Impact of the COVID-19 Pandemic on Cryptocurrency Markets: A DCCA Analysis
[Financial Technology and Innovation Sustainable Development](
/journal/fintech/special_issues/59RZMG405X
))
Abstract:
1.Introduction
[1](#B1-fintech-02-00017), [2](#B2-fintech-02-00017), [3](#B3-fintech-02-00017)].Cryptocurrencies are a relevant set of global financial assets [ [4](#B4-fintech-02-00017)], attracting investors’ interest due to their distinctive features (e.g., blockchain technology, decentralization, scarcity, high returns, low correlations with traditional assets, and susceptibility to speculative bubbles).Inter alia, the attention of academics and policymakers has also been attracted by these markets’ potential instability and contagion risks [ [5](#B5-fintech-02-00017), [6](#B6-fintech-02-00017), [7](#B7-fintech-02-00017)].
Several studies focusing on cryptocurrencies have assessed herding behavior [ [8](#B8-fintech-02-00017)], co-explosivity [ [9](#B9-fintech-02-00017)], contagions [ [10](#B10-fintech-02-00017)], interdependence [ [11](#B11-fintech-02-00017)], co-movements [ [12](#B12-fintech-02-00017)], information flows, and links with other financial assets [ [13](#B13-fintech-02-00017), [14](#B14-fintech-02-00017), [15](#B15-fintech-02-00017)].[16](#B16-fintech-02-00017)], thus enhancing the probability of financial contagions, especially in periods of turmoil.Both financial and non-financial shocks may promote financial contagions, and the risks posed by episodes such as natural disasters and pandemics are an emerging line of research [ [17](#B17-fintech-02-00017), [18](#B18-fintech-02-00017)].
The COVID-19 pandemic is one such distressing phenomenon.It has impacted financial and real markets across the world, provoking a range of effects that often elicit comparison with the effects of the global financial crisis of 2008 [ [19](#B19-fintech-02-00017), [20](#B20-fintech-02-00017)].The pandemic impacted daily market returns around the globe, froze economic activity, spiked uncertainty, endangered global financial stability [ [21](#B21-fintech-02-00017), [22](#B22-fintech-02-00017)], reduced income, disrupted transportation, services, and manufacturing industries, raised unemployment, and affected other major economic variables [ [23](#B23-fintech-02-00017), [24](#B24-fintech-02-00017)].However, such an extreme event provides an opportunity to study return spillovers among cryptocurrencies during highly uncertain and stressful periods.The links established in cryptocurrency markets during these phases are of special interest to investors and portfolio managers as they are directly related to return and volatility spillovers (i.e., contagions), and are relevant for risk management and portfolio diversification strategies [ [3](#B3-fintech-02-00017)].[25](#B25-fintech-02-00017)]).To ensure conceptual and methodological coherence, various contagion definitions have been adopted (for example, [ [26](#B26-fintech-02-00017), [27](#B27-fintech-02-00017), [28](#B28-fintech-02-00017)]).
In this study, we follow the precedent set by [ [26](#B26-fintech-02-00017)], which defines contagion as “a significant increase in cross-market linkages after a shock to one country (or group of countries)” [ [26](#B26-fintech-02-00017)] (p.2223).
In light of such a definition, a contagion would be considered a significant increase in correlation levels between cryptocurrencies due to the COVID-19 pandemic.On the contrary, if in a given cut-off moment, no significant increase in correlations is detected, there is no contagion (although there may be interdependence).
In this study, we considered 31 December 2019 as the cut-off moment, based on the date when the World Health Organization was notified about the first cases of the disease, which made the information about COVID-19 publicly available for investors (see, for instance, [ [29](#B29-fintech-02-00017), [30](#B30-fintech-02-00017), [31](#B31-fintech-02-00017), [32](#B32-fintech-02-00017), [33](#B33-fintech-02-00017)]).[1](#B1-fintech-02-00017), [3](#B3-fintech-02-00017), [7](#B7-fintech-02-00017), [11](#B11-fintech-02-00017), [12](#B12-fintech-02-00017)], among others).
