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Economic Policy Uncertainty, Energy and Sustainable Cryptocurrencies: Investigating Dynamic Connectedness during the COVID-19 Pandemic

Business School, Liaoning University, Shenyang 110036, China
VALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, Portugal
Department of Economic Sciences and Organizations, Polytechnic Institute of Portalegre, 7300-555 Portalegre, Portugal
Center for Advanced Studies in Management and Economics, Palácio do Vimioso, Largo Marquês de Marialva, 8, 7000-809 Évora, Portugal
Independent Researcher, Anchieta Street, 697, Nova Odessa, São Paulo 13380-009, Brazil
Department of Applied Finance, Macquarie Business School, Macquarie University, Sydney 2109, Australia
Faculty of Business Administration, South Asia University, 19/1 Perchkasem Road, Nong Khaem, Bangkok 10160, Thailand
Author to whom correspondence should be addressed.
Economies 2023, 11(3), 76;
Received: 3 November 2022 / Revised: 19 January 2023 / Accepted: 7 February 2023 / Published: 24 February 2023
(This article belongs to the Special Issue International Financial Markets and Monetary Policy 2.0)


The purpose of the research is to explore the dynamic multiscale linkage between economic policy uncertainty, equity market volatility, energy and sustainable cryptocurrencies during the COVID-19 period. We use a multiscale TVP-VAR model considering level (EPUs and IDEMV) and returns series (cryptocurrencies) from 1 December 2019 to 30 September 2022. The data are then decomposed into six wavelet components, based on the wavelet MODWT method. The TVP-VAR connectedness approach is used to uncover the dynamic connectedness among EPUs, energy and sustainable cryptocurrency returns. Our findings reveal that CNEPU (USEPU) is the strongest (weakest) NET volatility transmitter. IDEMV is the most consistent volatility NET transmitter among all uncertainty indices across the original returns and wavelet scales (D1~D6). Energy cryptocurrencies, i.e., GRID, POW and SNC, are more likely to receive volatility spillovers than sustainable cryptocurrencies during a turbulent period (COVID-19). XLM (XNO) is least (most) affected by volatility spillover in system-wide connectedness, and XLM (ADA and MIOTA) showed a consistent (heterogeneous) non-recipient behavior across the six wavelet (D1~D6) scales and original return series. This study uncovers the dynamic connectedness across multiscale, which will support investors considering different investment horizons (D1~D6).

