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The RSI is above the middle line which indicates that the bulls dominate the market dynamics. In addition, a bullish crossover signal was also produced by the Stochastic RSI. Levels of key resistors: BTC 0. Against bitcoin, today, after pushing over the model roof at 0.

The cryptocurrency had traded between 0. The first resistor is 0. In the other hand, 0. Followed are 0. As the bulls retain their momentum dominance after the end of March, the RSI stays above the median axis. A bullish crossover signal was also generated by the Stochastic RSI. Latest Trending. Mastercard files applications for NFT, Metaverse-based trademarks 3 hours ago. Supreme Court rules in favor of Greenidge Generation crypto mining company 4 hours ago.

Load More. Home Bitcoin News. The rationale is that the more people are exposed to intensified media hype on COVID, the more pessimistic they might feel about the economy and the more they favor the cryptocurrency. More directly, we use the social panic during the pandemic as another proxy. In this subsection, we quantitively identify the effects of the nexus between COVID pandemic and lockdown policies on Bitcoin price discrepancies.

We present sophisticated evidence by estimating Eq. Table 3 reports our baseline estimation results. In columns 1 , 2 , the Bitcoin price ratio is calculated on the benchmark of US price. Column 1 provides a raw estimate, where we include no additional controls and only consider country-fixed effects.

The effect of the uncertainty nexus on Bitcoin price discrepancies is 0. After including a set of control variables in column 2 , the coefficient remains statistically and economically significant, with an estimated coefficient of 0. Finally, in our preferred specification that additionally considers date-fixed effects shown in column 3 , the coefficient becomes 0.

Thus, it confirms the intuitive pattern uncovered by Fig. Note: In this table, we report the benchmark results. The dependent variables in columns 1 - 3 and columns 4 - 6 are the Bitcoin price ratio between each country and the US priced in USD and in Tether, respectively. As previously mentioned, we also compute the Bitcoin price ratio on the alternative benchmark of Tether. Columns 4 — 6 report the results using the new dependent variables.

The coefficients of the variable of interest across columns remain similar both in significance and in magnitude with that in columns 1 , 2. It therefore provides further evidence that uncertainty explains the Bitcoin price discrepancies during the pandemic. It means that after including a nexus of COVID bear and governmental intervention, the pandemic has little power to explain the Bitcoins price discrepancies.

This subsection provides robustness checks for our baseline results. Following Ding et al. Then, to address the concern by Spiegel and Tookes that confirmed cases might be problematic because of the differentiation in testing capacity over time and across countries, we also measure the severity using daily death cases and accumulated death cases. Columns 1 , 2 of Table 4 present the results. The new estimates remain statistically significant, ensuring the robustness of our results.

It also suggests that Bitcoin price discrepancies are co-moved with uncertainty level, thereby shedding light on the following analyses on the mechanisms of alternative investments. The impact of uncertainty nexus on Bitcoin price ratio: considering pandemic intensity. The dependent variables are the Bitcoin price ratio. Columns 1 - 4 respectively use the number of daily confirmed cases, accumulated confirmed cases, daily death cases, and accumulated death as alternative measures of pandemic.

Country and date fixed effects are included in all specifications. Alternative price measure and time of policy shock. Hence, we employ a simple average daily price instead of weighted average price to measure the price discrepancy in column 1 of Table 5. An argument might be that the shock should start when the lockdown policy was implemented, which was the time most of the people realized the severity of the pandemic.

We change the time spot from the first COVID wave to the implementation of the lockdown policy and report the estimates in column 2. The results in both columns are still consistent with our benchmark results. Notes: In this table, we perform a set of robustness checks.

Column 1 uses simple average daily price ratio as alternative dependent variable. Column 2 uses policy implementation as the time of shock. Column 3 clusters standard error at continental level. Column 4 changes time fixed effects to week level instead of date level.

Country fixed effects are included in all specifications. Alternative fixed effects and clusters. However, macro-economic shocks might also occur at the regional level and investors are likely to have imitative investment behaviors with those in neighboring countries. As such, in columns 3 - 4 of Table 5 , we re-estimate Eq.

The new estimates still bear positively significant coefficients, thereby confirming the positive influence of uncertainty and lockdown interactions. Dealing with potential endogeneity. In our context, reverse causality might not be a big issue.

That is to say, for any country, neither its vulnerability to COVID nor governmental decisions on implementing lockdown policies is likely to be determined by domestic Bitcoin price discrepancies. In this sense, the major if not only source of endogeneity would be the potential existence of omitted variables that simultaneously affect local uncertainty and domestic Bitcoin price discrepancies.

As a step further, within the uncertainty nexus, because the infection of COVID is exogenous, the only concern is that the implementation of lockdown policies is not random. Following Chetty et al. By doing so, if no omitted variables exist, the estimates from the above falsification should be close to zero. The empirical density of the estimated coefficients of the falsification from the simulation tests is presented in Fig. As expected, the distributions of the estimated coefficients after the falsification are centered around zero with a small standard deviation, suggesting the absence of a significant effect with the randomly constructed experiment.

