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Economic Research-Ekonomska Istraživanja
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rero20
Are green bonds and sustainable cryptocurrencies
truly sustainable? Evidence from a wavelet
coherence analysis
Inzamam Ul Haq, Apichit Maneengam, Supat Chupradit & Chunhui Huo
To cite this article: Inzamam Ul Haq, Apichit Maneengam, Supat Chupradit & Chunhui
Huo (2022): Are green bonds and sustainable cryptocurrencies truly sustainable? Evidence
from a wavelet coherence analysis, Economic Research-Ekonomska Istraživanja, DOI:
10.1080/1331677X.2022.2080739
To link to this article: https://doi.org/10.1080/1331677X.2022.2080739
© 2022 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 31 May 2022.
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Are green bonds and sustainable cryptocurrencies truly
sustainable? Evidence from a wavelet coherence analysis
Inzamam Ul Haq
a
, Apichit Maneengam
b
, Supat Chupradit
c
and
Chunhui Huo
d
a
Department of Management Sciences, COMSATS University Islamabad, Islamabad, Pakistan;
b
Department of Mechanical Engineering Technology, King Mongkut’s Institute of Technology North
Bangkok, Bangkok, Thailand;
c
Department of Occupational Therapy, Faculty of Associated Medical
Sciences, Chiang Mai University, Chiang Mai, Thailand;
d
Asia-Australia Business College, Liaoning
University, Shenyang, China
ABSTRACT
This article aims to explore the co-movement of daily returns
among S&P green bonds (GB/GBs), the top five sustainable cryp-
tocurrencies, Bitcoin, the Dow Jones Sustainability World Index
(DJSWI) and the Dow Jones Sustainability Emerging Market Index
(DJSEMI) to determine whether GBs, Bitcoin and sustainable cryp-
tocurrencies are truly sustainable; in addition, it investigates hedg-
ing and diversification opportunities. Using a partial wavelet
coherence framework to capture the bivariate co-movement, our
findings show strong (weak) positive co-movements among GB
(sustainable cryptocurrencies) and DJSWI returns, where GBs (sus-
tainable cryptocurrencies) have a heterogeneous leading role in
the short-term and long-term horizons. Results indicate moderate
positive (negative) co-movement among GBs and sustainable
cryptocurrencies (Bitcoin) and DJSWI in the short run (long run).
Overall, the results show GB (sustainable cryptocurrencies) acts as
a diversifier for Bitcoin and sustainable cryptocurrencies in most
cases (DJSWI). However, increasing Bitcoin returns adversely
impacts the DJSWI in the long run. Findings are equally impera-
tive for green investors, crypto traders and policymakers, where
investors and traders can earn financial and social returns, and
policy-makers can deploy suitable policies for the development of
sustainable cryptocurrency mining processes. The role of Bitcoin
is alarming for the United Nations Sustainable Development Goals
and global greener economy.
ARTICLE HISTORY
Received 29 September 2021
Accepted 11 May 2022
KEYWORDS
Green bonds; sustainable
cryptocurrencies; Bitcoin;
sustainability world index;
wavelet coherence;
diversifier
JEL CLASSIFICATIONS
G11; G15; Q56; O1; O2
1. Introduction
Sustainability is a key antecedent of sustainable social, environmental and economic
development. The Department of Economics and Social Affairs of the United Nations
launched the 2030 Agenda for Sustainable Development in 2015. It expounds 17
CONTACT Chunhui Huo huoch@lnu.edu.cn
ß2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA
https://doi.org/10.1080/1331677X.2022.2080739
development goals and 169 targets. The mission of the sustainable development goals
(SDGs) is to transform the worldwide society to a better sustainable future for the
entire planetary ecosystem by 2030. Addressing sustainability challenges requires an
enormous effort (de Oliveira et al., 2020). According to an estimate by Trade and
Development, US$5 to 7 trillion is indispensable to achieve the forecasted targets and
goals (de Oliveira et al., 2020). Therefore, the role of individual and institutional
investors is crucial to mobilize resources and sustainable investments.
Climate change has become an existential challenge for people living around the
globe (Xuefeng et al., 2021). Currently, greenhouse gas emissions are playing a devas-
tating role in fostering global warming due to the increasing pace of their rise. Rising
emissions could have irreversible consequences in damaging the ecosystem and life
on earth. These challenges are alarming and require immediate action to mitigate the
carbon emission rate and to circumvent environmental disasters. Hence, mobilizing
substantial capital flows is required to tackle climate challenges and to institute a
low-carbon economy. A serious global climate change movement requires approxi-
mately US$3.5 trillion every year from the energy sectors from 2020 to 2050 (Naeem
et al., 2021) and approximately US$110 trillion in total to reach the targets set by the
Paris climate change agreement by 2050 (Ferrer et al., 2021). In addition, the chal-
lenges of climate change, greenhouse emissions and global warming have turned the
attention of individual and institutional investors toward financial innovations to pro-
mote a greener global economy and sustainable development. The development of
energy technologies can promote environmental protections and decarbonize the
energy system (Van Hoang et al., 2019). Therefore, GBs and sustainable cryptocurren-
cies are promising innovations and avenues for the massive reallocation of funds.
