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Abstract

This study investigates the dynamic volatility transmission among green cryptocurrencies, ESG indices, and gold from 2018 to 2023, offering insights valuable to portfolio managers for risk management. We selected four green cryptocurrencies, three ESG indices, and gold as examples. We utilize the time-varying parameter vector autoregression (TVP-VAR) model to examine the relationships among the assets. This approach aims to analyze the dynamic interactions among many time series variables whose relationships fluctuate over time. Key findings include volatility patterns; that is, Gold, Tezos, Nano, and DJSEM exhibit higher volatility, while Cardano, Stellar, DJSW, and DJSNA show lower volatility. Gold plays a net volatility role, meaning it is a net receiver of volatility. Among green cryptocurrencies, Cardano and Stellar are net transmitters, while Nano and Tezos are net receivers. For ESG indices, DJSW and DJSNA are net transmitters, whereas DJSEM is a net receiver. While diversification across different asset classes is beneficial, the most effective hedging occurs within the same asset class, such as cryptocurrencies. The mixed outcomes prevent a clear categorization of asset classes as net transmitters or receivers, highlighting the complexity of volatility interactions. Policymakers should account for the systemic risk linked to green cryptocurrencies and ESG indices when formulating legislation.

Plain language summary

Connectedness between Green Cryptos, ESG indices and Gold

The connectedness volatility of ESG indices, green cryptocurrencies and gold markets provide significant value for portfolio managers and investors to overcome risks. This study examines the dynamic transmission of volatility between different asset classes, specifically focusing on green cryptocurrencies, Environmental, Social and Governance (ESG) indexes, and gold. We have selected four green cryptocurrencies, three ESG indexes, and gold as our study samples for the period covering from 2018 to 2023. Our data indicates that gold, Tezos, Nano, and DJSEM tend to experience higher levels of volatility, whereas Cardano, Stellar, DJSW, and DJSNA tend to have lower levels of volatility. In short, we are unable to categorize which asset type functions as net receivers and net transmitters. Furthermore, the findings demonstrate the advantages of diversification across three different asset classes, taking into account various costs associated with hedging. In order to mitigate the risk, portfolio managers and investors may utilize research insights to include environmentally-friendly cryptocurrencies and ESG indexes into their asset portfolios. Policymakers should take into account the systemic risk associated with green cryptocurrencies and ESG indexes when formulating laws.

Keywords

Green Crytocurrency ESG Indices gold volatility TVP-VAR

Introduction

As investors’ awareness of environmental, social, and governance (ESG) increases, the financial assets become developed and more complex. Not only ESG assets but also the emergence of blockchain technology currencies has grown very fast, which integrates the ESG concept into the green cryptocurrency. Consequently, the connectedness between these assets and the existing traditional ones becomes more sophisticated. Interestingly, gold has preserved its role as a safe-haven asset, especially during economic turmoil.

Much research has been conducted to investigate the relationship between cryptocurrency and gold; for example Bhuiyan et al. (2023) suggested the benefit of diversification between Bitcoin and gold in the short-term investment period. Nevertheless, Oosterlinck et al. (2023) revealed that gold and bitcoin complement each other during the crisis period. Bouri et al. (2017) also proposed that Bitcoin is the main asset in diversifying gold. In addition, the potential diversification among the ESG and cryptocurrencies has been explored by several scholars, such as Anwer et al. (2023) and Kakinuma (2023). Moreover Aloui et al., (2021) and Rabbani et al. (2023) further emphasized that the advantages of including FinTech and Islamic assets in a portfolio vary over time and contain Islamic stock and bonds over short and long investment horizons.

From the previous discussion, the connectedness between green cryptos, ESG stock indices, and gold has yet to be investigated. Green cryptocurrencies are digital assets designed to minimize environmental impact through energy-efficient consensus mechanisms and sustainable practices. While ESG stock indices are designed to measure the performance of companies that meet certain environmental, social, and governance (ESG) criteria. It brings the question of whether there is a similarity in the volatility connectedness between ESG indexes and green cryptos to regular stock indexes and bitcoin when measured against gold. The primary objective of this research is to examine the connectedness between spillover volatility in three unique yet interrelated asset classes: gold, the Environmental, Social, and Governance (ESG) index, and green cryptocurrencies. The study examines whether the ESG index, green crypto and gold have the benefit of diversification. The study used the time-varying parameter vector autoregression (TVP-VAR) approach to explore the interconnections among the different categories of assets mentioned. This approach aims to analyze the dynamic relationships between multiple time series variables whose relationships are not constant over time. This method is particularly useful in understanding how these relationships evolve due to structural changes, regime shifts, or other forms of non-stationarity (Nakajima, 2011).

