The adoption of environmental, social, and governance (ESG) criteria in the investment process is a rapidly rising international trend. ESG criteria comprise a set of standards regarding companies' operations that investors can use in selecting potential investments. ESG criteria address (1) environmental factors, as evidenced by a company's efforts to address climate change by reducing greenhouse gas emissions, with more than $30 trillion in total assets under management at the start of 2018.
According to the European Sustainable Investment Fund (EUROSIF), the last two decades show clear signs of SRI becoming integral to European fund management, while ESG integration, which remains by far the preferred strategy, has grown by 60%. In addition, ESG equity mutual funds have attracted record net flows in recent years (Koutsokostas and Papathanasiou, 2017; Koutsokostas et al., 2019) . Europe accounts for the largest concentration of ESG assets worldwide, totaling $14.1 trillion in 2018, followed by the US with $12 trillion; the latter increased by 38% from 2016.
Japan is the third largest market for sustainable investing after Europe and the US, with managed assets rising from 3% to 18% during the same period. SRI also grew by 42% over the same 2-year period in Canada and accounts for half of the country's total assets under management. Australia also has a high proportion of sustainable assets relative to total assets under management.
This study investigates the connectedness and spillover effects across various equity indices that comprise companies with the highest ESG performance for the period 10/1/2007-04/15/2020. Our sample consists of the daily closing prices of MSCI ESG Leaders Indices for 10 equity markets: the US, Australia, Canada, China, Europe, India, Japan, Russia, South Africa, and the UK. The selected indices invest in the most significant ESG markets in terms of depth and breadth, and represent a high percentage of SRI of global market capitalization. Our sample represent both developed and emerging market indices enabling us to analyze the spillover and portfolio diversification benefits across these well know market segments , Kenourgios et al., 2020 . In addition, a set of macroeconomic and financial variables, including the VIX implied volatility index, the US economic policy uncertainty index (EPU), the international crude oil price, and changes in the US 10-J o u r n a l P r e -p r o o f
year Treasury bond yield, are taken into account in order to identify possible channels that transmit spillovers between ESG markets. These variables represent well-known indicators of uncertainty in stock markets and reflect contingent fluctuations in the global financial system during periods of turmoil. We first conduct a Granger (1969) causality test in order to evaluate causality between our sample indices, then check for connectedness and spillover effects across the indices by employing the Diebold and Yilmaz (DY, 2012) method. 2 Motivated by the impact of global sustainable investing during the last two decades, and especially after the global financial crisis, we seek to answer five main research questions: (1) Do ESG equity markets interconnect during the last decade?
(2) Are there spillover effects across ESG markets, and do they display a static or dynamic pattern? (3) Which ESG markets play the role of net contributor/receiver for other markets? (4) Which transmission channels seem to operate between ESG markets? (5) How has the connectedness of ESG indices evolved and affected portfolio diversification benefits across time? SRI, along with ethical investments that adhere to Islamic or Shariah-compliant equity indices, is considered to be an alternative form of investment and, specifically, a form of ethical finance. This paper is also motivated by the rapid growth of ethical finance, which has spurred interest from investment practitioners and academics alike, as well as the ongoing research agenda that examines SRI and Islamic banking and finance to understand how they differ from the conventional approach. Although there is ample research on risk transmission and contagion between faith-based and conventional investment (see for example, Kenourgios et al., 2016; Shahzad et al., 2017; Hkiri et al., 2017; Umar et al., 2018 Jawadi et al., 2019 and references therein) , as well as on the performance of SRI and conventional investment (e.g., Hamilton et al. 1993; Climent and Soriano 2011; Oikonomou et al., 2018) , the connectedness of SRI has not been thoroughly investigated.