Since the first green bond was issued in 2017 by the European Investment Bank
(EIB), the GB market has become a rapidly growing segment of the capital markets. The
role of GBs is vital in the fund collections related to environmentally friendly projects to
combat climate change (Baker et al., 2018). It is a fixed-income capital market security
and differs from other fixed-income instruments due to its environmental protections
and sustainable features. The scope of GB is wider. A majority of the GBs issued between
2010 and 2019 were related to renewable energy, energy efficiency, clean transportation,
green buildings, pollution prevention and control. According to the International Capital
Market Association, the global bond market reached US$128.3 trillion in August 2020
1
,
and the GB market increased from US$11 billion in 2011 to US$349.1 in June 2020
(Naeem et al., 2021). However, the GB market accounts for less than 5% of the total
bond market and is still in its infancy (Naeem et al., 2021). A huge potential avenue for
future growth has spotlighted the GB market, and investors (individual and institutional)
foresee GB as a sustainable risk management tool in finance and economics (Haq et al.,
2021; Huynh et al., 2020;Leetal.,2021;Liuetal.,2021;Nguyenetal.,2021;Pham,
2021; Reboredo, 2018; Reboredo & Ugolini, 2020; Reboredo et al., 2020; Saeed et al.,
2020). Thus, it is of interest to investigate the GB market to validate the sustainable
development impact and risk management possibilities in the current market.
Sustainable cryptocurrency is an emerging buzzword in modern sustainable
finance. Sustainable cryptocurrencies and eco-friendly crypto options have received
enormous attention since Elon Musk (chief executive of Tesla) announced that they
2 I. ULHAQ ET AL.
are not going to accept Bitcoin as payment for electric vehicles. Bitcoin has a severe
impact on carbon emissions and sustainability. The mining process of conventional
cryptocurrencies involves high-powered computer systems, huge electricity require-
ments to run algorithms, and uses nonrenewable resources such as coal and fossil
fuels. In addition, these nonrenewable resources produce the worst consequences for
the environment and carbon footprints. The SolarCoin (SLR) generates 1 SolarCoin
for each megawatt hour by employing solar technology. The BitGreen (BITG) mining
process involves a low-energy proof of stake algorithm to ensure eco-friendly actions.
The cryptocurrency network utilizes a minimal power of 6 GWh only for Cardano
(ADA), and it is more energy efficient than Bitcoin. ADA was introduced by the co-
founder of Ethereum. Moreover, Steller (XLM) has its own consensus protocol (SCP).
Due to its personal SCP, the authentication cycle becomes shorter, which makes it an
energy-efficient and low-cost crypto asset. However, Ripple (XRP) uses an algorithm
called the Ripple protocol consensus algorithm (RPCA). Therefore, RPCA provides a
low cost and a secure rapid transaction speed.
2
The mining processes of sustainable
cryptocurrencies are reliant on sustainable and environmentally beneficial systems.
Therefore, the idea to create sustainable cryptocurrencies seems plausible, and the
future of modern sustainable finance and the cryptocurrency market lie in sustainable
cryptocurrencies.
Our motivations are as follows: First, participants in cryptocurrency and sustain-
able financial markets have heterogeneous investment horizons (e.g., amateur traders
versus informed long-term institutional investors) and investment objectives (conven-
tional (black) versus green investors) (Broadstock & Cheng, 2019), which not only
requires a differentiation between social returns and financial returns (Lucey et al.,
2021) due to environmental consequences (Y. Wang et al., 2022) but also the applica-
tion of a wavelet-based approach to make inferences in a time–frequency setting
(Bouri et al., 2020). Second, the academic hedging literature highlights potential dif-
ferences between conventional cryptocurrencies (e.g., Bitcoin and Ethereum) (Haq
et al., 2021; Koumba et al., 2020; Rubbaniy et al., 2021). In addition, the hedging and
diversifier roles of Bitcoin and other cryptocurrencies have increased in the last five
years (Haq et al., 2021); however, the hedge and diversifier role of GB with sustain-
able cryptocurrencies remains overlooked. Thus, the current research seeks to answer
two questions: Are GBs and sustainable cryptocurrencies truly sustainable? Does GB
act as a hedge or a diversifier for sustainable cryptocurrencies and Bitcoin? In add-
ition, in exploring these questions, we explore the leading and lagging roles of all
asset classes.
The current research contributes to the inclusive body of the literature in a few
ways. First, to the best of our knowledge, this is the first study to investigate the co-
movement among GB, sustainable cryptocurrencies, Bitcoin, DJSWI and DJSEMI
returns over the short and long run. Second, it contributes to the hedge and diversi-
fier literature (Arif et al., 2021; Hung, 2021; Le et al., 2021; Maltais & Nykvist, 2020;
Naeem et al., 2022; Naeem et al., 2021; Nguyen et al., 2021; Reboredo et al., 2020).
Third, it adds knowledge to the current debate concerning the negative consequences
of the accelerating Bitcoin mining practices (Naeem & Karim, 2021). Finally, it
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 3
prompts an inquiry of whether GBs and sustainable cryptocurrency are truly sustain-
able and improves sustainability around the globe.
Using a partial wavelet coherence framework, our empirical analysis presents five
significant outcomes. First, it finds that GB is positively correlated (in-phase) with the
DJSWI and DJSEMI, where the GB market leads the DJSWI and DJSEMI (lagging).
Second, it reveals the financial impact of COVID-19 by considering COVID-19 epi-
sode data, where GB is positively correlated (in-phase) with DJSWI and DJSEMI and
GB is leading the other two sustainability indices. Third, it adds knowledge to the
inclusive debate about sustainable cryptocurrencies. The top five sustainable crypto-
currencies do not present any pronounced strong positive (in-phase) correlation with
GB. Thus, GBs act as diversifiers for sustainable cryptocurrencies or weak hedge.
