Connectedness between cryptocurrencies, gold and stock markets in the presence of the COVID-19 pandemic

Ghorbel, Achraf; Loukil, Sahar; Bahloul, Walid Article Connectedness between cryptocurrencies, gold and stock markets in the presence of the COVID-19 pandemic European Journal of Management and Business Economics (EJM&BE) Provided in Cooperation with: European Academy of Management and Business Economics (AEDEM), Vigo (Pontevedra) Suggested Citation: Ghorbel, Achraf; Loukil, Sahar; Bahloul, Walid (2024) : Connectedness between cryptocurrencies, gold and stock markets in the presence of the COVID-19 pandemic, European Journal of Management and Business Economics (EJM&BE), ISSN 2444-8451, Emerald, Leeds, Vol. 33, Iss. 4, pp. 466-487, https://doi.org/10.1108/EJMBE-10-2021-0281 This Version is available at: https://hdl.handle.net/10419/325580 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Connectedness between cryptocurrencies, gold and stock markets in the presence of the COVID-19 pandemic Achraf Ghorbel, Sahar Loukil and Walid Bahloul Faculty of Economics and Management of Sfax, University of Sfax, Sfax, Tunisia Abstract Purpose –This paper analyzes the connectedness with network among the major cryptocurrencies, the G7 stock indexes and the gold price over the coronavirus disease 2019 (COVID-19) pandemic period, in 2020. Design/methodology/approach –This study used a multivariate approach proposed by Diebold and Yilmaz (2009, 2012 and 2014). Findings –For a stock index portfolio, the results of static connectedness showed a higher independence between the stock markets during the COVID-19 crisis. It is worth noting that in general, cryptocurrencies are diversifiers for a stock index portfolio, which enable to reduce volatility especially in the crisis period. Dynamic connectedness results do not significantly differ from those of the static connectedness, the authors just mention that the Bitcoin Gold becomes a net receiver. The scope of connectedness was maintained after the shock for most of the cryptocurrencies, except for the Dash and the Bitcoin Gold, which joined a previous level. In fact, the Bitcoin has always been the biggest net transmitter of volatility connectedness or spillovers during the crisis period. Maker is the biggest net-receiver of volatility from the global system. As for gold, the authors notice that it has remained a net receiver with a significant increase in the network reception during the crisis period, which confirms its safe haven. Originality/value –Overall, the authors conclude that connectedness is shown to be conditional on the extent of economic and financial uncertainties marked by the propagation of the coronavirus while the Bitcoin Gold and Litecoin are the least receivers, leading to the conclusion that they can be diversifiers. Keywords Connectedness, Cryptocurrencies, COVID-19 crisis, Gold, Spillover Paper type Research paper 1. Introduction Almost all markets have witnessed strong upheavals with the spread of the coronavirus disease 2019 (COVID-19) pandemic shifting to the major digital currencies, the stock indices, the oil price and commodities. The shifts in the mentioned asset volatility have proved costly for many markets. In fact, the increase of volatility has put business operations at the risk of affecting the financial system. Therefore, the global economy is in turmoil as a result of concerns over the coronavirus epidemic. No company is immune to the challenges caused by the health crisis; besides, there are understandable concerns about the damage caused to the worldwide economy. During the propagation of the COVID-19 worldwide, an insurmountable fear was behind a global stock market crash. The 2020 stock market crash, also referred to as the Coronavirus Crash, was a major and sudden global stock market crash that began on 20 February 2020. EJMBE 33,4 466 © Achraf Ghorbel, Sahar Loukil and Walid Bahloul. Published in European Journal of Management and Business Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http:// creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2444-8494.htm Received 17 October 2021 Revised 21 January 2022 Accepted 8 April 2022 European Journal of Management and Business Economics Vol. 33 No. 4, 2024 pp. 466-487 Emerald Publishing Limited e-ISSN: 2444-8494 p-ISSN: 2444-8451 DOI 10.1108/EJMBE-10-2021-0281 The impact of the COVID-19 on the volatility of markets exceeded the one caused by the 2008 global financial crisis and continues to have an effect (Zhang and Hamori, 2021a). The pandemic created an unprecedented level of risk, such as oil triggering stock markets, which was accompanied by heavy losses for investors. As a result, the Paris Stock Exchange fell from 8.39% to 4,707.91 points at the close, its worst session since 2008 [1]. Then, the Wall Street had its worst downturn since 2008 as the coronavirus fears have wiped off almost 32%, or roughly $9 trillion, from the value of the benchmark S&P index since its record closing high on February 19, 2020 [2]. Moreover, the Dow Jones entered “bear market” territory [3] as it fell by 1,465 points or 5.9%. This was enough to put it more than 20% lower than the index recent high point on 12 February 2020. On the other hand, the Nikkei reached its lowest point in 30 years amid worsening virus fears [4]. Recent research studies evaluated and quantified the unexpected outbreak effects of the global pandemic on the stock markets’ performance and proved its reducing effect in the USA (Yousfi et al.,2021),intheAfrican countries (Owusu Takyi and Bentum-Ennin, 2020), and in the USA, Japan and Germany, where the impact of the COVID-19 exceeded that of the 2008 financial crisis (Zhang and Hamori, 2021a), etc. On the other hand, although they are new digital currencies, which established a new distributed payment system on the basis of crypto-graphical protocols which can ensure anonymity, low cost and fast speed of peer-to-peer transactions, cryptocurrencies are not immune to this financial crash caused by the new pandemic. Therefore, the major cryptocurrencies has plummeted to its lowest level since March as a stronger dollar and investor nerves strip off nearly $140bn in cryptocurrency market cap. For example, over two days in January, it plunged to 21%, which is its biggest decline since March 2019. On the other hand, Ethereum fell to 12%. The smaller coins, XRP and Litecoin shed about 18% each [5]. The BTG, which was created in 2017 to counter the centralization of Bitcoin, was notably volatile during 2020 with record in March 2020 [6]. In fact, several researchers, such as Mnif et al. (2020),Demir et al. (2020),Umar and Gubareva (2020),Bergeron et al. (2020),Salisu and Ogbonna (2021) and Yarovaya et al. (2021), studied the impact of the COVID-19 on the cryptocurrency market efficiency. While correlations among most types of assets significantly increased, gold was the only asset to increase in value in 2020. At the time of the market