Our contributions to the literature are threefold. First, we add depth to the connectedness literature that focuses on several asset classes by investigating for the first time, to the best of our knowledge, the spillover effects between ESG equity indices. Second, we add breadth by using a comprehensive dataset that spans the last 12 years and features 10 major world ESG markets and four economic crises-the global financial crisis (GFC), the European sovereign debt crisis (ESDC), the oil crisis, and the COVID-19 pandemic-to compare connectedness patterns in ESG equity indices. Third, given that markets that adopt ESG criteria have been characterized by continual fluctuations during the last decade, we identify whether specific global risk factors could act as transmitting channels for spillovers between ESG equity indices in the same way they act for conventional ones, and how this mechanism has evolved over time. We believe that our analysis of connectedness will be valuable for global investors, traders, and portfolio managers in terms of whether diversification across ESG equity indices can be achieved during turbulent periods. Furthermore, our results will appeal to policymakers, because the co-movement of ESG indices should be taken into consideration in periods when financial stability is required.
Our analysis finds significant spillover effects between the MSCI ESG Leaders Indices and the selected macroeconomic and financial variables throughout the J o u r n a l P r e -p r o o f sample period. In general, total connectedness exhibits a static pattern during 10/1/2007 10/1/ -04/15/2020 10/1/ . However, during two subperiods-from mid-2011 10/1/ to mid-2013 10/1/ and mid-2015 to late 2017-characterized by turbulence in the Eurozone, the connectedness becomes dynamic. This implies that major economic and financial shocks kept the volatility of ESG markets high. The same holds for the coronavirus outbreak (Feb. 2020-April 2020), during which total connectedness exhibit a rise of approximately 30%. These findings suggest that the markets under consideration are strongly connected. Key players in ESG capital markets-notably, the US, Canada, Europe and the UK-transmit shocks to other markets (net transmitters), while Japan, China, and India are influenced by developed countries (net receivers). The VIX volatility index is found to be the main transmission channel that influences markets that comply with ESG criteria.
The rest of the paper is organized as follows. Section 2 reviews the literature on socially responsible investing. Section 3 describes the data and methodology. Section 4 presents and discusses the results, and Section 5 concludes.
In this section we give a brief review of the various strands of literature on the topic of ESG investments.
The first strand of studies deals with the comparative performance of ESG screened mutual funds with their conventional peers or the benchmarks. These studies document mixed results. Renneboog et al. (2008) find that the performance of SRI funds in the US, the UK and in many European and Asia-Pacific countries follows closely the performance of conventional funds and falls short of the performance of their equivalent benchmarks. Derwall and Koedijk (2009) also find the performance of SRI bond funds to be similar to the performance of their conventional peers for the J o u r n a l P r e -p r o o f period 1987-2003. However, they document that SRI balanced funds outperformed the conventional balanced funds by 1.3% per year. On the other hand, Gi-Bazo et al. (2010) document that the US SRI funds outperform the conventional funds for the period 1997-2005, by taking into account both gross and net returns. They also find that, to a certain degree, there are no significant differences in management fees between SRI and conventional funds, except that SRI funds tend to be cheaper than conventional funds when they are offered by the same management company. Finally, Joliet and Titova (2018) present an analysis of investment decisions taken by US equity SRI funds based on holdings. They conclude that both SRI and conventional funds incorporate ESG information as well as financial criteria in their investment decisions, however SRI funds achieve higher sustainability scores. industrial indices) and find that the latter show comparable levels of uncertainty, but vary with phases of the business cycle. On the other hand, Tripathi and Bhandari (2016) argue that socially responsible companies are better performers than general companies in the Indian stock market by generating significant abnormal returns. Oikonomou et al. (2018) also document that the screening criterion for ethical investing are not necessarily harmful for investors, since a portfolio featuring SRI yields superior risk-return trade-offs compared to the unrestricted portfolio of investments.
The issue of contagion and connectedness between various types of investments has been widely documented. (Akhtaruzzaman et al., 2014; Akhtaruzzaman and Shamsuddin, 2016; Awartani et al., 2016; Spierdijk and Umar, 2017; Akhtaruzzaman et al., 2019; Malik and Umar, 2019; Zaremba et al., 2020 , Naeem et al., 2020 .