Fourth, it notices a positive (in-phase) relationship between sustainable cryptocurren-
cies and DJSWI. It reveals a negative impact of increasing returns on the DJSWI in
the long run. The outputs show evidence of a moderate positive co-movement of
Bitcoin and GB returns.
The rest of this article unfolds as follows. Section 2 provides an overview of the
related literature. Section 3 describes the methods employed, and Section 4 presents
the data and an empirical analysis. Finally, Section 5 concludes this article and sug-
gests future research avenues.
2. Review of related studies
The earlier academic literature is divided into several parts, GB premium and yields,
volatility comparison, benefits of GB, price efficiency dynamics, and co-movements
between GB and conventional assets.
Many previous studies have focused on the yield and premium of GB (Ferrer
et al., 2021). Overall, researchers have found mixed GB outcomes. Previous studies
reported a positive GB premium, suggesting that GBs have the option to earn finan-
cial gains (Gianfrate & Peri, 2019; Zerbib, 2019). However, the premium of GBs
reported a negative moderate; hence, investors have to scarify the returns in the
name of the sustainability and environmental protection characteristics of GBs
(Bachelet et al., 2019). The recent literature has captured a wide picture of the pre-
mium and yields avenues (MacAskill et al., 2020). In short, the discussion of the GB
premium is still inclusive (Ferrer et al., 2021).
From another perspective, volatility comparisons and spillovers between GBs and
other fixed income asset markets have been studied earlier. Similarly, Pham (2016)
investigated the relationship between GB and standard fixed income markets, finding
a strong spillover risk between GBs and conventional fixed-come markets. Moreover,
volatility clustering is much stronger in green markets than in standard fixed income
markets. Another study by Broadstock and Cheng (2019) documented that the rela-
tionship between U.S. GBs and standard bonds is sensitive, where crude oil, news-
based risk proxies and economic conditions predict the association between both
markets. Recently, the connectedness between GB and equity markets was investi-
gated by Park et al. (2020), who reported the existence of volatility spillovers using a
multivariate GARCH model. A similar nature of research was conducted from the
4 I. ULHAQ ET AL.
Chinese perspective (Gao et al., 2021) but reported an insignificant spillover between
GBs and the equity market in China.
Another relevant body of the literature explores the benefits of GB issuance, not from
the issuer and investor perspective but for national benefit (Naeem et al., 2021). The
announcements about GB issuance increased the shareholder’s return (Lautsi, 2019)and
stock prices positively pinned with the announcement of GB issuance (Tang & Zhang,
2020). Likewise, the issuance of GBs adds value to Chinese shareholders (J. Wang et al.,
2020). In addition, the announcement of GB environmental issuances (Flammer, 2020)
as well as the financial performance of firms and the funding cost to climate change is
another compelling advantage of GBs (Flaherty et al., 2017). Similarly, GBs proved to be
a potential avenue to achieve sustainable development goals (Banga, 2019).
One additional area of research is price efficiency dynamics. Until lately, only lim-
ited studies have examined the multifractal features (Naeem et al., 2021). They con-
clude that the GB market is highly efficient and reacts more efficiently in black swan
events or periods of higher uncertainty, such as COVID-19. In contrast, a lack of effi-
ciency was found in green and other bonds (Karginova-Gubinova et al., 2020).
Furthermore, Karginova-Gubinova et al. (2020) suggested that the GB market is yet
immature; hence, institutional and regulatory changes should be made to improve
efficiency in Russian settings.
Several studies have investigated the co-movements and dependencies between
GBs and other asset classes or financial markets (Huynh et al., 2020; Le et al., 2021;
Liu et al., 2021; Nguyen et al., 2021; Pham, 2021; Reboredo, 2018; Reboredo &
Ugolini, 2020; Reboredo et al., 2020; Saeed et al., 2020). In an earlier contribution,
the dependencies were gauged by Reboredo (2018) between GB, conventional bonds
and fixed-income, energy and equity markets. They found that the GB market is co-
integrated with other corporate bonds using the bivariate. However, the GB prices
positively but not perfectly co-move with energy and stock markets. Therefore, ample
diversification potential exists in the GB market. In a similar domain, Reboredo and
Ugolini (2020) studied the price transmissions of several financial markets and the
GB market. Using a VAR (vector autoregressive) model, they concluded that the GB
market is more cointegrated with the U.S. currency markets and government bonds.
Similarly, another study by Reboredo et al. (2020) investigated network connected-
ness, where the number of asset classes and the GB market were studied over mul-
tiple investment horizons in the U.S. and European Union. Another recent study by
Liu et al. (2021) investigated the dynamic dependency structure between GB and glo-
bal energy clean energy markets using a copula approach alongside a conditional
value at risk model. The outputs reported a positive time-varying dependence.
Another promising area is focusing on the dynamic linkage between GBs and
other financial markets considering the unprecedented global episode of COVID-19
(Shahzad et al., 2021). For instance, Haq et al. (2021) investigated the dynamic condi-
tional correlation between GB, rare earth metals, clean energy stocks and the eco-
nomic policy uncertainty (EPU) index during the COVID-19 pandemic. They found
that the GB acted more as a hedge than a safe haven during the pandemic for EPU
but as a diversifier for other asset classes, such as clean energy stocks and rare earth
metals. In contrast, Gupta et al. (2021) have studied the impact of financial
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 5
uncertainty (news-based index) on U.S. interest rates (term structure). They con-
cluded that U.S. treasury securities proved to be a safe-haven during the pandemic
period. Similarly, Bouri et al. (2021) found the GB market to be a main transmitter
of volatility during the coronavirus period. They have investigated the connectedness
between bonds, currencies, crude oil, equities and gold during the pandemic. A simi-
lar set of asset classes were considered by Arif et al. (2021) to explore the hedging
and safe-haven properties of the GB market.