1. Introduction

Uncertainty has been considered one of the major concerns among investors and policymakers. Regarding investors and academics, uncertainty started with the analysis of standard deviation as a risk measure and evolved to the analysis of variables such as the Chicago Board Options Exchange Volatility Index (VIX) (Baker et al. 2016), the economic policy uncertainty index (EPU) (Al-Thaqeb and Algharabali 2019), cryptocurrency policy uncertainty index (UCRY policy) (Lucey et al. 2022) or index of cryptocurrency environmental attention (ICEA) (Wang et al. 2022). Importantly, financial crises are key predictors of volatility and uncertainty in financial markets (Karaömer 2022; Karim et al. 2022). Due to global interconnectedness, financial crises cause spillovers for the different economies and transmit to international financial markets country-wide (Gulzar et al. 2019). These events shake the trust of individual and institutional investors in financial institutions (Haq and Bouri 2022) and are considered major reasons to avoid or delay investments during these periods, considering potential losses and high uncertainty levels. Due to this, it is crucial to study the impact of uncertainty or volatility on the cryptocurrency market, focusing on energy and sustainable cryptocurrencies during the period of COVID-19.
Starting with Bitcoin, the first cryptocurrency introduced in 2009, there are already more than 19,850 cryptocurrencies, with more than 70 having a market value higher than $1 billion (Yousaf et al. 2022). Traditional cryptocurrency mining uses a tremendous amount of energy, which has drawn a lot of criticism (Gallersdörfer et al. 2020). Initially, studies on cryptocurrencies regarded all of them equally, but with time, some crypto assets have come to be seen as intrinsically different, especially in terms of sustainability. For instance, Haq and Bouri (2022) investigate the time-frequency co-movement between bitcoin, sustainable cryptocurrencies and sustainable financial markets and find that conventional cryptocurrencies, i.e., Bitcoin, have an adverse effect on sustainability. Contrarily, sustainable cryptocurrencies show a favorable impact on sustainability and sustainable financial assets. Likewise, Ren and Lucey (2022b) analyze clean and dirty cryptocurrencies, claiming that the clean energy crypto markets do not often exhibit herding behavior while the dirty energy crypto markets exhibit asymmetric and severe herding tendencies in negative markets. Ren and Lucey (2022a) explore the linkage of clean energy with green and dirty cryptocurrencies, finding that clean energy has weak connectedness with dirty and green cryptocurrencies, suggesting weak hedging or safe-haven properties of clean energy, regarding sustainable cryptocurrencies. Green cryptocurrencies are weakly connected with Bitcoin and Ethereum, while financial and macro-economic factors influence the tail dependence of carbon, dirty and green cryptocurrency markets (Pham et al. 2022). Policy and price uncertainty might influence the returns of sustainable and conventional (dirty) cryptocurrencies (Haq and Bouri 2022). However, how economic policy uncertainty and COVID-19 affect equity market volatility and the capacity to predict the returns of energy and sustainable cryptocurrencies is still a key question for sustainable policymakers and investors.
Interests in the cryptocurrency market have evolved during the last few years and crypto traders (amateur and informed investors) are now more concerned about the environmental and social effects of the conventional cryptocurrency market (Lucey et al. 2022; Wang et al. 2022). For instance, Elon Musk, the CEO of Tesla Corporation expressed that traditional cryptocurrency (Bitcoin) consumes large amounts of electricity and fossil fuel and produces a carbon footprint that makes the global environment dirty and unclean, and this could affect investors’ trust. Meanwhile, crypto investors are revising their priorities, with an increased preference for sustainable, green or clean cryptocurrencies (Haq and Bouri 2022; Pham et al. 2022; Ren and Lucey 2022a; Haq et al. 2022a). Generally, three well-known cryptocurrencies support energy trades in the renewable energy sector: Powerledger (POWR), GridPlus (GRID+) and SunContract (SNC) (Yousaf et al. 2022). Additionally, SolarCoin (SLR), Bitcoin Green (BITG), Cardano (ADA), Steller (XLM) and Ripple (XRP) are recognized as committed to sustainability (Haq and Bouri 2022; Haq et al. 2022a). In this research, we study the dynamic multiscale connectedness between economic policy uncertainty, energy and sustainable cryptocurrencies. Additionally, we explore the linkage between the Daily Infectious Disease Equity Market Volatility Tracker (IDEMV) and cryptocurrencies.
Previous research has investigated the connectedness between EPU and conventional cryptocurrencies from several methodological and empirical perspectives. A first strand of research analyzes the connectedness and the impact of EPU in traditional cryptocurrencies using different time series and empirical approaches (Bouri and Gupta 2021; Chen et al. 2021; Cheng and Yen 2020; Koumba et al. 2020; Papadamou et al. 2021; Wang et al. 2019; Wu et al. 2021; Yen and Cheng 2021). A second strand of literature is focused on the impact of EPU or risk measures on the time-varying relationship between cryptocurrency and financial markets, through the use of GARCH family models (Fang et al. 2017, 2019; Li et al. 2022; Mokni et al. 2020; Xiong et al. 2018; Zhao and Wang 2022). A third strand of research examines the connectedness between economic/financial risk measures and cryptocurrencies across time-and frequency domains (Ah Mand 2021; Al-Yahyaee et al. 2019; Haq and Bouri 2022; Jiang et al. 2021; Rubbaniy et al. 2021; Wu et al. 2021; Zhu et al. 2022). This area of research studies the impact of risk measures such as EPU, index of cryptocurrency environmental attention (ICEA), UCRY policy, UCRY price and cryptocurrency implied volatility index (VCRIX), and conventional cryptocurrencies. A final strand of research investigates the linkage (impact) between EPU and the cryptocurrency market (Chen et al. 2021; Jiang et al. 2021; Mokni et al. 2022; Wu et al. 2021).
To the best of our knowledge, no research has investigated the impact of EPU and IDEMV on energy and sustainable cryptocurrencies during the fragile economic and crisis period associated with COVID-19. The rest of the paper is designed as follows. Section 2 reviews related studies. Section 3 explains the data and TVP-VAR method. Section 4 presents the empirical findings and relates them to previous research. Finally, the last section concludes and presents the implications.

