Spillovers between cryptocurrencies, gold and stock markets: Implication for hedging strategies and portfolio diversification under the COVID-19 pandemic

Lamine, Ahlem; Jeribi, Ahmed; Fakhfakh, Tarek Article Spillovers between cryptocurrencies, gold and stock markets: Implication for hedging strategies and portfolio diversification under the COVID-19 pandemic Journal of Economics, Finance and Administrative Science Provided in Cooperation with: Universidad ESAN, Lima Suggested Citation: Lamine, Ahlem; Jeribi, Ahmed; Fakhfakh, Tarek (2024) : Spillovers between cryptocurrencies, gold and stock markets: Implication for hedging strategies and portfolio diversification under the COVID-19 pandemic, Journal of Economics, Finance and Administrative Science, ISSN 2218-0648, Emerald Publishing Limited, Bingley, Vol. 29, Iss. 57, pp. 21-41, https://doi.org/10.1108/JEFAS-09-2021-0173 This Version is available at: https://hdl.handle.net/10419/289669 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/ Spillovers between cryptocurrencies, gold and stock markets: implication for hedging strategies and portfolio diversification under the COVID-19 pandemic Ahlem Lamine Facult e des Sciences Economiques et de Gestion de Sfax, Universit e de Sfax, Sfax, Tunisia Ahmed Jeribi Facult e des Sciences Economiques et de Gestion de Mahdia, Mahdia, Tunisia, and Tarek Fakhfakh Facult e des Sciences Economiques et de Gestion de Sfax, Universit e de Sfax, Sfax, Tunisia Abstract Purpose –This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers. Design/methodology/approach –The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation. Findings –Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors. Originality/value –The paper’s empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis. Keywords Stock markets, Gold, Cryptocurrencies, Stablecoins, Hedging, Diversification, COVID-19 crisis Paper type Research paper Cryptocurrencies 21 © Ahlem Lamine, Ahmed Jeribi and Tarek Fakhfakh. Published in Journal of Economics, Finance and Administrative Science. 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 non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe 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/2077-1886.htm Received 2 September 2021 Revised 26 December 2021 6 October 2022 Accepted 28 August 2023 Journal of Economics, Finance and Administrative Science Vol. 29 No. 57, 2024 pp. 21-41 Emerald Publishing Limited 2077-1886 DOI 10.1108/JEFAS-09-2021-0173 1. Introduction In December 2019, the COVID-19 outbreak, identified in the Chinese city of Wuhan, is quickly spread to other parts of China and around the world. As the virus news moves far beyond China’s borders, the COVID-19 pandemic has significantly impacted stock markets around the world (Li, 2021;Mensi et al., 2021;Tan et al., 2021;Ghorbel et al., 2022). The coronavirus pandemic first affected China’s stock markets, and then the remaining stock markets around the globe. The COVID-19 recession began in the world in February 2020. The date of March 9, 2020, recorded the triggering of the circuit breakers on the US stock market for the first time since 1997. The 2020 global pandemic caused an unprecedented increase in the risk of global financial markets (Zhang et al., 2020). Tan et al. (2021) indicated low risk and high returns in the US market after the crisis. However, China stock markets exhibit high risk and low return. Mensi et al. (2021) found that the 2020 global pandemic intensified spillovers from commodity markets to the US and Chinese stock markets. Risk spillover research is important for market participants in managing assets, hedging risks and enhancing investment efficiencies. Gold is widely considered, in related literature, to be a good asset for hedging risks in financial markets (Shakil et al., 2018). After periods of financial uncertainty witnessed during the last decade, investors tend to search for new investment strategies that can offer diversification and/or hedge advantages. During the global economic and financial crisis of 2008, gold prices rose dramatically, while other assets suffered losses (Beckmann et al., 2015). As was the case for commodities in the early 2000s, and due to its high expected return and low association for large financial assets, Bitcoin may be a useful device for managing portfolios (Bouri et al., 2017,2020;Guesmi et al., 2019; Fakhfekh and Jeribi, 2020;Charfeddine et al., 2020;Schinckus, 2020;Jeribi and Fakhfekh, 2021;Schinckus