turmoil, investors are more interested in gold as a safe-haven asset (Baur and Lucey, 2010;Shahzad et al., 2019). This precious metal is unconnected with other assets (Baur and Lucey, 2010) and is still considered to be a zero-beta asset (McCown and Zimmerman, 2006). Among all the commodities, gold has the longest duration in the high volatility regime (Choix and Hammoudeh, 2010). In fact, the rising feeling of fear and the investors’pessimism observed during crises caused an increase of demand for gold, which results in an increase of volatility (Ghorbel, 2018). Moreover, several studies, such as those of Baur and McDermott (2010) and Creti et al. (2013), proved the safe-haven role of gold,particularly during the stock market crises (Anand and Madhogaria, 2012;Arouri et al., 2015;Chkili, 2016;Chen and Wang, 2017;Junttila et al., 2018). Given this volatile time, we intend to study the time-varying volatility and the volatility transmission mechanisms across the most widely traded cryptocurrencies, stock indices and gold. This would be essential for both international investors and policymakers. In fact, so far, the common consensus has proven the weak correlations between cryptocurrencies and other assets. However, several observations allow revisiting this consensus (Kristoufek, 2015; Yermack, 2013a,b;Blau, 2017;Bouri et al., 2018a;Jiang et al., 2021). Therefore, we study the pairwise and total connectedness among the stock indices, the major cryptocurrencies and gold. Thus, our empirical study sheds lights on the literature regarding the linkages between financial and commodity markets. We particularly use data relevant for eight popular cryptocurrencies, namely Bitcoin, Dash, Ethereum, Monero, Maker, Bitcoin Gold, Litecoin and Ripple, stock indices for seven developed countries (American index S&P500, Cryptocurrencies, gold and stock markets 467 British index FTSE, Japanese index Nikkei, German index Dax 30, Canadian index SP/TSX, French index CAC40 and Italian index FTSE MIB) and gold price. In retrospect, this study goes one step further and contributes to the existing literature in a number of ways. First, while several research studies on the relationship between the Bitcoin and other traditional assets emerged to assess whether the Bitcoin can be used as a safeheaven, a diversifier or a hedging asset (see, e.g. Bri ere et al., 2015;Dyhrberg, 2016;Bouri et al., 2018a,b,c;Baur et al., 2018a,b;Corbet et al., 2018;Feng et al., 2018;Giudici et al., 2018;Ji et al., 2018;Symitsi and Chalvatzis, 2019), our study focuses on the eight major cryptocurrencies. Second, our analysis during the COVID-19 pandemic enabled us to revisit the common consensus regarding the weak correlation between the cryptocurrencies and the stock markets and also detect the risk of contagion. Third, our study shows to what extent the relationship between gold, the stock indexes and the cryptocurrencies can be understood in a systemic way. Fourth, our hedging effectiveness analysis is set to assess the roles of the cryptocurrencies, the stock indexes and gold in a crisis period. Doing so, we extend the correlation analysis and help portfolio hedgers to make optimal portfolio allocations, engage in risk management and forecast future volatility in financial assets and commodity markets. We proceed as follows. The second section will present the literature review. In section 3, we discuss the construction of our sample and introduce the connectedness method proposed by Diebold and Yılmaz (2014) to investigate the investors’strategies in relation to cryptocurrencies, stock indices and gold, where we propose the data description and the summary statistics. In section 4, we provide results for the static and dynamic information spillover effect, and finally, in section 5, we conclude the paper. 2. Literature review A large strand of literature has focused on the mutual dependencies between cryptocurrencies, stock indexes, oil and other commodities (Kurka, 2017;Corbet et al., 2018;Tiwari et al., 2019,2020;Ji HoKwon, 2020;Yitong Hu et al., 2020;Ahsan Bhuiyan et al., 2021;Yonghong Jiang et al., 2021;Lahiani et al., 2021;Caferra and Vidal-Tom as, 2021). This line of thoughts is interesting and considered as a new topic because it especially considers the increased integration between financial markets in crisis period. Therefore, studying connectedness among different assets is important for two major reasons. First, the portfolio performance depends on the investor’s portfolio selection and on the structure of its components (Baum€ ohl et al., 2018). Second, policymakers could benefit from the information transmitted across assets to broadcast their policies (Ciner et al., 2013). This explains the existence of a large empirical literature trying to better understand the mutual dependencies among various asset classes. Moreover, recent studies have concentrated on the safe haven and the various roles of cryptocurrencies with respect to traditional assets (Bouri et al., 2018a,b,c;Selmi et al., 2018;Urquhart and Zhang, 2019), especially with the stock indices because of their universality (Dyhrberg, 2016;Bouri et al., 2018a,b,c,2020;Jiang et al., 2021). Using numerous methods and techniques, it was proved that the major cryptocurrencies are in general isolated from conventional assets (Dyhrberg, 2016;Aslanidis et al., 2019; Charfeddine et al., 2020;Bouri et al., 2020;Ghorbel and Jeribi, 2021a). However, the novel approach is to challenge this common consensus regarding the weak correlation between cryptocurrencies and the stock markets. This is explained by the fact that the cryptocurrency prices are determined by the same standard fundamental factors as in traditional assets (Kristoufek, 2015), besides their speculative nature (Yermack, 2013a,b; Blau, 2017;Bouri et al., 2018a,b,c) may increase information transmission, risk contagion and the downturn between cryptocurrencies and the stock markets during the COVID-19 EJMBE 33,4 468 pandemic. For their part, Jiang et al. (2021) proved their dependence. In fact, it becomes interesting to challenge this traditional consensus in crisis period marked by the spread of a new global pandemic COVID-19, which destabilized the economic and financial system in the first quarter of 2020. Furthermore, several studies presented some empirical findings on connectedness between cryptocurrencies, stocks and other assets. In this sense, Kurka (2017) documented a very low connectedness between Bitcoin and gold, oil, S&P500 and treasury notes. Moreover, Corbet et al. (2018) confirmed that Bitcoin, Ripple and Litecoin are isolated from other financial and economic assets, such as VIX, Bond, Gold, FX, S&P500 and GSCI. More recently, Tiwari et al. (2019) have used a copula-ADCC-EGARCH model to examine the timevarying asymmetric correlation between cryptocurrencies and stock returns in the USA markets. They found that Litecoin is the most efficient hedge asset againstthe risk in the USA stock