude oil future prices (WTI)

5.4 Empirical Results

81

Fig. 5.2 (continued)

return system and 2 for the volatility system based on the Schwarz criterion. We set the forecast horizon (h) to 100 in the analysis. In panel A of the return spillover table, there is an obvious spillover over of about 40.717% for DY 12. Moreover, the value in the last column represents “from spillover,” which shows the total directional spillover from other markets to the specified market. We call the value of the last row “to spillover” indicating the total directional spillover from the specified market to all other markets. Here, we mainly focus on the connectedness between MSCIESG and other variables. First, in terms of the connectedness from MSCIESG to other variables, the spillover from MSCIESG to STLFSI is 18.848%, while the connectedness from MSCIESG to the others are all in single digits. This result shows that the spillover index from MSCIESG to STLFSI is much larger than that of WEI, WGS1MO, and WTI for return, indicating the spillover over effect from MSCIESG to the financial market is

82

5 How Does the Environmental, Social, and Governance Index

Fig. 5.3 Time series of volatility data. Note The figures represent the MSCI USA ESG leader index (MSCIESG), St. Louis Fed financial stress index (STLFSI), weekly economic index (WEI), 1-month treasury constant maturity rate (WGS1MO), and West Texas intermediate crude oil future prices (WTI)

5.4 Empirical Results

83

Fig. 5.3 (continued)

much bigger—rather than the impact on GDP, interest rate market, and crude oil futures prices market. Second, regarding the connectedness from other markets to MSCIESG, the spillovers from STLFSI and WTI to MSCIESG are relatively high. It also indicates that the MSCIESG is impacted by the financial market and crude oil market. Notably, the spillover effect from crude oil future price to MSCIESG (the return spillover is 11.870%) is larger than that from MSCIESG to crude oil future price (the return spillover is 2.058%). Except for the total spillovers in the time domain, we investigate the spillover changes in different frequencies. We use Fourier transform to divide DY 12 into three frequency bands in the frequency domain based on Baruník and Krehik (2018). Here, we consider the short-term “Frequency S” is 1–4 weeks (roughly 1 month), the mid-term “Frequency M” means 5–12 weeks (a quarter), and we set

84

5 How Does the Environmental, Social, and Governance Index

Table 5.2 Summary statistics for return and volatility series A. Summary statistics for the return variation MSCIESG STLFSI WEI WGS1MO WTI Minimum − 0.072 − 0.010 − 11.450 − 0.590 − 0.151 Maximum 0.062 0.009 4.630 0.160 0.120 Mean 0.001 − 0.000 1.573 − 0.000 − 0.000 Std. dev. 0.010 0.001 2.364 0.048 0.023 Skewness − 0.741 0.137 − 3.361 − 5.962 − 0.935 Kurtosis 8.812 20.084 12.090 66.890 7.872 ADF − 16.730*** − 18.054*** − 2.162** − 13.411*** − 17.430*** B. Summary statistics for the volatility variation MSCIESG STLFSI WEI WGS1MO WTI Minimum 0.005 0.000 0.219 0.022 0.014 Maximum 0.051 0.009 2.772 0.505 0.084 Mean 0.009 0.001 0.379 0.035 0.020 Std. dev. 0.010 0.001 2.364 0.048 0.023 Skewness − 0.741 0.137 − 3.361 − 5.962 − 0.935 Kurtosis 8.812 20.084 12.090 66.890 7.872 ADF − 2.161** − 3.396 *** − 2.894*** − 8.037*** − 4.331*** Notes ADF is the abbreviation of the augmented Dickey–Fuller unit root test (1979). ***, **, and * represent a rejection of the null hypothesis, which means the series has no unit root at the 1%, 5%, and 10% significance levels, respectively