From the above discussion, we conclude that the body of the literature on diversi-
fication and hedging is advancing tremendously. However, the earlier literature
remains silent on exploring the socially and environmentally sustainable features of
GBs and cryptocurrencies. To the best of our knowledge, no study has investigated
the hedging and sustainability features of sustainable cryptocurrencies. Therefore, this
article seeks to fill the current literature gap in the existing studies.
3. Wavelet coherence
The wavelet coherence approach combines the time and frequency of the time series
in itself. It serves to estimate the association or co-movement between two time series
over time and frequency bands. This research considers the wavelet coherency as
defined by Torrence and Compo (1998) under the smoothing technique in both
domains. Here, there are two time series a(t) and b(t) and the cross-wavelet trans-
forms for them W
a
(u, s) and W
b
(u, S). Hence, the cross-wavelet transform can be
written as follows in Equation (1).
Wa,bðu,sÞ¼Waðu,sÞW
bðu,sÞ(1)
In Equation (1),’s’and ’u’are the scale and position index, respectively. Given any
two time series ’a’and ’b’, a continuous wavelet transform can be written for time
series ’a’and ’b’as Waðu,sÞand W
bðu,sÞ, respectively. A symbol on any series ‘b’,
as given in equation W
b, demonstrates a complex conjugate. Hence, a wavelet trans-
form gauges the association between any two time series ‘a’and ‘b’.
Torrence and Compo (1998) introduced a wavelet coherence approach that was used
to estimate cross-wavelet power. The purpose of the wavelet coherence approach is to
identify a notable covariance between any two times through the cross-wavelet power ser-
ies at each scale. Likewise, the purpose of wavelet coherence is quite similar to the cross-
wavelet power. However, it may not have high wavelet power. Hence, this study followed
theTorrenceandWebster(1999) method to estimate the squared wavelet coherence
between pairs. It is an extension of the Torrence and Compo (1998) method. Therefore,
squared wavelet coherence can be written as follows in Equation (2):
R2u,s
ðÞ
¼jSs
1Wa,bðu,sÞ
j2
Ss
1jWau,s
ðÞ
j2
hi
Ss
1jWbu,s
ðÞ
j2
hi (2)
In Equation (2), the smoothing operator is ’s’over time as well as space, and an
inconclusive R2u,s
ðÞ
defines the localized correlation in a squared form over the
6 I. ULHAQ ET AL.
time and frequency domains. In addition, the squared correlation coefficient ranges
from 0 R2u,s
ðÞ
1. The value of R2u,s
ðÞ
determines the co-movement between
any two time series, and a higher (lower) value of R2u,s
ðÞ
denotes a higher (lower)
co-movement. However, squared wavelet coherence faces an issue. It remains unable
to differentiate between positive or negative associations and is thus limited to cap-
turing co-movements from 0 to 1 only. Ultimately, Torrence and Compo (1998)
developed a solution to resolve this issue and recommended using the phase differ-
ence. The core purpose of phase difference is detecting different directions (positive/
negative) of co-movements between pairs. Hence, the phase difference can be written
as follows in Equation (3):
Øa,bu,s
ðÞ
tan1lm S s1Wa,bðu,sÞ
Re S s1Wa,bðu,sÞ
!
(3)
Figure 1. First difference return series.
Source: Authors’estimations.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 7
In Equation (3),lm expresses the imaginary smoothed part and Re expresses the
real part of the smoothed cross-wavelet transform.
Generally, the analysis of a cross-wavelet coherence estimation produces a figure. The
colourful figure has five key chunks (see Figures 2–5), colours from red to blue (warm and
cold colours), uniquely directed eight arrows in the black colour
,!,",#,&,%,.,-ðÞ, black contours, a cone of influence and two
axes (x-axis and y-axis). The (!Þ arrows indicate an out-of-phase (in-phase) association
between two time series or a negative (positive) correlation direction. Moreover, %ð.)
denotes that the first time series variable leads the second time series. For example, the -
arrow in the figure (see Figures 2–5) denotes a negative correlation or out-of-phase associ-
ation between any two series ‘a’and ‘b’under the leading effect of any first-time series ’a’.
In contrast, &has the reverse explanation. Generally, a zero phase difference indicates that
both time series ’a’and ’b’move in inhomogeneous directions. Black coloured curves in
Figures (2–5) show the black contours. These black contours indicate that the results
(coherence regions) are statistically significant at a significance level of 0.05 (5%), and an
ultimate u-shaped white solid line is the cone of influence in Figures (2–5).
4. Data descriptions and an empirical analysis
4.1. Data description
The research follows daily values (five days a week) for all variables. The sample
period for S&P GB, DJSWI, and DJSEMI is 1 September 2012 to 9 September 2021.
The sample period for the five sustainable cryptocurrencies SolarCoin, BitGreen,
Cardano, Steller and Ripple commence from different starting points due the latest
inception on 29 March 2014, 22 March 2018, 1 October 2017, 5 August 2014, 4
Figure 2. Wavelet coherence among green bond, sustainability world index and sustainability
emerging market index.