et al., 2021;Loukil et al., 2021). It is considered the oldest and the most famous cryptocurrency (Schinckus et al., 2020;Schinckus, 2021). During the global economic and financial crisis of 2020, financial market risks intensified, causing new challenges for financial risk managers. In order to define their portfolio strategies and hedge their risks, investors need to distinguish between three types of assets: diversifier, hedge and safe haven (Baur and Lucey, 2010). In this context, the most discussed safe-haven assets in the COVID-19 literature include gold (Belhassine and Karamti, 2021; Ghorbel and Jeribi, 2021a,b,c;Jeribi et al., 2021;Lahiani et al., 2021;Rubbaniy et al., 2021) and cryptocurrencies (Conlon and Mcgee, 2020;Mariana et al., 2020;Ghorbel and Jeribi, 2021a,b,c; Jeribi et al., 2021;Jeribi and Snene_Manzli, 2021;Guo et al., 2021;Jeribi et al., 2021). While cryptocurrencies can be considered as diversifier assets, their use as a medium of exchange is limited by their price volatility (Katsiampa, 2017;Fakhfekh and Jeribi, 2020;Jeribi and Masmoudi, 2021;Jeribi et al., 2022). Recently, Stablecoins, which are pegged to less volatile assets or currencies, have received attention from portfolio managers as well as academic researchers beyond the realm of cryptocurrency markets (Wang et al., 2020;Ante et al., 2021a, b;Hoang and Baur, 2021;Giudici et al., 2022;Grobys et al., 2021;Jalan et al., 2021; Kristoufek, 2021). Based on this crux, we attempt to examine the static and dynamic volatility spillover between US/Chinese stock market cryptocurrencies and gold with the outbreak of the Covid19 pandemic using Diebold and Yilmaz’s (2012) method. Our study contributes to the existing literature in three ways. First, we examine both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and during the COVID-19 pandemic crisis. Second, we use the digital asset and we distinct between cryptocurrencies (Bitcoin and Ether) and Stablecoins for a more detailed analysis. Third, we calculate the Diebold and Yilmaz (2012) indexes over a period ranging from August 2018 to January 2021 that covers several turbulence events, including the drop in oil prices and the COVID-19 pandemic. This methodology allows us to observe dynamic spillover during recent financial crises, to analyze the spillovers of risks JEFAS 29,57 22 over time without breaking down the study period into subsamples and to identify the receiver or transmitter of shocks. Our study helps investors to identify optimal diversification and hedging strategies during a crisis. Generally, our results show that gold, Bitcoin and Ether can be considered as safe havens during the COVID-19 crisis. The layout of this paper is as follows: Section 2 presents an overview of the literature. Section 3 gives an outline of the econometric methodology adopted. Section 4 is devoted to highlighting the relevant data. In Section 5, we report and analyze the empirical findings. Discussions and policy implications are presented in Section 6. Finally, Section 7 concludes. 2. Literature review Mensi et al. (2019) provided evidence of major volatility spillover effects between Bitcoin and precious metals. They show that Bitcoin heavily transmits net-positive spillovers to other commodities. Adebola et al. (2019) found indicated that it might be very difficult to determine the changes in the cryptocurrencies’market based on changes in the gold market and vice versa. Kang et al. (2019) observed a contagion increase during the European sovereign debt crisis. A relatively high degree of comovement between Bitcoin and gold futures prices for the period between 2012 and 2015 is indicated by wavelet coherence results. Shahzad et al. (2019) propose a new definition of a weak and strong safe haven which considers the lowest tails of both the safe-haven asset and the stock index, within a bivariate cross-quantilogram approach. Their main results show that gold, Bitcoin and the commodity index studied can be considered weak safe-haven assets. Huynh et al. (2020) investigated the spillover effects between different types of digital assets and their relationships with gold prices. Their results suggested that Bitcoin is still the most appropriate instrument for hedging, while Tether, as a cryptocurrency that has a strong anchor with the US dollar, is volatile. In the same line of results, Iqbal et al. (2021) indicated that digital assets served as an alternative investment tool in times of stress and