CRISIS AND CONTAGION IN CRYPTOCURRENCY MARKET
Bhavesh Garg*, Karan Rai**, Rishabh Pachoriya***, and Manik Thappa****
*Indian Institute of Technology Ropar, Punjab, India. Email: [email protected]
**Corresponding author. Indian Institute of Technology Ropar, Punjab, India.
E-mail: [email protected]
***Indian Institute of Technology Ropar, Punjab, India. E-mail: [email protected]
****Indian Institute of Technology Ropar, Punjab, India. E-mail: [email protected]
The paper examines whether an unanticipated event like the COVID-19 crisis has strengthened the contagion in the cryptocurrency market utilizing samples of data representing the pre-crisis and post-crisis periods. Employing the wavelet coherence and DCC-GARCH(1,1) models, we identify that the cryptocurrency market started integrating from 2018 as volatility within the market reduced. Our main finding is that the cryptocurrency market is highly interconnected and that the contagion strengthened during the crisis period. We draw appropriate policy implications from these findings.
Article history:
Received : September 20, 2022 Revised : December 11, 2022 Accepted : January 01, 2023 Available Online : February 28, 2023 https://doi.org/10.21098/bemp.v26i0.2369
Keywords: COVID-19; Cryptocurrency; Wavelet; Contagion.
JEL Classifications: C50; E44; G15.
ABSTRACT
I. INTRODUCTION
In this paper, we examine whether the occurrence of an unanticipated event like the COVID-19 economic crisis has strengthened the contagion in the cryptocurrency market. The cryptocurrency market has emerged as a new global investment financial asset. Its emergence is led by low transaction fees, no intermediation for settlement, and digital currency acceptance that have complemented its popularity (Urom et al., 2020; Khelifa et al., 2021). Nevertheless, the cryptocurrency market is also one of the most volatile financial markets, with persistent volatility, speculative bubble, financial instability, and contagion risk (Celeste et al., 2020).
Thus, it may be a significant source of vulnerability in financial markets in times of crisis (Conlon et al., 2020). Further, recent studies have also examined hedging (Hanif et al., 2022) and optimal portfolio composition properties (Sha and Song.
2021) of cryptocurrencies concerning regional market equities and Belt and Road equities, respectively. Hence, the cryptocurrency market is now an established financial market. Therefore, our study examines the increasing co-movement of cryptocurrencies using a time-frequency domain framework.
The empirical literature on volatility modelling of the cryptocurrency market has rapidly evolved in the last few years. Therefore, we have grouped it into four strands. The first strand of literature investigated the relationship between cryptocurrency prices and their trading volume (Balcilar et al., 2017; Bouri et al., 2019; Fousekis and Tzaferi, 2021; Katsiampa et al., 2019b; Koutmos, 2018) and found a positive correlation between these series. The second strand of literature examined the return volatility of cryptocurrencies and found the presence of high volatility persistence in the cryptocurrency returns (Osterrieder and Lorenz, 2017; Katsiampa, 2017; Lahmiri and Bekiros, 2018; Chu et al., 2017; Takaishi, 2020;
Baur and Dimpfl, 2018; Kakinaka and Umeno, 2021; Salisu and Ogbonna, 2021).
The third strand of literature examines the interdependencies and volatility spillovers within the cryptocurrency market (Katsiampa, 2019; Katsiampa et al., 2019a; Canh et al., 2019; Ji et al., 2019; Chaim and Laurini, 2019; Kumar and Anandarao, 2019). The studies found significant volatility spillovers between the cryptocurrencies, indicating increasing co-movement and interdependencies within the cryptocurrency market.
Recently, studies on the cryptocurrency market have focused on examining the time-frequency dynamics. Omane-Adjepong and Alagidede (2019) and Omane- Adjepong et al. (2019) employed the wavelet analysis and found that the volatility spillovers within the market are more influential on a time scale. Mensi et al.
(2019) provided evidence of strong co-movement between Bitcoin and Ethereum in both the time and frequency domains, suggesting Bitcoin is leading the other cryptocurrencies. Qureshi et al. (2020) identified a short- and long-run contagion in the cryptocurrency market, whereby Ripple and Ethereum are the primary origins of market contagion. Qiao et al. (2020) suggested that Bitcoin returns are ahead of other cryptocurrencies in low frequency.