market. While for the BRICS and developed countries, Lahiani et al. (2021) investigated the dependence between cryptocurrencies and the stock market returns and found evidence for the predicting role of BSE 30 for cryptocurrencies while the Bitcoin future reshaped the tail dependence between cryptocurrencies and the stock returns. As for Mokni et al. (2020), they took into account the economic policy uncertainty and proved its negative effect on the dynamic conditional correlation between Bitcoin and the USA stock markets only after the Bitcoin crash of December 2017. However, before the crash, they documented the existence of a positive association between the economic policy uncertainty and the weight of Bitcoin in the portfolio. Furthermore, in order to classify cryptocurrencies, Ji HoKwon (2020) proved that they are an alternative for a medium of exchange and a means of investment being far from a commodity. For their part, Ahsan Bhuiyan et al. (2021) also tried to identify the interrelationship between Bitcoin and the different asset classes. In fact, they found evidence of a strong bidirectional causality between gold and Bitcoin and a neutral relationship with the aggregate commodity index, crude oil, and the US dollar index. This relative isolation of Bitcoin proves its quality as a diversifier. As for Yonghong Jiang et al. (2021), they emphasized this finding through a novel quantile coherency approach. They proved that cryptocurrencies failed to be a strong hedge or safe haven against the stock markets while they could be diversifiers especially during the March 2020 market recession. To draw generalized conclusions, Yitong Hu et al. (2020) investigated the impact of the investor’s attention allocation on the worldwide stock returns during extreme the Bitcoin movements. They found that these shock events decrease worldwide the stock returns especially in the emerging countries. Considering the COVID-19 pandemic, Caferra and Vidal-Tom as (2021) studied the behavior of cryptocurrencies and stock markets. They found that the price dynamics during the pandemic depends on the type of the market. In other words, despite the fall of both cryptocurrencies and stock indexes, cryptocurrencies promptly rebounded, while stock markets were trapped in the bear phase. In the same line of thoughts, Ghorbel and Jeribi (2021a) investigated the relationships between the volatilities of five cryptocurrencies, American indices (S&P500, Nasdaq, and VIX), oil, and gold and found that cryptocurrencies are diversifiers during the stability period but not a safe haven for US investors during the coronavirus crisis. The previous empirical works have examined the volatility connectedness or spillover effects across different financial assets, which motivated us to use a newly developed systemic framework to investigate the volatility connectedness in the cryptocurrency market, stock market and gold during the crisis period. Therefore, this study is intended to fill the gap and explicitly incorporate these issues to revisit the crypto–stock–gold timevarying relationship from a global perspective. This paper also aims at answering the following questions: If the global financial markets, the crypto-currency market and gold are directly connected with financial markets, which assets can be diversifiers for investors? Cryptocurrencies, gold and stock markets 469 3. Methodology In this section, we will present the multivariate time-series approach proposed by Diebold and Yilmaz (2009,2012,2014) to investigate the crypto–gold–stock index relationship from a global perspective. In fact, the authors proposed an analytical framework that makes it possible to produce different types of connectivity from the same method: exposure, influence or global connectivity. Moreover, they made the data of different entities interact in a VAR/ VECM model and used generalized variance decomposition as the network adjacency matrix. This matrix gives an almost complete description of a network at a given time. The authors applied this method recursively to obtain the evolution of connectivity over time, which enabled them to paint a picture of the network of the major US financial institutions from a series of financial volatilities then analyze the changes in this network as the crisis unfolds. In fact, this approach fits our topic since our objective is to study the connectedness with network among the major cryptocurrencies (Bitcoin, Dash, Ethereum, Monero, Maker, Ripple, Litecoin and Bitcoin gold), the G7 stock indexes and the gold price over the COVID-19 pandemic period. We therefore used data for eight major cryptocurrencies, seven stock indexes and gold during 2020. Like Zhang and Hamori (2021b), we used the returns measured by the changes in the daily prices. To account for interdependence in financial markets, Diebold and Yilmaz (2009) introduce a simple measure of connectedness called the multivariate time-series approach. This approach based on a vector autoregressive model (VAR) and the generalized forecasting variance decomposition method which is used to look at spillover effects in the global financial market. Due to its simplicity and flexibility, this connectedness measure has been widely applied in information spillover (Diebold and Yilmaz, 2012;Zhang and Hamori, 2021b; Ji et al., 2018). The detailed procedure is as follows. First, consider aKvariable VAR model with plagged number: yt¼Xp i¼1Φiyt−iþ ε t(1) where ytis a (K31) vector of variables at date t,Φiis autoregressive coefficient matrix and ε t is a (K31) vector of error terms that are assumed to be serially uncorrelated. Given a stationary covariance of the VAR system, a moving average representation is written as yt¼P∞ j¼0Aj ε t−j, where the n3n, coefficient matrices Aj¼Φ1Aj−1þΦ2Aj−2þ...þΦpAj−p with A0is the n3nidentity matrix and Aj¼0 for j<0. To calculate the variance contribution of variable jto variable i,θijðHÞ,Koop et al. (1996) and Pesaran and Shin (1998) proposed the following H-step-ahead generalized forecast error variance decomposition: θijðHÞ¼ σ −1 jj PH−1 h¼0e0 iAhPej2 PH−1 h¼0e0 iAhPA0 hei(2) Σis the variance matrix of the vector of errors ε , σ jj is the standard deviation of ε jand ei is a selection vector with a value of one for the i th element, and zero elsewhere. Because the row sums of the variance decomposition matrix are not necessarily equal to one, each entry in the matrix θðHÞis normalized by the row sum and hence the row sum will be equal to one. Each entry in the k3kmatrix θðHÞ¼½θijðHÞ measures the contribution of variable jto the forecast error variance of variable iat horizon H,CH i←j. Note that in general CH i←j≠CH j←i, hence, the main diagonal elements of the θðHÞmatrix represent the ownvariable contributions, while the off-diagonal elements represent the cross-variable contributions. Table 1 illustrates the various connectedness measures and their relationships. EJMBE 33,4 470 Finally, net pairwise connectedness, directional connectedness and total connectedness can be calculated using the generalized forecast error variance decomposition approach (FEVD). 