Are cryptocurrencies becoming more interconnected?
Nektarios Aslanidis, Aurelio F. Bariviera, Alejandro Perez-Laborda
AAre cryptocurrencies becoming more interconnected?
Nektarios Aslanidis , Aurelio F. Bariviera ∗ , and Alejandro Perez-Laborda Universitat Rovira i Virgili, Department d’Economia, ECO-SOS, Reus, Spain Universitat Rovira i Virgili, Department of Business, Reus, Spain
October 1, 2020
Abstract
This paper studies the dynamic market linkages among cryptocurrencies during August 2015- July 2020 and finds a substantial increase in market linkages for both returns and volatilities.We use different methodologies to check the different aspects of market linkages. Financial andregulatory implications are discussed.
Keywords:
Cryptocurrencies; Market Linkages; Diversification
JEL codes:
C4; G01; G14
Bitcoin, the first-ever created cryptocurrency, stems from a proposal to bypass the establishedfinancial system to make peer-to-peer payments. Such initiative, born in the aftermath of thefinancial crisis of 2007-2008, was funneled using an anonymously posted white paper (Nakamoto,2009). The initial popularity of Bitcoin came from the libertarian point of view, advocating for acompetition of this peer-to-peer system with fiat money to overcome national boundaries. Despiteits impracticability as a means of payment , market participants soon acknowledged it as a newinvestment instrument. Popularity has been gaining momentum and stimulating the creation of newalternative cryptocurrencies (altcoins). Litecoin was created in 2011, Ripple in 2013, Dash, NEO,and Monero in 2014, to name a few. As of July 2020, there are more than 5000 cryptocurrencies, witha total market capitalization of $ B 344. Even though Bitcoin is still the leading player, accountingfor 60% of the market capitalization, it has been losing market participation, especially since June2017 (Coinmarket, 2020).Cryptocurrencies have been consolidated as an alternative investment to traditional assets. Con-sidering the increase in the number of coins, some cryptocurrency development companies begin todesign cryptocurrency indices to monitor the evolution of the market. Also, some investors havestarted considering investing in portfolios predominantly constituted by different cryptocurrencies.In light of these recent market developments, this paper aims at examining the dynamic marketlinkages in cryptocurrencies for both returns and volatility. We believe our findings have importantimplications for active diversification strategies in portfolios consisting of several cryptocurrencies,and for prudential regulation regarding the stability of the market. The paper tackles these ques-tions by studying the evolution of return and volatility linkages across cryptocurrencies since 2015.The contribution to the existing literature is multiple. First, we employ an extended sample that in-cludes recent data up to July 2020, thus covering recent events that might have largely affected these ∗ [email protected] According to Coinmap (2020), there are only 16614 venues (cafeterias, groceries, ATMs, etc.) in the world thataccept Bitcoin as means of payment. a r X i v : . [ q -f i n . S T ] S e p arkets, such as the COVID-19 pandemic. Second, we adopt different econometric methodologiesto cross-check the relevance of our findings. Third, we assess the linkages across frequency-ranges,which allow us to distinguish whether the transmitted shocks across cryptocurrencies have shortor long-run effects. This distinction is crucial to interpret connectedness in terms of systemic riskbecause market participants have different preferences over trading horizons. The rest of the paper is organized as follows: section 2 conducts a brief literature review; section3 outlines the methodology used in the paper; section 4 describes data and discusses the main results;finally, section 5 summarize the main conclusions.