Nevertheless, how the global cryptocurrency market would react to a shock as large as the current pandemic shock is still unknown. Of note, it begs the question: how would the current crisis impact the dependence between global cryptocurrencies and what are the possible implications of this impact for investors and policymakers?
With regard to the contribution, our study departs from the previous literature on two fronts. First, we analyse the cryptocurrency market’s contagion effect in a crisis period – as large as the COVID-19 crisis – vis-`a-vis a non-crisis period.1 Umar et al. (2022) have distributed their sample likewise to understand the time- frequency connectedness of NFTs and five major asset classes. We consider two sub- samples – one before the COVID-19 crisis and another post-COVID-19 outbreak.
We compare the findings and identify whether the pandemic has strengthened the interdependence between major cryptocurrencies since studies, such as Narayan et al. (2018) and Sharma et al. (2019), have shown that markets become more connected when an unanticipated event occurs, such as government shutdowns, terrorism, etc. Second, our study ascertains how global cryptocurrency markets react to a full-blown global economic crisis. The findings also inform us whether portfolio diversification would be a better strategy in a crisis. We chose eight major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), Lunacoin (LUNA), Ripple (XRP), Steller (XLM), Dogecoin (DOGE), Dash (DASH), and Litecoin (LTC).
The basis for the selection of cryptocurrencies stems from the fact that these cryptocurrencies account for most of the cryptocurrency market’s capitalization.
In this study, we extend the line of research by employing the wavelet coherence analysis and the DCC-GARCH models to examine the increasing co-movement during the crisis (contagion) and volatility dynamics among cryptocurrencies.
The rest of the paper is structured as follows. Section II presents the methodology. Section III discusses the data and empirical results. Section IV concludes.
II. METHODOLOGY A. Wavelet Coherence
Various studies have used wavelet coherence developed by Torrence and Compo (1998) for financial and economic analysis (see, for instance, Dewandaru et al., 2017;
Qureshi et al., 2020). The effectiveness of this methodology is that it can capture the co-movement in time and frequency scale. The wavelet coherence using the continuous wavelet transformation transforms the series into the continuous wave and signals (Demir et al., 2020).
With the help of Equation (1), the data generation process is started by this construction. The constructed data are transformed into synthetic data (Demir et al., 2020).
1 The studies of Phan and Narayan (2020), Narayan et al. (2020), Prabheesh, et al. (2020a), Prabheesh, et al. (2020b), Iyke and Ho (2021), Rai and Garg (2021), Garg and Prabheesh (2021), Prabheesh and Kumar (2021), Chowdhury and Garg (2022), and Iyke and Maheepala (2022) have examined the impact of COVID-19 on financial markets and oil prices.
(1)
(2)
The series is transformed to a signal by wavelet transformation in Equations (2) and (3). Then, in Equation (4), the wavelet coherence equation, which will be used for analysis, is specified.
(3)
(4)
(5)
(6) (7) B. Dynamic Conditional Correlation (DCC-GARCH)
The dynamic conditional correlation (DCC-GARCH) model proposed by Engle (2002) assumes that the conditional correlation varies over time. The DCC-GARCH model decomposes the matrix Ht as follows:
where, rt is the vector of returns and mt is the K*1 vector of parameters; et is the error vector, is the information available at t – 1, and Ht is the dynamic condition covariance matrix.
The matrix Ht has to be positive definite because it is a covariance matrix.
All the rt should be equal to or less than 1. The matrix is the diagonal matrix of the conditional standard deviations of the residuals. Moreover, rt is a matrix of time-varying conditional correlation, which is given by
where is the unconditional covariance matrix of the standardised errors ; is a symmetric positive definite matrix, and a and b are non-negative scalar parameters satisfying .
III. DATA AND RESULTS A. Data Properties
The daily data on eight major cryptocurrencies, viz. BTC, ETH, LUNA, XRP, XLM, DOGE, DASH, and LTC are collected from https://finance.yahoo.com/, and the period of analysis for the full sample spans between November 10, 2017 and April 26, 2022.2 The considered period is significant for understanding how the
2 For Luna coin we are using 27th July 2019 to 26th April 2022, 27th July 2019 to 31st December, and 01st January 2020 to 26th April 2022 as full sample, pre-crisis and post crisis samples respectively.
highly volatile cryptocurrencies react during a period of high uncertainty. First, we analyse the full sample and split it into two sub-samples: one representing the pre-crisis (before COVID-19) period and the other representing the crisis and post-crisis (during and post COVID-19) periods. Thus, the first sub-sample ranges from November 10, 2017 to December 31, 2019, and the second sub-sample is from January 1, 2020 to April 26, 2022. The daily returns are calculated as the first difference of the natural logarithm of the raw data of the cryptocurrencies. The resulting daily return series are illustrated in Figure 1.