13 weeks to infinity (more than a quarter) as the long-term “Frequency L.”1 Many previous studies, for example, Toyoshima and Hamori (2018) and Wang et al. (2019), report that the total return spillover index decreases with a decrease in the frequency band, and the long-term return spillover is very small. This shows that the future uncertainty of the return system is almost concentrated in the short-term in Baruník and Krehik (2018). However, as we can see in panel B of Table 5.3, the return spillover over of the long-term (17.659%) is larger than the short-term (15.461%), which indicates that the shocks coming from the return system have a significant influence on the long-term rather than the short-term. We can also find some reasonable interpretations in Fig. 5.3, considering the dynamic analysis with a moving-window. We see that in the overall changes of return spillover, the short-term maintains the largest proportion most of the time—also consistent with previous studies. However, the long-term return index suddenly increases and peaks in early April 2020 because of the epidemic of coronavirus (the red line circle in Fig. 5.4), resulting in the final average return spillover being mainly explained by the long-term. This further illustrates the huge impact of COVID-19. To check the robustness, we also use other case consisting of 1 month (1–4 weeks) for “Freq S,” half a year (5–24 weeks) for “Freq M,” and we define that more than half a year (25 weeks– infinity) is “Freq L.” We obtain similar results, which are shown in Appendix.

1

MSCIESG 59.599 18.848 5.450 2.856 2.058 5.842

MSCIESG MSCIESG 48.158 STLFSI 14.162 WEI 0.017 DGS1MO 0.514 WTI 1.314 To 3.201 Freq M: 5–12 weeks MSCIESG MSCIESG 7.732 STLFSI 3.484 WEI 0.193 DGS1MO 1.351 WTI 0.459 To 1.097

MSCIESG STLFSI WEI WGS1MO WTI To Panel B: BK 18 Freq S: 1–4 weeks

Panel A: DY 12

WEI 0.173 2.162 0.532 0.113 1.338 0.757 WEI 0.016 0.298 0.941 0.012 0.054 0.076

STLFSI 5.536 8.237 0.413 3.640 1.164 2.15

STLFSI 22.723 45.532 12.569 9.312 3.022 9.525

STLFSI 14.556 34.188 0.037 2.689 1.054 3.667

Table 5.3 Return spillovers for DY 12 and BK 18 WEI 0.386 2.966 26.345 0.167 1.777 1.059

DGS1MO 1.405 5.140 1.519 25.084 1.196 1.852

DGS1MO 3.651 9.653 0.217 38.508 3.919 3.488

WGS1MO 5.421 15.658 37.936 77.952 6.155 13.034

WTI 3.091 4.292 0.567 4.157 14.735 2.421

WTI 7.636 11.595 0.046 2.456 62.668 4.347

WTI 11.870 16.995 17.700 9.713 86.989 11.256

From 2.009 2.643 0.538 1.832 0.574 7.597 (continued)

From 5.203 7.514 0.063 1.155 1.525 15.461

From 8.080 10.894 14.731 4.410 2.602 40.717

5.4 Empirical Results 85

Freq L: 13 weeks–infinity MSCIESG STLFSI WEI DGS1MO WTI From MSCIESG 3.709 2.632 0.197 0.366 1.143 0.868 STLFSI 1.202 3.106 0.506 0.865 1.109 0.736 WEI 5.240 12.119 24.872 36.201 17.087 14.129 DGS1MO 0.991 2.983 0.042 14.360 3.101 1.423 WTI 0.285 0.803 0.385 1.039 9.586 0.503 To 1.544 3.707 0.226 7.694 4.488 17.659 Notes Freq S is the abbreviation of “Frequency S,” roughly corresponds to 1–4 weeks; Freq M is the abbreviation of “Frequency M,” roughly corresponds to 5–12 weeks; Freq L is the abbreviation of “Frequency L,” roughly corresponds to 13 weeks–infinity. The value is expressed as a percentage

Table 5.3 (continued)

86 5 How Does the Environmental, Social, and Governance Index

MSCIESG STLFSI WEI DGS1MO WTI To

MSCIESG STLFSI WEI DGS1MO WTI To Freq M: 5–12 weeks

MSCIESG STLFSI WEI DGS1MO WTI To Panel B: BK 18 Freq S: 1–4 weeks

Panel B: DY 12

STLFSI 0.642 2.763 0.064 1.658 0.091 0.491 STLFSI 2.804 6.280 1.080 2.618 1.031 1.507

MSCIESG 3.535 0.365 0.027 1.402 0.039 0.366

MSCIESG 5.848 1.844 0.201 0.759 0.375 0.636

MSCIESG 48.972 23.745 15.076 3.788 13.775 11.277

Table 5.4 Volatility spillovers for DY 12 and BK 18 STLFSI 18.997 32.843 21.448 7.088 15.176 12.542

