Has COVID-19 changed the correlation between cryptocurrencies and stock markets? © The Author(s) 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License Vol. 12, No. 2 (2023), pages 139-156 https://doi.org/10.17979/ejge.2023.12.2.9960 Submitted: Oct 5, 2023 Accepted: Nov 14, 2023 Published: Dec 5, 2023 Article Has COVID-19 changed the correlation between cryptocurrencies and stock markets? Ines Abdelkafi,1,* Youssra Ben Romdhane,2 Sahar Loukil 3 1 URAMEF, ESC, University of Sfax, Tunisia 2 LED, FSEG, University of Sfax, Tunisia 3 ARTIGE, FSEG, University of Sfax, Tunisia *Correspondence: ines.abdelkafi@escs.usf.tn Abstract. The COVID-19 pandemic has challenged the notion that cryptocurrencies are uncorrelated with traditional asset markets. This study uses VAR-OLS techniques to investigate the time-varying correlation between Bitcoin and three major European stock market indices from January 4, 2016, to February 26, 2021. Our results show that cryptocurrencies and stock markets are dependent during crisis periods, but not during non-crisis periods. This confirms the time-varying correlation between cryptocurrencies and stock markets, which depends on the extent and persistence of responses to own and cross shocks. To improve the robustness of our results, we also test the impact of government measures on Bitcoin and stock market indices, and find that they are both affected by these measures. Our study adds to the literature by examining the impacts of pandemics on the correlations between Bitcoin returns and the stock market, oil, and gold index returns, which have so far been unaddressed. Keywords: COVID-19; financial markets; Bitcoin; stock indices JEL classification:I1; D53; G15; G12; C22 1. Introduction The concept of safe haven value for investment is driven by investor loss aversion (Tversky & Kahneman, 1991), where investors are more concerned about avoiding losses than the associated potential gains (Hwang & Satchell, 2010). This loss aversion encourages investors to seek safe haven assets, i.e. assets that are not correlated or correlated negatively with traditional assets in times of market turbulence (Baur & Lucey, 2010). Various safe haven assets have been established at short to medium horizons, including gold (Bredin et al., 2015), currency (Ranaldo & Söderlind, 2010), long- term treasury bills (Flavin et al., 2014) and, more recently, cryptocurrencies. The role of alternative investments in improving the returns of traditional equity bond portfolios has long been the subject of academic research. Given the shortage of “traditional” alternative investment classes, the role of the increasingly important cryptocurrency markets becomes relevant. Cryptocurrency is a digital or https://creativecommons.org/licenses/by-nc/4.0/ 140 Abdelkafi et al. virtual currency that is exchanged between peers without the need for a third party. Since the introduction of Bitcoin in 2008, academic research has highlighted the low correlation between Bitcoin and traditional financial markets (Baur et al., 2018; Corbet et al., 2018). This correlation, however, became stronger after the introduction of futures contracts on Bitcoin in December 2017 (Matkovskyy & Jalan, 2019; Sami & Abdallah, 2021). This discovery led to a more in- depth investigation into the hedging and diversification properties of Bitcoin compared to traditional financial assets (Urquhart & Zhang, 2019; Guesmi et al., 2018; Bouri et al., 2017; Cretarola et al., 2021). Other authors suggest that the assets become more correlated during economic downturns. Thus, many studies have explored the response of cryptocurrency markets to the COVID-19 pandemic as well as changes in interactions between cryptocurrencies and other traditional asset classes (Lahmiri & Bekiros, 2020; Conlon & McGee, 2020; Mnif et al., 2020; Ali et al., 2020; Goodell & Goutte, 2021; Ji et al., 2020). Indeed, the COVID-19 global health crisis has the potential to slow down the global economy and increase the level of volatility in financial markets. Baker et al. (2020) have explained the stronger impact of the COVID-19 pandemic on equity markets than previous outbreaks of infectious diseases. This crisis is an opportunity to study much more about the evolution of the crypto-currency market as well as the interdependencies between the crypto-currency market and the traditional asset classes. The objective of this article is to examine the correlation between crypto-currencies and stock markets. We study the impact of the introduction of Bitcoin into investors' portfolios on their efficiency in the context of the COVID-19 pandemic. The remainder of this document is organized as follows. Section 2 presents the literature review. Section 3 describes the methodology. Section 4 examines the empirical results. Section 5 is devoted to discussion and the final section presents the main conclusions. 2. Literature review The usefulness of alternative investments is confirmed in the literature which documents that a planned asset allocation between various super form asset classes systematically involves both a strategic commodity portfolio and a full equity portfolio (Conover et al., 2010; Ciner et al., 2013; Li & Lucey, 2017; Gao & Nardari, 2018) recent studies suggest that there now appears to be a shortage of alternative assets that can be used to reduce the risk of declining equity investments (Bouri et al., 2019; Shahzad et al., 2019). Regarding portfolio diversification with cryptocurrencies, Chen and Vivek (2014) show that Bitcoin can play an important role in improving the efficiency of the portfolio. Based on an analysis of traditional assets and alternative investments, Bouri et al. (2017) show that investing in bitcoin offers significant diversification benefits. Bouri et al. (2019) document that hedging stocks with cryptocurrencies is beneficial. Inci and Lagasse (2019) explain the role of crypto-currencies in improving investment portfolios. They found that private and listed companies recorded huge profits in these assets. They confirmed that ownership of crypto-currencies varies between companies, ranging from investment objectives to supporting future plans to accept digital currencies as a means of payment for goods and services. Kajtazi and Moro (2019) demonstrate that the addition of Bitcoin, Has COVID-19 changed the correlation between cryptocurrencies and stock markets? despite its speculative characteristics, results in an improvement in the performance of asset portfolios in the US, China and Europe. Chan et al. (2019) suggested strong Bitcoin hedging properties against five international stock indexes, including the S&P 500. Urquhart and Zhang (2019) investigate the hedging, diversification and value-safe-haven properties of Bitcoin against global currency fluctuations. Their results support the assumption that alternative investments, in this case cryptocurrencies, add value by improving the performance of traditional financial assets. Platanakis et al. (2019) suggest that investors should include Bitcoin in their portfolios as it generates significantly higher risk-adjusted returns. In line with this thinking, Frankovic et al. (2021) analysed the relationship between the