Figure 1.
Closing Price Returns of Cryptocurrency
-0,5 -0,3 -0,4 -0,2 -0,1 0,0 0,2 0,1 0,8
11-1-2017 8-1-2018 5-1-2019 2-1-2020 11-1-2020 8-1-2021
BTC
-0,6 -0,4 -0,2 0,0 0,2
11-1-2017 8-1-2018 5-1-2019 2-1-2020 11-1-2020 8-1-2021
ETH
-0,6 -0,4 -0,2 -0,0 -0,6 -0,4 -0,2
11-1-2017 8-1-2018 5-1-2019 2-1-2020 11-1-2020 8-1-2021
XRP
-0,4 -0,2 -0,0 -0,6
-0,4
-0,2
11-1-2017 8-1-2018 5-1-2019 2-1-2020 11-1-2020 8-1-2021
XLM
-0,5 -0,0 -1,5
1,0
0,5
11-1-2017 8-1-2018 5-1-2019 2-1-2020 11-1-2020 8-1-2021
DOGE Figure 1.
Closing Price Returns of Cryptocurrency (Continued)
-0,6 -0,4 0,6 0,4 0,2
-0,2 0,0
11-1-2017 8-1-2018 5-1-2019 2-1-2020 11-1-2020 8-1-2021
DASH
-0,6 -0,4 0,4
0,2
-0,2 0,0
11-1-2017 8-1-2018 5-1-2019 2-1-2020 11-1-2020 8-1-2021
LTC
-0,6 -0,4 0,8
0,2 0,4 0,6
-0,2 0,0
6-27-2019 12-27-2019 6-27-2020 1-27-2020 6-27-2021 12-27-2021
Luna Figure 1.
Closing Price Returns of Cryptocurrency (Continued)
Table 1.
Descriptive Statistics
Panel A, B, and C represent the descriptive statistics for the full sample, pre-crisis, and post-crisis, respectively. S.D.
and J.B. stand for standard deviation and Jarque-Bera, respectively. * represents significance at the 1% level.
The descriptive statistics of daily returns are presented in Table 1. All return series have a positive mean in Panel A (full sample) and Panel C (post-crisis). In contrast, we find the mixture of positive (BTC, XLM, and DOGE) and negative (ETH, LUNA, XRP, DASH, and LTC) mean in Panel B (pre-crisis). The standard deviations for all eight cryptocurrencies are higher than their mean in all three samples, indicating a higher level of risk. The skewness and kurtosis statistics suggest a leptokurtic distribution and higher tail risks for all cryptocurrencies.
The Jarque-Bera test supports the presence of volatility and confirms the non- normality of the distribution of returns.
Mean S.D. Skewness Kurtosis J.B.
Panel A: Full Sample
BTC 0.001 0.041 -0.813 14.258 10424.4*
ETH 0.003 0.052 -0.981 13.377 7570.5*
LUNA 0.004 0.078 0.864 13.018 4327.52*
XRP 0.001 0.066 0.860 19.036 17654.3*
XLM 0.001 0.064 0.827 13.280 7359.4*
DOGE 0.003 0.061 4.916 87.444 7090.8*
DASH 0.001 0.081 0.253 13.205 49056.9*
LTC 0.001 0.056 -0.111 11.865 5338.1*
Panel B: Pre-crisis
BTC 0.001 0.042 -0.071 6.375 371.8*
ETH -0.001 0.051 -0.258 5.588 226.9*
LUNA -0.010 0.053 0.519 5.491 47.958*
XRP -0.001 0.065 2.039 21.602 11819.8*
XLM 0.001 0.066 0.972 9.764 1614.1*
DOGE 0.000 0.062 0.929 12.349 2960.4*
DASH -0.003 0.057 0.684 10.720 2002.8*
LTC -0.001 0.057 1.114 10.080 1795.1*
Panel C: Post-crisis
BTC 0.002 0.040 -1.768 25.646 18541.3*
ETH 0.00 0.052 -1.606 20.105 10690.6*
LUNA 0.007 0.082 0.812 12.578 3350.3*
XRP 0.001 0.067 -0.158 16.960 6881.1*
XLM 0.002 0.062 0.670 17.341 7359.4*
DOGE 0.005 0.095 5.559 85.871 246734.2*
DASH 0.001 0.065 -0.031 14.225 4446.9*
LTC 0.001 0.056 -1.333 13.831 4391.3*
B. Evidence of Contagion
The contour map exhibits wavelet coherence where the horizontal and vertical axes represent time and frequency (converted into time units of days). The strength of contagion (increasing co-movement) is indicated by the colour red (high coherence) and blue (low coherence), whereas the black contours signify significance at 5%.