Note: The figure indicates the wavelet coherency plot among the Green bond, Sustainability World Index and
Sustainability Emerging Market Index where the horizontal axis presents the time in days. The vertical axis depicts the
period (frequency) classified in 4, 8, 16, 32, 64, 128 and 256 days). The correlation (coherency band) is flaunted on
the right side of the figure in blue (0.0) to red (1.0) colours indicating the correlation range and the highest and low-
est correlation value (R
2
). The cone of influence is displayed in a curved solid white colour. Black-coloured contours at
different spots demonstrate the significance of the results at the 0.05 (5%) level. The arrows signal the phase differen-
ces, where forward arrows (!) are in the phase (positive relationship) connectedness and vice versa. Upward arrows
(") indicate that the first time series is leading the other (lagging) and vice versa. Forward upward and downward
arrows (&,%) denotes a phase (positive relationship) and the first time series is leading other (lagging) and (.,-)
vice versa.
Source: Authors’estimations and drawing.
8 I. ULHAQ ET AL.
August 2013 to 9 September 2021, respectively. In addition, the dataset covers the
data for Bitcoin from 1 January 2014 to 9 September 2021. First, (1
st
) difference daily
values are calculated for wavelet coherence analysis. The studied cryptocurrencies are
the top five sustainable cryptocurrencies
3
and the largest capped cryptocurrency,
Bitcoin. The data for GB, DJSWI and DJSEMI are given according to the available
cryptocurrency data. The data for cryptocurrencies (GB, DJSWI and DJSEMI) were
sourced from coinmarketcap.com (www.spglobal.com).
4.2. Basic statistics
The descriptive statistics for the level series and first difference are illustrated in
Table 1. The statistics in Table 2 show that all-time series positive means, except
Figure 3. Wavelet coherence of sustainable cryptocurrencies and the world sustainability index.
The figure indicates the wavelet coherency plot between sustainable cryptocurrencies and world sustainability index.
Refer Figure 1 for interpretations of the wavelet coherence output.
Source: Authors estimations and drawing.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 9
BITG. Bitcoins is the most volatile cryptocurrency with a standard deviation of
755.947; however, BITG is more volatile among the sustainable cryptocurrencies with
a standard deviation of 0.187. Interestingly, the DJSEMI proves more volatile than
the DJSWI, which obviates the fact that emerging markets are more volatile. All first
difference series are leptokurtic, indicating the presence of higher tail risks. The coef-
ficients of Jarque–Bera validates that all first difference series are non-normally dis-
tributed. (Table 3)
Figure A.1 (see Appendix A) and Figure 1 depict the level series and first differ-
ence return series, respectively, for all indices. Figure 1 delineates the presence of
momentous growth in GB, sustainability indices, and sustainable cryptocurrencies
except SLR and BITG. In addition, it also depicts the huge price appreciation in
Bitcoins, ADA, XLM and XRP since COVID-19 (December 2019).
Figure 4. Wavelet coherence of green bonds and cryptocurrencies.
Figure indicates the wavelet coherency plot between Green bonds and cryptocurrencies. Refer Figure 1 for interpreta-
tions of wavelet coherence output.
Source: Authors’estimations and drawing.
10 I. ULHAQ ET AL.
4.3. Wavelet coherence
Current research investigates the co-movement of sustainable cryptocurrencies with
GB and DJSWI using the wavelet coherence approach by Torrence and Webster
(1999). In addition, it captures the relationship (co-movements) between GB and sus-
tainability indices for world and emerging markets in the full sample and during the
COVID-19 period. The leading and lagging relationship is estimated by using the in-
phase and out-phase arrows in the wavelet coherence. The scale is divided into four
levels: 1–4 days, 4–8 days, 8–16 days, 16–32 days, 32–64 days, 64–128 days,
128–256 days and scales above 256 days.
Figure 2 displays the outcomes of wavelet coherence for the GB, DJSWI and the
DJSEMI considering the full sample estimation. Subfigure 2a shows that the right-
directed upward and downward arrows (&,%) indicate an in-phase relationship
(positive correlation) between GB and DJSWI from 2012 to 2014 and 2017 to 2021
on all scales. The subfigure further validates that GB is leading DJSWI. The black
contours on the left and right sides at multiple scales show a positive co-movement
at the 5% significance level. The results indicate that regardless of the hedge and
diversifier, GB increased world sustainability. In addition, Subfigure 2 b shows a
homogeneous co-movement to Subfigure 2a. However, the black contours indicate
that a positive correlation (in-phase) between GB and DJSEMI is more pronounced
in 2012 to 2013 and 2016 to 2021 at all scales. The outcome indicates that GBs are
not the only source of increasing world sustainability and sustainability in emerg-
ing markets.
In sum, the findings show a strong positive co-movement over short- and long-
term investment horizons between GB and sustainability indices (DJSWI and
DJSEMI). Moreover, GBs have a leading role in increasing the sustainability of the
world and emerging markets. Therefore, GB is not only a suitable risk management
tool but also positively associated with sustainability indices. It is evidence for the
idea that GBs are sustainable for the environment and key financial market partici-
pants (Ferrer et al., 2021). Additionally, the GB demonstrated an even stronger asso-
ciation and impact on sustainability during the unprecedented COVID-19 period
than in the full sample estimation. The current findings fill several research gaps
Figure 5. Wavelet coherence of Bitcoin, green bonds and world sustainability index.