However, little evidence is there regarding the connectedness of ESG leader J o u r n a l P r e -p r o o f investments. Reboredo and Ugolini (2019) is a notable exception who investigated the connectedness between green bonds and financial markets by using a structural vector autoregressive model. Their empirical findings show that the green bond market is a net spillover receiver, while treasury and currency markets are net spillover transmitters. Similarly, Reboredo et al. (2020) also demonstrate that the green bonds receive spillover effects from treasury and corporate bonds prices. This study contributes to this sparse strand of literature by discussing the connectedness of ESG leader equity indices.
The data for this study is obtained from Thompson Reuters DataStream. We use MSCI ESG leader equity indices as a proxy for the ESG investments. These indices are designed to include companies that have the highest environmental, social and governance rated performance. We employ daily frequency data of equity indices covering ten developed and developing markets; USA, Australia, Canada, China, Europe, India, Japan, Russia, South Africa, the United Kingdom. The daily frequency for calculating returns contributes to the enhancement of the predictive power of the models employed in this study. Also, daily data contains richer information than other data frequency (monthly, quarterly or weekly). The time-period of our analysis range from October 1, 2007 to April 15, 2020, enabling us to employ 3,273 daily observations. We choose to investigate the connectedness during the aforementioned period as: 1) socially responsible investing has witnessed a remarkable growth over the last decade; 2) markets adopting ESG criteria have endured intense fluctuations bond yield is used as a proxy for the long-term interest rates. In most cases, the relationship between the long-term interest rates and stock returns is inverse, as denoted by Shahzad et al. (2017) . Thus, a rise in the long-term interest rates shall reduce stock prices. However, a positive relationship between these variables cannot be excluded when the economic prospects are favorable (Ferrer et al. 2016 ). Finally, the WTI oil price is used as a proxy for the international crude oil price. As an essential input in the production of a variety of goods and services, oil prices constitute a major factor of uncertainty in economy, although the literature has not given concrete results concerning the relationship between oil and stock prices (Jones and Kaul, 1996; Miller and Ratti, 2009) . The data series for all the aforementioned risk factors are obtained from Thompson Reuters Datastream.
At a preliminary stage, the Granger (1969) causality test is firstly implemented in order to examine the relationships among the indices under consideration. The Granger (1969) test is commonly used in determining whether one time series is useful in forecasting another time series. To check for Granger (1969) causality, we assume the following y and x stationary time series: • β i are statistically significant, while γ i statistically insignificant, then X Granger-causes Y.
• β i are statistically insignificant, while γ i statistically significant, then Y Granger-causes X.
• both β i and γ i are statistically significant, then a bi-directional Granger causality occurs.
• both β i and γ i are statistically insignificant, then no Granger causality exists.
After the application of Granger (1969) causality test, we assess the connectedness among the sample markets, following the Diebold and Yilmaz (2012) approach. This J o u r n a l P r e -p r o o f approach is based on variance decomposition, in which the forecast error variance of a variable is decomposed into parts attributed to various variables in the system. Diebold and Yilmaz (2012) suggested the resulting H-step-ahead generalized forecast-error variance decomposition as follows:
where e j is a selection vector with jth element equal to one and zero otherwise, A h is the coefficient matrix multiplying the h-lagged shock vector in the infinite movingaverage representation of the non-orthogonalized VAR, Σ is the covariance matrix of the shock vector in the non-orthogonalized VAR and σ jj is the jth diagonal element of Σ. The spillover index yields a n × n matrix θ(Η) = [θ ij (H) This enables us to define a total spillover index as follows (Husain et al., 2019) :
Finally, we can record the directional spillovers received by market i from all other markets j and vice versa, i.e. the ones transmitted by market i to all other markets j, as computed in the following equations:
By subtracting Eq. (6) from Eq. (7), we estimate the net spillovers from each market to all other markets:
In this section we report the empirical results. We start with the results for Granger causality, thereafter we study the static connectedness for the entire sample period, followed by the dynamic connectedness. 7. Japan and the rest of the remaining markets are mutually interacting, except the cases of Japan-US, Japan -Europe, Japan -Russia, Japan -South Africa and Japan -UK, where these relations are one-directional (USA → Japan, Europe → Japan, Russia → Japan, South Africa → Japan, UK → Japan).