The arrows suggest the phase-difference indicating the lead and lag. The two scales (frequency) that we are focusing on are 1-64 (high frequency) days and 64-256 (low frequency) days, representing the pure (short-run) and fundamental (long-run) contagion. We analyse the increasing co-movement by taking pairs of cryptocurrencies for all three samples. Thus, we examine eighty-four pairs, and the results are reported in Figures 2 to 4. The results suggest that the cryptocurrencies in all three samples indicate in-phase (arrow pointing right) differences. Therefore, the results imply a positive correlation among all the cryptocurrencies suggesting no hedging opportunity within the cryptocurrency market.
Figure 2 reports the wavelet coherence results for the full sample. The result suggests that BTC-ETH, BTC-XLM, BTC-DASH, BTC-LTC, ETH-XRP, ETH-XLM, ETH-DASH, ETH-LTC, XRP-XLM, XRP-DASH, XRP-LTC, XLM-DASH, XLM- LTC, and DASH-LTC demonstrate the highest coherence in both high and low- frequency bands, suggesting the evidence of pure contagion and fundamental relationship. This further implies high coherence and positive relationship within the cryptocurrency market. Additionally, we find that in the pure contagion (in the short-run), BTC, ETH, and LTC are the three leading cryptocurrencies. Hence, they are the sources of contagion, disseminating the risk to other cryptocurrencies within the market. Concerning the fundamental relationship (in the long run), the evidence suggests BTC, XRP, and XLM are generally lagging behind other cryptocurrencies.
Conversely, DOGE shows strong coherence with BTC and XRP in high- frequency bands (pure contagion), whereas it shows strong coherence with ETH, LTC, and XLM in low-frequency bands (fundamental coherence). Concerning LUNA, we find minimal evidence of pure contagion with BTC, ETH, and LTC and a fundamental relationship with XRP and DASH. Thus, concerning the full sample analysis, our results imply that BTC, ETH, and LTC are the major cryptocurrencies driving the pure contagion in the cryptocurrency market. Additionally, XRP and DASH are leading the fundamental relationship. Moreover, LUNA and DOGE can be used to diversify the investment portfolio within the cryptocurrency market, considering the presence of comparatively less risk of pure contagion.
Figure 2. Wavelet Coherence Analysis of Cryptocurrencies – Full Sample The results of Wavelet Coherence for the different combinations of cryptocurrencies returns from November 10, 2017, to April 26, 2022 (full sample). The arrow represents the direction of relevance and lead-lag relationship. If the arrow points right (left), there is a positive (negative) correlation
Next, we investigate the co-movement among cryptocurrencies before the occurrence of an unanticipated crisis, i.e. COVID-19. The pre-crisis analysis explains the dynamic interdependency in the new moulding cryptocurrency market. The results are reported in Figure 3. We note that, for the pre-crisis period, there is evidence of strong coherence developing over time among BTC, ETH, XRP, XLM, DASH, and LTC in both the high and low-frequency bands. The empirical results imply that the market started integrating over time (2018 onwards) with increasing interdependencies. Further, BTC, ETH, and LTC are the three leading cryptocurrencies in the pre-crisis period.
On the contrary, DOGE has a strong coherence only at high frequency, suggesting pure contagion. In contrast, there is no significant evidence concerning LUNA. The paramount understanding from the pre-crisis analysis is that the cryptocurrency market started integrating in 2018 and further. Additionally, the results imply that LUNA provided a diversification opportunity during the pre- crisis period, considering the presence of no pure contagion.