The study holds significance for several reasons, such as assisting portfolio managers and investors in risk management. Moreover, investors who prioritize ESG aspects in their decision-making should note the significant influence that environmental and social assets have on the financial market. Examining the spillover volatility among various assets also yields valuable information about investor behavior and market sentiment. Additionally, it aims to shed light on how innovative and technical assets, that is, cryptocurrencies, affect the broader financial market. Policymakers can now evaluate the possible systemic risk associated with ESG investments and cryptocurrency.

This study extends the current theory on the diversification of asset classes. The study suggests the inclusion of green cryptocurrencies and ESG indexes in investment portfolios, in addition to traditional safe-haven assets such as gold, to achieve diversification benefits. The substantial impact of ESG indexes on environmentally friendly cryptocurrencies and the gold markets highlights their complex interconnectedness within the growing financial ecosystem. Gaining a profound comprehension of these spillover impacts can offer valuable insights for responsible portfolio management and strategies to mitigate risks. The research demonstrates a persistent trend in which the utilization of asset classes within the same category tends to yield greater efficacy, regardless of any potential variations in hedging expenses.

The remaining sections of the paper are organized as follows: The literature review is covered in Literature Review section. Data and Methods section pertains to the data and methods, whereas Empirical Findings section focuses on the empirical analysis. Finally, Conclusion section serves as a conclusion.

Literature Review

The financial media has traditionally regarded gold as a safe-haven asset. Several researchers have conducted tests on the propositions, yielding varying outcomes. For instance Baur and McDermott (2010), discovered that gold serves as a hedge and safe haven against most developed nations, including the European and US markets, particularly during times of crisis. However, little evidence suggests that gold acts as a safe haven asset in Australia, Canada, and some emerging economies. Additionally, the authors proposed that gold may function as a stabilizing force for the financial system by mitigating losses during periods of severe decline in the financial market. Using an alternative methodology Bulut and Rizvanoghlu (2020) arrived at similar findings. Their research demonstrates that just nine nations can be confirmed as robust safe havens for gold, whereas 21 countries exhibit a poor safe-haven status. Chemkha et al. (2021) present findings indicating that gold exhibits limited effectiveness as a safe-haven asset during the COVID-19 epidemic; in contrast, bitcoin does not serve as a reliable hedging tool because of its high volatility. In addition Kang et al. (2019) found that metals and gold are the primary receivers of shocks, suggesting that these commodities are advantageous as a hedging tool due to their limited correlation with other financial assets. In their study Nguyen and Liu (2017) found that gold and oil are considered safe haven assets in comparison to other financial instruments. However, Shahzad et al. (2019) suggested that gold and bitcoin may only be considered safe havens on certain occasions, since their effectiveness may vary across different stock markets and change with time. Furthermore Hussain Shahzad et al. (2020) claimed that Bitcoin exhibits lower diversification potential compared to gold due to its pronounced price stability. An Islamic bond index is also a hedging tool (Shahzad et al., 2019).

The study conducted by Yang et al. (2022) yields contrasting findings on the selection of Bitcoin and commodities as safe haven instruments. Empirical data demonstrates that Bitcoin possesses remarkable hedging capabilities over extended periods of time, whereas commodities exhibit significant hedging efficacy across all timeframes. Huynh et al. (2020) suggest that portfolio managers should incorporate gold into their cryptocurrency portfolio since it is more effective in reducing unforeseen volatility in the cryptocurrency market. Bitcoin might potentially be included into a portfolio of equities and USD assets (Aliu et al., 2023). Recent research conducted by Mensi et al. (2019, 2023) further supports the idea of mixing cryptocurrencies with gold as a superior hedging strategy. The assets amplify the benefits of diversity over short, medium, and long periods, as well as during both normal and volatile market conditions.

The studies above suggest that people view gold as a safe-haven investment, particularly during turbulent times. Investors will move their assets into gold as a value-preserving asset during periods of high volatility and uncertainty in other financial asset markets, such as ESG indices and crypto currencies. As a result, gold will be in high demand, increasing its price volatility. In other words, gold is a volatility receiver from other financial asset markets.

Hypothesis 1: Gold is a network’s net volatility receiver.