WEI 0.119 0.125 1.068 1.043 0.007 0.259

WEI 0.34 0.153 0.654 0.795 0.114 0.28

WEI 0.659 0.788 4.534 2.692 0.381 0.904

DGS1MO 2.631 5.259 4.046 25.957 1.748 2.737

DGS1MO 0.897 1.593 0.761 30.953 0.363 0.723

DGS1MO 11.798 20.392 37.496 72.18 17.863 17.51

WTI 3.056 3.733 1.321 5.580 5.924 2.738

WTI 0.98 0.951 0.163 3.52 3.187 1.123

WTI 19.574 22.232 21.446 14.252 52.805 15.501

From 1.722 2.192 1.330 2.000 0.632 7.876 (continued)

From 0.572 0.612 0.203 1.475 0.121 2.983

From 10.206 13.431 19.093 5.564 9.439 57.733

5.4 Empirical Results 87

Freq L: 13 weeks–infinity MSCIESG STLFSI WEI DGS1MO WTI From MSCIESG 28.045 13.447 0.571 12.618 14.656 8.258 STLFSI 7.996 27.227 0.544 22.800 16.185 9.505 WEI 2.144 11.504 11.378 43.121 14.079 14.169 DGS1MO 1.022 3.524 1.403 34.942 7.512 2.692 WTI 2.909 8.007 0.051 13.575 46.004 4.908 To 2.814 7.296 0.514 18.423 10.486 39.534 Notes Freq S is the abbreviation of “Frequency S,” which means 1–4 weeks; Freq M is the abbreviation of “Frequency M,” which represents 5–12 weeks; Freq L is the abbreviation of “Frequency L,” which represents 13 weeks–infinity. The value should consider as a percentage

Table 5.4 (continued)

88 5 How Does the Environmental, Social, and Governance Index

5.4 Empirical Results

89

Then, let us see the volatility spillover, which is shown in Table 5.4. First, in the time in panel A, the total volatility spillover (57.733%) is larger than the total return spillover (40.717%) in panel A. This shows that there are more future uncertainties in the volatility system. Relating to the connectedness from MSCIESG to other variables, the directional spillover over indices shows that the STLFSI (23.745%) is highly influenced by MSCIESG, followed by WEI (15.076%) and WTI (13.775%). About the impact from other variables to MSCIESG, we find except for WEI (0.659%), STLFSI (18.997%), WGS1MO (11.798%) and WTI (19.574%) show the connectedness impact to MSCIESG. The results of the analysis reveal that the risks may be propagated to each other among the value of corporate, paying attention to ESG, financial systemic risk, interest rate, and oil prices. Moreover, in the frequency domain, the total volatility spillover index increases with a decrease in the frequency, and the long-term volatility spillover will take the largest proportion, which is consistent with previous literature (Toyoshima and Hamori 2018; Wang et al. 2019).

5.4.2

Rolling-Window Analysis

In Sect. 4.4.1, we introduce the return and volatility impacts of the full sample in the time and frequency domains. The full sample analysis provides a simple summary of average spillovers, although it cannot demonstrate the potentially important cyclical and secular changes in spillovers. Then, we consider a dynamic analysis of the rolling-window combined with DY 12 and BK 18 to obtain the total return and volatility spillover index in the time dynamic. Here, we set the size of the rolling-window to 192 weeks,2 and we set the same frequency bands as the full sample analysis in Sect. 4.4.1. We use a 100-period-ahead forecast horizon (H). The results of the rolling-window total spillover index of return and volatility are showed in Figs. 5.4 and 5.5, respectively. The black line shows the total spillover index of DY 12, the DY total spillover index is decomposed into three frequency dynamics, followed by BK 18, and the remaining lines display the total spillover index of BK 18. In detail, the red line indicates the “Frequency S” for short-term (1–4 weeks), the green line means the “Frequency M” for mid-term (5–12 weeks), and the “Frequency L” for long-term (13 weeks to infinity) is shown as the blue line. In Fig. 5.4, the DY total spillover index varies from 15.894 to 80.007%. We can see that the total return spillover is mainly focused on the short-term most of the time, indicating that future uncertainties almost impact the short-term, which is consistent with the previous literature. However, contrary to the literature (Toyoshima and Hamori 2018; Zhang et al. 2020a; Liu et al. 2020), there is a