share prices of Australian companies holding crypto-currencies and their prices. They found that these companies adopt positions that are sensitive to fluctuations in crypto-currency prices. Similarly, Xu et al. (2022) examined the strategic reasons for crypto-currency integration by corporate treasury departments and explored the risk-return outcomes of these decisions and strategies. As a result, they confirmed in their study that the emergence of jumps in crypto-currencies increases the probability of jumps in the returns of said US companies. Empirical studies have also emerged to explore the response of cryptocurrency markets to the COVID-19 pandemic as well as changes in interactions between cryptocurrencies and other traditional assets. In the US context, Sharif et al. (2020) examined the reaction of crypto-currencies during the period of the health crisis. They found that the correlation between price volatility shocks on economic policy uncertainty and the oil market is also related to the spread of the COVID-19 pandemic in the US. They confirmed that the health crisis is a geopolitical threat. These results are confirmed by the study by Umar and Gubareva (2020). Al-Awadhi et al. (2020) found evidence of a significant negative impact of COVID-19 on the equity returns of all companies included in the Hang Seng Index and the Shanghai Composite Index. These results are confirmed with studies by Sharif et al. (2020) and Zhang et al. (2020). Lahmiri and Bekiros (2020) suggest that the cryptocurrency market has been relatively more volatile than international equity markets during the COVID-19 pandemic. The empirical results show the variable relationship over time between the cryptocurrency market and the US stock market or the price of the gold market. Recent data has shown that there has been a positive relationship that has varied over time between these two markets since COVID-19. Mishra et al. (2022) demonstrated a reduction in market linkage during a recession as compared to expansion. Hung (2019) suggested that portfolio managers need to adjust their asset allocations in times of turbulence or crisis when asset volatility shifts from one market to another. In this sense, Kim et al. (2020) examined the relationship of major financial assets, Bitcoin, Gold and S&P 500 with GARCH models. They show the relationship of Bitcoin with Gold and S&P 500. They also analyzed the relationships between the conditional correlation varying over time with the volatility of Bitcoin and the volatility of the S&P 500 by a marginal regression of the Gaussian copula (GCMR). The empirical results show that the S&P 500 and gold prices are statistically significant for Bitcoin in terms of log-back and volatility. 142 Abdelkafi et al. 3. Methods We examine the nexus between cryptocurrencies and stock markets before and after the COVID-19 pandemic. Our study retrieved global daily-frequency data from 4 January 2016 to 26 February 2021. We divide our sample to before COVID-19 and COVID-19 period around December 31, 2019 which corresponds to the date the first cases were discovered by Chinese authorities. Our choice is justified since investors are more sensitive to negative information. Data on cryptocurrencies and stock markets were collected respectively from Coinbase, retrieved from Federal Reserve Economic database, and the data of oil and gold price are extracted from dtastream. The data of governmental measures are extracted from Oxford Data base. These governmental measures are used herein as a proxy for reducing actions of global pandemic uncertainty. Moreover, we calculate the continuous compounded daily returns for all the series as 100 × ln ( Pt Pt−1 ), where Pt represents the daily closing price of each asset. Descriptive statistics results clearly show that during the COVID-19 outbreak, all indices return downturn dramatically. The mean returns of all stock market indices are very small, among which DAX30 is positive, while CAC40 and FTSEMIB turn out to be negative with a significant increase in volatility. As expected, the Bitcoin has high mean returns and standard deviations (Std. Dev.) This result suggests its potential hedge role to investors (Kajtazi & Moro, 2019; Kim et al., 2020). Besides, we can easily notice the free fall in oil price return and the remarkable increase in volatility. Finally, according to the results, gold’s return slightly decreased in crisis period amid a slight decrease in volatility. Thus, we confirm the previous findings of Bouri et al. (2020), who suggests the hedging role of gold, especially in mitigated periods. For government measures, we can easily notice that all countries are seriously implementing controlling the disease with a high index (at least 79%) and that the most applied measures are School closure, Workplace closures, Cancel public events, Testing policy and Contract tracing. On the other hand, Stay at home, and Restrictions on internal movement are comparatively less applied. This result could be explained by the difficulty of forcing people to stay at home and outlaw movements since it is socially difficult to accept and harmful for economic activities. The first confinement is marked by a drop in wages. France has a drop in wages of the poorest among the lowest in Europe. Its decline is lower than in Germany. This impact is particularly present in local businesses that suffer from this effect: confinement causes a considerable decrease in the number of people who use the shops (restaurants, small markets, bakeries, tourism, etc.). Table 1. Descriptive statistics for Bitcoin and Stock indices before and during COVID-19 Source: Authors calculations Before COVID-19 During COVID-19 Variable Obs Mean Std. Dev. Min Max Obs Mean Std. Dev. Min Max lnbitcoin 1028 0.0027301 0.0462737 -0.247405 0.221747 297 0.0062591 0.0520257 -0.497278 0.193671 dax30 1028 1.000292 0.0097051 0.931767 1.03506 297 0.0001463 0.0195434 -0.130549 0.104143 lncac40 1028 0.0002763 0.0095263 -0.083844 0.040604 297 -0.0001585 0.0193948 -0.130983 0.080561 lnftsemib 1028 0.0001299 0.0128752 -0.133314 0.049111 297 -0.000109 0.0212854 -0.185411 0.085495 Lngold 1028 0.0005163 0.02189 -0.082336 0.136944 297 0.0004485 0.0121427 -0.058928 0.042968 Lnwti 1028 1.000376 0.007489 0.968084 1.04022 297 0.0000159 0.064684 -0.601676 0.319634 Has COVID-19 changed the correlation between cryptocurrencies and stock markets? Table 2. Variables measurement and descriptive statistics for governmental responses Germany France Italy Variable Obs Mean Std. Dev. Min Max Mean Std. Dev. Min Max Mean Std. Dev. Min Max lnschoolcl~g 294 .811 .404 0 2 .791 .412 0 2 .817 .386 0 1 lnworkplace 294 .828 .381 0 1.5 .846 .374 0 2 .898 .327 0 2 lncancelev~s 294 .874 .342 0 2 .874 .350 0 2 .891 .312 0 1 lnstayhome 294 .495 .520 0 2 .573 .501 0 2 .686 .473 0 2 lnmouvements 294 .760 .450 0 2 .597 .504 0 2 .566 .509 0 2 lntestingp~s 294 .950 .259 0 3 .956 .226 0 2 .935 .260 0 2 lncontactt~g 294 .962 .203 0 2 .954 .223 0 2 .932 .252 0 1 Δcumcases 294 .0861 .322 0 4 .099 .410 0 5 .086 .259 0 2.575 Source: Authors calculations According to INSEE, “73% of companies report a decrease in their sales of more than 10%. and 35%, a decrease of more than 50% during the trigger period.” To get more detailed results, we present below the time series of each variable in Figure 1. FTSEMIB DAX30 BITCOIN CAC40 WTI Figure 1. CAC40, DAX30, WTi and Bitcoin return over the whole period. Source: Own elaboration. 