Meanwhile, our baseline estimate is 0. Distribution of simulated treatment estimates. Note: This figure plots the empirical distribution of simulated treatment effects for the benchmark. The PDF is constructed from estimates using the specification in column 3 of Table 3. No parametric smoothing is applied: the PDF appears smooth because of the large number of points used to construct it.

The vertical line shows the benchmark estimate reported in column 3 of Table 3. In this subsection, we analyze the potential mechanism through which uncertainty affects Bitcoin price discrepancies. To this end, we particularly examine whether people perceive cryptocurrency as a shelter under uncertainty. As previously discussed, we first examine whether intensive exposure to media hype on COVID enlarged Bitcoin price discrepancies.

We estimate Eq. Columns 1 , 2 of Table 6 provide the results. We also replicate the above procedure except that we scaled the two measures by US value hence using the ratio. Columns 3 — 4 of Table 6 show the new estimates are consistent with those in Columns 1 , 2. Note: In this table, we examine the effect of social media. The key variables used in columns 1 - 2 are the percentage of fake news about COVID and the media hype exposure by the percentage of all entities reported in the media alongside COVID The scaled measures by US value are shown in columns 3 - 4.

We then directly check whether social panic during the pandemic affects Bitcoin price discrepancies. The results are reported in columns 1 , 2 of Table 7. We obtain a significantly positive coefficient on our key variable for the former and a negative for the latter, implying that increments of uncertainty indeed stimulate the incentives of conducting hedging investment and enlarge Bitcoin price discrepancies.

We also scaled the two measures by US value and re-estimate Eq. The results, as shown in columns 3 — 4 of Table 7 , remain consistent. Note: In this table, we examine the effect of social sentiment. The level of news chatter that makes reference to panic or hysteria alongside COVID and the level of sentiment mentioned in the news alongside COVID as proxies of social panic are used in columns 1 , 2.

The results in this subsection suggest that hedging motivation is likely to be an important mechanism that explains the existence of price discrepancies in the Bitcoins market, especially during periods under high uncertainty. In this subsection, we examine several heterogeneous effects on the baseline estimation. First, we connect findings during the crisis on the role of uncertainty with previous knowledge on the role of capital controls.

That is, we examine whether the stringency of capital control has an effect on the effects of the pandemic and lockdown nexus. Column 1 of Table 8 presents the results. The estimate of the triple interacted term is significantly positive, implying that capital control remains important during the crisis, the uncertainty would be amplified in a more capital-regulated country, thereby resulting in larger Bitcoin price discrepancies. Note: In this table, we present the heterogeneity analyses results.

Second, we detect how containment and health policies affect the effects of the uncertainty. As shown in column 2 of Table 8 , the estimate of the triple interacted term is significantly positive, indicating that people in a society with stricter public health regulations are more likely to purchase Bitcoins during the pandemic and the following lockdown periods. Because we found that Bitcoin is perceived as an important alternative investment under uncertainty, it is plausible that people that are more uncertainty averse would purchase more Bitcoins during the pandemic.

As presented in column 3 of Table 8 , the estimate of the triple interaction term is significantly positive, which confirms the aforementioned conjecture. Fourth, enlightened by Fernandez-Perez et al. We use the Individualism Index to measure the extent of individualism within a country.

A larger indicator implies a higher degree of individualism. Column 4 of Table 8 reports the new results. The coefficient of the triple interaction term is significantly negative, suggesting that a culture of individualism would mitigate the willingness to buy Bitcoins under uncertainty.

A plausible explanation is that individualistic people might not perceive governmental lockdown policies as a signal of uncertainty because they usually do not comply with this regulation, and thus, social interaction remains. Finally, we delve into an important aspect of state capacity, which is the ability to effectively implement governmental policy.

It is reasonable to assume that a more centralized in terms of power government is more likely to penetrate society. Column 5 of Table 8 reveals the results. As expected, the estimate of the triple interaction term is significantly positive, indicating that a powerful government can indeed shut down the internal movements and consequently increase the level of uncertainty.

Unlike traditional financial markets that have experienced huge plunges, we observed active trading and recurrent price discrepancies in cryptocurrency markets across countries during the post-pandemic period. Using the beginning of the COVID pandemic in and subsequent lockdown policies as natural experiments, along with a DID identification strategy, this paper examines how the uncertainty nexus affects Bitcoin price discrepancies.

Our results are robust based on a set of tests. Finally, we conduct several heterogeneity analyses and show that countries with stringent domestic capital control, strict public health regulations, higher uncertainty aversion, collective culture, and a powerful government are more likely to be engaged in Bitcoin transactions.

At last, since recent literature proposes that model specification uncertainty is also notable for the study of cryptocurrency, further steps to extend this paper could be taken to utilize new modelling techniques e. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The authors thank the anonymous referee of the Journal of International Money and Finance for the very helpful comments and suggestions. Authorship is alphabetical — the three authors have contributed equally. All remaining errors are our own. Mazur et al. For example, Liu and Tsyvinksi questions the role of cryptocurrency production factors. Thus, the outbreak of COVID pandemic provides an opportunity to extend our insights on this topic.