2

We also employ several previous moving windows (96, 144, and 192 weeks) to test the robustness. The results are shown in the Appendix.

90

5 How Does the Environmental, Social, and Governance Index

Fig. 5.4 Total return spillover for DY 12 and BK 18. Note This figure shows the total return spillover among the MSCI USA ESG leader index (MSCIESG), St. Louis Fed financial stress index (STLFSI), weekly economic index (WEI), 1-month treasury constant maturity rate (WGS1MO), and WTI crude oil future prices (WTI). The total return spillover is calculated using the method of DY 12 and BK 18. The value should consider as a percentage

skyrocket in the total return index and peak at 80.007% in early April because of the COVID-19 (coronavirus) epidemic (the red line circle in Fig. 5.4). At the same time, the highest proportion is accounted for by the long-term spillover index, meaning that extreme crises like COVID-19 have a long-term impact on the return system. This phenomenon is consistent with the study of Zhang and Hamori (2020b), and it should be fully noticed. In Fig. 5.5, the DY total spillover index changes from 15.139 to 80.000%, and the volatility spillover changes display clear bursts (the red line circles in Fig. 5.5) without any trend in keeping with readily identified “crisis” events, the first one burst is consistent with the plummet of crude oil on January 6, 2016, the second one corresponds to the coronavirus crisis and the plummet of crude oil at early March 2020. Both bursts show the huge impact of crude oil prices. Moreover, the total volatility spillover is concentrated in the long-term, showing that risk events or future uncertainties influence the long-term. It also differs from the previous literature. Compared with the picture of the DY 12 and BK 18 total volatility spillover, the number of volatility skyrockets are higher than those in the return series. In the time dynamics, there is only one deep increase in return spillover and two bursts of volatility spillover. This indicates that volatility is more susceptible to risk events than returns in the system.

5.5 Conclusion

91

Fig. 5.5 Total volatility spillover for DY 12 and BK 18. Note This figure shows the total volatility spillover among the MSCI USA ESG leader index (MSCIESG), St. Louis Fed financial stress index (STLFSI), weekly economic index (WEI), 1-month treasury constant maturity rate (DGS1MO), and WTI crude oil future prices (WTI). The total volatility spillover is calculated using the method of DY 12 and BK 18. The value should consider as a percentage

5.5

Conclusion

This investigation studies the return and volatility spillovers among the MSCIESG and STLFSI, WEI, WGS1MO, and WTI in the time and frequency domains, using the DY 12 and BK 18 approaches. Focusing on the United States, we employ the weekly data from July 2009 to November 2020 (574 observations), ending on Friday. Furthermore, we also develop a moving-window to explore the cyclical and secular movements of return and volatility spillover as time flows. The main results are as follows: (a) In terms of average spillover over in the time domain, based on the return spillover, compared with the impact on GDP, interest rate, and crude oil future price, the MSCIESG return has a huge impact on the financial market. The financial market is also the greatest contributor of the return spillovers to the MSCIESG return, followed by crude oil future price return. This indicates that the spillover effect between the MSCIESG return and financial markets is relatively large. The return spillover index between MSCIESG and WTI shows that the MSCIESG return is easily affected by crude oil future price return and not vice versa. According to the volatility spillovers, the uncertainties from MSCIESG volatility mostly impact the financial market, followed by GDP and crude oil futures prices, and the connectedness from WTI, STLFSI, and WGS1MO is quite high, especially from WTI and STLFSI.