2. Literature Review and Related Studies

Several studies have investigated the relationship between EPU and conventional cryptocurrencies, i.e., Bitcoin and Ethereum, considering different perspectives. One of those perspectives analyzes the connectedness/impact of EPU on traditional cryptocurrencies (Bouri and Gupta 2021; Chen et al. 2021; Cheng and Yen 2020; Koumba et al. 2020; Papadamou et al. 2021; Wang et al. 2019; Wu et al. 2021; Yen and Cheng 2021). For instance, Wang et al. (2019) studied the risk spillover effect from EPU to Bitcoin using MVQM-CAViaR and the Granger causality method, finding that the spillover from EPU to Bitcoin is marginal and Bitcoin is a safe-haven or diversifier during the time of EPU shocks. Similarly, Cheng and Yen (2020) investigated the impact of EPUs on traditional cryptocurrencies using a predictive regression model and concluded that China-EPU predicts Bitcoin returns, while Koumba et al. (2020) investigated the dependence between EPU indices and traditional cryptocurrencies through the use of a D-Vince Copula approach, finding that US-EPU predicts Ethereum better than Bitcoin returns. Moreover, Ethereum has a higher effective hedge for EPU than Bitcoin. Bouri and Gupta (2021) studied the predictive power of news-based and internet-based EPU risk measures concerning Bitcoin returns, with both measures predicting Bitcoin returns positively. Notably, it is evident that not only does EPU predict Bitcoin returns positively but also the volatility of Bitcoin negatively (Yen and Cheng 2021). In this debate, Wu et al. (2021) found that the EPU Twitter-based index is also positively connected with returns of the top four cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple), considering the use of the Granger Causality test. However, more cryptocurrencies are linked to EPU in bearish market and less in bullish market conditions (Papadamou et al. 2021). The country-wide EPU shows a consistent volatility spillover effect on the cryptocurrency market, based on the DCC-GARCH model (Foglia and Dai 2021). A number of studies have validated that EPU has mixed (positive/negative) predicting ability of Bitcoin returns (Chen et al. 2021; Demir et al. 2018; Shaikh 2020; Wang et al. 2020).
Another research path focuses on the impact of EPU or risk measures on the time-varying relationship between cryptocurrency and financial markets (Fang et al. 2019; Li et al. 2022; Mokni et al. 2020). For example, Fang et al. (2019) studied the impact of EPU on the correlation patterns of Bitcoin-bond, using a GARCH-MIDAS approach, concluding that the global EPU index shows a negative impact on Bitcoin–bond pair correlations, but a positive impact on Bitcoin–commodities and Bitcoin–equities correlation patterns, reflecting the limited hedging ability of Bitcoin returns. With a similar objective, Mokni et al. (2020) studied the impact of EPU on Bitcoin–US stock correlation using the DDC-GARCH model, concluding that EPU has a positive effect on Bitcoin–SP500 before the crash and low-EPU periods, while having a negative impact on the conditional correlation, raising the possibility of using Bitcoin as a hedging tool when high uncertainty occurs. Additionally, Li et al. (2022) documented that EPU shows heterogenous effects on Bitcoin–SP500 and Bitcoin–Gold pairs (correlations), indicating that stock and cryptocurrency markets are sensitive to domestic and global economic and fiscal events. Therefore, it is relevant to investigate the volatility spillover effect of EPU and volatility measures on energy and sustainable cryptocurrencies.
The connectedness of economic or financial risk measures and cryptocurrencies across time and frequency domains has also originated several studies (Ah Mand 2021; Al-Yahyaee et al. 2019; Haq and Bouri 2022; Jiang et al. 2021; Rubbaniy et al. 2021; Wu et al. 2021; Zhu et al. 2022). For instance, Al-Yahyaee et al. (2019) investigated the co-movement between VIX and Bitcoin returns and the impact of EPU on the Bitcoin–VIX correlation, based on bivariate and multivariate wavelet coherence approaches in time and frequency domains. Those authors find heterogenous co-movement across time and investment horizons and conclude that VIX could be used to predict Bitcoin returns, at the same time as Bitcoin-uncertainty indices are time and frequency dependent. Ah Mand (2021) investigated the co-movement in both time and frequency domains between cryptocurrency uncertainties and cryptocurrency returns, concluding that cryptocurrency uncertainties predict cryptocurrency returns in all investment horizons. However, EPU and VIX failed to influence the co-movement between cryptocurrency returns and uncertainties. Jiang et al. (2021) analyzed the interconnectedness between EPU, COVID-19-induced equity market volatility index and traditional cryptocurrency market returns, based on a quantile coherency analysis approach. According to those authors, most traditional cryptocurrencies are effective hedges for high EPU and COVID-19-induced equity market volatility index during the COVID-19 pandemic. Focusing on COVID-19, Rubbaniy et al. (2021) explored the co-movement between financial and non-financial risk proxies and the returns of Bitcoin, Ethereum, and Ripple, using a wavelet coherence approach, finding that cryptocurrencies are safe-haven assets for non-financial market-based proxies, but acting like traditional assets against financial market-based proxies. Wu et al. (2021) studied the co-movement between EPU and four cryptocurrencies (Bitcoin, Ethereum, Ripple and Litecoin), using a wavelet coherence approach, and did not find a causal relationship between EPU and cryptocurrency returns during the COVID-19 pandemic.
Another strand of research investigated the nexus of energy, sustainable cryptocurrencies, and stock markets. For example, Haq et al. (2022a) consider the financial market and sustainability perspectives, studying the co-movement of green bonds, sustainable and traditional cryptocurrencies and major sustainability indices in both time and frequency domains, concluding that green bonds and sustainable cryptocurrencies are sustainable investment and risk management avenues for sustainable crypto traders and investors. Similarly, Pham et al. (2022) analyze the tail dependence between carbon prices and green and non-green cryptocurrencies, using quantile connectedness. In contrast to low-volatility times, they observe increased dependency during high-volatility periods. At times of low volatility, the relationship between carbon prices and cryptocurrencies returns is basically inexistent, while the interconnectedness between green cryptocurrencies and Bitcoin and Ethereum is marginal. Finally, those authors noticed that macroeconomic and financial uncertainties significantly affect the tail dependence between these variables. Ren and Lucey (2022a) investigated the relationship of clean energy with green and dirty cryptocurrencies using the DCC-GARCH model and found that clean energy is not a suitable hedge for both types of cryptocurrencies, but a weak safe haven. Additionally, clean energy is a more suitable safe haven for dirty cryptocurrencies than for clean cryptocurrencies. Haq and Bouri (2022) investigated the co-movement of sustainable and conventional cryptocurrencies and cryptocurrency uncertainty indices, using a wavelet coherence method and considering multiple investment horizons, finding that sustainable and conventional cryptocurrencies have a positive correlation with both cryptocurrency uncertainty indices (UCRY Price and UCRY Policy) in short-term investment horizons, showing a short-lived hedging ability of cryptocurrencies regarding cryptocurrency uncertainty indices.
The above discussion considers the idea that national and global economic factors are vulnerable to the stability of financial and cryptocurrency markets (Fang et al. 2019; Li et al. 2022), with the volatility spillover of EPU and IDEMV on energy and sustainable cryptocurrencies not being analyzed in previous research. Moreover, previous research is also scarce on the empirical evidence of multiscale analysis of EPU on energy and sustainable cryptocurrencies, considering the heterogeneity of investors and investment horizons (Haq and Bouri 2022; Haq et al. 2022b). Based on this, our research has two motivations: firstly, crypto institutional/individual investors and traders are turning toward sustainable cryptocurrencies due to both social and economic benefits, making it relevant to investigate the impact of economic and financial variables; secondly, investors have heterogeneous investment interests considering multiple investment horizons such as D1~D6, so it is crucial to investigate this connectedness considering six wavelet scales.