The early financial literature on cryptocurrencies mainly focuses on the assessment of the informa-tional efficiency of Bitcoin. Urquhart (2016) employs autocorrelation, runs, and variance ratio testsover 2010 -2016 and finds that although Bitcoin showed signs of inefficiency in the initial period of2010-2013, it evolved towards a more efficient market later on. Shortly afterward, Bariviera (2017)shows that although returns have become more efficient over time, volatility still exhibits substantialpersistence. Tiwari et al. (2018) later confirms these results.A different stream of the literature studies the relationships across cryptocurrencies, or betweencryptocurrencies and traditional assets. Corbet et al. (2018), for example, provides evidence of arelative detachment of Bitcoin, Ripple, and Litecoin, from stocks, government bonds, and gold in-dices, thus offering some diversification benefits for investors in the short term. In a similar vein,Aslanidis et al. (2019) find a positive but time-varying conditional correlation among cryptocur-rencies (Bitcoin, Ripple, Dash, Monero), and confirm their negligible relationship with traditionalassets. Additionally, Vidal-Tom´as et al. (2019) finds evidence of herding behavior during downmarkets, and that the smallest coins follow the path of the larger ones (not only that of Bitcoin).In a more recent paper Bouri et al. (2020) reports that the average return equicorrelation betweencryptocurrencies is upward trending, which suggests that market linkages are increasing over time.Kurka (2019) argues that although Bitcoin seems isolated from other financial assets over the entireperiod, market linkages arise when sub-periods are carefully examined.Perhaps, the most closely related works are Yi et al. (2018) and Ji et al. (2019). These twostudies are based on the connectedness methodology of Diebold and Yilmaz (2009, 2012, 2014)and quantify the interdependencies across cryptocurrencies using data up to 2018. In particular,Yi et al. (2018) analyzes return and volatility connectedness between six leading cryptocurrencies,stressing the importance of Bitcoin and Litecoin as sources of uncertainty. Ji et al. (2019) focusesonly on volatility linkages using a large set of coins.The authors document a period of increasinginterdependencies across volatilities from mid- 2016, emphasizing the role of cryptocurrencies otherthan Bitcoin in emitting uncertainty.We complement these two interesting studies in two ways. First, we study both return andvolatility linkages adopting a more general set of methodologies and a broader time coverage (up toJuly 2020), thus capturing the most recent events. In addition, we quantify the market linkages acrossfrequency ranges, determining the specific frequencies at which cryptocurrencies are more tightlyconnected. The frequency-domain analysis allows us to document new stylized facts about thecyclical properties of the transmission mechanism, which are essential to make an overall assessmentof systemic risk.For the sake of brevity, we refer to two recent surveys covering most aspects of cryptocurrenciesresearch topics (Corbet et al., 2019; Merediz-Sol`a and Bariviera, 2019). Consider, for example, that cryptocurrencies were tight at high frequencies only. In such a situation, transmittedshocks are not persistent, having short-term effects only. As a result, interdependencies would not be much of an issuefor an agent looking for long-run profits but would matter for a short-term trader. Methodology
We conduct the empirical analysis using three different methodologies to assess cryptocurrency mar-ket linkages. Our first approach, Principal Component Analysis (PCA), is a statistical method thatconverts a set of correlated variables into a set of uncorrelated components through an orthogo-nal transformation. PCA aims to reduce the dimension of the data retaining as much variance(information) as possible. See, e.g., Wei (2019) for further details about PCA methodology.Our second approach to assess cryptocurrency market linkages is based on the estimation ofcross-sectional dependence. Specifically, we first obtain the pair-wise cross-sectional correlationsof the cryptocurrencies, ˆ ρ ij . Then, we calculate the average correlations across all pairs as ˆ ρ =(2 /N ( N − (cid:80) N − i =1 (cid:80) Nj = i +1 ˆ ρ ij , and the associated cross-sectional dependence statistic of Pesaran(2015) as CD = [ T N ( N − / / ˆ ρ. Pesaran (2015) establishes that the implicit null hypothesis ofthe CD test is that of weak cross sectional dependence versus the alternative hypothesis of strongcross sectional dependence .Our third approach consists of constructing quantitative measures of market