Finally, the wavelet coherence maps during the crisis and post-crisis periods are presented in Figure 4. We find significant evidence of high-frequency contagion for all the pairs except the pairs concerning LUNA, where the presence of contagion was minimal. The results validate increasing interdependencies and market integration during the period of uncertainty. Further, our results suggest that pairs, such as BTC-ETH, BTC-LTC, ETH-XLM, ETH-LTC, XRP-XLM, XLM- LTC, and DASH-LTC experienced the contagion effect in both high and low- frequency bands, which is strong during the peak of COVID-19 (first and second waves). Moreover, we find that the BTC, ETH, DASH, and LTC are the origin of contagion in the cryptocurrency market as they were leading and disseminating.
DOGE and LUNA were able to escape the crisis more quickly than other currencies.
Therefore, considering the results of the sub-sample (post-crisis) analysis, the evidence suggests that DOGE and LUNA provide a suitable avenue for portfolio diversification within the cryptocurrency market in a high uncertainty period.
Further, the results indicate that there are no hedging benefits in the market among cryptocurrencies, only some diversification benefits.
Figure 3. Wavelet Coherence Analysis of Cryptocurrencies – Pre-crisis Period The results of Wavelet Coherence for the different combinations of cryptocurrencies returns from November 10, 2017, to December 31, 2019 (pre-crisis), explaining increasing co- movement (contagion).
Figure 4. Wavelet Coherence Analysis of Cryptocurrencies – Post-crisis Period The results of Wavelet Coherence for the different combinations of cryptocurrencies returns from January 1, 2020, to April 26, 2022 (post-crisis), explaining contagion among different cryptocurrencies.
C. Robustness Check
We check for the robustness of our results by implementing the DCC-GARCH(1,1) model. The results are reported in Table 2 for the full sample, pre-crisis period, and post-crisis period. We find that the sum of α and β is less than one for all models, thus satisfying the restriction. Further, all the β values indicating persistent volatility are significant at the 1% level. Overall, we find evidence of substantial dynamic interdependency among all the cryptocurrencies suggesting that the cryptocurrency market is highly integrated. The DCC-GARCH(1,1) results support our discussion that the cryptocurrency market is the most volatile financial market. Though there is a significant rise in volatility in the post-crisis period, there is a substantial decline in particular combinations concerning DOGE and LUNA. The DCC-GARCH(1,1) results hence imply that DOGE and LUNA are attractive portfolio diversification prospects in the face of high uncertainty in cryptocurrency markets. Thus, our results are firmly in line with the wavelet coherence results.
Table 2.
DCC Results
The values in the parenthesis are the std errors. *, **, and *** represent significance levels at 1%, 5%, and 10%, respectively.
Cryptocurrencies Full-Sample Pre-crisis Post-crisis
α β α β α β
BTC-ETH 0.062* 0.936* 0.065* 0.935* 0.073* 0.926*
(0.006) (0.007) (0.008) (0.008) (0.022) (0.025)
BTC-LUNA 0.026* 0.946* 0.000 0.924* 0.034* 0.944*
(0.009) (0.022) (0.000) (0.179) (0.010) (0.019)
BTC-XRP 0.064* 0.932* 0.072* 0.925* 0.018* 0.981*
(0.007) (0.008) (0.018) (0.011) (0.002) (0.002)
BTC-XLM 0.081* 0.908* 0.717* 0.925* 0.112* 0.851*
(0.011) (0.014) (0.011) (0.012) (0.026) (0.035)
BTC-DOGE 0.052* 0.947* 0.090* 0.813* 0.004 0.389*
(0.007) (0.008) (0.025) (0.049) (0.800) (0.014)
BTC-DASH 0.217* 0.606* 0.095* 0.892* 0.199* 0.439*
(0.029) (0.053) (0.015) (0.018) (0.040) (0.079)
BTC-LTC 0.044* 0.948* 0.049* 0.951* 0.153* 0.581*
(0.008) (0.008) (0.005) (0.006) (0.035) (0.104)
ETH-LUNA 0.039* 0.933* 0.000 0.926* 0.051* 0.927*
(0.009) (0.016) (0.000) (0.323) (0.010) (0.016)
ETH-XRP 0.040* 0.958* 0.058* 0.942* 0.018* 0.982*
(0.004) (0.005) (0.008) (0.008) (0.042) (0.002)
ETH-XLM 0.066* 0.920* 0.066* 0.920* 0.074* 0.894*
(0.009) (0.011) (0.009) (0.011) (0.017) (0.026)
Table 2.