92

5 How Does the Environmental, Social, and Governance Index

(b) As for the connectedness in the frequency domain, this finding is contrary to previous studies that have suggested that the total return spillover index decreases with the decrease in frequency, the long-term return spillover index of this study accounts for the largest proportion, meaning that the risks or uncertainties from the return system will work in the long-term rather than in the short-term. Following the present results (Baruník and Křehlík 2018), have demonstrated that the total volatility spillover index increases with a decrease in frequency. (c) Finally, we develop DY 12 and BK 18 employing a moving-window methodology. In the time domain, the total return spillover peaks during the COVID-19 crisis period, and the total volatility spillover index has two peaks. The former is consistent with the plummet of crude oil on January 6, 2016, the second one corresponds to the coronavirus crisis and the plummet of crude oil in early March 2020. In the frequency domain, the result shows that the total return spillover is mainly concentrated in the short-term. This means that the risks will have an impact on the return system in the short-term. The result differs from the results of earlier investigations (Toyoshima and Hamori 2018) but it is broadly consistent with Zhang et al. (2020b). The total volatility spillover focuses on the long-term, meaning that the uncertainties will have influence in the long-term.

Appendix This appendix includes the empirical results of the full sample analysis for return and volatility spillovers using different frequency bands to check the robustness. Tables 5.5 and 5.6 use different frequency bands: 1 month (1–4 weeks) for “Freq S,” half a year (5–24 weeks) for “Freq M,” and more than half a year (25 weeks– infinity) for “Freq L”. Figures 5.6 and 5.7 indicate the total return spillover and total volatility spillover using a window size equal to 192, and the different frequency bands we mentioned. Although the return spillover does not focus on the long-term, the total return spillover for the long-term still exceeds that of the mid-term, which is almost the same as that of the shot-term. It also shows the long-term impact of shocks in the return system. These tables and pictures indicate that our empirical results are robust to the choice of frequency bands. The appendix also indicates the robustness results. We consider three moving windows (96, 144, and 192 weeks) to check the robustness of the body part. Figures 5.8 and 5.9 indicate the total return and volatility spillover using a window size equal to 96. Figures 5.10 and 5.11 show the total return and volatility spillover using a window size equal to 144. Figure 5.8 through Fig. 5.11 use the same frequency bands as in Chap. 2 (Sect. 2.4: Empirical Results) of this article. These figures indicate that our empirical results are robust to the choice of window size (Figs. 5.6 and 5.7).

5.5 Conclusion

93

Fig. 5.6 Total return spillover for DY 12 and BK 18. Note We consider a new frequency band: 1 month (1–4 weeks) for “Freq S,” half a year (5–24 weeks) for “Freq M,” and more than half a year (25 weeks–infinity) for “Freq L”

Fig. 5.7 Total volatility spillover for DY 12 and BK 18. Note We consider a new frequency band: 1 month (1–4 weeks) for “Freq S,” half a year (5–24 weeks) for “Freq M,” and more than half a year (25 weeks–infinity) for “Freq L”

94

5 How Does the Environmental, Social, and Governance Index

Fig. 5.8 Total return spillover for DY 12 and BK 18. Note We consider the window size = 96

Fig. 5.9 Total volatility spillover for DY 12 and BK 18. Note We consider the window size = 96

5.5 Conclusion

95

Fig. 5.10 Total return spillover for DY 12 and BK 18. Note We consider the window size = 144

Fig. 5.11 Total volatility spillover for DY 12 and BK 18. Note We consider the window size = 144

ESG 59.599 18.848 5.450 2.856 2.058 5.842

ESG ESG 48.158 STLFSI 14.162 WEI 0.017 DGS1MO 0.514 WTI 1.314 To 3.201 Freq M: 5–24 weeks ESG ESG 9.552 STLFSI 4.140 WEI 0.443 DGS1MO 1.763 WTI 0.607 To 1.390

ESG STLFSI WEI WGS1MO WTI To Panel B: BK 18 Freq S: 1–4 weeks

Panel A: DY 12

WEI 0.173 2.162 0.532 0.113 1.338 0.757 WEI 0.032 0.398 2.091 0.014 0.073 0.104

STLFSI 6.882 9.887 0.977 4.872 1.582 2.863

STLFSI 22.723 45.532 12.569 9.312 3.022 9.525

STLFSI 14.556 34.188 0.037 2.689 1.054 3.667

Table 5.5 Return spillovers for DY 12 and BK 18 WEI 0.386 2.966 26.345 0.167 1.777 1.059