Most of these studies are limited to a relatively small number of cryptocurrencies, usually the three or four with the highest market capitalization (where BTC is always included—see, for example, [ [34](#B34-fintech-02-00017), [35](#B35-fintech-02-00017), [36](#B36-fintech-02-00017), [37](#B37-fintech-02-00017), [38](#B38-fintech-02-00017)]).
Evaluations are also often focused on the relationships between each cryptocurrency and BTC.Here, we try to improve knowledge of the behavior of cryptocurrency markets by using a larger set of 16 cryptocurrencies (doubling the number analyzed in [ [33](#B33-fintech-02-00017)]) in our evaluation of integration and contagions in cryptocurrency markets in the context of the COVID-19 pandemic.[39](#B39-fintech-02-00017), [40](#B40-fintech-02-00017), [41](#B41-fintech-02-00017)]).
We followed this methodological approach and estimated the DCCA correlation coefficient () and its variation (our measure of contagion) before and after 31 December 2019.The adopted approach allows the identification of possible non-linearities among variables, which are not accounted for when estimating simpler linear correlation coefficients.All possible pairs of cryptocurrencies in our sample were assessed, with the objective of providing more information about these markets’ complex dynamics.[42](#B42-fintech-02-00017)] and that has challenged risk and management activities [ [20](#B20-fintech-02-00017)].
Second, as we analyzed both periods before and after the beginning of the COVID-19 pandemic, we provide new evidence concerning cryptocurrency markets’ behavior when the global financial system is disturbed by a real extreme shock.Third, it provides evidence of integration and contagions occurring between cryptocurrencies emanating from a health crisis rather than a financial one.Fourth, it employs a methodology that not only accounts for nonlinearities, but also allows for an assessment of a contagion across different timescales; thus, it produces information on its short- and long-run impacts, which is relevant because the effects across shorter and longer timescales may differ.[Section 2](#sec2-fintech-02-00017)presents a brief literature review, and it provides recent empirical evidence of contagions in cryptocurrency markets.
In [Section 3](#sec3-fintech-02-00017), we present both the data and methodology, with results shown and discussed in [Section 4](#sec4-fintech-02-00017), and [Section 5](#sec5-fintech-02-00017)concludes.
2.Brief Literature Review
[43](#B43-fintech-02-00017)].One relevant sign of increasing market integration is the rising correlations across them (see, among others, [ [44](#B44-fintech-02-00017)]).Given its potential positive and negative real effects (for example, regarding positive effects, enhanced economic growth and welfare; conversely, regarding negative effects, increased risk of contagions), it is relevant to assess how individual financial markets relate to each other.[4](#B4-fintech-02-00017)] and are increasingly included in investors’ portfolios.It is thus important to analyze the co-movements between cryptocurrencies, as well as those between the cryptocurrency markets and other markets.One interesting feature that distinguishes the study of cryptocurrencies from those of other assets is that, given the former’s short history, observing the structural organizational process of markets from their inception is possible.[10](#B10-fintech-02-00017)], using DCCA, based on a sample between July 2016 and May 2019, analyzed the evidence that a contagion from BTC had transferred to the other considered cryptocurrencies.
Except for the USDT, the authors found evidence of a contagion being present in all the cryptocurrencies analyzed.Although Ref.
[ [45](#B45-fintech-02-00017)] used a different approach by making use of copula functions, it found similar results.Using coherence and cross-wavelet transform techniques, Ref.[ [46](#B46-fintech-02-00017)] studied the connection between BTC and five other major cryptocurrencies, identifying co-movements in the time–frequency space, with the main relationships occurring between BTC and Dash, Monero (XMR), Ripple (XRP), a lagged relationship with Ethereum (ETH), and out-of-phase movements with Litecoin (LTC).Ref.[ [47](#B47-fintech-02-00017)] considered five leading and liquid cryptocurrencies, using a sample from 2016 to 2018, and it investigated the dynamics of their multiscale interdependence.The authors identified high levels of dependence on a daily frequency scale, and a contagion with its origins in XRP and ETH.