3.1 Net pairwise connectedness Due to the asymmetric effect between two variables and because CH i←j≠CH j←i, we measure the net pairwise connectedness as the difference between CH i←jand CH j←i. Such difference, CH i←j−CH j←i, measures the net spillover effect from variable jto variable i. Based on net pairwise connectedness, a directional connectedness network can be built. In such network, each node represent an index, and a directional edge from jto iexists in the network if CH i←j−CH j←iis positive. 3.2 “From”and “To”, the total directional connectedness In Table 1,“From”column and “To”row measure the total directional connectedness from and to each market. Total directional connectedness “From”is defined as the information spillover from other markets to one market and this number is between 0 and 1. Whereas, total directional connectedness “To”represents the information spillover from one market to other markets, and this number is not bounded by 1. 3.3 Net total directional connectedness The difference between total directional connectedness “To”and “From”of one market measures the net information spillover contribution. 3.4 Total connectedness for the system The average of total directional connectedness “From”or “To”for all the variables measures the total connectedness of the system, which is a representative indicator of the market integration and convergence. The full-sample connectedness approach does not help us understand the connectedness dynamics, for this reason, Diebold and Yilmaz (2009) extend this measure by allowing for time-varying spillover effects. In the dynamic version of the measure, the used method Note(s): Following Diebold and Yilmaz (2009, 2014) and Zhang (2017), H is set to be 10 days Table 1. Connectedness table based on the FEVD approach Cryptocurrencies, gold and stock markets 471 remains the same, but it is applied in the overlapping sub-samples. In such case, the dynamic measure of connectedness is different from a simple average of the rolling-window measures, due to the fact that the latter is obtained from different VARs models. In the dynamic version, we will be able to analyze how individual components contribute to the system over time and how much information it gains from it. Also, the dynamic model allows us to show the timevarying connectedness in the system. In this paper, the choose of the size of the rolling window is selected based on guidelines indicating that it should not be too large or too small; otherwise, it leads to estimations bias. Therefore, we choose a rolling-window size of approximately 30% of daily observations (which equal to 135) [7]. We would note that several studies on time-varying parameter vector autoregressions (TVP-VAR) dynamic connectedness have progressively begun to appear (see, Gabauer and Gupta, 2018;Antonakakis et al., 2018,2019a,b,c;Chatziantoniou et al., 2022). Specifically, Antonakakis and Gabauer (2017) and Korobilis and Yilmaz (2018) both proved evidence of the superiority of TVP-VAR connectedness estimation. 4. Data and empirical results This section mainly presented the data and analyzes the static and dynamic spillover effect across global financial system for Gold, eight major cryptocurrencies (Bitcoin, Dash, Ethereum, Monero, Litecoin, Bitcoin Gold, Maker and Ripple) and major stocks of France, USA, Britain, Italy, Canada, Germany and Japan. We will focus on connectedness at a variety of levels, from pairwise connectedness for cryptocurrencies, stock indices and Gold to the total connectedness and from the static connectedness that measures the unconditional average of connectedness over the full sample to the dynamic that represents the conditional connectedness and its movements during a crisis period. The descriptive statistics of these return series are reported in Table 2 while the summary statistics of cryptocurrencies show that evidently, the unconditional variance of the Bitcoin is the lowest volatility, followed bythat ofRipple. This means that the Bitcoin exhibits the lowest volatility and thus remains the safest currency vis- a-vis the other studied cryptocurrencies; besides, it offers the highest average returns. Meanwhile, Bitcoin Gold has experienced the lowest return and the highest volatility. This indicates that it is the most volatile and thus, the riskiest. Amid indices, Dax30 and S&P500 offerthe highest average returns and FTSE the Variables Mean Standard deviation Min Max Skewness Kurtosis Jarque– Bera BITCOIN:BTC 0.226 4.833 49.728 20.078 2.508 29.161 19345 Dash 0.039 6.520 50.029 56.488 0.600 22.655 5028.9 ETHEREUM:ETH 0.169 5.823 57.987 21.063 2.414 25.229 2143.5 MONERO:XMR 0.090 5.591 51.954 17.630 2.192 18.888 2781 LITECOIN:LTC 0.234 5.144 14.723 29.062 0.586 4.290 4872.7 BITCOIN GOLD:BTG 0.268 6.570 54.495 71.658 1.938 46.286 1982.4 MAKER: MKR 0.101 6.552 81.821 31.419 4.233 60.814 37815.8 RIPPLE: XRP 0.106 4.995 18.813 32.182 1.123 7.492 10542.3 Gold 0.092 1.027 4.737 5.600 0.318 6.746 428.9 FTSE 0.018 1.456 11.512 8.667 1.470 14.893 18543 CAC40 0.018 1.624 13.098 8.056 1.766 14.642 14287.1 FTSEMIB 0.026 1.805 18.541 8.549 3.341 33.005 13982.7 DAX30 0.049 1.649 13.055 10.414 1.156 15.404 17251.6 NIKKEI 0.047 1.150 5.128 5.972 0.085 4.085 20465 SP/TSX 0.028 1.652 13.176 11.294 1.801 26.481 21587.1 S&P500 0.049 1.773 12.765 8.968 0.996 13.576 31578.2 Table 2. Descriptive statistics EJMBE 33,4 472 lowest return (even negative). Besides, the FTSEMIB has the highest volatility and NIKKEI the lowest one. This means that the NIKKEI exhibits the lowest volatility and thus remains the safest index while FTSEMIB is the riskiest. Moreover, compared to all cryptocurrencies and indices, gold presents the lowest volatility. It is a safe investment especially during crisis periods. Thus, we join Ghorbel and Jeribi (2021a,b) and Fakhfekh et al. (2021), who showed that gold is a safe haven during the COVID-19 pandemic period. The skewness statistics demonstrate that marginal distributions are asymmetrical to the left for Bitcoin, Ethereum, Maker, Monero and all stock indices for which the values are negative, except for the Dash, Litecoin, Bitcoin Gold, Ripple and gold. These positive values suppose that the marginal distributions are asymmetrical to the right. Then, the kurtosis statistics is used in order to test for the existence of heavy-tailed or light-tailed relative to a normal distribution. The obtained high values confirm the existence of fat tails in return distributions except for Litecoin, Ripple, gold and Nikkei with low values. Therefore, the assumption of Gaussian returns is rejected by the Jarque–Bera test for all digital and financial assets. All the cryptocurrencies and financial assets (gold and stock indices), as evidenced by the kurtosis and Jarque–Bera’s tests are far from the normal distribution. 