Regarding the transmission of volatility between ESG assets and cryptocurrencies, Hassan et al. (2022) examined the influence of cryptocurrency environmental attention (ICEA) on commodities, green bonds, and ESG stocks. The authors have demonstrated that there is no correlation between ICEA and the specified asset for the period from 2014 to 2021. Nevertheless Kakinuma (2023) demonstrated a limited correlation between the ESG index and Bitcoin, suggesting possible advantages in diversification. On the same page, Duan et al. (2023) stated that cryptocurrencies have superior diversification abilities when paired with ESG assets compared to their conventional counterparts. Lei et al. (2023) found that gold and palladium are the main recipients of risk, even in extremely adverse market situations, while studying the spillover volatility of metals and green assets. The authors discuss the consequences of allocating a safe haven portfolio between precious metals and ESG stocks. They confirm that gold is beneficial in providing diversification benefits and reducing possible losses. Umar et al. (2023) reported that green cryptocurrencies significantly transmit shocks across various asset systems, potentially affecting ESG stock indexes. The finding is particularly relevant in addressing dirty financial investment, for example, the fossil fuel market. The interdependence between the green cryptocurrency and fossil fuel markets has intensified during periods of crisis, such as the COVID-19 pandemic and the hostilities between Russia and Ukraine. The efficacy of using green cryptocurrency as a hedge against fossil fuel assets has been demonstrated significantly.

The studies above concluded that green cryptocurrencies transmit volatility to others financial assets, such as ESG indexes and gold. Green cryptocurrencies frequently prioritize reducing their impact on the environment, which might be of interest to investors who are concerned about environmental, social, and governance (ESG) factors. Changes in the performance of these digital currencies can have an impact on individuals' investment decisions in ESG markets and other correlated assets. Moreover, investors commonly perceive cryptocurrencies as high-risk investments with potential for high returns. Increased volatility in green cryptocurrencies can indicate a shift in the general market's risk perception, impacting other assets that are susceptible to risk, including ESG indices, and gold (Figure 1).

Hypothesis 2: Green cryptocurrency is a network’s net volatility transmitter.

Hypothesis 3: The ESG index is a network’s net volatility receiver.

Figure 1.

Diagram of theoretical frameworks.

Data and Methods

This analysis utilizes daily data on green cryptocurrency, ESG indicators, and gold prices. The specified time frame is from November 15, 2018, to November 3, 2023. Table 1 provides comprehensive information on the data, including its source. We selected four notables green cryptos, namely Cardano, Tezos, Stellar, and Nano, due to their representation of a wide range of use cases. For example, Cardano and Tezos are known as smart contracts and decentralized applications; Stellar is known for cross-border payments and financial inclusion, while Nano is known for peer-to-peer, low-latency, and eco-friendly transactions. We have selected three sustainability indices to represent developed, developing, and world countries. As for gold, we use it as a representation of a traditional and safe-haven asset.

Table 1.

Description of Data.

Variable	Description	Source	Number of Sample
Gold	Precious metal	Bloomberg	1,294 days
Cardano	Green crypto	Yahoo Finance	1,294 days
Tezos	Green crypto	Yahoo Finance	1,294 days
Stellar	Green crypto	Yahoo Finance	1,294 days
Nano	Green crypto	Yahoo Finance	1,294 days
DJSW	Dow Jones Sustainability World Index	S&P Dow Jones Indices website	1,294 days
DJSEM	Dow Jones Sustainability Emerging Markets Index	S&P Dow Jones Indices website	1,294 days
DJSNA	Dow Jones Sustainability North America Composite Index	S&P Dow Jones Indices website	1,294 days

We employ the time-varying parameter vector autoregression (TVP-VAR) model, as suggested by Diebold and Yilmaz (2012) and Antonakakis et al. (2020). The model effectively reflects the dynamic interactions and interdependencies across numerous time series variables, enabling the flexibility for parameters to vary over time. However, one of the model’s limitations is the need for significant computational resources, especially when dealing with large datasets or many variables. Estimating the model parameters over time involves complex algorithms and can be time-consuming.

The model for the overall connectedness index is as follows:

C_{t} (H) = \frac{\sum_{i, j = 1, i \neq j}^{m} {\tilde{Φ}}_{i j, t} (H)}{\sum_{i, j = 1}^{m} {\tilde{Φ}}_{i j, t} (H)} x 100 = \frac{\sum_{i, j = 1, i \neq j}^{m} {\tilde{Φ}}_{i j, t} (H)}{m} x 100

(1)

The overall connectedness index at time t and horizon H, denoted as $C_{t} (H)$ , measures the extent of total spillovers within a system of m variables. It is calculated as the ratio of the sum of off-diagonal elements of the generalized forecast error variance decomposition (FEVD) to the total sum of all elements, expressed as a percentage. This indicates the proportion of forecast error variance that is due to shocks across different variables, rather than within themselves. A higher value implies greater overall connectedness among the system’s variables.