interdependence(or connectedness) based on the vector autorregression (VAR) framework of Diebold and Yilmaz(2009, 2012, 2014). This methodology has also been used by other papers in the literature (Yiet al., 2018; Ji et al., 2019). Beside this, we also follow the approach of Barunik and Krehlik (2018),who extend the traditional Diebold-Yilmaz framework to the frequency-domain. The advantageof the frequency-domain is twofold. First, the frequency-domain analysis allows us to distinguishwhether shocks across cryptocurrencies have long- or short-term effects. Second, one can recoverstandard, (time-domain) indices by aggregating frequency-domain connectedness measures over allfrequencies. Thus, the approach in Barunik and Krehlik (2018) allows for a simultaneous assessmentover time and across frequencies. Corbet et al. (2018) for example, also rely on this approach whenassessing connectedness between cryptocurrencies and traditional assets. We briefly discuss themajor features of the frequency-domain measures in the supplementary material. However, for thestandard time-domain indices, we refer to Diebold and Yilmaz (2012, 2014). The empirical analysis employs daily data for seventeen major cryptocurrencies obtained from https://coinmarketcap.com . Since these cryptocurrencies were not launched at the same time,to capture more information we expand our sample adding more coins as we move through in time.Sample 1 includes seven important cryptocurrencies traded since (at least) August 2015. Sample2 adds three more cryptocurrencies with data starting in October 2016. Finally, Sample 3 addsseven more coins, and data coverage starts in October 2017. Returns are computed as the log-pricedifferences. We follow Diebold and Yilmaz (2012), and estimate daily range-based return volatilitiesfrom open, close, high, and low daily prices, as in Garman and Klass (1980). Given that realizedvolatilities are right-skewed but approximately Gaussian after taking logs (Andersen et al., 2001),we consider logarithmically transformed volatilities as time series for our estimations, as in Dieboldand Yilmaz (2016) or Demirer et al. (2018).We first conduct PCA for returns and volatilities, separately. Following the literature, we stan-dardize the data before applying PCA to prevent undue influence of a variable. We provide com-parative analysis, dividing the data into non-overlapping one-year samples. The percentage of thevariance explained by the first principal component is reported in Table 1. The table also providesthe squared component loadings, which are just the squared correlations between the first principalcomponent and each of the variables. Analogous to Pearson’s R-squared, the squared componentloading measures the percentage of the variance in that variable explained by the principal compo-nent. For further developments on cross-sectional dependence, we refer to the Special Issue edited by Bai et al. (2016)
Sample 1 (7 coins)
Returns Volatilities08/08/2015 08/08/2016 08/08/2017 08/08/2018 08/08/2019 08/08/2015 08/08/2016 08/08/2017 08/08/2018 08/08/201907/08/2016 07/08/2017 07/08/2018 07/08/2019 17/07/2020 07/08/2016 07/08/2017 07/08/2018 07/08/2019 17/07/2020% Variance explained by first PC 36% 37% 62% 80% 83% 41% 54% 70% 73% 71%Squared component loading BTC 76% 49% 60% 79% 86% 61% 63% 76% 76% 67%DASH 21% 39% 59% 78% 65% 36% 49% 69% 74% 69%ETH 7% 35% 80% 88% 93% 27% 66% 76% 85% 83%LTC 68% 36% 68% 79% 91% 43% 67% 75% 74% 83%XLM 27% 36% 49% 79% 74% 40% 55% 58% 64% 48%XMR 31% 41% 70% 83% 85% 39% 13% 71% 76% 72%XRP 19% 19% 50% 70% 87% 37% 61% 67% 63% 74%
Sample 2 (10 coins)
Returns Volatilities30/10/2016 30/10/2017 30/10/2018 30/10/2019 30/10/2016 30/10/2017 30/10/2018 30/10/201929/10/2017 29/10/2018 29/10/2019 17/07/2020 29/10/2017 29/10/2018 29/10/2019 17/07/2020%Variance explained by first PC 37% 65% 79% 82% 48% 75% 72% 72%Squared component loading BTC 52% 56% 79% 87% 55% 79% 76% 64%DASH 44% 62% 80% 66% 50% 76% 72% 75%ETC 52% 64% 72% 79% 54% 73% 74% 73%ETH 50% 80% 89% 92% 67% 81% 83% 81%LTC 46% 67% 78% 91% 57% 80% 72% 85%NEO 26% 66% 80% 84% 24% 78% 78% 65%XLM 27% 58% 76% 77% 49% 70% 55% 55%XMR 48% 74% 82% 86% 45% 78% 68% 75%XRP 13% 52% 78% 87% 54% 61% 68% 76%ZEC 16% 74% 79% 72% 25% 69% 71% 70%
Sample 3 (17 coins)