4.1 Static analysis of connectedness network for stocks, gold and cryptocurrencies Table 3 summarizes the estimation results of the static connectedness measures for each stock, cryptocurrency and gold, issued from the TVP-VAR model to study the fear connectedness and the risk transfer. The total connectedness in this VAR system is 59.3%, which is mainly due to the close link among the major stocks and cryptocurrencies and the global financial system. This indicates how much spillover effects exist within this system and that cryptocurrencies and stocks are not independent from the global financial system. The average influence of the stock indices is approximately 82.01%, while the average influence of cryptocurrencies is approximately 45.66%. In fact, the large value of the stock indices shows that the international stock market spillovers are more important than those of the cryptocurrency market as a source of market fluctuations. For cryptocurrencies, when we consider pairwise connectedness, we notice that only the contributions of Bitcoin, Dash, Ethereum and Monero are around 36% to the global system volatility, and consequently are overtaken by the information system. On the other hand, the contributions of Litcoin, Bitcoin Gold, Marker and Ripple are more important as they exceed 75%, except for Maker (52.8%). Moreover, we found that Litecoin is the least receiver from other cryptocurrencies. On the other hand, among the stock indices, we found that their own contributions are near 20%, except for SP 500 (0.077), which is overtaken by the system with an average volatility transmission of 78.4%. However, the pairwise connectedness values show that the contributions of CAC40, FTSE, FTSE MIB, DAX30 AND SP/TSX to the studied stocks range from 13% to 24%, while the contributions of NIKKEI and S&P500 to other stocks are less than 8%. According to the pairwise connectedness analysis, Litecoin is a diversifier in a cryptos’portfolio. Then, regarding gold, it becomes visible that it is a diversifier for cryptos. Besides, for a crypto and stocks portfolio, we found that the least transmitters of volatility are Dash,as a cryptocurrency and DAX 30, as a stock index. Table 3 indicated that the French index (CAC 40) is a diversifier in a portfolio of stocks and gold. Finally, for a stock index portfolio, FTSEMIB is the least receiver and so is a diversifier in this case. It is worth noting that in general, cryptos are diversifiers for a stock index which helps reduce volatility, especially in COVID-19 crisis. These findings will be proved checked through the following volatility connectedness analysis. The net connectedness study shows that Bitcoin contributes 81.3% to the total variation in this system. However, this system contributes 63.1% of the variation in Bitcoin returns, which results in the highest positive net Cryptocurrencies, gold and stock markets 473 fluctuation in the global system. Therefore, it is a safe haven for other cryptocurrencies and especially stock indices. Turning to the stock indices, we noticed that the net transmission of FTSE, CAC40, FTSEMIB and DAX30 significantly drops after the crisis period besides, the net reception of the volatility connectedness or the spillover of Nikkei and SP 500 is marked by a significant decline. This indicates a significant change in their characteristics due to instability. As for Sp/TSX, while the net connectedness is not significant during almost the sample period, it is marked by a great shock during the crisis period as it became a net transmitter. Finally, regarding gold, we noticed that it remains a net receiver with a significant increase in net reception during the crisis period. Overall, we can conclude that connectedness is shown to be conditional on the extent of economic and financial uncertainties marked by the propagation of the corona virus. This confirms the results of the Jeribi and Fakhfekh (2020) and Jeribi et al. (2020). Such comparative studies would improve our understanding of the portfolio strategies for international investors among different assets, especially during crisis period. Next, we construct the directional connectedness network based on the net pairwise connectedness. Figure 5 displays the network plot of the full-sample static implied volatility connectedness of each cryptocurrency, stock index and gold. Each of them is set as a node and a directional edge from ito jexists only if the net pairwise connectedness from ito jis positive. Then, the nodes represent the stock index, gold and currency series included in our analysis. The dark color of each node indicates the degree of the total “Net”connectedness of Figure 5. Directional connectedness network (in) EJMBE 33,4 480 the volatility indices, i.e. the net difference of “To all others”minus “From all others.”These would help us identify the quantum and directions of shocks. Using the node dark color and area, we attempted to convey full-scale information of the system-wise connectedness dynamics of the stock indices, gold and cryptocurrencies covered in this paper. To simplify visualization and interpretation, Figure 5 is based on only the maximum net pairwise connectedness from all the other nodes to each node i. Subsequently, in Figure 5, each node-is of degree 1, which reflects only the maximum information inflow from the other nodes. Similarly, in Figure 6, each node is of out-degree 1, which reflects only the maximum information outflow from each node to the other nodes. In Figure 5, NIKKEI and S&P500 are the largest receivers among stocks from the system followed by the rest of stocks. We also noticed that the distances between CAC40, DAX 30, FTSE, FTSEMIB and SP/TSX are very short. This reflects the high pairwise correlation among this set of stock indices. Furthermore, the disposition of NIKKEI and S&P500 in the figure reflects their low correlation with others. The same conclusions could be drawn from the thickness of the arrows. Besides, amid Cryptocurrencies, we figure out that DASH, MONERO, ETHEREUM and BITCOIN are not only the greatest receivers but also closely related to others in terms of volatility connectedness. They are followed by MAKER, which is not closely related to other studied cryptocurrencies, reflecting the low pairwise connectedness with others. More interestingly, we noticed that Ripple, Bitcoin Gold and Litecoin are the least receivers and disposed away from the other cryptocurrencies. Therefore, we can detect their potential status as hedge cryptocurrencies against systemic risk. Figure 6. Directional connectedness network (out) Cryptocurrencies, gold and stock markets 481 In fact, Figure 6 displays the maximum information outflow from each node to the other nodes. Moreover, through the disposition of each node, its darkness and the thickness of arrows, we noticed that concerning the stock indices; CAC40, DAX30, FTSE, FTSEMIB and SP/TSX are the greatest transmitters as they are the greatest receivers. They are also