uncertainty. Bitcoin and other cryptocurrencies performed better in comparison with other currencies. Jeribi and Fakhfekh (2021) applied the FIEGARCH-EVT-Copula and Hedge ratio analysis to assess the capabilities of cryptocurrencies to generate benefits from portfolio diversification as well as hedging strategies. They argue that the investor should hold more conventional financial assets than digital assets. Jeribi et al. (2022) studied the volatility dynamics and diversification benefits of Bitcoin under asymmetric and long memory effects. Their results indicated that the digital currency yields significant diversification benefits when being added to a well-diversified benchmark portfolio. Afast-growingbodyofresearchontheCoronavirus effects on traditional as well as digital markets has emerged. Conlon and McGee (2020) found that Bitcoin and Ether are not safe havens for the majority of international equity markets. By using several copula models, Garcia-Jorcano and Benito (2020) suggested that Bitcoin can be considered as a hedge asset against the US, European, Japanese and Chinese stock market index movements under normal market conditions. However, under extreme market conditions, Bitcoin changes to be a diversifier asset. Shahzad et al. (2020) investigated the safe haven and hedging characteristics of Bitcoin and gold for the stock markets of the G7 countries. They found that the diversification benefits offered by gold are comparatively more stable andmuchhigherthanthoseofBitcoin.Jeribi and Ghorbel (2021) used the generalized orthogonal autoregressive conditional heteroskedasticity (GO-GARCH) model to explore the hedging potential of gold and digital assets for investors in developed and BRICS countries. They found that the risks among developed stock markets can be hedged by gold and Bitcoin. This latter can be considered as the new gold for developed economies. Unlike Bitcoin, the authors provide evidence that gold can be considered as a hedge for China. In the same line of results, Ghorbel and Jeribi (2021a, b, c) indicated that Bitcoin and gold were Cryptocurrencies 23 considered hedges for the US investors before the coronavirus crisis. However, the results show that, unlike gold, digital assets are not a safe haven for US investors during the 2020 global financial crisis. Ahelegbey et al. (2021) used the extreme downside hedge and the extreme downside correlation to study the relationships among digital assets during stressful times. Their results indicated that digital assets can be clustered in two groups: speculative cryptocurrencies, which are mainly “givers”of tail contagion, such as Bitcoin, and technical cryptocurrencies, which are mainly “receivers”of contagion, such as Ether. However, Stablecoins are a world on their own. By employing the same wavelet spectrum approach, Karamti and Belhassine (2022) indicated that fear in the US market spread to all the other financial markets except for gold, SSE and cryptocurrencies, which can be diversifier assets for developing US portfolio strategies. Schinckus et al. (2021) considered anonymous cryptocurrencies like Monero, Dash and Verge as good diversifier assets, but their diversifying properties cannot be observed in decreasing markets. They also argued that Dash could be involved in the dynamics of Bitcoin and Ether the two largest pseudoanonymous cryptocurrencies (Bitcoin/Ether). Under the shadow of the 2020 pandemic disease, Elgammal et al. (2021) found unidirectional mean spillovers from energy markets to the precious metal and equity counterparts, and bidirectional return spillover effects between gold and equity markets. Using the directed acyclic graph, network topology, and spillover index, the empirical results of Guo et al. (2021) show that the contagion effect between Bitcoin and developed markets is strengthened during the 2020 crisis. The later cited authors found that Bitcoin always has a contagion effect with gold, while gold, the US dollar and the bond market are the contagion receivers of Bitcoin under the shock of the COVID-19. Their empirical results proved that Bitcoin is considered as a safe haven, hedge and diversifier asset in economic stable times but also found that the sustainability of the safe-haven property is undermined during the market turmoil. Using the methodologies of Diebold and Yilmaz (2012) and Barun ıkandK  rehl ık (2018),Nekhili et al. (2021) examined the time-frequency return and volatility spillovers between major commodity futures