[7](#B7-fintech-02-00017), [48](#B48-fintech-02-00017), [49](#B49-fintech-02-00017), [50](#B50-fintech-02-00017), [51](#B51-fintech-02-00017), [52](#B52-fintech-02-00017), [53](#B53-fintech-02-00017)].According to [ [45](#B45-fintech-02-00017)], BTC was the dominant contributor to return and volatility spillovers, contrary to [ [49](#B49-fintech-02-00017)], which found tight and time-varying volatility spillovers, but not with BTC as the leading contributor.Shared leadership between the BTC and LTC was also identified by [ [50](#B50-fintech-02-00017)], with ETH as the main net receiver.This evidence was corroborated by [ [51](#B51-fintech-02-00017)], which also highlighted the relevant links between these cryptocurrencies and various others.Conversely, Ref.[ [7](#B7-fintech-02-00017)] concluded that BTC, EHT, and LTC are the main net transmitters of volatility spillovers, with the short-term risk spillovers being stronger (in comparison to the medium- and long-term ones).These authors also found evidence of larger negative spillovers than positive ones, thus contradicting [ [52](#B52-fintech-02-00017)].Although they identified ETH and XRP as the main receivers of negative-return shocks, it was also possible to make conclusions regarding very weak positive-return spillovers for Dash and ETH.
Higher market capitalization cryptocurrencies exhibited leadership in terms of volatility spillover.Refs.[ [53](#B53-fintech-02-00017), [54](#B54-fintech-02-00017)] found evidence of frequent structural breaks, which were more relevant for larger cryptocurrencies, and small cryptocurrencies’ exhibited volatility spillover leadership.The diversity of these results justifies the interest in further and deeper assessments.[55](#B55-fintech-02-00017)], increased uncertainty, and panicked investors [ [56](#B56-fintech-02-00017)], with significant price falls in several markets.Both financial and real markets have suffered the consequences of the pandemic [ [57](#B57-fintech-02-00017), [58](#B58-fintech-02-00017)], and for the first time in their short life, cryptocurrency markets were also impacted by the global shock [ [59](#B59-fintech-02-00017)].[60](#B60-fintech-02-00017), [61](#B61-fintech-02-00017), [62](#B62-fintech-02-00017), [63](#B63-fintech-02-00017), [64](#B64-fintech-02-00017), [65](#B65-fintech-02-00017), [66](#B66-fintech-02-00017)], among others).Results have identified significant changes in co-movement patterns and in correlations during the pandemic period.
Moreover, they also showed the more influential role of altcoins during the crisis period compared with pre-pandemic times, changes in the structure of the cryptocurrency networks, and the intensification of the information flows between cryptocurrencies which simultaneously occurred with the abrupt fall in stock markets; this could warn of the possibility of contagions, and thus, increases in systematic risk.[20](#B20-fintech-02-00017), [61](#B61-fintech-02-00017), [67](#B67-fintech-02-00017)]), with mixed results.It is possible to find evidence of high symmetric dependence between cryptocurrencies during normal market conditions and an asymmetric one in bearish and bullish market conditions, negative dependence between cryptocurrencies and gold, thus indicating possible diversification opportunities for these assets during the pandemic, a low positive dependence between cryptocurrencies and gold under normal market conditions, low dynamic conditional correlations with other financial assets in stable periods, and weak or negative volatility dynamics before the pandemic, which became positive during the health crisis for the most assessed assets.[20](#B20-fintech-02-00017)] indicate that gold and cryptocurrencies can be used for hedge or diversification purposes across all timescales, Refs.
[ [20](#B20-fintech-02-00017), [30](#B30-fintech-02-00017), [35](#B35-fintech-02-00017), [36](#B36-fintech-02-00017)], for example, concluded that BTC does not act as a hedge in periods of financial turmoil (such as the COVID-19 period).On the other hand, Ref.[ [37](#B37-fintech-02-00017)] suggests that BTC is a safe haven investment.[68](#B68-fintech-02-00017), [69](#B69-fintech-02-00017)] estimated the generalized DCCA coefficient.