144 Abdelkafi et al. The trends of CAC 40, DAX30 and FTSE MIB stock market indices and prices of Bitcoin, Oil and Gold are similarly affected by COVID-19 in the first quarter of 2020 with clearly persistent high volatility for stock indices, gold and Bitcoin, while oil return almost rejoin it trend as before COVID-19 period. Concretely, during the outbreak of COVID-19 from 1rst January 2020 to 17 September 2020, the prices of cryptocurrencies and the stock indices of the world fell sharply first and then rose moderately. All series show a downward trend. 4. Results 4.1 Pearson matrix Table 3 presents the Pearson’s correlations between Bitcoin return, stock market, oil and gold indices returns. Table 3. Pearson matrix Before COVID-19 During COVID-19 lnbitc~n dax30 lncac40 lnftse~b gold lnwti lnbitc~n dax30 lncac40 lnftse~b gold lnwti lnbitcoin 1 1 dax30 0.004 1 0.390* 1 0.903 0.000 lncac40 -0.003 0.917* 1 0.384* 0.957* 1 0.931 0 0 0 lnftsemib -0.008 0.792* 0.829* 1 0.491* 0.908* 0.914* 1 0.795 0 0 0 0 0 gold 0.049 -0.268* -0.272* -0.240* 1 0.262* 0.163* 0.108 0.117* 1 0.118 0 0 0 0 0.005 0.062 0.043 lnwti -0.011 0.254* 0.302* 0.311* -0.034 1 0.161* 0.285* 0.292* 0.283* 0.074 1 0.730 0 0 0 0.264 0.005 0 0 0 0.202 Note: * p-value <0.05. Source: Authors calculations. Before COVID-19, it can be easily observed that in addition to the weak and positive correlations between the stock market returns of the three countries and Bitcoin, there is a positive correlation between Bitcoin and gold, proving their similarity. Therefore, we employ the VAR approach to study the heterogeneous relationships between these variables. Next, we check the robustness of our results and test whether bitcoin and stock market indices are similarly affected by government actions using a GLS regression. 4.1 VAR model We intend to figure out the existence of a potential link between cryptocurrencies and stock returns before and during COVID-19 period through VAR model. The lagged Bitcoin returns, stock returns, and oil and gold are used as exogenous factors to detect the mean spillover effect within the studied indices. The stationary tests of ADF and PP with the null hypothesis of having a unit root confirm that all indices are stationary at first level as indicated in Table 4. The lags orders are selected according to FPE, AIC, HQIC and SBIC criteria allowed in Tables 5, 6. and 7. Has COVID-19 changed the correlation between cryptocurrencies and stock markets? Table 4. Stationary tests Before COVID-19 During COVID-19 Stationary level I(0) Stationary at first difference I(1) Stationary level I(0) Stationary at first difference I(1) ADF test PP test ADF test PP test ADF test PP test ADF test PP test Case 1: Model without trend Case 1: Model without trend Case 1:Model without trend Case 1: Model without trend FTSE MIB -1.934 -1.934 -13.942 *** -29.949*** -2.08 -2.08 -13.941*** -29.949*** DAX30 -1.720 -1.720 -14.777*** -27.550*** -2.08 -2.09 -14.765*** -27.536*** CAC40 -1.518 -1.518 -14.625*** -25.483*** -1.67 -1.67 -25.489*** -25.489*** Bitcoin 2.990 2.990 -15.949*** -29.233*** 2.20 2.20 -29.429*** -29.429*** gold -1.698 -1.698 -30.922*** -30.922*** -1.86 -1.86 -30.957*** -30.957*** wti -2.851 -2.851 -29.718*** -29.718*** -2.84 -2.84 -29.712*** -29.712*** Note: *** statistical significance at 1%. Source: Authors calculations Table 5. Selection-order criteria before and after COVID-19 for Italy Selection-order criteria before COVID lag LL LR df p FPE AIC HQIC SBIC 0 2049.12 8.2e-15* -21.0837* -21.0564* -21.0163* 1 2063.69 29.157 16 0.023 8.3e-15 -21.069 -20.9326 -20.7321 2 2080.01 32.637 16 0.008 8.3e-15 -21.0723 -20.8267 -20.4659 3 2091.51 22.995 16 0.114 8.7e-15 -21.0259 -20.6712 -20.15 4 2109.72 36.422* 16 0.003 8.5e-15 -21.0487 -20.5849 -19.9032 Selection-order criteria during COVID-19 lag LL LR df p FPE AIC HQIC SBIC 0 535.012 4.8e-14 -19.3095 -19.2531 -19.1635* 1 563.308 56.592 16 0.000 3.1e-14 -19.7566 -19.4744* -19.0267 2 574.879 23.143 16 0.110 3.7e-14 -19.5956 -19.0875 -18.2817 3 602.427 55.095 16 0.000 2.5e-14 -20.0155 -19.2816 -18.1177 4 619.753 34.652* 16 0.004 2.5e-14* -20.0637* -19.104 -17.5819 Source: Authors calculations Table 6. Selection-order criteria before and after COVID-19 for France Selection-order criteria before COVID lag LL LR df p FPE AIC HQIC SBIC 0 2113.24 4.2e-15* -21.7447* -21.7175* -21.6774* 1 2123.72 20.954 16 0.180 4.5e-15 -21.6878 -21.5514 -21.3509 2 2138.02 28.605 16 0.027 4.6e-15 -21.6703 -21.4248 -21.0639 3 2147.04 18.045 16 0.321 4.9e-15 -21.5984 -21.2437 -20.7225 4 2163.06 32.037* 16 0.010 4.9e-15 -21.5986 -21.1347 -20.4531 Selection-order criteria during COVID-19 lag LL LR df p FPE AIC HQIC SBIC 0 529.101 6.0e-14 -19.0946 -19.0381 -18.9486* 1 554.908 51.615 16 0.000 4.2e-14 -19.4512 -19.1689* -18.7213 2 570.998 32.18 16 0.009 4.2e-14 -19.4545 -18.9464 -18.1406 3 596.131 50.266 16 0.000 3.1e-14* -19.7866 -19.0527 -17.8887 4 612.474 32.686* 16 0.008 3.2e-14 -19.799* -18.8393 -17.3173 Source: Authors calculations 146 Abdelkafi et al. Table 7. Selection-order criteria before and after COVID-19 for Germany Selection-order criteria before COVID-19 lag LL LR df p FPE AIC HQIC SBIC 0 2120.86 3.9e-15* -21.8233* -21.796* -21.7559* 1 2131.18 20.634 16 0.193 4.1e-15 -21.7647 -21.6283 -21.4278 2 2145.85 29.346 16 0.022 4.2e-15 -21.751 -21.5055 -21.1446 3 2154.89 18.073 16 0.320 4.5e-15 -21.6792 -21.3245 -20.8033 4 2170.5 31.24* 16 0.013 4.5e-15 -21.6753 -21.2115 -20.5299 Selection-order criteria during COVID-19 lag LL LR df p FPE AIC HQIC SBIC 0 535.6 4.7e-14 -19.3309 -19.2744 -19.1849* 1 560.191 49.183 16 0.000 3.5e-14 -19.6433 -19.361* -18.9134 2 574.783 29.184 16 0.023 3.7e-14 -19.5921 -19.084 -18.2782 3 598.892 48.218 16 0.000 2.8e-14* -19.887* -19.1531 -17.9891 4 612.624 27.465* 16 0.037 3.2e-14 -19.8045 -18.8448 -17.3227 Source: Authors calculations Then, the Granger causality and VAR results quality are validated in tables 10, 11 and 12 for Italy, France and Germany, respectively before and after the COVID-19 pandemic crisis. It is confirmed that all the eigenvalues lie inside the unit circle. Consequently, VAR satisfies stability condition for France, Italy and Germany before and during COVID-19. After running the VAR, we support our findings by implementing the impulse function in figure 2. Table 8. Granger causality for Italy Italy Before Covid After Covid Equation Excluded Prob> chi2 Prob> chi2 dLnΔFTSEMIB dlnΔBitcoin 0.864 0.018** dLnΔFTSEMIB dlnΔgold 0.265 0.587 dLnΔFTSEMIB dllnΔwti 0.402 0.009** dLnΔFTSEMIB ALL 0.596 0.003** dlnΔBitcoin dLnΔFTSEMIB 0.944 0.000*** dlnΔBitcoin dlnΔgold 0.24 0.095* dlnΔBitcoin dllnΔwti 0.093* 0.007** dlnΔBitcoin ALL 0.192 0.000*** dlnΔgold dLnΔFTSEMIB 0.72 0.002** dlnΔgold dlnΔBitcoin 0.81 0.033** dlnΔgold dllnΔwti 0.048** 0.013** dlnΔgold ALL 0.193 0.005** dllnΔwti dLnΔFTSEMIB 0.055* 0.080* Source: Authors calculations Has COVID-19 changed the correlation between cryptocurrencies and stock markets? Table 9. Granger causality for France France