Returns Volatilities03/10/2017 03/10/2018 03/10/2019 03/10/2017 03/10/2018 03/10/201902/10/2018 02/10/2019 17/07/2020 02/10/2018 02/10/2019 17/07/2020% Variance explained by first PC 54% 74% 81% 54% 74% 81%Squared component loading ADA 47% 85% 84% 47% 85% 84%BCH 47% 69% 88% 47% 69% 88%BNB 38% 54% 89% 38% 54% 89%BTC 54% 76% 87% 54% 76% 87%DASH 59% 78% 63% 59% 78% 63%EOS 54% 80% 90% 54% 80% 90%ETC 63% 72% 78% 63% 72% 78%ETH 78% 89% 93% 78% 89% 93%LTC 62% 79% 92% 62% 79% 92%MIOTA 58% 71% 75% 58% 71% 75%NEO 63% 84% 78% 63% 84% 78%TRX 34% 68% 87% 34% 68% 87%XLM 44% 76% 76% 44% 76% 76%XMR 71% 80% 84% 71% 80% 84%XRP 49% 77% 86% 49% 77% 86%XTZ 23% 45% 64% 23% 45% 64%ZEC 70% 77% 69% 70% 77% 69% ρ , computed using different num-ber of coins in each sample and in non-overlapping 1-year windows. Coins and windows dates arethe same as in Table 1Next, we conduct the Pesaran (2015) test for cross-sectional dependence. Our results stronglyreject the null hypothesis of weak in favor of strong cross-sectional dependence in all cases . Theaverage correlation across all pairs (for returns and volatilities, separately) is plotted in Figure 1.The increasing cross-sectional correlations over time are consistent with previous findings obtainedusing PCA. Again, towards the end of the sample the increase in correlation is slightly strongerin returns than in volatilities. Thus, our results support previous findings of steadily increasingcross-cryptocurrency market linkages.Finally, to quantify the strength of these market linkages we carry out a more extensive linkageanalysis using the methodologies developed in Diebold and Yilmaz (2009, 2012, 2014) and Barunikand Krehlik (2018). For each dataset, we analyze the dynamic evolution of connectedness estimatingVARs for returns and for volatilities in a rolling window fashion. As in Barunik and Krehlik (2018),the usual (time-domain) connectedness indices are computed by aggregating frequency connectednessover all ranges, but the results are identical to those obtained from finite-horizon formulas with the Detailed results of CD test are not reported in the paper, but they are available upon request. The results in the paper are based on a vector autoregression of order four and a rolling window length of 365days (one year) (a) Returns(b) Volatilities
Figure 2: Total connectedness in returns and volatilties, using rolling windows.Figure 2 depicts the dynamic evolution of the total connectedness indices for returns (upperpanel) and for volatilities (bottom panel) using the three samples of coins. Total connectedness(also called spillover index) measures, on average, the percentage of the variance explained by shockstransmitted across coins. As Figure 2 shows, total connectedness exhibits a substantial upward trendfor both returns and volatilities. Specifically, the index for returns rises from nearly 25% to 80% over2016-2020 while for volatilities from roughly 30% to 76% during the same period. Thus, more thanthree quarters of the return and volatility variances are currently explained by shocks transmittedacross cryptocurrencies. Notice that, despite the different starting dates, the magnitude and patternof total connectedness are very similar across the three samples, that is, our findings are robust andtherefore independent of the number of coins considered.Since 2019 total connectedness stabilizes around 0.8, leaving little room for further increases.Importantly, we observe a sudden upward jump around March 2020 in all plots, which is contem-poraneous to stock market crashes worldwide arising from the COVID-19 pandemic. This resultindicates that such a global shock has helped to knit even tighter relationships among cryptocur-rencies. This finding broadens the results by Goodell and Goutte (2020) regarding the influence ofCOVID-19 on Bitcoin prices.The directional connectedness indices offer further insight into the evolution of the cryptocurrencymarket linkages. In Table 2 we report the complete connectedness tables for returns (also knownas spillover table) estimated using the first and last years of the seven coins sample (Sample 1).Results in Table 2 provide strong evidence of an across the board increase in return connectednessalready depicted in Figure 2. Moreover, a comparison of the pairwise and the total FROM and TOconnectedness indices shows that the relative importance of the different cryptocurrencies have also6able 2: Return connectedness table for Sample 1 (7 coins)