The figure indicates the wavelet coherency plot between Bitcoin, green bonds and the world sustainability index.
Refer Figure 1 for interpretations of wavelet coherence output.
Source: Authors’estimations and drawing.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 11
highlighted in previous research (Broadstock & Cheng, 2019; Haq et al., 2021) and
demonstrate the significant utility of GBs in terms of social and financial returns. The
current results establish GB as a sustainable investment for global investors and
investors from emerging markets to build a greener economy, as emphasized in ear-
lier research (Arif et al., 2021; Ferrer et al., 2021; Naeem et al., 2021; Naeem et al.,
2021) GBs are similar to conventional bonds, but they mainly focus on green and
sustainable projects that are thus friendly to the environment (Saeed et al., 2020).
Current findings disagree with Maltais and Nykvist (2020), who argued that GBs
have a false image of being more sustainable or impactful toward the environment
than other municipal and corporate bonds.
Figure 3 depicts the outcomes of the wavelet coherency between the top five sus-
tainable cryptocurrencies and DJSWI. The right-directed arrows in the middle of
Subfigure 3a from 2019 to 2020 show a positive relationship or co-movement (in-
phase) between ADA and DJSWI on 16–32-day and 32–64-day scales. They also indi-
cate that ADA is leading the DJSWI. Black contours in the subfigure validate that
positive co-movements are significant at the 5% significance level. Backward arrows
on the left side, middle and right side on the 64-day scale in Subfigure 3 b show a
marginally positive correlation (out of the phase) between BITG and DJSWI in the
short term (16–32-day and 32–64-day scales). Although several black contours show
a zero phase difference at the 5% significance level, the relationship is not pro-
nounced through multiple scales and time periods. Subfigure 3c and Subfigure 3e
show a homogenous co-movement pattern, where both SLR and XRP show a positive
relationship (in-phase) co-movement with DJWSI. The right-directed arrows
(!,&,%) at the bottom, left and right side at 16 days, 64 days and ahead of the 256-
day scale confirm the position relationship, and the black contours validate that the
outcomes are significant at the 5% significance level. In addition, the right-directed
arrows confirm that SLR and XRP lead the DJWSI in the short and long term.
Subfigure 3d shows a positive relationship (in-phase) between XLM and DJWSI on
16-day and 64-day scales from 2017 to 2019. Right directed arrows (!,&,%) con-
firm the positive association, where black contours show results that are significant at
the 5% significance level. These findings indicate that sustainable cryptocurrencies
increased the DJSWI, except for BITG. The relationship between sustainable
Table 1. Descriptive statistics.
Mean Std. Dev. Skewness Kurtosis Jarque–Bera Probability Observations
DJSEMI 0.152 9.618 0.682 7.898 2502.560.000 2323
DJSWI 0.487 12.128 1.469 21.705 34699.040.000 2323
GB 0.008 0.419 0.702 9.858 4742.9620.000 2323
ADA 0.000 0.093 16.45 434.175 79940200.000 1025
BTC 22.089 755.947 0.706 31.677 68596.070.000 1997
BITG 0.005 0.187 1.677 46.270 71026.670.000 904
SLR 0.000 0.037 16.032 583.876 272871040.000 1934
XLM 0.000 0.020 0.291 101.067 739747.20.000 1845
XRP 0.000 0.054 2.198 125.371 13150860.000 2104
Note: The table reports the descriptive statistics of the differenced return of closing prices. denotes the rejection
of the null hypothesis at the 1% significance level. Dow Jones Sustainability Emerging Market Index (DJSEMI). Dow
Jones Sustainability World Index (DJSWI), S&P Green Bonds (GB), Cardano (ADA), Bitcoin (BTC), BitGreen (BITG),
SolarCoin (SLR), Stellar (XLM), and Ripple (XRP).
Source: Authors’estimations.
12 I. ULHAQ ET AL.
cryptocurrencies and DJSWI remains low in most cases, but several red, light red and
yellow spots with black borders show a significant relationship on different scales.
In summary, the outputs show mixed co-movement patterns between sustainable
cryptocurrencies and DJSWI. The dynamic co-movement over different investment
horizons and time periods supports the idea that sustainable cryptocurrencies were
prompted as a source of increasing global sustainability. Sustainable cryptocurrencies
(SLR and XRP) have strong long-term co-movements, and BITG, SLR and XRP co-
move positively with DJSWI on 16–32-day and 32–64-day scales, particularly in 2021
and 2013, respectively. XLM and ADA also have a short-term co-movement with
DJSWI on 16–32-day and 32–64-day scales; moreover, XLM also has a long-term
co-movement.
Overall, all selected sustainable cryptocurrencies have strong positive co-movement
in the short run. Only two sustainable cryptocurrencies (SLR and XRP) co-move (in-
phase) with DJSWI in the long run (ahead of a 256-day scale). These outcomes
answer the difficult question raised by Arps (2018) that cannot be answered by inves-
ting in the role of sustainable cryptocurrencies in terms of global and social sustain-
ability. These results are novel because no study has investigated the co-movement
between sustainable cryptocurrencies and DJSWI through the wavelet coher-
ence approach.
Figure 4 shows the wavelet coherency between GB and the top five sustainable
cryptocurrencies (ADA, BITG, SLR, XLM and XRP). Subgroup 4a demonstrates
wavelet coherence between GB and ADA. The black contours at the lower left corner
Table 2. Philips–Perron test of unit root (1st difference returns).