Before Covid After Covid Equation Excluded Prob> chi2 Prob> chi2 dLnΔCAC40 dlnΔBitcoin 0.657 0.033 dLnΔCAC40 dlnΔgold 0.378 0.287 dLnΔCAC40 dllnΔwti 0.285 0.018 dLnΔCAC40 ALL 0.57 0.005 dlnΔBitcoin dLnΔCAC40 0.36 0 dlnΔBitcoin dlnΔgold 0.175 0.083 dlnΔBitcoin dllnΔwti 0.061 0.006 dlnΔBitcoin ALL 0.134 0 dlnΔgold dLnΔCAC40 0.961 0.013 dlnΔgold dlnΔBitcoin 0.818 0.09 dlnΔgold dllnΔwti 0.039 0.014 dlnΔgold ALL 0.204 0.021 dllnΔwti dLnΔCAC40 0.302 0.002 dllnΔwti dlnΔBitcoin 0.747 0.248 dllnΔwti dlnΔgold 0.02 0.244 dllnΔwti ALL 0.042 0.013 Source: Authors calculations Table 10. Granger causality for Germany Germany Before Covid After Covid Equation Excluded Prob> chi2 dLnΔDAX30 dlnΔBitcoin 0.824 0.078* dLnΔDAX30 dlnΔgold 0.054* 0.484 dLnΔDAX30 dllnΔwti 0.098* 0.012** dLnΔDAX30 ALL 0.102 0.012** dlnΔBitcoin dLnΔDAX30 0.984 0.000*** dlnΔBitcoin dlnΔgold 0.251 0.149 dlnΔBitcoin dllnΔwti 0.093 0.010*** dlnΔBitcoin ALL 0.192 0.000*** dlnΔgold dLnΔDAX30 0.648 0.001** dlnΔgold dlnΔBitcoin 0.796 0.043** dlnΔgold dllnΔwti 0.045 0.008** dlnΔgold ALL 0.187 0.003** dllnΔwti dLnΔDAX30 0.462 0.014** dllnΔwti dlnΔBitcoin 0.737 0.329 dllnΔwti dlnΔgold 0.017 0.34 dllnΔwti ALL 0.054 0.066* Notes: *p< 0.1, ** p < 0.05, *** p < 0.01 Source: Authors calculations Table 11. Stability test for Italy Eigenvalue Modulus Be fo re C O VI D .603695 + .03151711i .604517 .603695 - .03151711i .604517 .4107565 + .01133293i .410913 .4107565 - .01133293i .410913 All the eigenvalues lie inside the unit circle. VAR satisfies stability Af te r CO VI D .8291133 .829113 .4813131 + .07542815i .487188 .4813131 - .07542815i .487188 .4038571 .403857 All the eigenvalues lie inside the unit circle. VAR satisfies stability Source: Authors calculations -1 -.5 0 .5 1 Im ag in ar y -1 -.5 0 .5 1 Real Roots of the companion matrix -1 -.5 0 .5 1 Im ag in ar y 1 5 0 5 1 Roots of the companion matrix 148 Abdelkafi et al. Table 12. Stability test for France Eigenvalue Modulus Be fo re C O VI D .5998576 .599858 .5304404 .53044 .4077736 + .01516735i .408056 .4077736 - .01516735i .408056 All the eigenvalues lie inside the unit circle. VAR satisfies stability Af te r CO VI D .7005232 .700523 .5066378 + .09266491i .515042 .5066378 - .09266491i .515042 .3528257 .352826 All the eigenvalues lie inside the unit circle. VAR satisfies stability diti Source: Authors calculations Table 13. Stability test for Germany Eigenvalue Modulus Be fo re C O VI D -.118457 0 -.0539891 0 .0078151 .028414 .0078151 -.028414 All the eigenvalues lie inside the unit circle. VAR satisfies stability Af te r CO VI D .0033594 .1356476 .0033594 -.1356476 -.1243459 0 .0044669 0 All the eigenvalues lie inside the unit circle.VAR satisfies stability condition. Source: Authors calculations -1 -.5 0 .5 1 Im ag in ar y -1 -.5 0 .5 1 Real Roots of the companion matrix -1 -.5 0 .5 1 Im ag in ar y 1 5 0 5 1 Roots of the companion matrix -1 -.5 0 .5 1 Im ag in ar y -1 -.5 0 .5 1 Real Roots of the companion matrix -1 -.5 0 .5 1 Im ag in ar y Roots of the companion matrix Has COVID-19 changed the correlation between cryptocurrencies and stock markets? Figure 2. Impulse response functions. Source: the authors. Germany France Italy Be fo re C O VI D -1 9 D ur in g CO VI D -1 9 -.5 0 .5 1 -.5 0 .5 1 0 5 10 0 5 varbasic, lnbitcoin, lnbitcoin varbasic, lnbitcoin, lndax30 varbasic, lndax30, lnbitcoin varbasic, lndax30, lndax30 95% CI impulse response function (irf) step Graphs by irfname, impulse variable, and response variable -.5 0 .5 1 -.5 0 .5 1 0 5 10 0 5 varbasic, lnbitcoin, lnbitcoin varbasic, lnbitcoin, lncac40 varbasic, lncac40, lnbitcoin varbasic, lncac40, lncac40 95% CI impulse response function (irf) step Graphs by irfname, impulse variable, and response variable -.5 0 .5 1 -.5 0 .5 1 0 5 10 0 5 10 varbasic, lnbitcoin, lnbitcoin varbasic, lnbitcoin, lnftsemib varbasic, lnftsemib, lnbitcoin varbasic, lnftsemib, lnftsemib 95% CI impulse response function (irf) step Graphs by irfname, impulse variable, and response variable -.5 0 .5 1 -.5 0 .5 1 0 5 10 0 5 varbasic, lnbitcoin, lnbitcoin varbasic, lnbitcoin, lndax30 varbasic, lndax30, lnbitcoin varbasic, lndax30, lndax30 95% CI impulse response function (irf) step Graphs by irfname, impulse variable, and response variable -.5 0 .5 1 -.5 0 .5 1 0 5 10 0 5 varbasic, lnbitcoin, lnbitcoin varbasic, lnbitcoin, lncac40 varbasic, lncac40, lnbitcoin varbasic, lncac40, lncac40 95% CI impulse response function (irf) step Graphs by irfname, impulse variable, and response variable -.5 0 .5 1 -.5 0 .5 1 0 5 10 0 5 varbasic, lnbitcoin, lnbitcoin varbasic, lnbitcoin, lnftsemib varbasic, lnftsemib, lnbitcoin varbasic, lnftsemib, lnftsemib 95% CI impulse response function (irf) step Graphs by irfname, impulse variable, and response variable BITCOIN-BITCOIN BITCOIN-BITCOIN BITCOIN-BITCOIN BITCOIN-BITCOIN BITCOIN-BITCOIN BITCOIN-BITCOIN BITCOIN-INDEX 30 BITCOIN-INDEX 30 INDEX 30-BITCOIN INDEX 30-BITCOIN INDEX 30-INDEX 30 INDEX 30-INDEX 30 BITCOIN-CAC40 BITCOIN-CAC40 CAC40-BITCOIN CAC40-BITCOIN CAC40-CAC40 CAC40-CAC40 BITCOIN-FTSE MIB BITCOIN-FTSE MIB FTSE MIB-BITCOIN FTSE MIB-BITCOIN FTSE MIB-FTSE MIB FTSE MIB-FTSE MIB 150 Abdelkafiet al. 4.2 Impulse response function results Figure 2 provides the responses of the variables to their own and cross-shocks during the two periods. The impulse–response graph places one impulse in each row and one response variable in each column. The horizontal axis is time. The vertical axis is in units of the variables measured in percentage points. 4.3 LS and GLS regressions In order to confirm our findings, we test if, in the case of COVID-19, both stock indexes and Bitcoin are affected by governmental actions to control the pandemic through an OLS regression. If so, we confirm the existence of coherency between Bitcoin and stock indices during mitigated periods. We test for and residual autocorrelation. Since results confirm the existence of such problems (see Table 14), we turn to estimate GLS regression for the following models (Table 15): Bitcoin = f (governmental measures), FTSE MIB = f (governmental measures of Italy) CAC= f(governmental measures of France), DAX30= f(governmental measures of Germany) Table 14. Estimation of OLS regression Italy France Germany lnΔBitcoin lnΔFTSEMIB lnΔBitcoin lnΔBitcoin lnΔBitcoin lnΔCAC40 Coeff Coeff Coeff Coeff Coeff Coeff lnschoolclosing .0445947*** .0258763*** .0278783** .006253 -.011966 .0059655 lnworkplace -.0177996 -.003466 -.0208287 .0080479 .0615218*** .0231909** lncancelevents -.0199816 -.0279787*** -.0012075 -.0109665 -.039791** -.0244232*** lnstayhome -.0125133 .0004167 .0065614 .0015334 .0102935 .0004281 lnmouvements .0166239* .0013323 -.0083567 -.0013301 -.0037976 -.0006247 lntestingpolicies .038037 .0123604 .0022607 -.0132201 .0111725 .0020282 lncontacttracing -.0514426 -.0066714 -.0091534 .0096287 -.038255* -.0037589 Δcumcases .0044208 -.0023159 -.0043617 -.0034895 .0022145 .0032284 _cons .0151017 .0006138 .0114601 .0014226 .0239506 -.0008544 R-sq 0.0516 0.1022 0.0319 0.0415 0.0579 0.1036 Hetheroscedasticity test 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Durbin' 0.0107 0.0134 0.0212 0.8027 0.0067 0.4364 Notes: *p< 0.1, ** p < 0.05, *** p < 0.01. Source: Authors calculations Table 15. Estimation of GLS regression Italy France Germany lnΔBitcoin lnΔFTSEMIB lnΔBitcoin lnΔCAC40 lnΔBitcoin lnΔDax30 lnschoolclosing .0445947*** .0258763*** .0278783** .006253 -.011966 .0059655 Lnworkplace -.0177996 -.003466 -.0208287 .0080479 .0615218*** .0231909** Lncancelevents -.0199816 -.0279787** -.0012075 -.0109665 -.039791** -.0244232*** Lnstayhome -.0125133 .0004167 .0065614 .0015334 .0102935 .0004281 Lnmouvements .0166239*** .0013323 -.0083567 -.0013301 -.0037976 -.0006247 lntestingpolicies .038037 .0123604 .0022607 -.0132201 .0111725** .0020282 lncontacttracing -.0514426 -.0066714 -.0091534 .0096287 -.038255 -.0037589 Δcumcases .0044208 -.0023159 -.0043617 -.0034895 .0022145 .0032284 _cons .0151017 .0006138 .0114601 .0014226 .0239506* -.0008544 Notes: *p<0.1, ** p < 0.05, *** p < 0.01. Source: Authors calculations Has COVID-19 changed the correlation between cryptocurrencies and stock markets? 