3. Methodology

3.1. Data

This study considers the daily data of three EPU indices (USA, China and the UK) as well as a Daily Infectious Disease Equity Market Volatility Tracker (IDEMV), sourced from Additionally, three energy cryptocurrencies (Powerledger—POWR, GridPlus—GRID, and SunContract—SNC) and five sustainable cryptocurrencies (SolarCoin—SLR, Bitcoin Green—BITG, Cardano—ADA, Steller—XLM and Ripple—XRP) were considered, due to their sustainable mechanisms and mining processes. The daily closing prices for cryptocurrencies were sourced from The closing prices of energy and sustainable cryptocurrencies were transformed into returns with the daily return as R i , t = l n P i , t P i , t 1 . The dataset starts on 1 December 2019 and ends on 30th September 2022, covering the period of turmoil related to the COVID-19 pandemic, and the returns of energy and sustainable cryptocurrencies are found in Figure 1.

3.2. Maximum Overlap Discrete Wavelet Transform Method

We used the Percival and Walden (2000) maximum overlap discrete wavelet transform (MODWT) in order to decompose the original EPU and cryptocurrency return series into six wavelet components (i.e., D1, D2… D6), focusing on the multiscale analysis and considering the importance of investment horizons (short-term, medium-term and long-term). This wavelet technique can distinguish between the main types of variability and examine each wavelet component at a resolution according to its scale (Maghyereh et al. 2019). The MODWT, a non-orthogonal transform, outperforms the discrete wavelet transform (DWT) in several ways, including non-specific sample length, constant conversion process, incremental resolution at larger scales, and a more asymptotically efficient wavelet variance estimate (Cui et al. 2021). In numerous existing research studies, the MODWT has been used to divide the original return series into various wavelet components as part of the wavelet-based analytic framework (Cui et al. 2021; Maghyereh et al. 2019).
Equations (1)–(4) can be used to get the wavelet coefficients V j , t and scaling coefficients S j , t of the return series ( R i , t ) at the jth level:
V ~ i , j = I = 0 L j 1 x ~ j , l   R t j   m o d   T   t = 0 , 1 , , T 1
S ~ i , j = I = 0 L j 1 y ~ j , l   R t j   m o d   T   t = 0 , 1 , , T 1
Considering the wavelet filter length represented by L, and x ~ j , l = x j , l 2 j 2 and y ~ j , l = y j , l 2 j 2 as the wavelet filter and the scale filter, respectively. We have the following properties (Khalfaoui et al. 2015):
l = 0 L j 1 x ~ l = 0 , l = 0 L j 1 y ~ l = 0 ; l = 0 L j 1 x ~ 2 l = l = 0 L j 1 y ~ 2 l = 1 2 l ; + y ~ l y ~ l + 2 n = + x ~ l x ~ l + 2 n
Following Cui et al. (2021) and Maghyereh et al. (2019), we employed the MODWT wavelet filter for decomposition, due to its linear phase and symmetric properties. The MODWT can be expressed as follows:
R t = A j t + j 1 J B j ( t )
where A j t = I = + x l A J 1 ( t + 2 j 1 × l ) represents the smoothed form of the return series R ( t ) at the scale J. Furthermore, B j t = I = + y l A J 1 ( t + 2 j 1 × l ) expresses the detailed wavelet components that can capture the local dynamics of R(t) over the sample period at each scale j, where J = ( 1 , 2 , , J ) . The wavelet decomposition of the level series for EPUs is found in Figure 2. In addition, the level and return series of IDEMV and cryptocurrencies are decomposed into D1~D6 as presented in Figure A2.

3.3. TVP-VAR Approach

The TVP-VAR approach is the combination of Time-Varying Parameters (TVP) and Vector Autoregressive (VAR). Considering the entire dataset, the static approach considers the use of a vector autoregressive model, whereas the dynamics are estimated using a rolling-window VAR method. Initially proposed by Primiceri (2005), the TVP-VAR has been applied by Antonakakis et al. (2020) and developed by Diebold and Yilmaz (2009, 2012) and Diebold and Yilmaz (2014), being used in this study to assess the dynamic connectedness among economic policy uncertainty indices, energy and sustainable cryptocurrencies. Generally, it is a widely adopted approach to track and assess spillovers in a specified network (Bouri et al. 2021) because it offers researchers and practitioners both a static and a dynamic approach to time series network analysis. This model estimates potential changes in the degree to which EPUs and cryptocurrencies are interconnected in order to demonstrate whether the linear structure is derived from the likelihood of shocks or from the extension of the change mechanism response (Karim and Naeem 2022). The model also offers odd characteristics to spot probable structural breaks and offers compelling explanations to understand the relationship between EPU indices and cryptocurrencies.
Previous research highlighted several additional benefits of using this approach, which are key motivations behind using the TVP-VAR model (Adekoya and Oliyide 2021; Bouri et al. 2021; Haq et al. 2022a). First, it enables the variance to change via a Kalman Filter estimation with forgetting components. Second, it eliminates the need to arbitrarily select the rolling-window size. Third, it does not cause a loss of observations during the estimation process. Finally, it can be applied to low-frequency datasets.
The model equation can be written as follows:
C t = β 0 , t + β 1 , t Y t 1 + + β p , t Y t p + u t + X t Θ t + u t
with C t indicating the vector of the dependent variable with dimension n × 1 and β 0 , t p , t as n × n dynamic coefficients varying over time, which are rewritten as the Θ t matrix (Haq et al. 2022a; Karim and Naeem 2021) and with u t representing structural shocks and n × 1 has zero mean with a heteroskedastic distribution.
It is also possible to represent
D t = [ 1 , C t 1 , , C t p ]
with D t as an n × k matrix that incorporates both the intercept and the lags of time-varying variables and
Ω t = M t 1 H t ( M t 1 )
with the term Ω t indicating the time-varying variance-covariance matrix. Therefore, the variance-covariance matrix of cryptocurrencies and green financial assets returns series can be written as in Equation (7), where M t 1 and H t represent the simultaneous relationship between time series and stochastic connectedness, respectively.
The transition in dynamic parameters over time is assumed to be as follows:
Θ t = Θ t 1 + v t , v t N ( 0 , S )
α t = α t 1 + ξ t , ξ t N ( 0 , Q )
Here, the time-varying parameters are estimated through Equations (8) and (9) by following the random walk process (Kamal and Hassan 2022).
Finally, we can get
ln   h i t 1 = l n   h i , t 1 + σ t μ i , t , μ i , t N ( 0,1 )
to estimate the stochastic connectedness using the random walk process. Overall, the error term is determined to be independent of the transition equation. Therefore, the variables’ coefficients vary independently to maintain efficient and simplified estimates (Haq et al. 2022a; Karim and Naeem 2021; Primiceri 2005).