Period 08/08/ 2015 - 07/08/2016. Total connectedness: 0.32BTC DASH ETH LTC XLM XMR XRP Total fromBTC 0.47 0.05 0.01 0.30 0.05 0.09 0.04 0.53DASH 0.08 0.79 0.01 0.06 0.02 0.02 0.02 0.21ETH 0.02 0.02 0.88 0.01 0.02 0.03 0.01 0.12LTC 0.32 0.04 0.01 0.52 0.05 0.04 0.04 0.48XLM 0.09 0.02 0.02 0.08 0.67 0.03 0.09 0.33XMR 0.13 0.03 0.03 0.07 0.03 0.70 0.02 0.30XRP 0.05 0.02 0.02 0.05 0.11 0.02 0.73 0.27Total to 0.69 0.17 0.10 0.57 0.27 0.23 0.21Period 19/07/2019 - 18/07/2020. Total connectedness: 0.79BTC DASH ETH LTC XLM XMR XRP Total fromBTC 0.20 0.09 0.17 0.15 0.10 0.15 0.13 0.80DASH 0.12 0.26 0.14 0.14 0.10 0.13 0.12 0.74ETH 0.15 0.10 0.19 0.16 0.11 0.14 0.15 0.81LTC 0.15 0.10 0.16 0.19 0.12 0.14 0.15 0.81XLM 0.12 0.08 0.14 0.14 0.22 0.13 0.16 0.78XMR 0.15 0.10 0.15 0.14 0.11 0.21 0.13 0.79XRP 0.13 0.09 0.16 0.15 0.14 0.13 0.20 0.80Total to 0.82 0.57 0.92 0.89 0.68 0.82 0.84 changed dramatically over time. During the first year, spillovers are mostly driven by Bitcoin andLitecoin, while other coins seem less interconnected. This result is consistent with the findings in Yiet al. (2018) using data up to February 2018. However, the situation changes remarkably in recentyears, since all coins are now about equally important in transmitting/receiving return spillovers(shocks across markets represent between 74% to 80% of the return variances of coins).The directional connectedness analysis of the volatility linkages is similar to that of returns. Seesupplementary material for detailed results. Overall, our results show that cryptocurrencies have become increasingly interconnected in bothreturn and volatility over the recent period, emerging as a compactly integrated market. Theseresults complement the previous findings on volatility in Ji et al. (2019) and highlight that thecryptocurrency market is becoming significantly more vulnerable to within shock transmissions.We further adopt the Barunik and Krehlik (2018) approach to evaluate connectedness in thefrequency domain (high vs. low frequencies). The frequency domain approach would help us disen-tangle the specific frequencies that have most contributed to the observed rise in connectedness. Thehigh-frequency range includes frequencies with periods from one to seven days (one week), while thelow-frequency range frequencies with periods longer than one week. To the best of our knowledge,the frequency domain method is not performed in the other studies in the literature focusing on theexisting relationships across cryptocurrencies, such as Yi et al. (2018) and Ji et al. (2019).Figure 3 plots connectedness measures by frequency ranges obtained using the seven-coin sample(Sample 1)– corresponding figures for the other samples with more coins can be found in the Supple-ment. The upper panel of 3 depicts the decomposition of the total connectedness index in 2 into twofrequency connectedness components: the high frequency and low frequency components. Noticethat connectedness at the two frequency ranges add up the total connectedness index. The middlepanel plots the connectedness created within the specific ranges. Finally, the bottom panel plotsthe weights used to transform within connectedness into the frequency connectedness components,which measures the relative importance of high and low fluctuations on total variance. The supplement also provides connectedness tables obtained with the seventeen coins sample (Sample 3)
This paper broadens previous studies on cryptocurrency market linkages. We tackle this issue byan ensemble of methodologies to examine return and volatility linkages across the major coins overthe last five years. To account for the fact that new coins are being introduced in the market, weconduct our analysis using extended samples with an increasing number of coins. Irrespective of themethodology adopted, we document that the cryptocurrency market has experienced a strong overallincrease in market linkages (return and volatility). As of July 2020, only few coin-specific shocks arenot transmitted to the rest of the coins (less than 20%). The insights provided by the frequency-domain approach have provided new stylized facts on the shock transmission mechanism acrosscryptocurrencies. The paper uncovers that the transmitted shocks have mostly short-term effects onreturns. This result is in line with the view that the cryptocurrency market makes significant stepstowards becoming efficient. Although a significant part of volatility connectedness is still createdat low-frequencies, we show that volatility transmission at high frequencies has currently becomeconsiderably more important.Our results have several practical implications. First, there are now limited diversification ben-efits in the cryptocurrency market, with active portfolio re-balancing becoming mostly irrelevant.Second, cryptocurrency indices hardly add any information about market evolution beyond thatconveyed by any individual cryptocurrency. Third, from a regulatory perspective, if cryptocurren-cies were to become legal tender at some point in time, policymakers should evaluate the potentiallydisruptive effects of such a highly interconnected market. Nevertheless, the observed decline of therelative importance of low-frequency transmission is favoring long-term investors in terms of smallerexposure to systemic risk. 9
UPLEMENTARY MATERIALA Connectedness in the frequency domain.