5. Discussion 5.1 Theoretical implications The results of Pearson matrix illustrate a negative correlation between gold and returns of stocks and oil, confirming the hedging role of gold acknowledged by Yousaf et al. (2021) and Salisu et al. (2021). The outcomes of Soomro et al. (2022) show the results of developed further intention and trust of investors towards cryptocurrency adoption. During COVID-19, all correlations are positive and particularly, the correlations between Bitcoin and the indices are stronger than those between gold and indices. This result seems to imply that a cryptocurrency is more likely to be a diversifier or a weak hedge for stock markets rather than a strong hedge during crisis period. However, some studies show that there are heterogeneous relationships between cryptocurrencies and stock market indices (Bouri et al., 2017; Feng et al., 2018; Shahzad et al., 2019) and the BDS1 test indicates that all series have nonlinear structures. According to results shown in Table 8, before COVID-19, we notice the absence of causality between Bitcoin and the stock index in Italy, while there is a unidirectional causality from oil to Bitcoin and from FTSE MIB to oil and bidirectional causality between oil and gold. But things changed after the first discovered cases of COVID-19. We interestingly find bidirectional causality between Bitcoin and the stock index, oil and stock index and Bitcoin and gold. Table 8 shows that before COVID-19, in France, almost like Italy, there was a unidirectional causality from oil to Bitcoin and a bidirectional causality between oil and gold. After the announcement of the first COVID-19 cases, we found that causality relations significantly changed. In fact, we prove the existence of directional causality between Bitcoin and CAC40, oil and CAC40 and Bitcoin and gold (Table 8). For the case of Germany, we notice the absence of causality between DAX30 and Bitcoin before COVID-19, suggesting their independence, while gold and oil caused Bitcoin variation, in line with Aysan (2021). After the outbreak of the pandemic, the results confirm the existence of bidirectional causality between DAX30 and Bitcoin. Furthermore, interestingly, gold is affected by the stock index, Bitcoin and oil (see Table 8). Our results join Huang et al. (2021). Finally, we note bidirectional causality between the stock index and oil, confirming previous studies of Mariana et al. (2021). To conclude, we clearly notice the significant pattern change of correlations between studied assets and the strengthening of directional causalities during crisis periods, according to Mishra et al. (2022). Besides, we confirm the previous results of Corbet et. al (2018) supporting the common consensus regarding weak correlations between cryptocurrencies and stock market and re-examine it during a crisis period. Thus, we join the line of thoughts of Jiang et al. (2021); Bouri et al. (2017) and Kristoufek (2015). At last, investors and decision-makers would reconsider their investment strategies in mitigated periods since cryptocurrencies cannot be used as a strong hedge against the risks of stock indices. According to the results, school closing and movement restrictions measures to control the 1 The Broock, Dechert and Scheinkman (BDS) test (Broock et al. 1996) to test the linearity of all the returns series. 152 Abdelkafiet al. spread of COVID-19 enter a significant and positive impact on the FTSE MIB, reflecting its reducing effect on uncertainty and consequently rebuilding investor’s confidence in the Italian stock market. Bachman (2020) and Sarkis et al. (2020) prove that while trying to save lives, some governmental measures controlling COVID-19 spread are economically efficient, whereas others lack financial efficiency. Thus, we prove the previous results of Corbet et al. (2018), who prove turning to the impact on the cryptocurrency market. We find that the “stay at home” measure and the growth of the number of confirmed cases have a negative impact on Bitcoin. In the case of France, international travel restricting measures, testing policy and the growth number of confirmed cases have a negative impact on the stock return CAC40. These measures should generate negative impacts (see Tables 7 and 8). Figure 2 details that responses of Bitcoin to the effect of a one-standard-deviation impulse of stock return (DAX30, CAC40 and FTSE MIB) are more pronounced during COVID-19. Specifically, it declines slightly after one day and then peaks at one percentage point increase before declining. It should be noted that the Bitcoin response is not persistent. Conversely, stock return response to choc due to Bitcoin is barely significant. Besides, responses to own chocks show that an impulse to Bitcoin and each stock return causes a decline by about one percentage point over the following day. They respond strongly to their own shock. Our findings imply that the investors in Bitcoin and stock markets during the COVID-19 pandemic would face an abnormal initial impact on shock after the pandemic compared to stable periods. 5.2 Policy implications The OECD (2020) announced that the health crisis linked to COVID-19 has severely affected the tourism sector in France. The losses are 20 billion euros in 2020, and it has lost between 70% and 80% of its turnover. Finally, COVID-19 cases are a negative sign for investors worldwide, and France was among the worst-affected countries. As for CAC40, Bitcoin is also negatively affected by international travel restricting measures. This is quite understandable since most Bitcoin transactions are for international travelers. For Germany, we find that workplace closing has a positive and significant effect on Bitcoin and DAX30 while the cancel events measure has a negative and significant effect. Besides, testing policy measures has a positive and significant effect on Bitcoin. In doing so, both Bitcoin and stock indices are influenced by governmental measures. The results are useful for portfolio diversification and risk management. Cardona-Montoyaet al. (2022) point out that the pandemic has taught us to improve biosecurity measures and that financial strength, remote working and income diversification are key factors in dealing with negative shocks. Valerio Roncagliolo and Villamonte Blas (2022) argued that the stock market index could serve as a precautionary measure against possible crises in the financial market and thus inform measures to reduce the financial stress impact of on economies. Thus, policymakers would have to reduce uncertainties in financial markets by reducing policy inconsistencies and enhancing monetary and fiscal policy coordination that would guarantee the effective implementation of policy decisions that would reduce the impact of the pandemic on the global economy. Has COVID-19 changed the correlation between cryptocurrencies and stock markets? 