and currency markets. The results show that the intermediateand long-term return spillovers are dominated by short-term spillovers. Given the extreme volatility of Bitcoin, investors may rather need a safe haven against Bitcoin (Hoang and Baur, 2021). Grobys et al. (2021) concluded that the volatility of Bitcoin is a fundamental factor that drives the Stablecoins’volatilities. Using the generalized vector autoregressive framework and directed spillovers based on the forecast error variance decompositions, Kristoufek (2021) investigated the spillovers within the major cryptocurrencies and Stablecoins. He found no evidence that Stablecoins boost the prices of other cryptocurrencies. Using the DCC-GARCH and time-varying copula models, Wang et al. (2020) examined the risk-dispersion abilities of gold-pegged and USD-pegged Stablecoins against traditional digital currencies and also compared their risk-dispersion abilities with their underlying assets. Their empirical results show that Stablecoins can serve as safe havens in specific situations, but the safe-haven property of Stablecoins changes across market conditions. They also indicated that gold-pegged Stablecoins perform worse as safe havens than USD-pegged ones. Hoang and Baur (2021) found that Stablecoins are considered as safe havens against Bitcoin. Jalan et al. (2021) studied the performance of five gold-backed Stablecoins during the 2020 global pandemic and compared them to Bitcoin, Tether and gold. They found that gold-backed cryptocurrencies were susceptible to volatility transmitted from gold markets. In addition, the safe-haven potential of gold-backed cryptocurrencies is not comparable to gold. However, Wassiuzzaman and Abdul-Rahman (2021) provided evidence on the safe-haven property of gold-backed cryptocurrencies. JEFAS 29,57 24 3. Method To study the spillover between the US and Chinese stock market indices, gold, cryptocurrencies and Stablecoins, we use the econometric model presented by Diebold and Yilmaz (2012). This approach is based on N-variable vector autoregression (VAR) and generalized variance decomposition methods. The starting point for the analysis is the covariance stationary N-variable VAR (p) presented as follows: xt¼X p i¼1 wixt−iþ ε t(1) where xtis a N* 1 vector of endogenous variables wiis a N*Nmatrix of loading coefficients related to lag i, and ε tis and i.i.d process with mean 0 and covariance matrix Σ. The above VAR(p) model in Eq. (1) could be reparameterized as an infinite moving average process as follows: xt¼X ∞ i¼0 Ai ε t−i(2) where Aiis an N * N matrix coefficient matrices, which obey the recursion Ai¼Pi s¼1wi−sAs with Ai¼0 for i< 0. To explain the system dynamics, the key is the moving average coefficients or variance decompositions. The calculation of variance decomposition often proceeds via precise orthogonalization of VAR shocks. The Cholesky orthogonalization factor produces orthogonalized innovations and derives order-dependent variance decomposition. Likewise, the structural VAR model maintains assumptions from one theory or another. We apply the Generalized Forecast Error Variance Decomposition (GFEVD) approach of Koop et al. (1996) and Pesaran and Shin (1998), which accounts for correlated stocks appropriately. The generalized version of H-step variance decomposition matrices can be defined as follows: DgðHÞ¼hdg ijðHÞi;where dg ijðHÞ¼ σ −1 jj PH−1 h¼0ðe0AhPejÞ2 PH−1 h¼0e0AhPA0 hei (3) where σ jj is the standard deviation of the error term for the jth equation, and e0is the selection vector, with ith element unity and zeros otherwise. In the generalized VAR, the sum of the elements in each row of the variance decomposition matrix is not equal to one PN j¼1dg ijðHÞ≠1: Then, each element of the variance decomposition matrix is normalized as follows: e dijðHÞ¼ dg ijðHÞ PN j¼1dg ijðHÞ(4) Note that by construction, PN j¼1e dijðHÞ¼1andPN i;j¼1e dijðHÞ¼N:The matrix DgðHÞ¼½dg ij ðHÞ permits us to define total spillover index, directional and pairwise spillover indices, net directional and net pairwise spillover indices. Cryptocurrencies 25 This produces a total spillover index defined as SgðHÞ¼ PN i;j¼1 i≠je dijðHÞ N* 100 (5) This index measures the contribution of spillovers of return (volatility) shocks across selected asset classes to the total variance of forecast errors. The generalized VAR framework allows directional impacts to be inferred. Two basic variants of the measurement of gross directional impacts could be