In this section, we briefly discuss the major features of the frequency domain connectedness devel-oped in Barunik and Krehlik (2018). Like in the typical, time-domain Diebold and Yilmaz (2012)framework, connectedness measures in the frequency domain are based on the generalized VAR(Pesaran and Shin, 1998) to deal with (possibly) correlated innovations, but the authors employthe spectral decomposition of the variance instead of the forecast error variance decomposition. Werefer to Barunik and Krehlik (2018) for further details.Let i be the imaginary unit, and R = ( a, b ) : a, b ∈ ( − π, π ) , a < b the targeted frequency range.The share of a shock to variable k in variable j ’s fluctuations at the band R is given by:Θ Rj,k = 12 π (cid:90) ba P j ( ω ) f j,k ( ω ) dω, The terms f j,k ( ω ) and P j ( ω ) are the generalized causation (cross-)spectrum of the VAR and thepower of variable j at frequency ω , and the definite integral can be approximated by summationsfor Fourier frequencies ω j = 2 πj/T , j = 1 , . . . T / R . Like inthe time-domain, these shares are normalized as (cid:101) Θ dj,k = Θ Rj,k / (cid:80) k Θ ∞ j,k , where Θ ∞ j,k denotes thecontribution over all frequencies, and arranged after in a matrix of normalized contributions. The within connectedness at the frequency range R is defined from these normalized contributions as: W C R = 1 − T r (cid:110) (cid:101) Θ R (cid:111)(cid:80) (cid:101) Θ R . Within connectedness quantifies the contribution of shock transmission on the fluctuations atthe specific range of frequencies R , on average in the system. However, this index does not accountfor the relative importance of these fluctuations on the total system variance. As a result, overallconnectedness can be low even if within connectedness is high at a particular range. To account forthis, within connectedness is weighted by the relative importance of the band to define frequencyconnectedness at the range: F C R = W C R (cid:80) (cid:101) Θ R (cid:80) (cid:101) Θ ∞ The authors show that frequency connectedness decomposes total (time-domain) connectednessin components at different ranges. Specifically, let C h be the DY total connectedness index at aforecast horizon h (i.e., the spillover index). Consider a set of ranges R s that form a partition of thespace ( − π, π ). Then it can be shown that:lim h →∞ C h = (cid:88) R s F C R s Notice that equality holds on the limit. However, VAR variance decomposition typically convergefast, and the previous equation delivers very good approximations for finite horizons as well, providedthose are not too short.
A.1 Connectedness across frequency ranges for Samples 2 and 3
Below we provide return and volatility connectedness measures for the high and for the low frequencyranges using Sample 2 (10 coins) and Sample 3 (17 coins). Corresponding figures for Sample 1 arereported in the main text. 10igure 4: Connectedness across frequency ranges for Sample 2 with 10 coins (Returns: left panels;Volatilities: right panels). High frequencies: 1-7 day period. Low frequencies: period longer than 7days. 11igure 5: Connectedness across frequency ranges for Sample 3 with 17 coins (Returns: left panels;Volatilities: right panels). High frequencies: 1-7 day period. Low frequencies: period longer than 7days.