DGS1MO 1.568 5.648 3.276 31.279 1.662 2.431

DGS1MO 3.651 9.653 0.217 38.508 3.919 3.488

WGS1MO 5.421 15.658 37.936 77.952 6.155 13.034

WTI 3.716 4.933 1.359 5.434 19.442 3.088

WTI 7.636 11.595 0.046 2.456 62.668 4.347

WTI 11.870 16.995 17.700 9.713 86.989 11.256

From 2.440 3.024 1.211 2.416 0.785 9.876 (continued)

From 5.203 7.514 0.063 1.155 1.525 15.461

From 8.080 10.894 14.731 4.410 2.602 40.717

96 5 How Does the Environmental, Social, and Governance Index

Freq L: 25 weeks–infinity ESG STLFSI WEI DGS1MO WTI From ESG 1.889 1.285 0.181 0.202 0.518 0.437 STLFSI 0.546 1.456 0.406 0.357 0.468 0.355 WEI 4.990 11.554 23.722 34.444 16.296 13.457 DGS1MO 0.579 1.751 0.04 8.165 1.823 0.839 WTI 0.137 0.385 0.366 0.573 4.879 0.292 To 1.250 2.995 0.198 7.115 3.821 15.380 Notes Freq S is the abbreviation of “Frequency S,” which means 1–4 weeks; Freq M is the abbreviation of “Frequency M,” which represents 5–24 weeks; Freq L is the abbreviation of “Frequency L,” which represents 25 weeks–infinity. The value should consider as a percentage

Table 5.5 (continued)

5.5 Conclusion 97

ESG STLFSI WEI DGS1MO WTI To

ESG STLFSI WEI DGS1MO WTI To Freq M: 5–24 weeks

ESG STLFSI WEI DGS1MO WTI To Panel B: BK 18 Freq S: 1–4 weeks

Panel B: DY 12

STLFSI 0.642 2.763 0.064 1.658 0.091 0.491

STLFSI 6.314 12.198 3.215 3.659 3.209 3.279

ESG 3.535 0.365 0.027 1.402 0.039 0.366

ESG 13.050 5.325 1.003 1.169 1.683 1.836

ESG 48.972 23.745 15.076 3.788 13.775 11.277

Table 5.6 Volatility spillovers for DY 12 and BK 18 STLFSI 18.997 32.843 21.448 7.088 15.176 12.542

WEI 0.170 0.193 1.826 1.335 0.014 0.342

WEI 0.34 0.153 0.654 0.795 0.114 0.28

WEI 0.659 0.788 4.534 2.692 0.381 0.904

DGS1MO 5.097 9.438 9.025 32.801 4.917 5.695

DGS1MO 0.897 1.593 0.761 30.953 0.363 0.723

DGS1MO 11.798 20.392 37.496 72.18 17.863 17.51

WTI 6.589 7.798 3.456 7.516 13.719 5.072

WTI 0.980 0.951 0.163 3.52 3.187 1.123

WTI 19.574 22.232 21.446 14.252 52.805 15.501

From 3.634 4.551 3.340 2.736 1.965 16.225 (continued)

From 0.572 0.612 0.203 1.475 0.121 2.983

From 10.206 13.431 19.093 5.564 9.439 57.733

98 5 How Does the Environmental, Social, and Governance Index

Freq L: 25 weeks–infinity ESG STLFSI WEI DGS1MO WTI From ESG 32.386 12.041 0.150 5.804 12.005 6.000 STLFSI 18.055 17.882 0.443 9.361 13.484 8.268 WEI 14.046 18.169 2.054 27.71 17.827 15.551 DGS1MO 1.217 1.770 0.562 8.425 3.217 1.353 WTI 12.053 11.876 0.254 12.583 35.899 7.353 To 9.074 8.771 0.282 11.092 9.306 38.525 Notes Freq S is the abbreviation of “Frequency S,” which means 1–4 weeks; Freq M is the abbreviation of “Frequency M,” which represents 5–24 weeks; Freq L is the abbreviation of “Frequency L,” which represents 25 weeks–infinity. The value should consider as a percentage