The directional connectedness to others (or “TO”) quantifies the impact that shocks to variable i have on all other variables j, highlighting its role as a transmitter of spillovers:

C_{i \to j, t} (H) = \frac{\sum_{i, j = 1, i \neq j}^{m} {\tilde{Φ}}_{i j, t} (H)}{\sum_{j = 1}^{m} {\tilde{Φ}}_{ji, t} (H)} x 100

(2)

Conversely, the directional connectedness from others (or “FROM”) captures how much of variable i’s variance is explained by shocks from all other variables, indicating its sensitivity or exposure to external disturbances:

C_{i \leftarrow j, t} (H) = \frac{\sum_{i, j = 1, i \neq j}^{m} {\tilde{Φ}}_{i j, t} (H)}{\sum_{i = 1}^{m} {\tilde{Φ}}_{i j, t} (H)} x 100

(3)

The net directional connectedness identifies whether a variable is a net contributor (transmitter) or recipient (receiver) of shocks in the system by subtracting the “FROM” component from the “TO” component:

C_{i, t} = C_{i \to j, t} (H) - C_{i \leftarrow j, t} (H)

(4)

Additionally, the net pairwise directional connectedness (NPDC) assesses the bilateral spillover relationship between any two variables, measuring the dominant direction of influence:

{N P D C}_{i j} (H) = ({\tilde{Φ}}_{ji, t} (H) - {\tilde{Φ}}_{i j, t} (H)) x 100

(5)

Lastly, the hedge ratio, as introduced by Kroner and Sultan (1993), helps determine the optimal proportion of a hedging asset required to minimize portfolio risk. It indicates how much of one asset is needed to hedge against price movements in another. It suggests that if a long position in one asset is hedged by a short position in another asset, the optimal hedge ratio between the two assets can be determined as follows:

β_{i j, t} = h_{i j, t} / h_{jj, t}

(6)

Where $β_{i j, t}$ is a hedge ratio with a one-dollar long position in one asset and a one-dollar short position in a second asset. Hedging Effectiveness is based on Ederington (1979) and Antonakakis et al. (2020) estimation and is defined as follows:

r_{β} = x_{it} - β_{jit} x_{jt}

(7)

r_{w} = w_{i j} x_{it} + (1 - w_{ijt}) x_{jt}

(7)

H E_{i} = 1 - \frac{V a r (r_{w β})}{V a r (r_{u n h e d g e})}

(8)

$V a r (r_{w β})$ is the variance of portfolio returns, and $V a r (r_{u n h e d g e})$ is the variance of the un-hedge portfolio. The per cent decrease in the risk of an unhedged portfolio is denoted by “HE.” As the HE increases, there is a corresponding decrease in risk, that is, it indicates better hedging effectiveness, and vice versa.

Empirical Findings

Descriptive Statistics and Preliminary Analysis

Table 2 reports the descriptive statistics for gold, a selection of green cryptocurrencies (including Bitcoin, Cardano, Stellar, and Nano), and environmental, social, and governance (ESG) indices. The mean returns for all assets are approximately zero, indicating that the average performance of these series is centered close to the mean, which is typical for financial return data. Among the assets, Cardano exhibits the highest average return, suggesting stronger performance over the sample period compared to other cryptocurrencies and indices.

Table. 2.

Summary Statistics of Gold, Green Cryptocurrency, and ESG Indicators.

Variables\Stats	Mean	Std. Dev.	Skewness	Ex. kurtosis	Q(7)	LM (5)	ADF (22)
Gold	0.0004	0.0091	−0.3379	3.7500	12.6048**	51.7149***	−34***
Cardano	0.0013	0.0625	−0.1287	5.6937	15.3896**	60.9151***	−24***
Tezos	−0.0003	0.0692	−0.7259	8.3656	22.0393***	69.7511***	−18***
Stellar	−0.0005	0.0623	0.8777	13.277	3.9936	40.8501***	−36***
Nano	−0.0006	0.0728	0.8937	15.873	12.8242*	81.5431***	−16***
DJSW	0.0003	0.0104	−1.1957	17.118	87.1539***	326.918***	-8***
DJSEM	7.9907e-005	0.0102	−1.2188	11.425	60.8924***	475.396***	−12***
DJSNA	0.0004	0.0135	−0.7696	15.879	164.797***	479.053***	−8***

Note. Q is Ljung-Box autocorrelation; LM is Langrage Multiplier; ADF is Augmented Dickey Fuller test.