4. Results and Discussion

4.1. Summary Statistics

Figure A1 indicates the evolution of economic policy uncertainty and cryptocurrency prices during the COVID-19 pandemic period. The IDEMV, UKEPU and USEPU indices followed a dynamic pattern over time, and a sharp hike can be noticed near the COVID-19 outbreak, from the start of 2020 to the end of 2021. However, policy uncertainty became slightly more stable after 2021 except for the CHEPU whose fluctuations were homogenous and followed more dispersion in 2022. The prices of energy and sustainable cryptocurrencies followed a spike at the start of 2021, and cryptocurrency prices were always high during COVID-19. However, GRID and POW prices increased in 2022. These findings suggest that at times of high policy uncertainty and equity market volatility, investors prefer the cryptocurrency market as an attractive investment avenue, increasing the demand for cryptocurrencies and their prices. These findings are consistent with Huynh et al. (2021a), who documented the relationship between financial markets and uncertainty during the coronavirus period.
Table 1 reports the output of descriptive statistics, which includehte mean, standard deviation, skewness, kurtosis, and the Jarque–Bera and Augmented Dickey–Fuller test. In Panel A (original return series), UKEPU (CNEPU and USEPU) remains the most (least) volatile EPU index. Mean returns of all cryptocurrencies are positive with ADA and POW (XLM, XRP, GRID) showing the highest (lowest) positive returns. Among all cryptocurrencies, GRID (XLM) is the most (least) volatile. Additionally, returns of cryptocurrencies have negative skewness coefficients (with the exception of the XLM returns) and kurtosis values above three, indicating negative skewness and leptokurtic characteristics. By using the Jarque–Bera (JB) statistic, we rejected the normal hypothesis of the return (cryptocurrency) and level (EPU) distributions at the 1% level of significance, confirming that the original return series have non-normal distributions. Applying the Augmented Dickey– Fuller test, we conclude that all return (cryptocurrency) and level (EPU) series are stationary.
Further descriptive statistics of wavelet components are also presented in Table 1. The results of wavelet components are homogenous to the original returns. In particular, the crypto returns and EPU indices have mean values of zero over all time horizons, indicating that positive and negative shocks balance one another over longer investment horizons (Cui et al. 2021; Maghyereh et al. 2019). The lower the scales, the greater the unconditional volatility as measured by the standard deviation (high-frequency components). Wavelet components of cryptocurrency returns exhibit larger swings at several scales. Additionally, we see that the wavelet scales for crypto returns and EPU indices are all skewed and leptokurtic. The non-normality of the wavelet components was also confirmed by the JB statistic results. Interestingly, the returns of cryptocurrencies and EPU indices are closer to normality and follow somewhat non-normal distribution at higher wavelet scales, which is consistent with earlier research (Cui et al. 2021; Maghyereh et al. 2019). Additionally, we use the ADF unit root test to check if each wavelet component is stationary. The returns and level series of cryptocurrencies and EPU level series are stationary considering a 1% level of significance, respectively.
Table 2 reports the unconditional correlation between EPUs, IDEVM, energy and sustainable cryptocurrencies. The results of Panel A show that the correlation coefficients of CNEPU and IDEVM with MIOTA, GRID and POW are negative. Contrarily, the coefficient signs of UKEPU and USEPU with energy and sustainable cryptocurrencies are predominantly positive for the original return series, meaning that we could find safe-haven properties of energy and sustainable cryptocurrencies for UKEPU and USEPU. The unconditional correlation coefficients of EPUs and IDEVM are mostly negative with both types of cryptocurrencies at D1 (2 to 4 days), D2 (4 to 8 days) and D3 (8 to 16 days) scales, indicating lower safe-haven avenues across very short and short wavelet scales. For the D4 (16 to 32 days) scale, XLM shows positive correlation coefficients with EPUs and IDEVM, suggesting that an increase in EPUs leads to an increase in XLM returns. However, conditional correlation coefficients of SNC are negative and other cryptocurrencies show heterogeneous signs. These findings suggest mixed safe-haven properties of energy and sustainable cryptocurrencies during the COVID-19 period for EPUs and IDEVM. Noticeably, in a medium-term investment horizon, the correlation coefficients show positive signs between EPUs/energy and EPUs/sustainable cryptocurrencies. However, IDEVM shows predominantly negative signs with both classes of cryptocurrency. These findings reveal that energy and sustainable cryptocurrencies could be seen as having a safe-haven behavior for policy uncertainty.