Table 5.6 (continued)

5.5 Conclusion 99

100

5 How Does the Environmental, Social, and Governance Index

References Baruník J, Křehlík T (2018) Measuring the frequency dynamics of financial connectedness and systemic risk*. J Financ Economet 16(2):271–296. https://doi.org/10.1093/jjfinec/nby001 Caplan L, Griswold JS, Jarvis WF (2013) From SRI to ESG: the changing world of responsible investing. Commonfund Institute Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a): 427–431. https://doi.org/10. 2307/2286348 Diebold FX, Yilmaz K (2009) Measuring financial asset return and volatility spillovers, with application to global equity markets. Econ J 119(534):158–171. https://doi.org/10.1111/j.14680297.2008.02208.x Diebold FX, Yilmaz K (2012) Better to give than to receive: predictive directional measurement of volatility spillovers. Int J Forecast 28(1):57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006 Diebold FX, Yilmaz K (2014) On the network topology of variance decompositions: measuring the connectedness of financial firms. J Econ 182(1):119–134. https://doi.org/10.1016/j. jeconom.2014.04.012 Diebold FX, Yilmaz K (2015) Trans-Atlantic equity volatility connectedness: U.S. and European financial institutions, 2004–2014. J Financ Econometrics nbv021. https://doi.org/10.1093/ jjfinec/nbv021 Edmans A (2011) Does the stock market fully value intangibles? Employee satisfaction and equity prices. J Financ Econ 101(3):621–640. https://doi.org/10.1016/j.jfineco.2011.03.021 Friedman M, Friedman R (1990) Free to choose: a personal statement. Houghton Mifflin Harcourt Goyal MM, Aggarwal K (2014) ESG index is good for socially responsible investor in India. Asian J Multi Stud 2(11):92–96 IPE European Institutional Asset Management Survey (2009). http://www.ethe.org.gr/files/pdf/ F0F8B046A579490FB61B837740CC1755.pdf Jain M, Sharma GD, Srivastava M (2019) Can sustainable investment yield better financial returns: a comparative study of ESG indices and MSCI indsices. Risks 7(1):15. https://doi.org/10.3390/ risks7010015 Koop G, Pesaran MH, Potter SM (1996) Impulse response analysis in nonlinear multivariate models. Journal of econometrics, 74(1): 119–147. https://doi.org/10.1016/0304-4076(95)01753-4 Torre LM, Mango F, Cafaro A, Leo S (2020) Does the ESG index affect stock return? Evidence from the Eurostoxx50. Sustainability 12(16):6387. https://doi.org/10.3390/su12166387 Liu G, Hamori S (2020) Can one reinforce investments in renewable energy stock indices with the ESG index? Energies 13(5):1179. https://doi.org/10.3390/en13051179 Liu T, He X, Nakajima T, Hamori S (2020) Influence of fluctuations in fossil fuel commodities on electricity markets: evidence from spot and futures markets in Europe. Energies 13(8):1900. https://doi.org/10.3390/en13081900 Pesaran HH, Shin Y (1998) Generalized impulse response analysis in linear multivariate models. Economics letters, 58(1): 17–29. https://doi.org/10.1016/s0165-1765(97)00214-0 Toyoshima Y, Hamori S (2018) Measuring the time-frequency dynamics of return and volatility connectedness in global crude oil markets. Energies 11(11):2893. https://doi.org/10.3390/ en11112893 Wang B, Wei Y, Xing Y, Ding W (2019) Multifractal detrended cross-correlation analysis and frequency dynamics of connectedness for energy futures markets. Phys A 527:121194. https:// doi.org/10.1016/j.physa.2019.121194 Zhang W, He X, Nakajima T, Hamori S (2020a) How does the spillover among natural gas, crude oil, and electricity utility stocks change over time? Evidence from North America and Europe. Energies 13(3):727. https://doi.org/10.3390/en13030727 Zhang Y, He X, Nakajima T, Hamori S (2020b) Oil, gas, or financial conditions-which one has a stronger link with growth? North American J Econ Financ 54:101220. https://doi.org/10.1016/ j.najef.2020.101220