DCC Results Continued
The values in the parenthesis are the std errors. *, **, and *** represent significance levels at 1%, 5%, and 10%, respectively.
Cryptocurrencies Full-Sample Pre-crisis Post-crisis
α β α β α β
ETH-DOGE 0.044* 0.955* 0.043* 0.946* 0.034* 0.501
(0.007) (0.007) (0.011) (0.016) (0.000) (0.104)
ETH-DASH 0.0910* 0.816* 0.046* 0.935* 0.124* 0.646*
(0.023) (0.053) (0.014) (0.021) (0.033) (0.081)
ETH-LTC 0.043* 0.956* 0.037* 0.963* 0.049* 0.951*
(0.004) (0.004) (0.005) (0.005) (0.008) (0.008)
LUNA-XRP 0.057* 0.902* 0.001 0.909* 0.064* 0.905*
(0.012) (0.031) (0.009) (0.088) (0.012) (0.023)
LUNA-XLM 0.050* 0.913* 0.000 0.920* 0.057* 0.915*
(0.018) (0.030) (0.000) (0.063) (0.018) (0.024)
LUNA-DOGE 0.051* 0.921* 0.010 0.844* 0.063* 0.924*
(0.016) (0.019) (0.029) (0.051) (0.018) (0.017)
LUNA-DASH 0.038** 0.923* 0.000 0.879* 0.043* 0.926*
(0.018) (0.032) (0.000) (0.253) (0.019) (0.026)
LUNA-LTC 0.054* 0.916* 0.000 0.918* 0.067* 0.912*
(0.013) (0.017) (0.000) (0.127) (0.012) (0.014)
XRP-XLM 0.064* 0.921* 0.059* 0.933* 0.063* 0.912*
(0.007) (0.009) (0.012) (0.014) (0.011) (0.015)
XRP-DOGE 0.078* 0.919* 0.0626* 0.916* 0.054* 0.946*
(0.007) (0.007) (0.019) (0.033) (0.000) (0.000)
XRP-DASH 0.113* 0.846* 0.084* 0.908* 0.038* 0.930*
(0.014) (0.019) (0.023) (0.026) (0.010) (0.016)
XRP-LTC 0.057* 0.941* 0.057* 0.941* 0.061* 0.938*
(0.006) (0.006) (0.006) (0.006) (0.017) (0.018)
XLM-DOGE 0.088* 0.907* 0.073* 0.793* 0.112* 0.887*
(0.008) (0.008) (0.024) (0.071) (0.009) (0.009)
XLM-DASH 0.113* 0.846* 0.071* 0.921* 0.206* 0.544*
(0.019) (0.014) (0.013) (0.015) (0.037) (0.072)
XLM-LTC 0.078* 0.914* 0.060* 0.933* 0.090* 0.895*
(0.008) (0.009) (0.011) (0.012) (0.014) (0.018)
DOGE-DASH 0.056* 0.938* 0.080* 0.870* 0.035* 0.500
(0.009) (0.011) (0.022) (0.043) (0.000) (0.130)
DOGE-LTC 0.036* 0.962* 0.117* 0.764* 0.003* 0.230*
(0.008) (0.008) (0.040) (0.085) (0.000) (0.000)
DASH-LTC 0.076* 0.864* 0.041* 0.957* 0.078** 0.797*
(0.018) (0.033) (0.007) (0.007) (0.031) (0.088)
2017201820192020202020212022
0,2 0,0 -0,2
0,40,80,81,0BTC-ETH ETH-DOGE 0,2 0,00,40,80,81,0 2017201820192020202020212022
2017201820192020202020212022
BTC-XRP 0,2 0,0 -0,20,40,80,81,0BTC-XLM 0,2 0,0 -0,20,40,80,81,0 2017201820192020202020212022 BTC-DASH -0,2 -0,4 -0,80,00,4 0,20,8 0,81,2 1,0 2017201820192020202020212022
BTC-DOGE 0,2 0,0 -0,20,40,80,81,0 2017201820192020202020212022
BTC-LTC 0,2 0,0 -0,20,40,80,81,0 2017201820192020202020212022
ETH-XRP 0,2 0,00,40,80,81,0 2017201820192020202020212022 ETH-XLM 0,2 0,00,40,80,81,0 2017201820192020202020212022
ETH-DASH 0,2 0,00,40,80,81,0 2017201820192020202020212022
ETH-LTC 0,6 0,50,70,80,91,0 2017201820192020202020212022
XLM-DOGE -0,20,2 0,00,8 0,40,81,0 2017201820192020202020212022
XLM-DASH -0,2 -0,4 -0,80,00,4 0,20,8 0,81,0 2017201820192020202020212022
XRP-DASH -0,2 -0,4 -0,80,00,4 0,20,8 0,81,0 2017201820192020202020212022 XLM-LTC 0,2 0,00,40,80,81,0 2017201820192020202020212022
XRP-DOGE 0,2 0,0 -0,20,40,80,81,0 2017201820192020202020212022
XRP-LTC 0,2 0,00,40,80,81,0 2017201820192020202020212022
XRP-XLM 0,3 0,20,5 0,40,7 0,60,9 0,81,0 2017201820192020202020212022