Russia-Europe and Russia-South Africa. Furthermore, a one-way Granger causality occurs in the cases of Australia, Japan, UK and Canada (Russia → Australia, Russia → Japan, Russia → UK, Russia → Canada). 
Next, we analyze the static connectedness of the variables under consideration by estimating the spillover matrix for the entire sample period ranging from 10/1/2007 to 04/15/2020. Other notable values reported in Table 3 are the directional connectedness of the US, Canada and Europe to Australia (10.35%, 10.10% and 10.21%, respectively) and
from Europe and the UK to South Africa (11.73% and 10.91%, respectively). Finally, Japan seems to be affected by the US market to a great extent (12.19%) and Russia by the Canadian market (10.29%), but once again these relationships are primarily onedirectional.
The row sum of the pairwise connectedness is the total directional connectedness from other markets to each of the ten markets. Stated differently, the "FROM" column estimates the share of shocks received from other markets in the total variance of the forecast error for each market. As shown in Table 3 , the total directional connectedness in the "FROM" column ranges from 10.34% to 78.65%.
Similarly, the column sum of the pairwise connectedness is the total directional connectedness from a specific market to all other markets. In other words, the "TO" row measures the sum of shocks transmitted from a specific market to the variance of the forecast error for each market. Furthermore, Table 3 shows that the total directional connectedness in the "TO" row varies between a low of 0.28% (EPU) and a very high of 116.30% (US). US, Europe, Canada and the UK have one of the highest values of connectedness (exceeding 100%) to other markets. South Africa and
Australia also exhibit significant connectedness to others.
The subtraction of "total directional connectedness from others" from "total directional connectedness to others" gives us the "net total directional connectedness J o u r n a l P r e -p r o o f to others". As it can be seen from the last row of who document the United States as a significant contributor of spillover effects through financial firms. When it comes to China, our results are incongruous with their findings though, as they report significant spillovers transmitted from the Chinese market, which in our case does not apply. As concerns the macroeconomic and financial variables of our analysis, the majority of them are identified as net receivers of spillovers, with the exception of the volatility index (VIX) which marginally appears as a net transmitter.
Figure 2 depicts all the net directional connectedness generated between the pairs of the sample markets. As shown in Figure 2 , it is evident that Europe and Canada are net contributors of spillovers to other markets, whereas the EPU index displays the weakest net pairwise directional connectedness. The USA is primarily a net contributor, but it appears to be a net receiver also, to a lesser extent though.
Total directional connectedness reaches 63.90% (Table 3) , a value rather high, indicating the strong correlation between markets that adopt the ESG criteria. One reason for this high value of total connectedness could lie on the fact that the included markets in our analysis abide by the ESG standards. Thus, both industry-wide, and macroeconomic shocks as well, have a significant impact on each one of these markets and can be transmitted to other markets, resulting in a high degree of total connectedness (Diebold and Yilmaz, 2014) . 
After analyzing the connectedness over the entire sample period, we proceed and study the dynamics of connectedness over the sample period by employing a rolling window approach. We employ a rolling window of approximately 9 months and analyze the changing dynamics in connectedness over the period 7/7/2008-04/15/2020. The size of the rolling window is based on the trade-off between having enough data points for estimation and the time period to analyze the dynamic movement. 5
The total connectedness among the markets during the sample period is depicted in Figure 3 and reveals some intriguing patterns. The total connectedness' distribution is quite low for the period covered, showing that the markets' interaction is relatively stable over time.