(Sig at 5%) *** (Sig at 1%).

In terms of volatility, cryptocurrencies exhibit the highest standard deviations, followed by ESG indices and then gold. This ranking reflects the inherently volatile nature of digital assets, particularly Bitcoin, whose price fluctuations are more pronounced than those of traditional or ESG-aligned financial instruments.

An examination of the skewness values reveals that, with the exception of gold and Cardano, all variables lie outside the range of −0.5 to 0.5, suggesting significant asymmetry and deviation from normality. Most of the assets show negative skewness, indicating a distribution with a longer left tail and implying a greater probability of extreme negative returns. Notably, Stellar and Nano are the exceptions, displaying positive skewness, which implies a tendency for more frequent large positive returns.

All return series demonstrate leptokurtic behavior, as reflected in kurtosis values substantially exceeding the normal distribution benchmark of 3. This heavy-tailed characteristic implies the presence of extreme values or outliers, which is common in financial time series and indicates the potential for large, unexpected shocks.

To assess the time series properties of the data, several diagnostic tests were conducted. The Ljung-Box Q statistic reveals significant autocorrelation in all series, with the sole exception of Stellar, suggesting that past returns contain information useful for predicting future values. The Lagrange Multiplier (LM) test confirms the presence of ARCH/GARCH effects in the majority of series, highlighting time-varying volatility dynamics. Finally, the Augmented Dickey-Fuller (ADF) test indicates that all series are stationary at the 1% significance level, confirming that the return series are suitable for use in models that assume stationarity.

Collectively, the evidence from these statistical tests supports the use of a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with time-varying volatility. The significant autocorrelation, volatility clustering, and structural non-normalities make the TVP-VAR framework an appropriate methodological choice, as it accommodates evolving relationships and dynamic volatility among the variables over time.

Time-Varying Spillover and Interdependence Analysis

Table 3 presents the dynamic spillover effects across the individual assets and the overall system. The “From Others” row at the bottom of the table reflects the extent to which each asset receives spillovers from the rest of the system, indicating its role as a receiver of shocks. Conversely, the “To Others” column at the far right captures how much each asset contributes to the spillovers affecting other assets, signifying its role as a transmitter. The results indicate that the Dow Jones Sustainability World Index (DJSW) serves as the dominant transmitter of shocks within the system, followed closely by Cardano and Stellar. In contrast, the Dow Jones Sustainability Emerging Markets Index (DJSEM) exhibits the weakest spillover transmission among the major ESG indices, suggesting a more passive role in the network of connectedness. Nano has the lowest transmission rate compared to other environmentally friendly cryptocurrencies. Finally, gold has the lowest transmission rate compared to all other assets. Several variables, including supply and demand dynamics, investor sentiment, and changes in interest rates impact the price of gold. Investors commonly utilize gold as a hedge against inflation and the depreciation of currencies. Gold is sought after as a safe haven, as investors prefer it during market downturns since it can preserve and appreciate its value (Lei et al., 2023). These factors contribute to the minimal volatility of gold as a transmitter.

Table 3.

Dynamic Connectedness Between Gold, Green Cryptos, and ESG Index.

Variables	Gold	Cardano	Tezos	Stellar	Nano	DJSW	DJSEM	DJSNA	From Others
Gold	79.68	2.49	1.82	2.23	1.97	4.69	3.54	3.59	20.32
Cardano	1.93	35.79	15.67	21.97	14.59	4.18	1.90	3.96	64.21
Tezos	1.94	17.16	40.46	15.87	13.79	4.50	1.95	4.33	59.54
Stellar	1.47	22.68	15.00	36.95	15.03	3.61	1.73	3.52	63.05
Nano	2.12	16.54	14.12	16.70	41.75	3.79	1.59	3.38	58.25
DJSW	2.58	4.13	4.04	3.80	3.27	37.29	13.34	31.56	62.71
DJSEM	3.16	3.72	3.31	3.27	3.24	21.49	45.24	16.57	54.76
DJSNA	2.21	4.15	4.27	3.80	3.17	33.52	8.74	40.15	59.85
To Others	15.40	70.88	58.24	67.64	55.05	75.79	32.78	66.91	TCI
Net	−4.92	6.67	−1.31	4.59	−3.19	13.08	−21.98	7.06	55.34

Note. “From Others” denotes the directional connectivity that each asset receives from the system. “To Others” indicates how each asset distributes its influence to other assets. The term “Net” indicates whether the variable is a net receiver or transmitter of the volatility to the network.