This suspension should account for uncertainty. J Int Money Finance. Published online Mar Author information Copyright and License information Disclaimer. All rights reserved. Elsevier hereby grants permission to make all its COVIDrelated research that is available on the COVID resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source.

Abstract The past decades have witnessed recurrent price discrepancies in cryptocurrency markets across countries. Introduction It was the best of times, it was the worst of times, regarding cryptocurrencies. Literature review The starting point of this work is the recent recognition of the existence of cryptocurrency price discrepancies and consequently arbitrage opportunities worldwide.

Data and stylized facts We use a diverse set of datasets to examine the effect of the pandemic and lockdown nexus on Bitcoin price discrepancies. Table 1 Summary statistics. Open in a separate window. Bitcoin price discrepancies We retrieve data on tick-level Bitcoin trading from Bitcoincharts, a leading provider of data relevant to the Bitcoin network.

Capital controls We obtain data on the level of country-specific capital controls from a database managed by Fernandez et al. Table 2 Bitcoin price discrepancy and capital control. Var Price Ratio 1 2 3 4 5 6 Capital Control 0. Other country-specific characteristics This paper also utilizes data from other sources. Empirical analysis 4. Empirical strategy 4. Effects of pandemic and lockdown nexus on Bitcoin price discrepancies The existence of large and recurrent arbitrage opportunities in cryptocurrency markets across countries has recently been recognized Makarov and Schoar, Specifically, we explore a DID function with the form:.

Role of social panic and media hype Another key question is on the mechanism through which lockdown policies affect Bitcoin price discrepancies. Baseline results In this subsection, we quantitively identify the effects of the nexus between COVID pandemic and lockdown policies on Bitcoin price discrepancies. Table 3 The impact of uncertainty nexus on Bitcoin price ratio: baseline estimations.

Robustness checks This subsection provides robustness checks for our baseline results. Table 4 The impact of uncertainty nexus on Bitcoin price ratio: considering pandemic intensity. Table 5 The impact of uncertainty nexus on Bitcoin price ratio: robustness checks. Mechanisms In this subsection, we analyze the potential mechanism through which uncertainty affects Bitcoin price discrepancies.

Table 6 The mechanisms: social media. Table 7 The mechanisms: social sentiment. Var Price Ratio 1 2 3 4 Panic Index 0. Heterogeneous effect of regional characteristics In this subsection, we examine several heterogeneous effects on the baseline estimation.

Table 8 The heterogeneity effects on Bitcoin price ratio. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement The authors thank the anonymous referee of the Journal of International Money and Finance for the very helpful comments and suggestions.

Table A1 Variable definitions. Lockdown Dummy variable that equals 1 if a country implements stay-at-home policy, and 0 otherwise. The tick-level Bitcoin price is averaged to daily by equal weight. Capital Control Index ranges from 0 to 1.

A large number represents high level of overall capital controls in a country. Capital Outflow Control Index ranges from 0 to 1. A large number represents high level of capital outflow controls in a country. Amount The number of Bitcoins traded in a country. Volume The volume of Bitcoins traded in a country in US dollar terms.

Stock Index The daily stock market return in a country. A positive number represents optimal social sentiment during the pandemic, while a negative number represents the opposite. Panic Ratio The ratio of fake news index in a country to that in the US.

Sentiment Ratio The ratio of fake news index in a country to that in the US. Health Policy Index Index ranges from 0 to A high score indicates a high level of public health regulation and control. Uncertainty Avoidance Index Index ranges from 0 to A high score suggests low tolerance for uncertainty and ambiguity. Individualism Index Index ranges from 0 to A high score indicates that people place high priority on attaining personal goals rather than the well-being of the group.

Power Distance Index Index ranges from 0 to A high score indicates unequal distribution of power in a society. Table A2 Bitcoin data coverage and source. Table A3 Account verification requirements of each exchange. Proof of residency: mails or bills. References Ante L. The Fundamental Drivers of Cryptocurrency Prices.

Working Paper. Cryptocurrencies as an Asset Class? An Empirical Assessment. The Cross-Section of Cryptocurrency Returns. Working paper. Bouri E. Finance Res. Asset Manag. Markets Finance Trade. Salience and Taxation: Theory and Evidence. Bitcoin Microstructure and the Kimchi Premium.

Conlon T. Safe Haven or Risky Hazard? Tokenomics: Dynamic Adoption and Valuation. Corporate Immunity to the Covid pandemic. China Econ. Fernandez A. IMF Econ. Public Econ. Is Bitcoin Really Untethered? Hofstede G. McGraw Hill; New York: Cultures and Organizations: Software of the Mind.

A Pre- and Post-Covid Analysis. Risks and Returns of Cryptocurrency.

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