DOGE-LTC 0,2 0,0 -0,20,40,80,81,0 2017201820192020202020212022
DOGE-DASH 0,2 0,0 -0,4-0,20,40,80,81,0 2017201820192020202020212022
DASH-LTC 0,2 0,0 -0,20,40,80,81,0 2017201820192020202020212022 LUNA-BTC 0,2 0,10,30,40,50,7 0,6 20192020202020212022
LUNA-ETH 0,2 0,00,10,30,40,50,8 0,7 0,6 20192020202020212022
LUNA-XRP 0,2 0,0 0,20,40,8 0,6 20192020202020212022
LUNA-XLM 0,2 0,0 0,20,40,8 0,6 20192020202020212022
LUNA-DOGE 20192020202020212022
0,2 0,0 0,2
0,4
0,8 0,6
LUNA-DASH 20192020202020212022
0,2 0,0
0,4
0,8 0,6
LUNA-LTC 20192020202020212022
0,2 0,0 -0,2
0,4
0,6
0,8
1,0
Figure 5. DCC Analysis of Cryptocurrencies – Full Sample Dynamic Conditional correlation of different combinations of cryptocurrencies from November 10, 2017 to April 26, 2022 (full sample).
Figures 5, 6, and 7 represent the DCC between the cryptocurrencies in full, pre-crisis, and post-crisis periods. Figure 5 shows that the dynamic correlations were highly volatile during 2017 and early 2018 after which volatility increased during the peak of COVID-19. Further, the evidence suggests that the volatility of dynamic correlation had decreased significantly. The interdependency increased within the cryptocurrency market. Figure 7 helps us understand that the crisis affected the cryptocurrency market, and the dynamic correlation became volatile due to COVID-19 and its high uncertainty, increasing the co-movement. Further, the interdependency within the cryptocurrency market has increased significantly, except for some pairs containing DOGE and LUNA. Therefore, the evidence suggests that DOGE and LUNA provide an avenue for portfolio diversification for crypto-investors within the cryptocurrency market.
Figure 6. DCC Analysis of Cryptocurrencies – Pre-crisis Period Dynamic Conditional correlation results for the pre-crisis period
Figure 7. DCC Analysis of Cryptocurrencies – Post-crisis Period Dynamic Conditional correlation results for the post-crisis period.
IV. CONCLUSION
This paper examined whether the occurrence of an unanticipated event, such as the COVID-19 crisis, has strengthened the contagion in the cryptocurrency market.
To this end, the paper utilized one full sample and two sub-samples representing the pre-crisis and post-crisis periods. The study employed wavelet coherence analysis to identify the interdependency between cryptocurrencies in both time and frequency domains. We then conducted robustness checks by applying a DCC-GARCH model. Overall, we found that the cryptocurrency market started integrating in 2018, indicating an increase in cryptocurrency co-movement.
Notably, the contagion strengthened significantly during the COVID-19 crisis, wherein BTC, LTC, XLM, XRP, ETH, and DASH exhibited significantly increasing co-movement. However, DOGE and LUNA showed weak interdependence, indicating less contagion risk. The findings imply that in the face of high uncertainty caused by the unanticipated occurrence of the COVID-19 crisis, DOGE and LUNA can be utilized as potential prospects for portfolio diversification within the cryptocurrency market to minimize loss associated with the contagion risk.
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