defined. First, spillover received by market i from all other markets j (All to I) by: Sg i:ðHÞ¼ PN j¼1 i≠je dijðHÞ N3100 (6) Second, spillover from market i to all other markets (ito All) by: Sg :iðHÞ¼ PN j¼1 i≠je djiðHÞ N3100 (7) Thus, the net directional impact from market I to all other markets can be measured by Sg iðHÞ¼Sg :iðHÞSg i:ðHÞ(8) The net pairwise spillovers provide information on the net transmission of shocks from market l to market j: Sg ij ðHÞ¼0 B @e djiðHÞe dijðHÞ N 1 C A3100 (9) The Diebold and Yilmaz (2012) measures are flexible (allow for quantifying both returns and volatility spillovers in markets over time) and they are dynamic. The dynamics of the spillover indices are generated by a moving window, which facilitates the study of shock transmission during and outside crisis periods. 4. Data and preliminary statistics The empirical study involves 622 daily observations of American and Chinese stock market indices, four popular cryptocurrencies (Bitcoin, Ether, Tether, and TrueUSD), and gold sampled from August 24, 2018, to January 29, 2021. We select here two major pseudoanonymous cryptocurrencies, Bitcoin and Ether (Schinckus, 2021), and two anonymous USDpegged Stablecoins namely Tether and True. Daily time series data are collected for traditional assets from DataStream. The S&P500 and SSE indices are assumed to represent traditional diversified financial portfolios for U.S and Chinese investors. Data concerning cryptocurrencies were collected from the Coin Market Cap basis. Diebold and Yilmaz (2012) indices make it possible to study return and volatility spillovers. For return spillovers, the VAR model is applied directly to the series of daily JEFAS 29,57 26 returns. The daily stock market indices and cryptocurrencies price returns are computed on a continuous basis as the difference in logarithm between two consecutive observations: rt¼lnðptÞlnðpt−1Þ(10) r t : Return of the asset for daily t; p t : Price of the asset for daily t; p t1 : Price of the asset for daily t1. Table 1 presents summary statistics of returns. All assets recorded mean positive returns during this period, whereas Tether presents the lowest risk and Ether presents the highest risk. All asset returns, except for Ether and TrueUSD, have negative skewness. All market returns have kurtosis values higher than three. In addition, the assumption of Gaussian returns is rejected by the Jarque–Bera test for all assets. During the COVID-19 pandemic period, the returns of all the assets (except for Ether and TrueUSD) showed an increase compared to the returns of the precrisis period. Returns for Tether and TrueUSD, however, fell and even became negative for Tether. In addition, all assets (except for Ether and TrueUSD) have experienced larger standard deviations during the crisis and are therefore becoming riskier. For Tether and TrueUSD, falling yields are SP500 SSE Bitcoin Ether Tether TrueUSD Gold Period: August 24, 2018 to January 29, 2021 Mean 0.000522 0.000468 0.003718 0.004542 7.65E-06 5.87E-06 0.000725 Median 0.000899 0.000440 0.001389 0.000276 0.000000 9.99E-05 0.001138 Maximum 0.093828 0.063217 0.222361 0.418981 0.021375 0.033774 0.043905 Minimum 0.119841 0.076832 0.391816 0.440031 0.025683 0.022214 0.057225 Std. dev. 0.015566 0.012259 0.045865 0.062722 0.003185 0.003960 0.009700 Skewness 0.600665 0.122950 0.680647 0.317689 0.494695 1.279497 0.534478 Kurtosis 17.04405 8.628538 14.02468 12.09609 23.72030 24.63131 7.926998 Jarque-Bera 5140.807 821.2961 3192.890 2151.314 11134.26 12276.70 657.6901 Period (before Covid-19 crisis): August 24, 2018 to Novembre 29, 2019 Mean 0.000296 0.000226 0.001432 0.000151 1.57E-05 8.55E-06 0.000616 Median 0.000509 4.00E-05 0.000297 0.001732 0.000000 0.000100 0.000645 Maximum 0.049594 0.056007 0.222361 0.346948 0.021375 0.033774 0.032974 Minimum 0.032864 0.052233 0.144450 0.200309 0.025683 0.022214 0.021156 Std. dev. 0.009601 0.011694 0.044440 0.058480 0.004292 0.005390 0.007261 Skewness 0.254844 0.173037 0.421106 0.664608 0.389235 0.970459 0.517927 Kurtosis 6.654687 6.268712 6.960277 8.087132 13.81378 13.81487 4.816010 Jarque-Bera 183.2557 145.4072 220.6240 372.0654 1581.943 1624.805 58.82483 Period (during Covid-19 crisis): Decembre 01, 2019, to January 29, 2021 Mean 0.000767 0.000730 0.006195 0.009628 1.08E-06 2.97E-06 0.000843 Median 0.001857 0.001016 0.002946 0.002772 0.000000 9.99E-05 0.001776 Maximum 0.093828 0.063217 0.157128 0.418981 0.005076 0.007978 0.043905 Minimum 0.119841 0.076832 0.391816 