highly correlated since they are closely disposed and the linking arrows are thick. We remarkably noticed that NIKKEI and S&P500 are the lowest transmitters among others; besides, they are graphically disposed far from the mentioned group of correlated indices. We can conclude that they are not significantly connected to the other indices. Turning to cryptocurrencies, we figure out that Ethereum and Bitcoin are the greatest transmitters followed by Monero and Dash as they significantly transmit volatility to Monero and Dash. On the other hand, Bitcoin Gold, Ripple, Maker and Lietcoin are the lowest transmitters since the nodes are clearer and smaller, indicating their low influence of volatility. Besides, they are graphically dispersed and the arrows are not thick, reflecting the low pairwise connectedness between them. Finally, we can easily notice that gold is a low transmitter as it is a low receiver. Besides, the gold node is graphically disposed away from others with thin arrows meaning that it is not significantly connected with other cryptocurrencies and stock indices which indicating it is a safe haven during the pandemic COVID-19. 5. Conclusion This study investigates the connectedness between cryptocurrencies, gold and G7 stock indices and taking into account the effect of the COVID-19 crisis. According to the pairwise connectedness analysis, Litecoin is a diversifier in a cryptos’ portfolio. When we consider gold in portfolio assets, it becomes visible that gold is a diversifier for cryptos. Besides, for a crypto and stocks portfolio, we find that the least transmitters of volatility are Dash, as crypto-currency and DAX 30, as a stock index. At last and not least, CAC 40 is a diversifier in a portfolio of stocks and gold. Finally, for a stock index portfolio, FTSEMIB is the least receiver and so, it is a diversifier in this case. It is worth noting that in general, cryptocurrencies are diversifiers for a stock index portfolio and allow volatility reduction especially in crisis period. On the other hand, dynamic connectedness results do not significantly differ from static connectedness, we just mention that Bitcoin Gold becomes a net receiver. The scope of connectedness is maintained after the shock for most of cryptocurrencies, except for Dash and Bitcoin Gold, which join previous level. For the stocks, the high spillover is detected for all the studied stocks but loses scope after the crisis period and almost returns to normal values, except for Nikkei and SP&500. However, Bitcoin has always been the greatest net transmitter of volatility connectedness or spillovers for cryptocurrencies Gold and G7 stock indices, during the COVID-19 crisis. On the other hand, Maker is the greatest net-receiver of volatility from the global system. As for gold, we noticed that it remains a net receiver with a significant increase in the net reception during the crisis period. Overall, we can conclude that connectedness is shown to be conditional on the extent of economic and financial uncertainties marked by the propagation of the corona virus. NIKKEI and S&P500 are the greatest receivers among stocks from the system followed by the rest of stocks, including CAC40, DAX30, FTSE, FTSEMIB and SP/TSX, which are the greatest transmitters as they are the greatest receivers. Therefore, this confirms the contagion of the COVID-19 crisis between G7 stock markets. In contrary, NIKKEI and S&P500 are the lowest transmitters. As a consequence, the American and Japanese stock markets are more attractive to investors since they are less exposed to the shocks of other markets. However, Bitcoin Gold and Litecoin are the least receivers from the other cryptocurrencies, leading to the conclusion that they can be diversifiers during crisis. As for EJMBE 33,4 482 Ethereum and Bitcoin, they are the greatest transmitters from other cryptocurrencies, which confirm their weight and influence on the crypto-currency market. On the other hand, gold is a low transmitter and receiver, which confirms that it is a safe haven. Therefore, the investors can diversify their portfolios in order to reduce theirs risks, by adding Bitcoin Gold and Litecoin, when investing in Gold and G7 stock markets. Notes 1. https://www.tellerreport.com 2. https://www.reuters.com 3. https://www.barrons.com 4. https://kyodonews.net 5. https://www.businessinsider.fr 6. https://cryptonaute.fr0 7. Our results are quite robust to 100 and 150 rolling-window size as well. The results are available from the corresponding author upon request. References Anand, R. and Madhogaria, S. (2012), “Is gold a ‘safe-haven’?–An econometric analysis”,Procedia Economics and Finance, Vol. 1 No. 1, pp. 24-33. Antonakakis, N. and Gabauer, D. (2017), “Refined measures of dynamic connectedness based on TVP-VAR”. Antonakakis, N., Gabauer, D., Gupta, R. and Plakandaras, V. (2018), “Dynamic connectedness of uncertainty across developed economies: a time-varying approach”. Antonakakis, N., Chatziantoniou, I. and Gabauer, D. (2019a), “Cryptocurrency market contagion: market uncertainty, market complexity, and dynamic portfolios”,Journal of International Financial Markets, Institutions and Money, Vol. 61, pp. 37-51, ISSN 1042-4431, doi: 10.1016/j. intfin.2019.02.003. Antonakakis, N., Gabauer, D. and Gupta, R. (2019b), “Greek economic policy uncertainty: does it matter for Europe? Evidence from a dynamic connectedness decomposition approach”,Physica A: Statistical Mechanics and Its Applications, Vol. 535, 122280, ISSN 0378-4371, doi: 10.1016/j. physa.2019.122280. Antonakakis, N., Gabauer, D. and Gupta, R. (2019c), “International monetary policy spillovers: evidence from a time-varying parameter vector autoregression”,International Review of Financial Analysis, Vol. 65, 101382, ISSN 1057-5219, doi: 10.1016/j.irfa.2019.101382. Arouri, M.H., Lahiani, A. and Nguyen, D.K. (2015), “World gold prices and stock returns in China: insights for hedging and diversification strategies”,Economic Modelling, Vol. 44 No. 1, pp. 273-282. Aslanidis, N., Bariviera, A.