Index

A A-DCC, 29 A-DCC model, 22, 25 ADF test, 29 Anomalies, 15 AR-EGARCH, 29 AR-GARCH, 58 (AR(k)-EGARCH (p, q)) model, 23 ARMA, 77 Augmented Dickey-Fuller (ADF), 77 B Barunik and Krehlik methodology, 54 BK 18, 79 C Capital Asset Pricing Model (CAPM), 13 Causality test, 17 Conditional correlations, 31 Connectedness, 81 Copula, 38 Coronavirus (COVID-19), 25, 63, 84, 90, 92 Corporate Social Responsibility (CSR), 21 COVID-19 pandemic crisis, 33 Crisis, 92 Crude oil price crash, 63 Crude oil prices, 25 D Diebold and Yilmaz approach, 54 Dummy variables, 33

DY 12, 79 Dynamic conditional correlations, 34 E Economic fluctuations, 67 Efficient Market Hypothesis (EMH), 11, 17 EGARCH, 24 Empirical distribution function, 41 Environmental, Social, and corporate Governance (ESG), 1, 21, 22, 26, 29, 31, 34, 53, 71 ESG indicators, 9 ESG information, 10 ESG investment, 1, 2, 4, 9, 13, 16, 17, 54, 71 ESG investment strategy, 6, 12 ESG investors, 9 ESG Quant, 34 European Renewable Energy Total Return Index, 43 External economy, 8 F Fiduciary duty, 3 Financial, 72 Financial turmoil, 69 Fourier, 75 Fourier transformation, 56 Frequency, 75 Frequency bands, 60 Frequency domain, 56 Full-sample, 60

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 T. Nakajima et al., ESG Investment in the Global Economy, Kobe University Social Science Research Series, https://doi.org/10.1007/978-981-16-2990-7

101

102 G GARCH, 77 Generalized Forecast Error Variance Decomposition (GFEVD), 73 Generalized variance decompositions, 55 Global Sustainable Investment Alliance (GSIA), 4 Goodness-of-Fit (GoF) tests, 44 Green Bond index, 23 I Impact, 92 Information transmission, 69 Investment, 34 Investment horizon, 54 J Jarque–Bera test, 28 L Logarithm prices, 58 London Bullion Market Association (LBMA) gold, 58 Long term, 62, 90 M Macroeconomics, 72 Modern Portfolio Theory (MPT), 13, 15 Moving-window, 84 MSCIESG, 76 MSCI Europe ESG Leaders index, 57 MSCI USA ESG Leaders index, 57 MSCI World ESG Leader Index, 43 N New York (COMEX) gold, 58 Nonparametric estimate, 44 O Ordering, 73 P Pairwise spillover effects, 57 Portfolio performance, 46 Portfolio risk, 18 Principles for Responsible Investment (PRI), 2, 3

Index R Reinforcement investment, 37 Renewable energy stocks, 22 Return, 28, 77 Return spillover effects, 62 Rolling-window, 89 S Scope of stakeholders, 9 Secular, 89 Shareholder value, 8 Sharp increase, 67 Shocks, 92 Short term, 62 Skewed t distribution, 41 Sklar’s theorem, 38 Socially Responsible Investing (SRI), 72 Spillover, 91 Stakeholder, 8 Static spillover effects, 63 Statistics, 77 Susceptible, 90 Sustainability index, 23, 31 Sustainable, 72 Sustainable and Responsible Investment (SRI), 37 Sustainable investment, 53 T Tail dependence, 39 Time, 74 Time domain, 55 Time-varying Copula, 39 Time-varying spillover effects, 63 Total spillover effects, 57 V Volatility spillover effects, 63 W Weekly, 76 Weighting strategies, 46 Wilder Hill Clean Energy Index, 43 Wilder Hill New Energy Global Innovation Index, 43