Note: In this figure, the total connectedness between MSCI ESG Leaders Indices and the macroeconomic and financial variables is displayed. The spillovers are estimated using a 9-month rolling sample window.
Moreover, total connectedness remains at a high level throughout 2008-2020, indicating that this interaction is strong. However, two big cycles can be identified that prove that connectedness is gravely affected by political turmoil; the first one Besides the aforementioned two big cycles, it is evident that total connectedness also exhibited an unprecedented rise of approximately 30% during February-April of 2020. This increasing trend is attributable to the outbreak of the COVID-19 pandemic.
The loss of value of all major stock market indices due to the coronavirus pandemic triggered shocks across the financial markets, the ESG included, and boosted connectedness among the latter from 58% to 76% within a period of two months.
Finally, Figure 3 depicts some other smaller cycles, however no significant fluctuations in the total directional connectedness is observed during these cycles. separately for each market. The plots for directional connectedness "to" others are illustrated in Figure 4 , the plots for directional connectedness "from" others in Figure   5 and for the "net" directional connectedness in Figure 6 .
We start by looking into endogenous shocks at market level to explain the downward trend of the net spillover effects shown in Figure 6 . The downward spiral of US at the end of 2008 is related to the mitigation of the financial crisis' impact on the domestic economy. Furthermore, the rise of long-term interest rates by the Fed in 2015 reduced the spillover transmissions due to the sizeable price spillovers received by SRI from currency markets but transmitting negligible reverse effects, as J o u r n a l P r e -p r o o f highlighted by Reboredo and Ugolini (2019) . Similarly, the drop in the net spillover effects transmitted by Europe at the end of 2014 is associated with ECB's announcement of quantitative easing program of a total amount of €1.1 trillion to spur inflation and growth, as quantitative easing programs produce significant spillovers only after their implementation Papathanasiou et al., 2020; Riaz et al., 2019 Riaz et al., , 2020 . Another noticeable feature from Figures 4-6 is that the "to" connectedness plots' variation is larger compared to the equivalent in the "from" connectedness plots. This difference is intuitive though because when a market large in depth and breadth receives a shock, it is expected that this shock will be transmitted and have an even larger spillover effect on other markets. Since the size of our sample markets vary, the directional connectedness "to" others also varies across markets over the rollingsample windows in Figure 4 . On the other hand, while shocks transmitted to others by each market may be large, the magnitude of the shock received by each market will be comparatively smaller (Diebold and Yilmaz, 2014) . Thus, the "from" connectedness plots in Figure 5 are characterized by less variation in comparison to the "to" J o u r n a l P r e -p r o o f In order to further investigate the differences in the variations between the "to"
and "from" directional connectedness, Figure 7 depicts the quartiles of distribution of "to" and "from" directional connectedness. We incorporate the first and third quartile J o u r n a l P r e -p r o o f (Q1, Q3) of the distributions to have a distinct picture of the observed differences. As mentioned earlier, it is evident that the variation of the "from" directional connectedness is comparatively much lower than the variation of the "to"
connectedness. Figure 7 . Rolling distribution of total directional connectedness Total directional connectedness "to" Total directional connectedness "from"
Note: In this figure we plot the time series of daily 25%, mean and 75% distributions of the "to" and "from" total directional connectedness. The rolling estimation window width is 9 months.
Overall, our empirical results of both Granger (1969) 
The rise of sustainable investing over the last two decades, together with its immense growth potential, have stimulated interest of academics, practitioners, and policy makers. It is important to analyze performance of ESG markets during major economic and financial shocks, especially during turbulent time-periods. This paper examines the co-movement and transmissions between ESG indices and a set of influential macroeconomic and financial variables for the period ranging from J o u r n a l P r e -p r o o f