-F. and Mart ınez-Iba~ nez, O. (2019), “An analysis of cryptocurrencies conditional cross correlations”,Finance Research Letters, Vol. 31, pp. 130-137. Aydo gan, B., Vardar, G. and Taço glu, C. (2022), “Volatility spillovers among G7, E7 stock markets and cryptocurrencies”,Journal of Economic and Administrative Sciences, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/JEAS-09-2021-0190. Baum€ ohl, E., Ko cenda, E., Ly ocsa,  S. and V yrost, T. (2018), “Networks of volatility spillovers among capital markets”,Physica A, Vol. 490, pp. 1555-1574. Baur, D.G. and Lucey, B.M. (2010), “Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold”,The Financial Review, Vol. 45, pp. 217-229. Cryptocurrencies, gold and stock markets 483 Baur, D.G. and McDermott, T.K. (2010), “Is gold a safe haven? International evidence”,Journal of Banking and Finance, Vol. 34 No. 8, pp. 1886-1898. Baur, D.G., Dimpfl, T. and Kuck, K. (2018a), “Bitcoin, gold and the US dollar –a replication and extension”,Finance Research Letters, Vol. 25, pp. 103-110, ISSN 1544-6123, doi: 10.1016/j.frl. 2017.10.012. Baur, D.G., Hong, K. and Lee, A.D. (2018b), “Bitcoin: medium of exchange or speculative assets?”, Journal of International Financial Markets, Institutions and Money, Vol. 54, pp. 177-189. Bergeron, A., D ecary-H etu, D. and Giommoni, L. (2020), “Preliminary findings of the impact of COVID-19 on drugs crypto markets”,International Journal of Drug Policy, Vol. 83, 102870, ISSN 0955-3959. Bhuiyan, R.A., Husain, A. and Zhang, C. (2021), “A wavelet approach for causal relationship between bitcoin and conventional asset classes”,Resources Policy, Vol. 71, 101971, ISSN 0301-4207. Blau, B.M. (2017), “Price dynamics and speculative trading in bitcoin”,Research in International Business and Finance, Vol. 41, pp. 493-499. Bouri, E., Gupta, R. and Roubaud, D. (2018a), “Herding behaviour in cryptocurrencies”,Finance Research Letters. doi: 10.1016/j.frl.2018.07.008. Bouri, E., Lau, C.K.M., Lucey, B. and Roubaud, D. (2018b), “Trading volume and the predictability of return and volatility in the cryptocurrency market”,Finance Research Letters. doi: 10.1016/j.frl. 2018.08.015. Bouri, E., Shahzad, S.J.H. and Roubaud, D. (2018c), “Co-explosivity in the cryptocurrency market”, Finance Research Letters. doi: 10.1016/j.frl.2018.07.005. Bouri, E., Shahzad, J., Roubaud, D., Kristoufek, L. and Lucey, B. (2020), “Bitcoin, gold, and commodities as safe havens for stocks: new insight through wavelet analysis”,Quarterly Review of Economics and Finance. doi: 10.1016/j.qref.2020.03.004. Bri ere, M., Oosterlinck, K. and Szafarz, A. (2015), “Virtual currency, tangible return: portfolio diversification with bitcoin”,Journal of Asset Management, Vol. 16, pp. 365-373, doi: 10.1057/ jam.2015.5. Caferra, R. and Vidal-Tom as, D. (2021), “Who raised from the abyss? A comparison between cryptocurrency and stock market dynamics during the COVID-19 pandemic”,Finance Research Letters, Vol. 43, 101954, doi: 10.1016/j.frl.2021.101954. Charfeddine, L., Benlagha, N. and Maouchi, Y. (2020), “Investigating the dynamic relationship between cryptocurrencies and conventional assets: implications for financial investors”,Economic Model, Vol. 85, pp. 198-217. Chatziantoniou, I., Gabauer, D. and de Gracia, F.P. (2022), “Tail risk connectedness in the refined petroleum market: a first look at the impact of the COVID-19 pandemic”,Energy Economics, 106051. Chen, K. and Wang, M. (2017), “Does gold act as a hedge and a safe haven for China’s stock market?”, International Journal of Financial Studies, Vol. 5 No. 3, p. 18. Chkili, W. (2016), “Dynamic correlations and hedging effectiveness between gold and stock markets: evidence for BRICS countries”,Research in International Business and Finance, Vol. 38 No. 1, pp. 22-34. Choi, K. and Hammoudeh, S. (2010), “Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment”,Energy Policy, Vol. 38, pp. 4388-4399, doi: 10.1016/ j.enpol.2010.03.067. Ciner, C., Gurdgiev, C. and Lucey, B.M. (2013), “Hedges and safe havens: an examination of stocks, bonds, gold, oil and exchange rates”,International Review of Financial Analysis, Vol. 29, pp. 202-211. Corbet, S., Meegan, A., Larkin, C., Lucey, B. and Yarovaya, L. (2018), “Exploring the dynamic relationships between cryptocurrencies and other financial assets”,Economics Letters, Vol. 165, pp. 28-34. EJMBE 33,4 484 Creti, A., Jo€ ets, M. and Mignon, V. (2013), “On the links between stock and commodity markets’ volatility”,Energy Economics, Vol. 37, pp. 16-28, ISSN 0140-9883, doi: 10.1016/j.eneco.2013.01.005. Demir, E., Bilgin, M.H., Karabulut, G. and Doker, A.C. (2020), “The relationship between cryptocurrencies and COVID-19 pandemic”,Eurasian Economic Review, Vol. 10, pp. 349-360, doi: 10.1007/s40822-020-00154-1. Diebold, F.X. and Yılmaz, K. (2014), “On the network topology of variance decompositions: measuring the connectedness of financial firms”,Journal of Econometrics, Vol. 182 No. 1, pp. 119-134, ISSN 0304-4076, doi: 10.1016/j.jeconom.2014.04.012. Diebold, F.X. and Yilmaz, K. (2009), “Measuring financial asset return and volatility spillovers, with application to global equity markets”,Economic Journal, Vol. 119, pp. 158-171. Diebold, F.X. and Yilmaz, K. (2012), “Better to give than to receive: predictive directional measurement of volatility spillovers”,International Journal of Forecasting, Vol. 28, pp. 57-66. Dyhrberg, A.H. (2016), “Hedging capabilities of Bitcoin. Is it the virtual gold?”,Finance Research Letters, Vol. 16, pp. 139-144. Fakhfekh, M., Jeribi, A., Ghorbel, A. and Hachicha, N. (2021), “Hedging stock market prices with WTI, Gold, VIX and cryptocurrencies: a comparison between DCC, ADCC and GO-GARCH models”, International Journal of Emerging Markets.doi:10.1108/IJOEM-03-2020-0264. Feng, W., Wang, Y. and Zhang, Z. (2018), “Can cryptocurrencies be a safe haven: a tail risk perspective analysis”,Applied Economics, Vol. 50 No. 44, pp. 4745-4762. Gabauer, D. and Gupta, R. (2018), “On the transmission mechanism of country-specific and international economic uncertainty spillovers: evidence from A TVP-VAR connectedness decomposition approach”,Economics Letters, Vol. 171, pp. 63-71. Ghorbel, A. (2018), “Does gold a safe haven during the crisis period? Explanation by behavioural finance for the GCC and BRIC markets”,International Journal of Markets and Business Systems, Vol. 3 No. 4, pp. 333-361. Ghorbel, A. and Jeribi, A. (2021a), “Contagion of COVID-19 pandemic between oil and financial assets: the evidence of multivariate Markov switching GARCH models”,Journal of Investment Compliance, Vol. 22 No. 2, pp. 151-169, doi: 10.1108/JOIC-01-2021-0001. Ghorbel, A. and Jeribi, A. (2021b), “Volatility spillovers and contagion between energy sector and financial assets during COVID-19 crisis period”,Eurasian Econ Rev, Vol. 11, pp. 449-467, doi: 10. 1007/s40822-021-00181-6. Giudici, P. and Abu, H.I. (2018), “What determines bitcoin exchange prices? A network VAR approach”,Finance Research Letters, Vol. 28, doi: 10.1016/j.frl.2018.05.013. Hu, Y., Li, X. and Shen, D. (2020), “Attention allocation and international stock return comovement: evidence from the Bitcoin market”,Research in International Business and Finance, Vol. 54, 101286, ISSN 0275-5319. Jeribi, A. and Fakhfekh, M. (2020), “Determinants of G7 and Chinese stock market returns during COVID-19 outbreak”,Business Excellence and Management, pp. 256-266, S.I., doi: 10.24818/ beman/2020.S.I.1-20. Jeribi, A. and Ghorbel, A. (2021), “Forecasting developed and BRICS stock markets with cryptocurrencies and gold: generalized orthogonal generalized autoregressive conditional heteroskedasticity and generalized autoregressive score analysis”,International Journal of Emerging Markets, ahead-of-print, doi: 10.1108/IJOEM-06-2020-0688. Jeribi, A. and Kammoun Masmoudi, W. (2021), “Investigating dynamic interdependencies between traditional and digital assets during the COVID-19 outbreak: implications for G7 and Chinese financial investors”,Journal of Research in Emerging Markets, Vol. 3 No. 3, pp. 60-80, doi: 10. 