5.3 Future research agenda The results are useful for portfolio diversification and risk management. Future studies may consider a larger sample covering Europe and North America. In addition, the overall level of stock market indices used could mask the potential heterogeneity of the gold hedging ability across stock market indices. Therefore, future studies could expand our analysis by considering the level of stock market indices by sector of activities. Another possible direction is to adopt the artificial neural network approach to study the relationship between cryptocurrencies and stock market indices. 6. Conclusions Previous studies have provided strong evidence of the hedging and safe-haven properties of commodities relative to equity indices in times of stress. The unprecedented outbreak of COVID-19 has had a negative impact on human health and caused economic gridlock and uncertainty in financial markets around the world. Due to the recent evidence of a stronger impact of the COVID-19 pandemic on stock markets than previous epidemics and the lack of related empirical studies on the link between gold, crypto-currencies and stock markets, we analyse this missing insight for France, Italy and Germany before and during the COVID-19 epidemic. Specifically, we use the VAR model to understand the relationship between crypto-currencies and stock market returns before and during the COVID-19 period. The main results are summarised below: In the Italian and French contexts and for the period from 4 January 2016 to 31 December 2019, we show the non-existence of causality between Bitcoin and the stock market index. Instead, we note unidirectional causality from oil to Bitcoin and from FTSE MIB to oil and bidirectional causality between oil and gold. In the German context, we find no causality between the DAX30 and bitcoin prior to COVID-19, suggesting their independence, while gold and oil cause Bitcoin volatility index. In contrast, for the period from 31 December 2019 to 26 February 2021, we find that things change in France and Italy as we find bidirectional causality between bitcoin and the stock market index, oil and the stock market index and bitcoin and gold. In Germany, the empirical results confirm the existence of bidirectional causality between the DAX30 and bitcoin. It is also interesting to note that gold is affected by the stock market index, bitcoin and oil. Finally, we find bidirectional causality between the stock market index and oil, which confirms previous studies by Mariana et al. (2021). Second, we support our findings by implementing the impulse function to study both own and cross shocks for Bitcoin and each stock market before and during the global COVID-19 pandemic. The results show that Bitcoin's responses to the effect of an increase in stock market performance at one standard deviation (DAX30, CAC40 and FTSE MIB) are more pronounced during the COVID-19 pandemic. Third, we test, during the pandemic, which stock market indices and bitcoin are affected by government actions to control the pandemic via an OLS regression. We find that Bitcoin's response to the effect of an increase in stock market performance (DAX30, CAC40 and FTSE MIB) is more pronounced during the COVID-19 period. 154 Abdelkafiet al. Specifically, it declines slightly after one day before peaking at a one percentage point increase, before declining. Consequently, bitcoin's reaction is not persistent. On the other hand, the reaction of stock market returns to the bitcoin shock is not very significant. Investors are now better informed about the role of gold in hedging the risk of certain Asian stock markets, not only in normal times, but also during the catastrophic event of the COVID-19 epidemic. Therefore, the results have important policy implications for investors and authorities. The analysis highlights the importance of clarifying the link between crypto-currencies and stock markets in the short and long term in order to establish policies aimed at stabilising stock markets. In addition, they are useful for portfolio diversification and market risk management. References Al-Awadhi, A.M., Al-Saifi, K., Al-Awadhi, A, & Alhamadi, S. (2020). Death and contagious infectious diseases: impact of the COVID-19 virus on stock market returns. Journal of Behavioral and Experimental Finance,27(C), 100326. https://doi.org/10.1016/j.jbef.2020.100326 Ali, M., Alam, N., Aun, S, & Rizvi, R. (2020). Coronavirus (COVID-19) - An epidemic or pandemic for financial markets. Journal of Behavioral and Experimental Finance, 27(C), 1-9. https://doi.org/10.1016/j.jbef.2020.100341 Aysan, A. F., Bergigui, F, & Disli, M. (2021). Blockchain -Based Solutions in Achieving SDGs after COVID-19. Journal of Open Innovation: Technology, Market and Complexity, 7 (2), 1-16. https://doi.org/10.3390/joitmc7020151 Bachman, D. (2020). COVID-19 could affect the global economy in three main ways”, available at: https://www2.deloitte.com/us/en/insights/economy/covid-19/economic-impact-covid- 19.html (accessed 10 April 2023). Baker, S.R., Bloom, N., Davis, S.J., & Terry, S.J. (2020). "COVID-Induced Economic Uncertainty," NBER Working Papers 26983, National Bureau of Economic Research, Inc. Baur, D.G., &Dimpfl, T. (2018). The asymmetric return-volatility relationship of commodity prices. Energy Economics, 76 (C), 378-387. https://doi.org/10.1016/j.eneco.2018.10.022 Baur, D.G., Hong, K, & Lee, A.D. (2018). Bitcoin: Medium of Exchange or Speculative Assets? Journal of International Financial Markets Institutions and Money, 54, 177-189. https://doi.org/10.1016/ j.intfin.2017.12.004 Baur, D.G, & Lucey, B.M. (2010). Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold. The Financial Review, 45 (2), 217-229. https://doi.org/10.1111/j.1540-6288.2010.00244.x Bouri, E., Gupta, R., Tiwari, A, & Roubaud, D. (2017). Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Finance Research Letters, 23, 87-95. https://doi.org/10.1016/j.frl.2017.02.009 Bouri, E., Shahzad, S.J.H., Roubaud, D., Kristoufek, L., & Lucey, B. (2020). Bitcoin, gold. and commodities as safe havens for stocks: New insight through wavelet analysis. The Quarterly Review of Economics and Finance, 77(C), 156-164. https://doi.org/10.1016/j.qref.2020.03.004 Bouri, E., Shahzad, S.J.H, & Roubaud, D. (2019). Cryptocurrencies as hedges and safe-havens for US equity sectors. The Quarterly Review of Economics and Finance, 75(C), 294-307. https://doi.org/ 10.1016/j.qref.2019.05.001 Bredin, D., Conlon, T, & Poti, V. (2015). Does gold glitter in the long-run? Gold as a hedge and safe haven across time and investment horizon. International Review of Financial Analysis, 41, 320-328. https://doi.org/10.1016/j.irfa.2015.01.010 Cardona-Montoya, R.A., Cruz, V., & Mongrut, S.A. (2022). Financial fragility and financial stress during the COVID-19 crisis: evidence from Colombian households. Journal of Economics,Finance and Administrative Science, 27(54), 376-393. https://doi.org/10.1108/JEFAS-01-2022-0005 Chan, W.H., Le, M., & Wu, Y.W. (2019). Holding Bitcoin longer: The dynamic hedging abilities of Bitcoin. The Quarterly Review of Economics and Finance, 71 (C), 107-113. https://doi.org/ 10.1016/j.qref.2018.07.004 Chen. Y. Wu, & Vivek, P.K. (2014). The Value of Bitcoin in Enhancing the Efficiency of an Investor’s Portfolio. Journal of Financial Planning, 27, 44-52. Available at : https://www.financialplanning association.org/article/journal/SEP14-value-bitcoin-enhancing-efficiency-investors-portfolio https://doi.org/10.1016/j.jbef.2020.100326 https://doi.org/10.1016/j.jbef.2020.100341 https://doi.org/10.3390/joitmc7020151 https://www2.deloitte.com/us/en/insights/economy/covid-19/economic-impact-covid-19.html https://www2.deloitte.com/us/en/insights/economy/covid-19/economic-impact-covid-19.html https://ideas.repec.org/p/nbr/nberwo/26983.html https://ideas.repec.org/s/nbr/nberwo.html https://ideas.repec.org/s/nbr/nberwo.html https://doi.org/10.1016/j.eneco.2018.10.022 https://doi.org/10.1016/j.intfin.2017.12.004 https://doi.org/10.1016/j.intfin.2017.12.004 https://doi.org/10.1111/j.1540-6288.2010.00244.x https://doi.org/10.1016/j.frl.2017.02.009 https://doi.org/10.1016/j.qref.2020.03.004 https://doi.org/10.1016/j.qref.2019.05.001 https://doi.org/10.1016/j.qref.2019.05.001 https://doi.org/10.1016/j.irfa.2015.01.010 https://www.emerald.com/insight/search?q=Ra%C3%BAl%20Armando%20Cardona-Montoya https://www.emerald.com/insight/search?q=Vivian%20Cruz https://www.emerald.com/insight/search?q=Samuel%20Arturo%20Mongrut https://www.emerald.com/insight/publication/issn/2218-0648 https://www.emerald.com/insight/publication/issn/2218-0648 https://doi.org/10.1108/JEFAS-01-2022-0005 https://doi.org/10.1016/j.qref.2018.07.004 https://doi.org/10.1016/j.qref.2018.07.004 https://www.financialplanningassociation.org/article/journal/SEP14-value-bitcoin-enhancing-efficiency-investors-portfolio https://www.financialplanningassociation.org/article/journal/SEP14-value-bitcoin-enhancing-efficiency-investors-portfolio Has COVID-19 changed the correlation between cryptocurrencies and stock markets? (accessed 10 April 2023). Ciner, C., Gurdgiev, C, & Lucey, B.M. (2013). Hedges and safe havens: An examination of stocks, bonds, gold, oil and exchange rates. International Review of Financial Analysis, 29, 202-211. https://doi.org/ 10.1186/s40854-020-00199-w Conover, M., Jensen, G., Johnson, R. & Mercer, J. (2010). Is Now the Time to Add Commodities to Your Portfolio? The Journal of Investing, 19(3), 10-19. https://doi.org/10.3905/joi.2010.19.3.010 Conlon, T, & McGee, R. (2020). Safe haven or risky hazard? Bitcoin during the COVID-19 bear market. Finance Research Letters, 35, 101607. https://doi.org/10.1016/j.frl.2020.101607 Corbet, S., Meegan, A., Larkin, C., Lucey, B, & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28-34. https://doi.org/10.1016/j.econlet.2018.01.004 Cretarola, A., Figà-Talamanca, G. & Grunspan, C. (2021). Blockchain and cryptocurrencies: economic and financial research. Decisions in Economics and Finance, 44, 781–787. https://doi.org /10.1007/s10203-021-00366-3 Feng, W., Wang, Y., & Zhang, Z. (2018). Can cryptocurrencies be a safe haven: a tail risk perspective analysis. Applied Economics, 50(44), 4745-4762. https://doi.org/10.1080/00036846 .2018.1466993 Flavin, T., Morley, C.E, & Panopoulou, E. (2014). Identifying safe haven assets for equity investors through an analysis of the stability of shock transmission. Journal of International Financial Markets, Institutions and Money, 33, 137-154. https://doi.org/10.1016/j.intfin.2014.08.001 Frankovic, J., Liu, B, & Suardi, S. (2021). On spillover effects between cryptocurrency-linked stocks and the cryptocurrency market: evidence from Australia. Global Finance Journal, 54(C), 100642. https://doi.org/10.1016/j.gfj.2021.100642 Gao, X, & Nardari, F. (2018). Do Commodities Add Economic Value in Asset Allocation? New Evidence from Time-Varying Moments. Journal of Financial and Quantitative Analysis, 53(1), 365-393. https://doi.org/10.1017/S002210901700103X Goodell, J.W, & Goutte, S. (2021). Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis. Finance Research Letters, 38, 101625. https://doi.org/10.1016/j.frl.2020.101625 Guesmi, K., Saadi, S., Abid, I, & Ftiti, Z. (2018). Portfolio diversification with virtual currency: Evidence from bitcoin”, International Review of Financial Analysis, 63, 431-437. https://doi.org/10.1016/ j.irfa.2018.03.004 Huang, Y., Duan K., & Mishra T. (2021). Is Bitcoin really more than a diversifier? A pre-and post-COVID-19 analysis. Finance Res Lett, 34(C), 102016. http://dx.doi.org/10.1016/j.frl.2021.102016 Hung, N.T. (2019). Return and volatility spillover across equity markets between China and Southeast Asian countries. Journal of Economics, Finance and Administrative Science, 24 (47), 66-81. https://doi.org/10.1108/JEFAS-10-2018-0106 Hwang, S., & Satchell, S.E. (2010). How loss averse are investors in financial markets? Journal of Banking and Finance, 34(10), 2425-2438, http://dx.doi.org/10.2139/ssrn.383942 Inci, A.C., & Lagasse, R. (2019). Cryptocurrencies: applications and investment opportunities. Journal of Capital Markets Studies, 3 (2), 98-112. https://doi.org/10.1108/JCMS-05-2019-0032 Ji, Q., Zhang, D, & Zhao, Y. (2020). Searching for safe-haven assets during the COVID-19 pandemic. International Review of Financial Analysis,71(C), 101526. https://doi.org/10.1016/j.physa .2019.04.124 Kajtazi, A., & Moro, A. (2019). The role of bitcoin in well diversified portfolios: A comparative global study. International Review of Financial Analysis, 61, 143-157. https://doi.org/10.1016/j.irfa. 2018.10.003 Kim, J.M., Kim, S.T, & Kim, S. (2020). On the Relationship of Cryptocurrency Price with US Stock and Gold Price Using Copula Models. Mathematics, 8(11), 1-15. https://doi.org/10.3390/math8111859 Kristoufek, L. (2015). What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis. PLOS ONE, 10(4), e0123923. https://doi.org/10.1371/journal.pone.0123923 Lahmiri, S., & Bekiros, S. (2020). The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets. Chaos, Solitons & Fractals, 138(c), 109936. https://doi.org/10.1016/j.chaos.2020.109936 Li, S., & Lucey, B.M. (2017). Reassessing the role of precious metals as safe havens–What colour is your haven and why? Journal Commodity Markets,7(c), 1-14. https://doi.org/10.1016/j.jcomm .2017.05.003 Mariana, C.D., Ekaputra, I.A, & Husodo, Z.A. (2021). Are Bitcoin and Ethereum safe-havens for stocks during the Covid-19 pandemic? Finance Research Letters, 38(c), 101798, 1-6. https://doi.org/10.1016/ j.frl.2020.101798 Matkovskyy, R., & Jalan, A. (2019). From Financial Markets to Bitcoin Markets: a Fresh Look at the Contagion Effect. Finance research letters, 31(c), 93-97. https://doi.org/10.1016/j.frl. 2019.04.007 https://doi.org/10.1186/s40854-020-00199-w https://doi.org/10.1186/s40854-020-00199-w