4.2. Evidence from TVP-VAR Approach

Table 3 reports the output of the TVP-VAR dynamic connectedness approach. We investigate the connectedness between EPU level series and cryptocurrency returns (energy and sustainable cryptocurrencies) from 1st December 2019 to 30th September 2022. Generally, the output of TVP-VAR across multiple scales shows heterogeneous connectedness. Notably, the total connectedness index followed an increasing trajectory from a low-frequency scale to a high-frequency scale. Table 3 shows the total connectedness indices or system-wide connectedness for Panel A (40.41%), Panel B (42.13%), Panel C (47.66%), Panel D (54.78%), Panel E (63.86%), Panel F (63.91%) and Panel G (65.12%). These findings suggest that the connection became stronger with the frequency of scale. They also show that the connectedness is stronger in the medium-term than in the very short and short term. These findings are consistent with previous research (Cui et al. 2021).
The results of Panel A (original returns) reveal that EPU failed to act as a spillover transmitter. However, COVID-19-induced equity market volatility remains the strongest volatility transmitter as the NET spillover coefficient is 26.76%. Interestingly, energy cryptocurrencies (GRID, POW and SNC) are major volatility recipients among cryptocurrencies with NET connectedness values of −8.18%, −10.59% and −7.25%, respectively. However, only one sustainable cryptocurrency (XNO) is a recipient of volatility transmission, with a NET connectedness coefficient of 6.74%. Results of Panel B (2 to 4 days scale) demonstrate that CNEPU, UKEPU and IDEMV are NET volatility transmitters regarding uncertainty measures, with NET connectedness values of 14.08%, 6.73%, and 13.80%, respectively. Among energy and sustainable cryptocurrencies, XNO (−18.45%) and XRP (−1.00%) are NET volatility recipients, as well as GRID and SNC, with NET connectedness coefficients of 10.41% and 14.94%, respectively. For wavelet scales of 4–8 days (Panel C), all considered uncertainties are NET volatility transmitters, with UKEPU being the leading transmitter, followed by CNEPU, with NET connectedness values of 13.18 and 8.05%, respectively. Among energy cryptocurrencies (sustainable), POW and SNC (ADA and XNO) are higher (lower) NET volatility receivers, with NET connectedness coefficients of −17.04% and −16.27% (−2.26% and −18.99%). Considering the results of Panel D (8 to 16 days), only CNEPU and USEPU are NET volatility transmitters, with NET connectedness values of 16.49% and 4.73%, respectively. Among cryptocurrencies, only XRP is the highest NET recipient, showing a NET connectedness of −23.75%, while two other energy cryptocurrencies have negative values: GRID (−5.96%) and POW (−1.60%). Notably, Panel D is the only wavelet scale where more than three cryptocurrencies are volatility recipients. Focusing on the output of Panel E (16 to 32 days), all uncertainty indices are volatility transmitters, CNEPU being the leading volatility transmitter with a value of 31.11% of NET connectedness, followed by UKEPU (18.59%), and with IDEMV as the weakest volatility transmitter (5.75%).
Among cryptocurrencies, all energy cryptocurrencies (GRID, POW and SNC) are volatility recipients, with values of −35.15%, −11.22% and −7.64%, with sustainable cryptocurrencies (ADA, MIOTA, XNO) also as volatility recipients, with NET connectedness values of −4.39%, −14.86% and −6.10%. The results of Panel F (scales from 32 to 64) confirm that EPUs (CNEPU, UKEPU and USEPU) and IDEMV indices are major transmitters, while all energy cryptocurrencies (GRID, POW and SNC) are NET volatility receivers and MIOTA, XNO and XRP returns are also volatility recipients with sustainable cryptocurrencies (−4.10%, −19.43% and −2.51%).
Finally, the results of Panel G (64 to 128 days) show that all EPUs (CNEPU, USKEPU, USEPU), as well as IDEMV, are NET volatility transmitters. More specifically, the NET connectedness values are 1.68%, 27.42% and 28.58%, for CNEPU, USKEPU and USEPU, with IDEMV showing the least volatility spillover effect, with a NET connectedness value of 0.95%. Among sustainable cryptocurrencies, MIOTA, XNO and XRP are volatility recipients (NET connectedness values of −1.73%, −30.47% and −3.09%), with two energy cryptocurrencies (GRID and POW) also as significant volatility receivers, with NET connectedness values of −18.20% and −23.05%, respectively (see Figure 3).
Generally, we find that EPUs and COVID-19 induced equity market volatility are consistent with the results of Foglia and Dai (2021), who concluded that the cryptocurrency market is a NET volatility receiver from EPU, with a peak in 2015, and dropping down gradually. The role of UKEPU and CNEPU in volatility transmission is supported by previous research (Cheng and Yen 2020; Foglia and Dai 2021) which found that Chinese restrictions influence the cryptocurrency market and that the UK is a net source of volatility contribution. These findings are particularly corroborated by earlier research, which found that the spillover from EPU to Bitcoin is marginal and Bitcoin is a safe-haven or diversifier during the time of EPU shocks (Wang et al. 2019). Moreover, more cryptocurrencies are linked to EPU in bearish market and less in bullish market conditions (Papadamou et al. 2021).
On the other hand, only XLM shows consistent non-recipient behavior of volatility from EPU and IDEMV across all wavelet components and original return series, also consistent with previous research, which documented negative connectedness between cryptocurrencies, EPU and IDEMV, while traditional cryptocurrencies are effective hedges for high EPU. Additionally, Ah Mand (2021) documented that EPU and VIX failed to influence traditional cryptocurrency returns and uncertainties. Nor did Wu et al. (2021) find a causal relationship between EPU and traditional cryptocurrency returns during the COVID-19 pandemic. Therefore, the cryptocurrency market generally has low hedging and safe-haven properties. Our study confirmed that only XLM can be considered a safe-haven sustainable cryptocurrency during the turbulent period of COVID-19. Finally, our findings show that few sustainable cryptocurrencies (MIOTA and ADA) were not volatility recipients from EPUs and IDEMV, although showing scale-dependent safe-haven properties. These findings are consistent with Al-Yahyaee et al. (2019), who found that Bitcoin-uncertainty co-movement indices are time and frequency dependent.