B Connectedness tables at selected subsamples
Below we provide tables with results for: (i) Volatility connectedness for Sample 1 (Table 3) com-puted using the first year and the last years of the sample period. The corresponding tables forreturns are provided in the main text; (ii) Return (Table 4) and volatility (Table 5) connectednessfor Sample 3 computed using the last year of the sample period.12able 3: Pairwise volatility connectedness for sample 1
Period 08/08/ 2015 - 07/08/2016. Total connectedness: 0.32BTC DASH ETH LTC XLM XMR XRP Total fromBTC 0.53 0.05 0.04 0.26 0.02 0.04 0.05 0.47DASH 0.10 0.72 0.05 0.05 0.01 0.05 0.02 0.28ETH 0.05 0.09 0.73 0.03 0.04 0.05 0.01 0.27LTC 0.31 0.03 0.04 0.55 0.01 0.03 0.03 0.45XLM 0.09 0.06 0.06 0.05 0.64 0.06 0.05 0.36XMR 0.09 0.09 0.11 0.06 0.04 0.59 0.01 0.41XRP 0.10 0.04 0.05 0.05 0.09 0.03 0.64 0.36Total to 0.69 0.17 0.10 0.57 0.27 0.23 0.21Period 19/07/2019 - 18/07/2020. Total connectedness: 0.71BTC DASH ETH LTC XLM XMR XRP Total fromBTC 0.28 0.10 0.15 0.16 0.06 0.12 0.12 0.72DASH 0.10 0.31 0.13 0.16 0.08 0.10 0.10 0.69ETH 0.14 0.12 0.25 0.18 0.08 0.10 0.13 0.75LTC 0.12 0.12 0.16 0.27 0.08 0.11 0.13 0.73XLM 0.08 0.12 0.13 0.12 0.35 0.08 0.13 0.65XMR 0.12 0.11 0.12 0.16 0.07 0.27 0.13 0.73XRP 0.10 0.10 0.14 0.16 0.10 0.11 0.29 0.71Total to 0.66 0.67 0.84 0.95 0.47 0.63 0.75
Table 4: Pairwise return connectedness for sample 3
Period 19/07/2019 - 18/07/2020. Total connectedness: 0.91ADA BCH BNB BTC DASH EOS ETC ETH LTC MIOTA NEO TRX XLM XMR XRP XTZ ZEC Total fromADA 0.09 0.06 0.06 0.06 0.04 0.06 0.05 0.07 0.07 0.06 0.06 0.06 0.06 0.06 0.06 0.04 0.05 0.91BCH 0.06 0.08 0.06 0.06 0.05 0.07 0.06 0.07 0.07 0.05 0.06 0.06 0.05 0.06 0.06 0.04 0.06 0.92BNB 0.06 0.06 0.08 0.06 0.05 0.06 0.05 0.07 0.06 0.05 0.06 0.06 0.05 0.06 0.06 0.04 0.05 0.92BTC 0.06 0.07 0.07 0.08 0.05 0.06 0.05 0.07 0.07 0.05 0.06 0.06 0.05 0.06 0.06 0.04 0.05 0.92DASH 0.05 0.07 0.06 0.05 0.10 0.06 0.06 0.06 0.06 0.05 0.05 0.06 0.05 0.06 0.05 0.03 0.08 0.90EOS 0.06 0.07 0.06 0.06 0.05 0.08 0.06 0.07 0.07 0.05 0.06 0.06 0.05 0.06 0.06 0.03 0.05 0.92ETC 0.06 0.07 0.06 0.06 0.05 0.06 0.10 0.06 0.07 0.05 0.05 0.06 0.05 0.06 0.06 0.04 0.06 0.90ETH 0.06 0.07 0.07 0.06 0.05 0.06 0.05 0.08 0.07 0.05 0.06 0.06 0.05 0.06 0.06 0.04 0.05 0.92LTC 0.06 0.07 0.06 0.06 0.05 0.07 0.06 0.07 0.08 0.05 0.06 0.06 0.05 0.06 0.06 0.04 0.05 0.92MIOTA 0.06 0.06 0.07 0.06 0.05 0.06 0.06 0.07 0.06 0.09 0.06 0.06 0.05 0.06 0.06 0.04 0.05 0.91NEO 0.06 0.06 0.06 0.06 0.04 0.06 0.05 0.07 0.07 0.05 0.09 0.06 0.05 0.06 