Regarding the spillover receiver, Cardano receives the most spillover, followed by Stellar and DJSW. Once again, DJSEM ranks as the least-performing receiver among ESG indices, while gold stands as the least-performing receiver among all assets. Based on the “Net” designation, it is evident that gold, Tezos, Nano, and DJSEM are the net recipients of spillovers from the network. DJSEM is the asset that receives the greatest net amount. The results support our hypotheses 1 and 3 in terms of gold and DJSEM. Furthermore, the findings have been supported by Kilic et al. (2022), Lamine et al. (2023), and Wan et al. (2024). The index seeks to include the highest decile of the 800 most prominent firms in 20 developing economies, selected according to long-term economic, environmental, and social benchmarks. The distinctive attributes and market dynamics of the firms listed in the DJSEM Index may result in them experiencing the greatest net receiver volatility. However, the results do not validate hypothesis 2 concerning Tezos and Nano. According to CoinMarketCap, the world’s most-referenced price-tracking website for crypto assets in the rapidly growing cryptocurrency space, Tezos and Nano’s market capitalization places them 86th and 323rd out of 10,100 active crypto currencies, respectively. It means that both Tezos and Nano are not the main players in the market, which is why they are “net receivers” of network volatility.

On the other hand, Cardano, Stellar, DJSW, and DJSNA serve as the primary transmitters for the system. DJSW is the most well-regarded network transmitter. Gupta and Chaudhary, (2023); Shaik and Rehman, (2023) reached a similar conclusion. The index consists of the foremost global sustainability frontrunners and covers the best-performing decile of the largest 2,500 firms in the S&P Global BMI, selected according to rigorous long-term economic, environmental, and social benchmarks. Finally, the overall connectivity indicator stands at 55.34%, indicating that 55.34% of the assets are being transmitted. The results support hypothesis 2, but do not support hypothesis 3. According to Dow Jones Sustainability Indices (DJSI), a family of indices that evaluates companies’ sustainability performance, the DJS World and DJS North America indexes are the most prominent within the family. Both of them include the sustainability leaders’ companies globally for the DJS World index and regionally (USA and Canada) for the DJS North America index. This is the reason they are considered "net transmitters" of volatility.

Figure 2 displays the maximum overall interconnectedness of all assets in 2018 and the midpoint of 2020, with an average value approaching 80%. Two crises occurred during that time, including the sharp decline of the US stock market and the outbreak of COVID-19. The correlation between the three asset classes is likely strong during the economic downturn. Furthermore, the average connectivity generally falls between 40% and 60%.

Figure 2.

Dynamic Spillover “Total Connectedness Index (TCI)” of Assets.

Based on Figure 3, Cardano and Stellar have more spillover from the system than the other two cryptocurrencies, Nano and Tezos. Within the scope of ESG indices, both DJSW and DJSNA exhibit comparable amounts of spillover, averaging between 50% and 80%. Gold has the lowest spillover reception compared to other class assets. The peak value (60%) occurred with the beginning of the COVID-19 pandemic in early 2020.

Figure 3.

Dynamic Spillover—“From Others.”

As shown in Figure 4, Cardano and Stellar exhibit stability as transmitters, maintaining an average value range of 50% to 90%. Meanwhile, Nano and Tezos have a comparatively lower number of transmitters, falling within the value range of 40% to 70%. When it comes to ESG indices, DJSW is the top transmitter, whereas DJSEM is the bottom transmitter. Finally, gold has the lowest transmitter compared to other assets. Regarding Figure 5, gold and DJSEM are classified as net receivers starting from 2019. Conversely, DJSW, DJSNA, and Cardano have been the primary transmitters of volatility since 2019.

Figure 4.

Dynamic Spillover—“To Others.”

Figure 5.

Dynamic Spillover—“Net Transmitter or Receiver.”

The Net Pairwise Directional Connectedness (NPDC) in Figure 6 shows that gold has greater volatility coming from the ESG index compared to cryptocurrencies. Within the context of ESG indexes, DJSEM receives net inflows from both DJSW and DJSNA. As the NPDC level is close to zero for pairings of cryptocurrencies and ESG indexes, it indicates a low connection between both asset classes.