0.440031 0.007525 0.004904 0.057225 Std. Dev 0.020143 0.012858 0.047311 0.066744 0.001094 0.001113 0.011799 Skewness 0.577464 0.374363 1.692600 0.012895 0.351648 0.726792 0.769852 Kurtosis 12.36359 10.29505 20.42597 14.63133 13.01237 13.84644 6.856260 Jarque-Bera 1105.217 667.7485 3912.789 1679.832 1250.881 1486.996 214.0812 Table 1. Summary statistics of returns Cryptocurrencies 27 followed by falling risk. The modern portfolio theory, in which the risk-averse investors will only be willing to take on more risk in exchange for a higher return, holds true for our assets. Moreover, the volatility series are not directly observable and must be estimated. GARCH models are the most appropriate models that represent volatility in the financial markets. Table 2 summarizes the GARCH models used and Table 3 reports the results of the estimation of the GARCH models. Table 3 reports the GARCH model estimation of US and Chinese stock market indices, gold, cryptocurrencies (Bitcoin, Ether) and Stable coins (Tether, TrueUSD). 5. Results First, we present and discuss static spillover indices. Then, we examine the results of the dynamic analysis based on the rolling window spillover index estimation. Tables 4 and 5 show the static total and directional returns and volatility spillover indices for the US and Chinese stock markets, respectively. These indices give an overall average view of return and volatility shocks over the entire study period. The columns represent the US (Chinese) markets indices, Bitcoin, Ether, gold, Tether and TrueUSD. The total spillover indices appear in the first row of the table. Next, the directional spillover indices measure the effects of shocks received by market i from all other markets, the spillovers transmitted by market i to other markets, and finally the difference between these two measures. The last part of the table presents the pairwise indices. All results are based on a vector autoregressive model of order 2 and generalized variance decompositions of 30-day-ahead forecast errors. Assets Mean specification Model Volatility specification Model SP500 ARMA (1,2) yt¼a0þa1yt−1þ ε tþb1 ε t−1þb2 ε t−2TGARCH(1,1) σ 2 t¼C0þ α 1 ε 2 t−1þγ1St−1 ε 2 t−1þβ1 σ 2 t−1 SSE MA(5) yt¼a0þb5 ε t−5GARCH(1,1) σ 2 t¼C0þ α 1 ε 2 t−1þβ1 σ 2 t−1 Bitcoin MA(7) yt¼a0þb7 ε t−7GARCH(1,1) σ 2 t¼C0þ α 1 ε 2 t−1þβ1 σ 2 t−1 Ether ARMA((1,1) yt¼a0þa1yt−1þ ε tþb1 ε t−1GARCH(1,1) σ 2 t¼C0þ α 1 ε 2 t−1þβ1 σ 2 t−1 Tether ARMA(1,1) yt¼a0þa1yt−1þ ε tþb1 ε t−1GARCH(1,1) σ 2 t¼C0þ α 1 ε 2 t−1þβ1 σ 2 t−1 TrueUSD ARMA(1,1) yt¼a0þa1yt−1þ ε tþb1 ε t−1GARCH(1,1) σ 2 t¼C0þ α 1 ε 2 t−1þβ1 σ 2 t−1 Gold ARMA(1,1) yt¼a0þa1yt−1þ ε tþb1 ε t−1TGARCH(1,1) σ 2 t¼C0þ α 1 ε 2 t−1þγ1St−1 ε 2 t−1þβ1 σ 2 t−1 SP500 SSE Bitcoin Ether Tether TrueUSD Gold Mean equation a 0 0.0007 ** 0.0005 0.0032 ** 0.0047 *** 2.04 10–6 *** 3.27 10–7 0.0007 *** a 1 0.0837 * ––0.9917 *** 0.7296 *** 0.6549 *** 0.9958 *** b 1 –––0.9950 *** 0.9821 *** 0.9816 *** 0.9959*** b 2 0.09*** –––––– b 5 –0.07634 ** ––––– b 7 ––0.07596 * –––– Conditional variance equation C 0 4.73 10–6 *** 7.98 10–6*** 0.0003 *** 0.0004 *** 9.74 10–9 1.48 10–8 * 2.18 10–6*** α 1 0.1561*** 0.1055*** 0.1797*** 0.1214*** 0.2249*** 0.3151*** 0.1204*** γ 1 0.2356*** ––– – 0.1552*** β 1 0.7378*** 0.8442*** 0.6850*** 0.8022*** 0.7705*** 0.6807*** 0.8766*** Note(s): ***, ** and * denote significance at 1, 5 and 10% level, respectively Table 2. Summary of the selected models Table 3. Estimate parameters of GARCH models JEFAS 29,57 28 consistent with Wang et al. (2020) and Schinckus et al. (2021). We suggest that investors use Stablecoins with caution to avoid an extremely negative effect on their portfolios. Stablecoins appear to be different investment products than pseudo-anonymous cryptocurrencies (Bitcoin, Ether). 6.2 Policy/managerial implications The empirical findings of this study provide insightful information for portfolio managers and investors. For instance, portfolio managers can use suitable tools to account for the risk spillover between digital and traditional asset returns in order to adapt their hedging strategy to the shock risk size and maturity. Also, US and Chinese investors may consider gold and cryptocurrencies (Bitcoin, Ether) as alternative assets from a portfolio management perspective. Such assets offer hedging and diversification benefits for the US and Chinese investors. Stablecoins appear to be different from traditional cryptocurrencies. Tether and TrueUSD are far from being safe havens