5. Conclusions and Implications

Using the TVP-VAR technique from 1 December 2019 to 30 September 2022, our paper provides evidence of the connectedness between national economic policy uncertainty, energy, and sustainable cryptocurrencies during the turbulent period of the COVID-19 pandemic. Furthermore, we looked into how the COVID-19 equities market volatility, energy, and sustainable cryptocurrencies are interconnected. In general, focusing on the volatility transmission perspective, our findings reveal that CNEPU (USEPU) is the strongest (weakest) NET volatility transmitter, followed by UKEPU, among the EPUs. Additionally, IDEMV is the most volatile NET transmitter among all uncertainty indices across the original returns and wavelet scales (D1~D6). Considering volatility recipients, energy cryptocurrencies (GRID, POW and SNC) are more likely to receive volatility spillovers than sustainable cryptocurrencies during the period under analysis. Notably, XLM (XNO) is the least (most) affected by volatility spillover in system-wide connectedness, and XLM showed a consistent behavior as non-recipient, across the six wavelet (D1~D6) scales. Moreover, the additional least effected sustainable cryptocurrencies are ADA and MIOTA as summarized in Table 4.
These findings have several relevant implications. Firstly, cryptocurrency traders and sustainable investors should exercise caution when diversifying their portfolios between traditional assets, which are impacted by equity-economic news and political uncertainties, with the returns of cryptocurrencies showing consistent fluctuation throughout the period. Secondly, in the case of participants, institutional investors can choose energy and sustainable cryptos in the cryptocurrency market that offer greater diversification and reduce higher risks following periods of economic instability. Along with considering the potential advantages of diversifying their portfolios while focusing on the multiscale findings, portfolio managers can also acquire a variety of investment options to prevent significant losses. Our analysis identifies a variety of cryptocurrencies with various levels of risk absorption and diversification and their corresponding ramifications. Thirdly, because we discovered pass-through mechanisms between the economy and digital markets, cryptocurrencies are also regarded as a component of traditional investment channels. The results suggest that those who plan to invest in or trade on the cryptocurrency market should keep a watch on the volatility of the equity market as well as regular news coverage of issues such as economic growth, political shifts, and catastrophes. In a similar line, stabilizing the financial system and monetary policies should also include stabilizing the cryptocurrency markets. The role of government is crucial in protecting the environment from fund inflows through effective supervision (Tran et al. 2022). Finally, policymakers must promote energy and sustainable investments for portfolio diversification since the cryptocurrency market has faced various concerns. Regulators and investors should consider this investment opportunity when constructing a risk-free portfolio, according to implications drawn from the fact that high EPUs and the COVID-19 equity market volatility index also transmitted volatility spillovers. Sustainable and crypto investors can also look at a number of energy-related and sustainable cryptocurrencies with the lowest risk and highest return, which exhibit less volatility throughout the COVID-19 period.
There are a few limitations to our study. Firstly, we considered the COVID-19 period to study the multiscale relationship. These findings inherit the pandemic flavor. Secondly, we employed the multiscale TVP-VAR approach to examine the dynamic connectedness, indicating TVP-VAR-specific output. Finally, our study focused on energy/sustainable cryptocurrencies and daily EPU measures. Future research needs to uncover the connectedness with other country-level EPU indices on a monthly or investigate the impact of financial and economic uncertainties on the sectoral level (Huynh et al. 2021b). Another extension to the current study is employing dynamic connectedness models, i.e., LASSO-VAR.

Author Contributions

Conceptualization, I.U.H., P.F., D.D.Q. and N.H.; Methodology, I.U.H., P.F., D.D.Q., N.H. and S.S.; Formal analysis, I.U.H., P.F., D.D.Q. and N.H.; Data curation, I.U.H., P.F., D.D.Q. and N.H.; Writing—original draft, I.U.H., P.F., D.D.Q. and N.H.; Writing—review & editing, I.U.H., P.F., D.D.Q. and N.H. All authors have read and agreed to the published version of the manuscript.