Figure 6.

Dynamic Spillover—“Net Pairwise Directional Connectedness (NPDC).”

Hedge Ratio and Hedging Effectiveness

This section provides a detailed explanation of the hedge ratio and hedging effectiveness (HE). As stated before, a higher HE value signifies superior hedging efficacy. The hedging ratio represents the proportion of a one-dollar long position in one asset that may be hedged by shorting a second asset. As an illustration, a long position in gold worth one dollar can be hedged by shorting 14 cents in DJSW to minimize risk. Table 4 reveals that the most cost-effective method of hedging is pairing gold with green cryptocurrencies and DJSEM with Nano. Specifically, taking a long position in gold (DJSEM) may be hedged by shorting 2 cents worth of green cryptocurrencies (Nano). This finding is consistent with Bouri et al. (2017) and Mensi et al. (2019), who suggested that cryptocurrency is the main asset in diversifying gold. It is also consistent with the fact that gold has greater volatility coming from the ESG index compared to cryptocurrencies. Furthermore, the hedging that incurs the highest costs, on average, occurs when hedged by ESG indexes, with a range of 1.04–2.16.

Table 4.

Hedging Ratio and Hedging Effectiveness.

Hedge Position	Hedge ratio	HE
Long position in Gold
Hedged by Cardano	0.02	0.04
Hedged by Tezos	0.02	0.03
Hedged by Stellar	0.02	0.08
Hedged by Nano	0.02	0.13
Hedged by DJSW	0.14	0.06
Hedged by DJSEM	0.16	0.04
Hedged by DJSNA	0.06	0.06
Long position in Cardano
Hedged by Gold	0.98	0.03
Hedged by Tezos	0.60	0.47
Hedged by Stellar	0.80	0.61
Hedged by Nano	0.57	0.43
Hedged by DJSW	1.96	0.12
Hedged by DJSEM	1.18	0.05
Hedged by DJSNA	1.47	0.11
Long position in Tezos
Hedged by Gold	0.81	0.03
Hedged by Cardano	0.74	0.46
Hedged by Stellar	0.73	0.45
Hedged by Nano	0.60	0.40
Hedged by DJSW	2.16	0.14
Hedged by DJSEM	1.24	0.06
Hedged by DJSNA	1.70	0.13
Long position in Stellar
Hedged by Gold	0.72	0.02
Hedged by Cardano	0.76	0.60
Hedged by Tezos	0.56	0.43
Hedged by Nano	0.54	0.45
Hedged by DJSW	1.76	0.10
Hedged by DJSEM	1.18	0.04
Hedged by DJSNA	1.34	0.09
Long position in Nano
Hedged by Gold	0.82	0.02
Hedged by Cardano	0.71	0.40
Hedged by Tezos	0.61	0.38
Hedged by Stellar	0.73	0.43
Hedged by DJSW	1.95	0.09
Hedged by DJSEM	1.04	0.03
Hedged by DJSNA	1.42	0.08
Long position in DJSW
Hedged by Gold	0.18	0.08
Hedged by Cardano	0.06	0.18
Hedged by Tezos	0.05	0.20
Hedged by Stellar	0.06	0.17
Hedged by Nano	0.05	0.14
Hedged by DJSEM	0.63	0.42
Hedged by DJSNA	0.72	0.85
Long position in DJSEM
Hedged by Gold	0.17	0.05
Hedged by Cardano	0.03	0.07
Hedged by Tezos	0.03	0.07
Hedged by Stellar	0.03	0.06
Hedged by Nano	0.02	0.04
Hedged by DJSW	0.56	0.42
Hedged by DJSNA	0.34	0.24
Long position in DJSNA
Hedged by Gold	0.13	0.07
Hedged by Cardano	0.07	0.17
Hedged by Tezos	0.07	0.19
Hedged by Stellar	0.07	0.16
Hedged by Nano	0.05	0.14
Hedged by DJSW	1.18	0.86
Hedged by DJSEM	0.16	0.25

Note. Hedging Effectiveness (HE): A higher value indicates superior hedging efficacy. The hedging ratio shows how shorting a second asset can hedge a one-dollar long position in one asset.

Regarding hedging effectiveness, it is generally observed that hedging is most successful when the same type of asset is used for hedging. For instance, cryptocurrencies are hedged using other cryptocurrencies, and ESG indices are hedged using similar indexes. To mitigate 86% of the return variability of a one-dollar investment in DJSW, one may offset it by shorting $1.18 in DJSNA. Furthermore, when gold functions as a hedge, the hedging effectiveness (HE) is often minimal. In summary, our research emphasizes the advantages of diversifying investments among different types of assets, even though certain assets require higher costs for hedging.