Although they did not find significant cross-correlations in 2018 and 2019 between cryptocurrencies and other assets, this changed in 2020, when the cryptocurrency markets appeared to have become more connected with other financial markets.Ref.[ [69](#B69-fintech-02-00017)] also concluded that during the turbulent periods of the COVID-19 pandemic, cryptocurrencies were strongly cross-correlated, although the higher levels of cross-correlation were registered with other assets (the latter were, however, less independent among themselves).As the pandemic became a more normal feature of everyday life, cross-correlations between cryptocurrencies and other markets tended to decrease.[21](#B21-fintech-02-00017), [68](#B68-fintech-02-00017), [69](#B69-fintech-02-00017), [70](#B70-fintech-02-00017), [71](#B71-fintech-02-00017), [72](#B72-fintech-02-00017), [73](#B73-fintech-02-00017)]).These studies provide evidence of several spillovers in both regimes, but also structural changes in spillovers in late 2018 and early 2020.There were also stronger cross-correlations between cryptocurrency markets during the COVID-19 pandemic.Results suggest that cryptocurrencies acted as net receivers and transmitters of shocks during the COVID-19 pandemic, and that this event enhanced the spillovers and increased the integration of cryptocurrency markets.
[69](#B69-fintech-02-00017)].One of the reasons for this is that most past research focused almost exclusively on BTC, or at most, on the four or five most important cryptocurrencies [ [74](#B74-fintech-02-00017)].Samples of the main cryptocurrencies were used in most studies that focused on contagion, interdependence, or integration in cryptocurrency markets (e.g., [ [75](#B75-fintech-02-00017)]).Most of these studies evaluated the relationships between those cryptocurrencies and BTC.The other possible links between the other cryptocurrencies have been explored less.
To fill these gaps in the literature, we considered a sample of 16 cryptocurrencies and evaluated relationships between all possible pairs.[76](#B76-fintech-02-00017), [77](#B77-fintech-02-00017)], this paper uses the DCCA and a variation of the .This approach produces new insights into these markets’ reactions to a global non-financial shock, and it allows analyses across different timescales, thus providing more detailed information on the structure of correlations.
The obtained results are useful given that there are distinct preferences depending on the investment time horizon.Furthermore, the DCCA is robust in terms of evaluating power-law cross-correlations between two series regardless of their (non) stationarity (e.g., [ [78](#B78-fintech-02-00017)]).
3.Data and Methods
[66](#B66-fintech-02-00017)], the less well-known and less capitalized a cryptocurrency is, the less liquid and less reliable its related data are, thus justifying the use of cryptocurrencies with high market capitalization levels.We used an open-source database ( https://coinmarketcap.com, accessed on 31 January 2021), which is considered to be an appropriate database with which to conduct research [ [79](#B79-fintech-02-00017)].
The sample selection considered various degrees of market capitalization and different underlying business models for cryptocurrencies.Due to data availability constraints, the time series of the different cryptocurrencies had distinct starting dates.Aiming to preserve all the possible information contained within each time series, all data available before the cut-off moment (31 December 2019) were considered.All time series ended by 30 January 2021 (details in [Table 1](#fintech-02-00017-t001)).
Cryptocurrencies’ daily returns were calculated as , where is the return of cryptocurrency i at period t, and and are the prices at time t and t − 1, respectively.[26](#B26-fintech-02-00017)]), as well as the studies of [ [41](#B41-fintech-02-00017)] or [ [80](#B80-fintech-02-00017)].The non-linearity of data makes the use of classic linear approaches inappropriate; thus, the evaluation of a contagion between cryptocurrencies is based on the DCCA (commonly used in the finance literature, see for example [ [81](#B81-fintech-02-00017), [82](#B82-fintech-02-00017), [83](#B83-fintech-02-00017), [84](#B84-fintech-02-00017)]), the and variations thereof.DCCA does not require that the analyzed series are stationary, and it allows the establishment of cross-correlations (contagion effects) in both regimes by directly using the properties of the moments of the series (either linear or nonlinear relationships).