https://doi.org/10.3905/joi.2010.19.3.010 https://doi.org/10.1016/j.frl.2020.101607 https://doi.org/10.1016/j.econlet.2018.01.004 https://doi.org/10.1007/s10203-021-00366-3 https://doi.org/10.1007/s10203-021-00366-3 https://doi.org/10.1080/00036846.2018.1466993 https://doi.org/10.1080/00036846.2018.1466993 https://doi.org/10.1016/j.intfin.2014.08.001 https://doi.org/10.1016/j.gfj.2021.100642 https://doi.org/10.1017/S002210901700103X https://doi.org/10.1016/j.frl.2020.101625 https://doi.org/10.1016/j.irfa.2018.03.004 https://doi.org/10.1016/j.irfa.2018.03.004 http://dx.doi.org/10.1016/j.frl.2021.102016 https://doi.org/10.1108/JEFAS-10-2018-0106 http://dx.doi.org/10.2139/ssrn.383942 https://doi.org/10.1108/JCMS-05-2019-0032 https://doi.org/10.1016/j.physa.2019.04.124 https://doi.org/10.1016/j.physa.2019.04.124 https://doi.org/10.1016/j.irfa.2018.10.003 https://doi.org/10.1016/j.irfa.2018.10.003 https://doi.org/10.3390/math8111859 https://doi.org/10.1371/journal.pone.0123923 https://doi.org/10.1016/j.chaos.2020.109936 https://doi.org/10.1016/j.jcomm.2017.05.003 https://doi.org/10.1016/j.jcomm.2017.05.003 https://doi.org/10.1016/j.frl.2020.101798 https://doi.org/10.1016/j.frl.2020.101798 https://doi.org/10.1016/j.frl.2019.04.007 https://doi.org/10.1016/j.frl.2019.04.007 156 Abdelkafiet al. Mishra, A.K., & Patwa, J.A. (2022). Return and volatility spillover between India and leading Asian and global equity markets: an empirical analysis. Journal of Economics, Finance and Administrative Science, 27 (54), 294-312. https://doi.org/10.1108/JEFAS-06-2021-0082 Mnif, E., Jarboui, A., & Mouakhar, K. (2020). How the cryptocurrency market has performed during COVID 19? A multifractal analysis. Finance Research Letters, 36(c), 101647. https://doi.org/10.1016 /j.frl.2020.101647 OECD (2020). Global financial markets policy responses to COVID-19. available at: https://www.oecd.org/coronavirus/policy-responses/global-financial-markets-policy- responses-to-covid-19-2d98c7e0/ (accessed 10 April 2023). Platanakis, E., Sakkas, A., & Sutcliffe, C. (2019). Harmful diversification: Evidence from alternative investments. The British Accounting Review, 51(1), 1-23. https://doi.org/10.1016/ j.bar.2018.08.003 Ranaldo, A., & Söderlind, P. (2010). Safe Haven Currencies. Review of Finance, European Finance Association, 14 (3), 385-407. http://dx.doi.org/10.2139/ssrn.999382 Salisu, A.A., Vo, X.V, & Lawal, A. (2021). Hedging Oil Price Risk with Gold during COVID19 Pandemic. Resources Policy,70(c),101897. https://doi.org/10.1016/j.resourpol.2020.101897 Sami, M., & Abdallah, W. (2021). How does the cryptocurrency market affect the stock market performance in the MENA region? Journal of Economic and Administrative Sciences, 37 (4), 741- 753. https://doi.org/10.1108/JEAS-07-2019-0078 Sarkis, J., Dewick, P., Strauss, S., Schroeder, P.M., Vazquez-Brust, D., & Kruger, R. (2020). CE Forum- Post- COVID. Summary Report. https://doi.org/10.13140/RG.2.2.25795.43047 Shahzad, S.J.H., Bouri, E., Roubaud, D., Kristoufek, L., & Lucey, B. (2019). Is Bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis, 63(c), 322- 330. https://doi.org/10.1016/j.irfa.2019.01.002 Sharif, A., Aloui, C., & Yarovaya, L. (2020). COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach. International Review of Financial Analysis, 70(c), 101496 https://doi.org/10.1016 /j.irfa.2020.101496 Soomro, B.A., Shah, N., & Abdelwahed, N.A.A. (2022). Intention to adopt cryptocurrency: a robust contribution of trust and the theory of planned behaviour. Journal of Economic and Administrative Sciences, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JEAS- 10-2021-0204 Tversky, A., & Kahneman, D. (1991). Loss Aversion in Riskless Choice: A Reference-Dependent Model. The Quarterly Journal of Economics, 106(4), 1039-1061, https://doi.org/10.2307/2937956 Umar, Z., & 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. 28, 100404. https://doi.org/10.1016/j.jbef.2020.100404 Urquhart, A., & Zhang, H. (2019). Is Bitcoin a hedge or safe haven for currencies? An intraday analysis. International Review of Financial Analysis, 63(c), 49-57. https://doi.org/10.1016/j.irfa .2019.02.009 Valerio Roncagliolo, F.C., & Villamonte Blas, R.N. (2022). Impact of financial stress in advanced and emerging economies. Journal of Economics, Finance and Administrative Science, 27 (53), 68- 85. https://doi.org/10.1108/JEFAS-05-2021-0063 Xu, F., Bouri, E., & Cepni, O. (2022). Blockchain and crypto-exposed US companies and major cryptocurrencies: the role of jumps and co-jumps”, Finance Research Letters, 50. https://doi.org/10.1016/j.frl.2022.103201 Yousaf, I., Ali, S., Naveed, M., & Adeel, I. (2021). Risk and Return Transmissions from Crude Oil to Latin American Stock Markets During the Crisis: Portfolio Implications. SAGE Open, 11 (2), 1-11. https://doi.org/10.1177/21582440211013800 Zhang, D., Hu, M., & Ji, Q. (2020). Financial Markets under the Global Pandemic of COVID- 19”, “Finance Research Letters. 36 (c), 101528. https://doi.org/10.1016/j.frl.2020.101528 https://www.emerald.com/insight/search?q=Aswini%20Kumar%20Mishra https://www.emerald.com/insight/search?q=Jash%20Ashish%20Patwa https://www.emerald.com/insight/publication/issn/2218-0648 https://www.emerald.com/insight/publication/issn/2218-0648 https://doi.org/10.1108/JEFAS-06-2021-0082 https://doi.org/10.1016/j.frl.2020.101647 https://doi.org/10.1016/j.frl.2020.101647 https://www.oecd.org/coronavirus/policy-responses/global-financial-markets-policy-responses-to-covid-19-2d98c7e0/ https://www.oecd.org/coronavirus/policy-responses/global-financial-markets-policy-responses-to-covid-19-2d98c7e0/ https://doi.org/10.1016/j.bar.2018.08.003 https://doi.org/10.1016/j.bar.2018.08.003 http://dx.doi.org/10.2139/ssrn.999382 https://doi.org/10.1016/j.resourpol.2020.101897 https://www.emerald.com/insight/search?q=Mina%20Sami https://www.emerald.com/insight/search?q=Wael%20Abdallah https://www.emerald.com/insight/publication/issn/1026-4116 https://doi.org/10.1108/JEAS-07-2019-0078 https://doi.org/10.13140/RG.2.2.25795.43047 https://doi.org/10.1016/j.irfa.2019.01.002 https://doi.org/10.1016/j.irfa.2020.101496 https://doi.org/10.1016/j.irfa.2020.101496 https://www.emerald.com/insight/search?q=Bahadur%20Ali%20Soomro https://www.emerald.com/insight/search?q=Naimatullah%20Shah https://www.emerald.com/insight/search?q=Nadia%20A.%20Abdelmegeed%20Abdelwahed https://www.emerald.com/insight/publication/issn/1026-4116 https://www.emerald.com/insight/publication/issn/1026-4116 https://doi.org/10.1108/JEAS-10-2021-0204 https://doi.org/10.1108/JEAS-10-2021-0204 https://doi.org/10.2307/2937956 https://doi.org/10.1016/j.jbef.2020.100404 https://doi.org/10.1016/j.irfa.2019.02.009 https://doi.org/10.1016/j.irfa.2019.02.009 https://www.emerald.com/insight/search?q=Flavio%20C%C3%A9sar%20Valerio%20Roncagliolo https://www.emerald.com/insight/search?q=Ricardo%20Norberto%20Villamonte%20Blas https://www.emerald.com/insight/publication/issn/2218-0648 https://doi.org/10.1108/JEFAS-05-2021-0063 https://doi.org/10.1016/j.frl.2022.103201 https://doi.org/10.1177/21582440211013800 https://doi.org/10.1016/j.frl.2020.101528 1. Introduction 2. Literature review 3. Methods 4. Results 5. Discussion 6. Conclusions References