Theoretical Contribution

In the end, the study suggests that green cryptocurrencies and ESG indexes, like gold, are regarded as potential options for investment assets. ESG indices, particularly DJSW and DJSNA, significantly impact the unexpected fluctuations in both green cryptocurrencies and gold markets. Simply put, newly acquired information in the stock markets significantly influences other markets. According to efficient market theory, the study of connectedness can help to clarify the speed and efficiency of information transmission across various markets. If volatility shocks in certain green cryptocurrencies rapidly affect gold or certain ESG indices, and vice versa, it suggests a high degree of market integration and efficiency. Regarding the behavioral finance theory, analyzing how different markets react to volatility can provide insights into investor sentiment and behavior. For example, if increased volatility in green cryptocurrencies consistently leads to increased volatility in gold, it may indicate a flight-to-safety behavior among investors. Finally, in terms of risk management theory, portfolio managers and investors quantify the risk spillover across green digital, green stock, and traditional assets. This will enable them to adjust their hedging strategy accordingly.

Various factors, such as macroeconomic variables, market sentiment, and technology advancements, might affect the relationships among green cryptocurrencies, ESG indexes, and gold. Moreover, factors such as market structure, trading hours, liquidity, and participation points of view may provide valuable insights into the connections between green cryptocurrencies, ESG indices, and gold. Future studies could potentially mitigate these challenges by exploring a greater diversity of contributing factors and variations in market structure.

Conclusion

Moreover, as a traditional asset, gold is a net receiver of volatility from the network. Meanwhile, only two out of four green cryptos are net volatility transmitters, namely Cardano and Stellar; the other two, Nano and Tezos, are net receivers. The ESG indices demonstrate that DJSW and DJSNA are net transmitters of volatility, whereas DJSEM is a net receiver. In short, because the outcomes are mixed, we are unable to categorize which asset class functions as net receivers and net transmitters.

Furthermore, the findings demonstrate the advantages of diversification across three different asset classes, taking into account the various costs associated with hedging. We generally observe from hedging effectiveness that the most successful hedge is when we use the same type of asset for hedging. For example, we hedge cryptocurrencies using other cryptocurrencies, and we hedge ESG indices using other ESG indices.

The implications of the studies are as follows: Knowledge of dynamic volatility and connectedness can be helpful in developing tactical asset allocation strategies. Investors and fund managers can adjust their portfolios based on anticipated volatility spillovers to optimize returns and manage risks. The study of connectedness can illuminate the speed and efficiency of information transmission across various markets. If volatility shocks in certain green cryptocurrencies rapidly affect gold or certain ESG indices, and vice versa, it suggests a high degree of market integration and efficiency. Regarding the behavioral finance theory, analyzing how different markets react to volatility can provide insights into investor sentiment and behavior. For example, if increased volatility in green cryptocurrencies consistently leads to increased volatility in gold, it may indicate a flight-to-safety behavior among investors. As for policymakers, they should take into account the systemic risk associated with green cryptocurrencies and ESG indexes when formulating laws.

Limitations and Further Research

Numerous factors, including macroeconomic variables, market sentiment, and technological developments, influence the interactions between green cryptocurrencies, ESG indices, and gold. Capturing all these influences in a single model is challenging. Furthermore, differences in market structure, trading hours, liquidity, and market participants between cryptocurrencies, ESG indices, and gold can affect the connectedness analysis. Future research could address some of these limitations by examining a broader range of influencing factors and market structure differences.

Footnotes

Acknowledgements

The author would like to acknowledge the support of Prince Sultan University for this publication.

ORCID iD

Nevi Danila

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to acknowledge Prince Sultan University’s support in paying the Article Processing Charges (APC) for this publication.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available on request.

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Connectedness of Dynamic Volatility Over Green Cryptos,the ESG Indices,and Gold: TVP-VAR Approach

Abstract

Plain language summary

Keywords

Introduction

Literature Review

Data and Methods

Empirical Findings

Descriptive Statistics and Preliminary Analysis

Time-Varying Spillover and Interdependence Analysis

Hedge Ratio and Hedging Effectiveness

Theoretical Contribution

Conclusion

Limitations and Further Research

Footnotes

Acknowledgements

ORCID iD

Funding

Declaration of Conflicting Interests

Data Availability Statement

References