Consequently, there is no sample reduction, and all original observations are used (an advantage, especially when the number of observations is not very high).[85](#B85-fintech-02-00017)] to evaluate long-term power-law cross-correlations between two time series of equal lengths N.It is a generalization of the DFA, proposed by [ [86](#B86-fintech-02-00017)], to a context where interest lies in the study of the joint behavior of two distinct time series of equal lengths N.DCCA produces results for different timescales through the detrended covariance function, .
In this study, the DFA is not applied directly, but the DFA exponent values are used to calculate the .[87](#B87-fintech-02-00017)].Although the exponent allows quantification of the long-range power-law correlation and identification of seasonality, it does not quantify the level of identified cross-correlations [ [88](#B88-fintech-02-00017)].To obtain such a quantification (with the DFA and DCCA approaches), it is thus necessary to use the , proposed by [ [87](#B87-fintech-02-00017)].
[89](#B89-fintech-02-00017)], the is obtained using two time series, and , with equal lengths N (k represents two equidistant observations), starting with the integration of those time series in order to obtain two new ones and , with .Then, both integrated time series are divided into (N − n) overlapping boxes of equal lengths n, with .Subsequently, the local trend of each box, and , is calculated by a least-squares fit of each series.The detrended series are obtained by subtracting each trend from their original values.The detrended covariance of the residuals for a specific box is then calculated as: [90](#B90-fintech-02-00017)] tested this coefficient and compared it with the linear correlation coefficient.Regarding its efficiency, the study concluded that it displays the desirable properties of a correlation coefficient; indeed, it is composed of values between −1 and 1 (−1 ≤ ρDCCA ≤ 1, see [ [91](#B91-fintech-02-00017)] for a full description of the coefficient’s properties).
Thus, the interpretation is straightforward: if there is no cross-correlation; if or , there is perfect cross-correlation, or perfect anti cross-correlation, respectively.[92](#B92-fintech-02-00017)], and they capture the level of market integration [ [44](#B44-fintech-02-00017)].
To examine the statistical significance of (identifying the critical values), and to test the null hypothesis for (classical test), Ref.[ [92](#B92-fintech-02-00017)] proposed a set of procedures that we followed in order to empirically confirm the existence of cross-correlation between time series.However, as we wanted to assess the (non)existence of contagions in cryptocurrency markets during the pandemic, we considered two periods (before and after the onset of the pandemic), and thus, in accordance with [ [93](#B93-fintech-02-00017)], we calculated the as: [26](#B26-fintech-02-00017)], there is evidence of contagion.If the correlation coefficients have decreased in the period after the cut-off moment, and dependence between markets declined.
4.
Results and Discussion
4.1.Descriptive Statistics
[Table 2](#fintech-02-00017-t002)presents the descriptive statistics of the cryptocurrencies’ returns.To assess the stationarity of these series, a standard Augmented Dickey–Fuller (ADF) test was performed (using the StataSE 15® (64-bit) software, from StataCorp LLC, Lakeway Drive, College Station, TA, USA).The test’s was rejected in all cases, thus suggesting that the examined series of returns are all stationary (results not shown, but available upon request).[33](#B33-fintech-02-00017), [60](#B60-fintech-02-00017)], meaning that during the first period, there was a higher probability of large positive return variations than negative ones, which could be a sign of increased sensitivity to the effects of the pandemic.In contrast, during the second period, negative returns were more frequent, thus reflecting the turmoil and uncertainty provoked by the pandemic.
Regarding kurtosis, high values (i.e., leptokurtic distributions) were observed in both periods, thus suggesting that the returns do not follow a normal distribution; this is consistent with the existence of fat-tails, a well-known stylized fact in financial markets.This is also a justification for using nonlinear, rather than linear, techniques.Although the USDT is a stable cryptocurrency (pegged to the USD), it exhibited an extremely high kurtosis value before 31 December 2019.Shortly after it was launched in 2014, questions were raised concerning whether its issuer was setting aside enough collateral to maintain the dollar peg.The issuing company started reporting its reserves in 2017, due to mounting investors’ doubts.This could be an explanation for the high kurtosis observed in this cryptocurrency during the first period that was assessed.According to a report examining June 2018 by Freeh Sporkin & Sullivan, LLP, after that date, all tethers in circulation were fully backed by USD reserves.