during the COVID-19 crisis and do not offer diversification benefits for American and Chinese investors. In addition, speculators may opt for a spread strategy to improve their portfolio returns in both traditional and digital markets. Figure 4. Dynamic spillover between Bitcoin/Ether and gold Cryptocurrencies 35 Figure 5. Dynamic spillover between US/Chinese stock markets and Stablecoins JEFAS 29,57 36 6.3 Future research agenda Future research can use other methodologies such as the quantile connectedness approach as well as bivariate VAR for each pair or multivariate VAR for both traditional and digital markets to study the effect of the 2022 Russian invasion of Ukraine on the behavior of traditional and digital markets. As well, given that NFTs and DeFi have received growing attention, strategic asset allocation in NFT and DeFi markets can be studied and the role of traditional and digital safe havens and hedges during war crisis times. In addition, Latin America has seen impressive levels of cryptocurrency adoption over the last few years. We can study linkages between digital assets, especially Stablecoins and Latin American equity markets. These linkages help American Latin investors to determine whether cryptocurrencies can reduce the equity risk during crisis periods. 7. Conclusion This paper investigates the dynamic linkages between stock markets, cryptocurrencies and gold. It attempts to provide suggestions for portfolio risk management. We analyze volatility return and risk spillover effects between these markets using the Diebold and Yilmaz (2012) spillover index. Our empirical results show statistically significant risk spillovers among financial markets, which intensified during the recent COVID-19 crisis. The Diebold and Yilmaz (2012) indices show that Bitcoin, Ether and gold are net receivers of return and volatility shocks. In the context of portfolio management analysis, the results show that a mix of cryptocurrencies (Bitcoin, Ether), gold and equities could offer diversification opportunities for the Americans and Chinese during the COVID-19 pandemic crisis. Moreover, we show that, in periods of turbulence, both American and Chinese investors turn to the Bitcoin, Ether and gold markets to minimize the impact of the crisis on their portfolios and therefore their wealth. Indeed, we find that the introduction of gold and cryptocurrencies (Bitcoin, Ether) can improve the performance of the US and Chinese traditional diversified financial portfolios. 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(2021), “Performance of gold-backed cryptocurrencies during the COVID-19 crisis”,Finance Research Letters, Vol. 43, 101958. Yousaf, I., Hanif, H., Ali, S. and Moudud-Ul-Huq, S. (2021), “Linkages between gold and Latin American equity markets: portfolio implications”,Journal of Economics, Finance and Administrative Science, Vol. 26 No. 52, pp. 237-251. Zhang, D., Hu, M. and Ji, Q. (2020), “Financial markets under the global pandemic of COVID-19”, Finance Research Letters, Vol. 36, 101528. About the authors Ahlem Lamine holds a PhD in Finance from the University of Sfax (Tunisia), Faculty of Economics and Management, teaching Finance, and Portfolio management. She is a member of the LABORATORY research “Probability and Statistics”specializing in financial modeling. His research areas include portfolio selection, international diversification, neural networks, and Stochastic Dominance. Ahmed Jeribi is an associate Professor of Finance at the University of Monastir (Tunisia), Faculty of Economics and Management, teaching Finance, His research areas include Cryptocurrency, and Risk Management. Tarek Fakhfakh is an associate Professor of Finance at the University of Sfax (Tunisia), , Faculty of Economics and Management, teaching Finance, Financial market, Portfolio management, Derivatives, Asset Pricing and Financial Econometrics tools. He is a member of the LABORATORY research “Probability and Statistics”specializing in financial modeling. His research areas include Financial Economics, Portfolios selection, Derivatives, Asset Pricing, Risk Management, Risky debt pricing, and Numerical Method in Finance. He has published various papers in the Group for Research in Decision Analysis (GERAD) report and in referred international journals, including Economic Modeling and the European Journal of Operational Research. Tarek Fakhfakh is the corresponding author and 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 41