0.06 0.04 0.05 0.91TRX 0.06 0.07 0.06 0.06 0.04 0.07 0.05 0.07 0.07 0.05 0.06 0.08 0.05 0.06 0.07 0.04 0.05 0.92XLM 0.06 0.06 0.06 0.05 0.04 0.06 0.05 0.06 0.06 0.06 0.06 0.06 0.10 0.06 0.07 0.04 0.05 0.90XMR 0.05 0.06 0.07 0.07 0.05 0.06 0.05 0.07 0.06 0.05 0.05 0.06 0.05 0.09 0.06 0.04 0.05 0.91XRP 0.06 0.06 0.06 0.06 0.04 0.06 0.05 0.07 0.07 0.05 0.06 0.07 0.06 0.06 0.09 0.04 0.05 0.91XTZ 0.05 0.06 0.07 0.06 0.04 0.05 0.05 0.07 0.06 0.05 0.05 0.05 0.05 0.06 0.06 0.12 0.05 0.88ZEC 0.06 0.06 0.06 0.05 0.07 0.06 0.05 0.07 0.06 0.05 0.05 0.06 0.05 0.06 0.05 0.04 0.10 0.90Total to 0.92 1.03 1.02 0.96 0.76 0.98 0.86 1.08 1.04 0.84 0.88 0.96 0.79 0.92 0.97 0.60 0.86
Period 19/07/2019 - 18/07/2020. Total connectedness: 0.87ADA BCH BNB BTC DASH EOS ETC ETH LTC MIOTA NEO TRX XLM XMR XRP XTZ ZEC Total fromADA 0.13 0.06 0.06 0.05 0.05 0.06 0.05 0.07 0.07 0.06 0.06 0.06 0.05 0.04 0.06 0.03 0.05 0.87BCH 0.05 0.12 0.05 0.05 0.06 0.07 0.07 0.07 0.08 0.04 0.05 0.05 0.03 0.04 0.06 0.04 0.05 0.88BNB 0.05 0.06 0.13 0.06 0.05 0.06 0.05 0.07 0.07 0.05 0.06 0.06 0.03 0.05 0.06 0.04 0.05 0.87BTC 0.05 0.07 0.06 0.13 0.05 0.06 0.04 0.07 0.08 0.05 0.05 0.05 0.03 0.06 0.06 0.03 0.04 0.87DASH 0.05 0.08 0.06 0.05 0.13 0.07 0.06 0.06 0.07 0.05 0.05 0.04 0.04 0.05 0.05 0.03 0.08 0.87EOS 0.05 0.07 0.05 0.05 0.06 0.11 0.06 0.07 0.08 0.05 0.05 0.06 0.03 0.05 0.06 0.03 0.05 0.89ETC 0.05 0.08 0.05 0.04 0.06 0.07 0.13 0.06 0.08 0.05 0.04 0.05 0.04 0.04 0.06 0.03 0.06 0.87ETH 0.05 0.07 0.07 0.06 0.05 0.07 0.05 0.11 0.08 0.06 0.06 0.06 0.03 0.04 0.06 0.03 0.05 0.89LTC 0.05 0.07 0.06 0.06 0.05 0.07 0.06 0.07 0.12 0.05 0.06 0.06 0.04 0.05 0.06 0.04 0.05 0.88MIOTA 0.06 0.06 0.06 0.05 0.05 0.06 0.05 0.07 0.07 0.13 0.06 0.05 0.04 0.04 0.06 0.04 0.06 0.87NEO 0.06 0.06 0.06 0.05 0.05 0.06 0.04 0.07 0.07 0.05 0.14 0.06 0.04 0.04 0.05 0.04 0.06 0.86TRX 0.06 0.06 0.07 0.04 0.04 0.06 0.05 0.06 0.07 0.06 0.06 0.13 0.04 0.04 0.07 0.03 0.05 0.87XLM 0.06 0.06 0.05 0.04 0.06 0.05 0.05 0.06 0.06 0.06 0.05 0.06 0.16 0.04 0.06 0.03 0.06 0.84XMR 0.05 0.06 0.06 0.06 0.05 0.06 0.05 0.06 0.08 0.05 0.06 0.05 0.04 0.13 0.07 0.03 0.05 0.87XRP 0.06 0.06 0.06 0.05 0.05 0.06 0.05 0.06 0.07 0.06 0.05 0.07 0.05 0.05 0.13 0.03 0.05 0.87XTZ 0.04 0.06 0.06 0.04 0.04 0.06 0.04 0.06 0.07 0.05 0.06 0.05 0.04 0.04 0.05 0.19 0.05 0.81ZEC 0.05 0.07 0.05 0.05 0.08 0.06 0.05 0.06 0.07 0.05 0.06 0.05 0.04 0.04 0.05 0.04 0.14 0.86Total to 0.85 1.05 0.92 0.80 0.86 1.02 0.80 1.05 1.16 0.84 0.88 0.86 0.61 0.71 0.94 0.54 0.86
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