30585/jrems.v3i3.689. Jeribi, A., Snene Manzli, Y. and Chamsa, D. (2020), “Emerging stock markets reaction to COVID-19: can cryptocurrencies be a safe haven”,Journal of Management and Economic Studies, Vol. 2 No. 3, pp. 152-165, doi: 10.26677/TR1010.2020.619. Cryptocurrencies, gold and stock markets 485 Jeribi, A., Jena, S.K. and Lahiani, A. (2021), “Are cryptocurrencies a backstop for the stock market in a COVID-19-led financial crisis? Evidence from the NARDL approach”,International Journal of Financial Studies, Vol. 9 No. 3, p. 33, doi: 10.3390/ijfs9030033. Ji, Q., Bouri, E. and Roubaud, D. (2018), “Dynamic network of implied volatility transmission among US equities, strategic commodities, and BRICS equities”,International Review of Financial Analysis, Vol. 57, pp. 1-12. Ji Ho, K. (2020), “Tail behavior of Bitcoin, the dollar, gold and the stock market index”,Journal of International Financial Markets, Institutions and Money, Vol. 67, 101202, ISSN 1042-4431. Jiang, Y., Lie, J., Wang, J. and Mu, J. (2021), “Revisiting the roles of cryptocurrencies in stock markets: a quantile coherency perspective”,Economic Modelling, Vol. 95, pp. 21-34, ISSN 0264-9993. Junttila, J., Pesonen, J. and Raatikainen, J. (2018), “Commodity market based hedging against stock market risk in times of financial crisis: the case of crude oil and gold”,Journal of International Financial Markets, Institutions and Money, Vol. 56, pp. 255-280, ISSN 1042-4431, doi: 10.1016/j.intfin.2018.01.002. Koop, G., Pesaran, M.H. and Potter, S. (1996), “Impulse response analysis in nonlinear multivariate models”,Journal of Econometrics, Vol. 74, pp. 119-147. Korobilis, D. and Yilmaz, K. (2018), “Measuring dynamic connectedness with large Bayesian VAR models”, available at: https://ssrn.com/abstract53099725 or http://dx.doi.org/10.2139/ssrn.3099725. Kristoufek, L. (2015), “Power-law correlations in finance-related Google searches, and their crosscorrelations with volatility and traded volume: evidence from the Dow Jones Industrial components”,Physica A: Statistical Mechanics and Its Applications, Vol. 428, pp. 194-205, ISSN 0378-4371, doi: 10.1016/j.physa.2015.02.057. Kumar Tiwari, A., Dolapo Raheem, I. and Kang, S.H. (2019), “Time-varying dynamic conditional correlation between stock and cryptocurrency markets using the copula-ADCC-EGARCH model”,Physica A: Statistical Mechanics and Its Applications, Vol. 535, 122295, ISSN 0378-4371. Kurka (2017), “Do cryptocurrencies and traditional asset classes influence each other?”,EconStor. Lahiani, A., Jeribi, A. and Jlassi, B.N. (2021), “Nonlinear tail dependence in cryptocurrency-stock market returns: the role of Bitcoin futures”,Research in International Business and Finance, Vol. 56, 101351, doi: 10.1016/j.ribaf.2020.101351. McCown, J.R. and Zimmerman, J.R. (2006), “Is gold a zero-beta asset? Analysis of the investment potential of precious metals”. Mnif, E., Jarboui, A. and Mouakhar, K. (2020), “How the cryptocurrency market has performed during COVID 19? A multifractal analysis”,Finance Research Letters, Vol. 36, 101647, ISSN 1544-6123. Mokni, K., Noomen Ajmi, A., Bouri, E. and Vinh Vo, X. (2020), “Economic policy uncertainty and the Bitcoin-US stock nexus”,Journal of Multinational Financial Management, Vols 57-58, 100656, ISSN 1042-444X. Pesaran, M.H. and Shin, Y. (1998), “Generalized impulse response analysis in linear multivariate models”,Economics Letters, Vol. 58, pp. 17-29. Salisu, A.A. and Ogbonna, A.E. (2021), “The return volatility of cryptocurrencies during the COVID-19 pandemic: assessing the news effect”,Global Finance Journal, 100641, ISSN 1044-0283. doi: 10. 1016/j.gfj.2021.100641. Selmi, R., Mensi, W., Hammoudeh, S. and Bouoiyour, J. (2018), “Is Bitcoin a hedge, a safe haven or a diversifier for oil price movements? A comparison with gold”,Energy Economics, Elsevier, Vol. 74 No. C, pp. 787-801. Shahzad, S.J.H., Bouri, E., Roubaud, D., Kristoufek, L. and Lucey, B. (2019), “Is Bitcoin a better safe-haven investment than gold and commodities?”,International Review of FinancialAnalysis, Vol. 63, pp. 322-330. Symitsi, E. and Chalvatzis, K.J. (2019), “The economic value of Bitcoin: a portfolio analysis of currencies, gold, oil and stocks”,Research in International Business and Finance, Vol. 48, pp. 97-110, ISSN 0275-5319, doi: 10.1016/j.ribaf.2018.12.001. EJMBE 33,4 486 Takyi, P.O. and Bentum-Ennin, I. (2020), “The impact of COVID-19 on stock market performance in Africa: a Bayesian structural time series approach”,Journal of Economics and Business, 105968, ISSN 0148-6195. doi: 10.1016/j.jeconbus.2020. Tiwari, A.K., Adewuyi, A.O., Albulescu, C.T. and Wohar, M.E. (2020), “Empirical evidence of extreme dependence and contagion risk between main cryptocurrencies”,The North American Journal of Economics and Finance, Vol. 51, 101083. Tiwari, A.K., Raheem, I.D. and Kang, S.H. (2019), “Time-varying dynamic conditional correlation between stock and cryptocurrency markets using the copula-ADCC-EGARCH model”,Physica A: Statistical Mechanics and its Applications, Vol. 535, 122295. Umar, Z. and Gubareva, M. (2020), “A time–frequency analysis of the impact of the Covid-19 induced panic on the volatility of currency and cryptocurrency markets”,Journal of Behavioral and Experimental Finance, Vol. 28, 100404, ISSN 2214-6350. Urquhart, A. and Zhang, H. (2019), “Is Bitcoin a hedge or safe haven for currencies? An intraday analysis”,International Review of Financial Analysis, Vol. 63, pp. 49-57. Yarovaya, L., Matkovskyy, R. and Jalan, A. (2021), “The effects of a ‘black swan’event (COVID-19) on herding behavior in cryptocurrency markets”,Journal of International Financial Markets, Institutions and Money, Vol. 75, 101321, ISSN 1042-4431. Yermack, D. (2013a), “Is bitcoin a real currency? An economic appraisal”, NBER Working Paper 19747. Yermack, D. (2013b), “Is bitcoin a real currency?”,SSRN Electronic Journal. doi: 10.2139/ssrn.2361599. Yousfi, M., Zaied, Y.B., Cheikh, N.B., Lahouel, B.B. and Bouzgarrou, H. (2021), “Effects of the COVID-19 pandemic on the US stock market and uncertainty: a comparative assessment between the first and second waves”,Technological Forecasting and Social Change, Vol. 167, 120710, ISSN 0040-1625. Zhang, W. and Hamori, S. (2021a), “Crude oil market and stock markets during the COVID-19 pandemic: evidence from the US, Japan, and Germany”,International Review of Financial Analysis, Vol. 74, 101702, ISSN 1057-5219. Zhang, Y. and Hamori, S. (2021b), “Do news sentiment and the economic uncertainty caused by public health events impact macroeconomic indicators? Evidence from a TVP-VAR decomposition approach”,The Quarterly Review of Economics and Finance, Vol. 82, pp. 145-162. Corresponding author Achraf Ghorbel can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] Cryptocurrencies, gold and stock markets 487