Index t-statistics P value
BTC 46.6850.000
BITG 27.4970.000
XLM 40.8540.000
SLR 51.0450.000
ADA 35.8690.000
XRP 34.0450.000
GB 42.2370.000
DJSWI 43.9410.000
DJSEMI 38.7930.000
Note: The table represents the results of the unit root test. denotes the rejection of the null hypothesis at the 1%
significance level. Refer Table 1 for abbreviations.
Source: Authors’estimations.
Table 3. Portmanteau (Q) test for serial correlation.
Index t-statistics Probability
BTC 214.19090.000
BITG 225.80320.000
XLM 162.77550.000
SLR 448.67060.000
ADA 111.93780.000
XRP 459.67990.000
GB 75.7123 0.006
DJSWI 203.61130.000
DJSEMI 100.68100.000
Note: The table reports results for the Ljung-Box statistics of autocorrelation of t returns for serial correlation. ()
denotes the rejection of the null hypothesis at the 1% (10%) significance level. Refer Table 1 for abbreviations.
Source: Authors’estimations.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 13
show a positive co-movement between GB and ADA, where GB leads the ADA price.
Several other spots highlighted in yellow and light-red confirm moderate co-move-
ment at the 5% significance level. However, several backward arrows (.) ahead of
the 256 scale demonstrate a negative co-movement (out-phase) during the COVID-19
period. Subfigure 4 b also portrays backward arrows (.,-) where BITG is leading the
GB. In contrast, the forward arrows in the middle indicate a positive co-movement (in-
phase). More areas are shown in blue, which indicates a low co-movement between the
GB and BITG. Subfigure 5c demonstrates several black contours filled with a light-red
colour, which show a co-movement between GB and SLR; however, most of the area
remains blue except a few backward and downward ( ,#), indicating a negative co-
movement (in-phase). In Subfigure 4d, the right upward directed arrows (%) indicate
that GB and XLM have a positive relationship or co-movement (in-phase) ahead of the
256 scale and that GB leads XLM returns in the long run. In addition, several other
black contours filled with the light-red colour validate the moderate co-movement at a
5% significance. Black contours filled with light-red and yellow colours show a moder-
ate co-movement in Subfigure 4e on different scales. There is no phase; thus, both series
are moving in the same direction, and the series is now leading or lagging. All these
findings demonstrate that GB acts as a diversifier with sustainable cryptocurrencies
because the GB showed a moderate co-movement or correlation but not a perfect cor-
relation except with SLR and ADA. ADA and SLR showed few moderate negative co-
movements in the long run; therefore, GB evidenced hedging properties for ADA and
SLR. In summary, the pronounced blue colour in all subfigures indicates that GB and
sustainable cryptocurrencies have a weak or no co-movement; therefore, GB acts as a
weak hedge against sustainable cryptocurrencies.
Overall, our results reveal a positive moderate and weak co-movement between GB
and sustainable cryptocurrencies on short-term investment horizons (1–4-day, 4–16-
day and 16–64-day scales), except SLR, where it negatively co-moves with GB from
2017 to 2018 and from mid-2020 to 2021. Therefore, GB acts as a hedge for the SLR
64–128-day and 128–256-day scales for SLR, and previous studies found GB to be a
hedge (Arif et al., 2021; Naeem et al., 2021; Naeem et al., 2021). The light red colour
shows a moderate correlation (but not perfect), and the blue colour indicates no co-
movement; therefore, GB acts as a diversifier in the short-term investment horizon
and as a weak hedge where no correlation exists (blue colour). These findings par-
tially match previous research by Haq et al. (2021).
Figure 5 captures the wavelet coherence output of Bitcoin with GB and DJSWI.
Black contours are present in Subfigure 5a, and most of them are light red and yel-
low. A black contour in light red with right-directed arrows (&) on the 32–64-day
scale during 2020 indicates that GB and Bitcoin returns have positive (in-phase) co-
movements during 2020. However, Figure 5b shows several light-red and yellow con-
tours outlined in black validate a moderate data co-movement during the entire sam-
ple period. These findings show that GBs act as diversifiers of Bitcoin. Interestingly,
the left-directed arrows (.) in the right bottom corner above the 256-day scale show
a negative relationship or co-movement (out of phase). These arrows also confirm
that the DJSWI is leading, and Bitcoin is lagging. The black contour on the subfigure
validates the results at the 5% significance level. Current output shows that world
14 I. ULHAQ ET AL.
sustainability and Bitcoin have a negative relationship, indicating that price increases
for Bitcoin decrease sustainability.
In summary, our results express a moderate co-movement between GB and
Bitcoin in the long-term investment horizon (ahead of the 256-day scale) from 2020
to 2021 and a strong positive leading impact on Bitcoin returns in the short-term
investment horizon in 2020. The moderate co-movement suggests that diversification
avenues exist between Bitcoin and GBs in the long-term investment horizon; how-
ever, there is no significant correlation in the short-term investment horizon. This
may be due to the long-term investment nature of the GB asset class (Haq et al.,
2021). Interestingly, the wavelet coherency showed a strong-negative co-movement
between Bitcoin returns and DJSWI in the long-term investment horizon (ahead of a
256-day scale). Additionally, this co-movement remained strong at the 4–16-day and
16–64-day scales from 2016 and 2020 to 2021. These findings suggest that the
increasing Bitcoin value and returns have a negative impact on world sustainability.