This could be an explanation for the alignment of the kurtosis values of the USDT with those of the other cryptocurrencies in our sample during the second analyzed period.
4.2.∆ρDCCA Analysis
[89](#B89-fintech-02-00017), [94](#B94-fintech-02-00017)] for 90%, 95%, and 99%.[Figure 1](#fintech-02-00017-f001)depicts the lower (LL_99%) and upper (UL_99%) critical values (due to their proximity to zero, they are practically imperceptible).
If the estimated values are outside the referred limits (LL and UL), the correlation is statistically significant, and if positive, it can be interpreted, according to [ [10](#B10-fintech-02-00017), [26](#B26-fintech-02-00017)], as evidence of a contagion.Conversely, if the estimates lie within the critical values, the variation between correlations is not significant.
In accordance with [ [44](#B44-fintech-02-00017)], a positive value for can also be interpreted as an increase in integration between markets.[20](#B20-fintech-02-00017), [95](#B95-fintech-02-00017)].
The statistically significant increase in the correlation coefficients between the majority of cryptocurrencies may indicate that the respective markets are integrated (contradicting, for example, [ [20](#B20-fintech-02-00017)]), and thus, that there was an increase in systemic risk.Stronger integration was found between the XTZ market and the remaining cryptocurrency markets, as well as between BSV and the other markets (as can be seen by the higher values of the ).[10](#B10-fintech-02-00017)], for short timescales, the null hypothesis of was rejected in all cases and (except for USDT), thus suggesting that there is evidence of contagion (corroborating the findings in [ [47](#B47-fintech-02-00017)]) and highlighting the contribution of this study.Thus, the crisis caused by the COVID-19 pandemic seems to have affected cryptocurrency markets, increasing integration (in accordance with [ [33](#B33-fintech-02-00017), [60](#B60-fintech-02-00017), [69](#B69-fintech-02-00017)], among others) and suggesting that movements in one cryptocurrency reflect movements in other cryptocurrencies.
[47](#B47-fintech-02-00017)].[34](#B34-fintech-02-00017)]).The distinct behavioral patterns of both short and long timescales suggest that investors need to constantly update their positions (short vs.
long) and consider the distinct preferences for different time horizons when building investment portfolios.
5.
Conclusions
[3](#B3-fintech-02-00017)]).Exceptions to this general conclusion are USDT (across short timescales) and XRP and USDT (across long timescales).
This result hints that these cryptocurrencies could have safe-haven properties in periods of turmoil in the cryptocurrency markets.[20](#B20-fintech-02-00017), [33](#B33-fintech-02-00017), [60](#B60-fintech-02-00017), [69](#B69-fintech-02-00017)], that cryptocurrency markets became more integrated after the onset of the pandemic.
This means that, as a whole, they became more exposed to the effects of shocks, thus providing yet another example of the so-called correlations breakdown (i.e., that diversification becomes more difficult, precisely when it is more necessary).This evidence leads us to conclude that the analyzed cryptocurrency markets are neither immune to non-financial shocks affecting the global economy, nor independent from the global financial system.[30](#B30-fintech-02-00017)]), this study highlights that regulatory oversight and monitoring are needed to prevent, for example, financial instability and systemic risk.[96](#B96-fintech-02-00017), [97](#B97-fintech-02-00017), [98](#B98-fintech-02-00017)]), there is an urgent need to study cryptocurrency investments from the perspective of ESG investments.