P.F. is pleased to acknowledge financial support from Fundação para a Ciência e a Tecnologia (grant UIDB/05064/2020).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Figure A1. Evolution of EPU and cryptocurrencies.
Figure A1. Evolution of EPU and cryptocurrencies.
Economies 11 00076 g0a1
Figure A2. Wavelet decomposition graphs for all assets.
Figure A2. Wavelet decomposition graphs for all assets.
Economies 11 00076 g0a2aEconomies 11 00076 g0a2bEconomies 11 00076 g0a2c


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Figure 1. Returns of energy and sustainable cryptocurrencies.
Figure 1. Returns of energy and sustainable cryptocurrencies.
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Figure 2. Wavelet decomposition graphs.
Figure 2. Wavelet decomposition graphs.
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Figure 3. Dynamic return connectedness.
Figure 3. Dynamic return connectedness.
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Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
Panel A: Descriptive statistics (Original data)
Panel B: Descriptive statistics of D1 (2 to 4 days)
Panel C: Descriptive statistics of D2 (4 to 8 days)
Panel D: Descriptive statistics of D3 (8 to 16 days)
Panel E: Descriptive statistics of D4 (16 to 32 days)
Panel F: Descriptive statistics of D5 (32 to 64 days)
Panel G: Descriptive statistics of D6 (64 to 128 days)
Note: For abbreviations, M = Mean, SD = Standard deviation, Skew. = Skewness, Kurt. = Kurtosis, JB = Jarque–Bera, Prob. = Probability, ADF = Augmented Dickey–Fuller test, Obs. = Observations.
Table 2. Correlation Matrix.
Table 2. Correlation Matrix.
Panel A: Correlation Matrix (Original data)
Panel B: Correlation Matrix of D1 (2 to 4 days)
Panel C: Correlation Matrix of D2 (4 to 8 days)
Panel D: Correlation Matrix of D3 (8 to 16 days)
Panel E: Correlation Matrix of D4 (16 to 32 days)
Panel F: Correlation Matrix of D5 (32 to 64 days)
Panel G: Correlation Matrix of D6 (64 to 128 days)
Note: All unconditional correlation coefficients are significant at 1% or 0.001 significance level.
Table 3. Return Connectedness.
Table 3. Return Connectedness.
Panel A: Return Connectedness (Original data)
NET−3.370−1.400−7.13026.7602.5007.0007.710−6.7400.700−8.180−10.590−7.250TCI = 40.41
Panel B: Return Connectedness (2 to 4 days)
NET14.0806.730−17.51013.8000.06012.72014.080−18.450−1.000−10.4100.820−14.940TCI = 42.13
Panel C: Return Connectedness (4 to 8 days)
NET8.05013.1800.6300.040−2.26013.0508.050−18.9905.1306.420−17.040−16.270TCI = 47.66
Panel D: Return Connectedness (8 to 16 days)
NET16.490−4.510−22.8404.7301.6508.46016.4909.600−23.750−5.960−1.6001.240TCI = 54.87
Panel E: Return Connectedness (16 to 32 days)
NET31.11018.590−9.7705.750−4.390−14.86031.110−6.1002.580−35.150−11.220−7.640TCI = 63.86
Panel F: Return Connectedness (32 to 64 days)
NET13.62024.59022.64014.4503.900−4.17013.620−19.430−2.510−27.920−11.220−27.590TCI = 63.91
Panel G: Return Connectedness (64 to 128 days)
NET1.68027.42028.5800.95011.650−1.7301.680−30.470−3.090−18.200−23.0504.570TCI = 65.12
Table 4. Summary of NET Volatility Transmitters and Recipients based on TVP-VAR.
Table 4. Summary of NET Volatility Transmitters and Recipients based on TVP-VAR.
Volatility TransmittersVolatility Recipients
Original ReturnsNoNoNoYesNoNoNoYesNoYesYesYes
D1:(2–4 days)YesYesNoYesNoNoNoYesYesYesNoYes
D2:(4–8 days)YesYesYesYesYesNoNoYesNoNoYesYes
D3:(8–16 days)YesNoNoYesNoNoNoNoYesYesYesNo
D4:(16–32 days)YesYesNoYesYesYesNoYesNoYesYesYes
D5:(32–64 days)YesYesYesYesNoYesNoYesYesYesYesYes
D6:(64–128 days)YesYesYesYesNoYesNoYesYesYesYesNo
Note: The highlighted box with “Yes” (No) in the volatility transmitters column confirms EPUs and IDEMV are the NET transmitters (non-transmitters). The highlighted box with “Yes” (No) in the volatility recipients column confirms energy and sustainable cryptocurrencies are the NET recipients (non-recipients).
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Haq, I.U.; Ferreira, P.; Quintino, D.D.; Huynh, N.; Samantreeporn, S. Economic Policy Uncertainty, Energy and Sustainable Cryptocurrencies: Investigating Dynamic Connectedness during the COVID-19 Pandemic. Economies 2023, 11, 76.

AMA Style

Haq IU, Ferreira P, Quintino DD, Huynh N, Samantreeporn S. Economic Policy Uncertainty, Energy and Sustainable Cryptocurrencies: Investigating Dynamic Connectedness during the COVID-19 Pandemic. Economies. 2023; 11(3):76.

Chicago/Turabian Style

Haq, Inzamam Ul, Paulo Ferreira, Derick David Quintino, Nhan Huynh, and Saowanee Samantreeporn. 2023. "Economic Policy Uncertainty, Energy and Sustainable Cryptocurrencies: Investigating Dynamic Connectedness during the COVID-19 Pandemic" Economies 11, no. 3: 76.

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