This outcome is consistent with previous studies (De Vries, 2018; Li et al., 2019),
where they found that increased Bitcoin is harmful for sustainability due to high
energy consumption and carbon emissions around the globe (Gallersd€
orfer et al.,
2020; Onat et al., 2021). Recently, Elon Musk also announced that Tesla will no lon-
ger accept Bitcoin because Bitcoins are using massive amounts of fossil fuel for trans-
actions and mining. Although cryptocurrencies have a promising future, they produce
severe negative externalities to sustainable ecosystems and greener global economies.
5. Concluding remarks
This article investigates the co-movement among GB, DJSWI and Dow Jones sustain-
ability emerging markets indices. In addition, this study investigates the co-movement
among five sustainable cryptocurrencies, Bitcoin and DJSWI. The wavelet coherence
approach captures co-movements over multiple scales and time. The main results
reveal several conclusions. First, we find a strong positive co-movement of GB with
both indices, i.e., DJSWIDJSWI and DJSEMI. Generally, the wavelet coherence shows
strong co-movement over the short and long run among GB, DJSWI and DJSEMI. In
addition, GB returns lead both sustainability indices over short- and long-term invest-
ment horizons. This indicates that GBs are sources of increasing global sustainability
as well as increasing sustainability in emerging markets. Second, sustainable crypto-
currencies and DJSWI show strong but heterogeneous co-movement in both short-
term and long-term horizons, except for BITG. Institutional investors, speculators,
Bitcoin accepting companies and other market participants accelerate the use and
investment of sustainable cryptocurrencies other than Bitcoin, as sustainable crypto-
currencies to ensure a sustainable global environment and achieve sustainable devel-
opment goals. Third, the co-movements of the GB remain heterogeneous and
intermittent with the top five sustainable cryptocurrencies, i.e., SLR, BITG, ADA,
XLM and XRP, over the short and long run, indicating diversification benefits for GB
for sustainable cryptocurrencies (except for SLR) due to the internal short- and long-
term wavelet coherence. GB is more like a diversifier with ADA in the short run and
a hedge in the long run and a diversifier with XRP in the short run and a hedge for
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 15
SLR on the 64–128-day scale (short-term). In addition, wavelet coherence presents a
strong positive co-movement between GB and Bitcoin in short horizons up to the 64-
day scale from 2018 to 2021 but with a moderate co-movement (correlation) but it is
not perfectly positive, suggesting that GB acts as a diversifier with Bitcoin in the
long-term investment horizon from 2018 to 2021 (COVID-19). Moreover, a strong
negative relationship between Bitcoin and DJSWI in the long run alone shows an
unstable strong negative relationship in the short run, suggesting that increasing
Bitcoin returns are deteriorating world sustainability.
Our findings offer several key policy implications for crypto traders, green invest-
ors, and sustainability stakeholders in terms of hedging strategies and sustainability
policy. First, green investors and sustainability stakeholders need to understand that
it is perfectly possible to fight against climate change through investment in GBs and
sustainable cryptocurrencies to promote a sustainable global economy. Moreover, pol-
icy-makers should look into the role of sustainable cryptocurrencies and deploy poli-
cies in support of developing systems for sustainable cryptocurrencies. Second,
Bitcoin is a serious detriment to the world’s sustainability, which should compel
major improvements to the mining process. Hence, the mining process must refrain
from worsening global sustainability to ensure a greener global economy. Third, aside
from environmental and social benefits, GBs appear to be a potential diversification
avenue against sustainable cryptocurrencies for green, conventional, amateur crypto
traders and informed long-term institutions. However, GB and sustainable cryptocur-
rencies do not offer significant hedging benefits for sustainability investors (emerging
and global) and sustainable crypto traders (except SLR) in the short- and long-run
investment horizons. Fourth, despite the adverse role of Bitcoin toward sustainability,
Bitcoin proves to be a hedge against DJSWI, suggesting that crypto traders can earn
hedging benefits when considering Bitcoin against the DJSWI in the long-term invest-
ment horizon. In summary, beyond the diversification and hedging gains, these sus-
tainable financial assets can help mitigate the climate change crisis and meet the
rising demand for environmentally and socially responsible investments.
This research was conducted while the COVID-19 pandemic was not yet over. In
addition, it is not exclusively based on a COVID-19 event-specific dataset. Moreover,
the time period significantly differs for cryptos due to different inception times and
data availability. Hence, future research should explore the safe-haven properties of
GBs and sustainable cryptocurrencies using the same time spans. We suggest investi-
gating the price efficiency and inefficiency of sustainable cryptocurrencies. In add-
ition, direct portfolio implications, such as hedging effectiveness should be
considered. Sustainable cryptocurrencies are understudied; hence, they provide ample
research opportunities to academicians, young scholars and students.
Notes
1. https://www.icmagroup.org/Regulatory-Policy-and-Market-Practice/Secondary-Markets/
bond-market-size/
2. See for more details; https://ripple.com/files/ripple_consensus_whitepaper.pdf/
3. See the list of sustainable cryptos; https://www.leafscore.com/blog/the-9-most-sustainable-
cryptocurrencies-for-2021/
16 I. ULHAQ ET AL.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Inzamam Ul Haq http://orcid.org/0000-0003-4237-6287
Apichit Maneengam http://orcid.org/0000-0002-5989-5764
Supat Chupradit http://orcid.org/0000-0002-8596-2991
Chunhui Huo http://orcid.org/0000-0003-4087-4389
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Appendix A
Figure A.1. Original data.
Source: Coinmarketcap and S&P Global.
20 I. ULHAQ ET AL.