We intend to develop such a study as part of our future research (using, for example, the recently created cryptocurrency environmental attention (ICEA) index in [ [99](#B99-fintech-02-00017)]) in order to help the environmentally concerned investors who are willing to include crypto assets in their portfolio while contributing to the achievement of the ESG goals.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Cryptocurrency||Start Date||Market Capitalization (USD)|
|1||Bitcoin||BTC||29 April 2013||162,684,945,903||61.77%|
|2||Ethereum||ETH||7 August 2015||26,164,459,704||9.93%|
|3||Ripple||XRP||4 August 2013||26,164,459,704||9.93%|
|4||Bitcoin Cash||BCH||23 July 2017||6,059,789,428||2.30%|
|5||Bitcoin SV||BSV||9 November 2018||4,290,029,659||1.63%|
|6||Tether||USDT||25 February 2015||4,643,212,805||1.76%|
|7||Litecoin||LTC||29 April 2013||3,889,681,824||1.48%|
|8||EOS||EOS||1 July 2017||3,366,250,140||1.28%|
|9||BinanceCoin||BNB||25 July 2017||3,138,663,736||1.19%|
|10||Tezos||XTZ||2 October 2017||2,103,907,641||0.80%|
|11||ChainLink||LINK||20 September 2017||1,520,607,569||0.58%|
|12||Cardano||ADA||1 October 2017||1,268,987,677||0.48%|
|13||Stellar||XLM||5 August 2014||1,183,231,787||0.45%|
|14||TRON||TRX||13 September 2017||1,136,886,287||0.43%|
|15||Monero||XMR||21 May 2014||1,143,443,765||0.43%|
|16||Huobi Token||HT||3 February 2018||1,063,188,577||0.40%|
|Total||249,821,746,206||94.86%|
|Cryptocurrency||Before 31 December 2019||After 31 December 2019|
|Mean||Stdev.||Skewness||Kurtosis||Mean||Stdev.||Skewness||Kurtosis|
|BTC||0.0016||0.0427||−0.1527||10.7409||0.0039||0.0414||−3.4812||44.5290|
|ETH||0.0024||0.0714||−3.4274||74.6109||0.0060||0.0551||−2.5411||29.9171|
|XRP||0.0015||0.0727||2.0756||32.9133||0.0021||0.0660||−0.3960||26.4318|
|BCH||−0.0008||0.0794||0.6179||10.4098||0.0018||0.0603||−1.8145||24.2868|
|BSV||0.0008||0.0901||0.8643||19.9132||0.0015||0.0814||2.8755||46.5471|
|USDT||−0.0001||0.0211||−12.2749||829.3628||0.0000||0.0055||0.1522||37.9746|
|LTC||0.0009||0.0645||1.7163||28.5632||0.0030||0.0540||−1.5536||16.3358|
|EOS||0.0010||0.0827||2.2245||27.6377||0.0030||0.0545||−2.0790||22.8957|
|BNB||0.0055||0.0787||1.3888||15.1944||0.0003||0.0502||−3.3523||38.3843|
|XTZ||−0.0004||0.0751||0.1255||10.5396||0.0019||0.0634||−2.1090||24.3520|
|LINK||0.0027||0.0812||0.7048||7.1339||0.0065||0.0711||−1.4227||18.0953|
|ADA||0.0003||0.0792||2.9094||29.3140||0.0061||0.0623||−1.1089||14.6842|
|XLM||0.0015||0.0754||2.0089||19.6020||0.0050||0.0668||1.6195||21.9256|
|TRX||0.0023||0.0963||2.1343||19.3240||0.0022||0.0545||−2.2636||24.9947|
|XMR||0.0016||0.0703||0.6497||9.6001||0.0029||0.0509||−2.4056||26.4712|
|HT||0.0009||0.0518||0.6165||7.6063||0.0021||0.0431||−3.5911||49.8863|
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Almeida, D.; Dionísio, A.; Ferreira, P.; Vieira, I.
Impact of the COVID-19 Pandemic on Cryptocurrency Markets: A DCCA Analysis.FinTech 2023, 2, 294-310.https://doi.org/10.3390/fintech2020017
Almeida D, Dionísio A, Ferreira P, Vieira I.Impact of the COVID-19 Pandemic on Cryptocurrency Markets: A DCCA Analysis.FinTech.2023; 2(2):294-310.
https://doi.org/10.3390/fintech2020017Chicago/Turabian Style
Almeida, Dora, Andreia Dionísio, Paulo Ferreira, and Isabel Vieira.2023.”Impact of the COVID-19 Pandemic on Cryptocurrency Markets: A DCCA Analysis” FinTech 2, no.2: 294-310.https://doi.org/10.3390/fintech2020017.
