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Table 1 Summary of literature

From: Liquidity connectedness in cryptocurrency market

No References Empirical model (s) Data period Variables used Key findings
1 Omane-Adjepong and Alagidede (2019) Multiscale wavelet method; Linear and nonlinear causality; GJR-GARCH 8 May 2014 to 12 February 2018 BitShare, Bitcoin, Litecoin, Ripple, Monero, Stellar, and DASH Pairwise ranking for diversification and multiple correlations exist; returns (volatility) interactions are scale- and proxy-sensitive; relatively efficient diversification over the short- and medium-terms; and the direction of shock transmission seems non-homogeneous
2 Balli et al. (2020) Baruník and Křehlík (2018); Continuous Wavelet Transform; Rolling-Window Wavelet Correlation 5 August 2014 to 23 July 2018 Bitcoin, Litecoin, Ripple, Monero, Stellar, Dash, EPU Index, VIX, OVX, and GVZ Despite drift resemblance across all phases, the short-term connectedness is considerably higher than the medium- and long-term counterparts; increasing connectedness coincides with the popularity of cryptocurrencies; rising economic uncertainty leads to decreasing connectedness
3 Zięba et al. (2019) Minimum-Spanning Tree (MST); VAR 01 September 2015 to 02 May 2018 78 cryptocurrencies including Bitcoin Bitcoin was the essential cryptocurrency before 2017, after which Dogecoin has assumed this leading role; causality exists among cryptocurrencies, apart from Bitcoin
4 Yi et al. (2018) Diebold and Yılmaz (2014); Diebold and Yılmaz (2014) with LASSO-VAR 4 August 2013 to 1 April 2018;
1 December 2016 to 1 April 2018
52 cryptocurrencies Market capitalization partly drives the cryptocurrency connectedness; unpopular cryptocurrencies, such as Maidsafe Coin become volatility transmitters
5 Katsiampa et al. (2019) BEKK-MGARCH 7 August 2015 to 10 July 2018 Bitcoin, Ethereum, and Litecoin Shock transmission between Litecoin (Ethereum) and Bitcoin is bi-directional; conditional correlations are time-varying and predominantly positive
6 Xu et al. (2020) TENET Framework 18 April 2016 to 16 May 2019 23 Cryptocurrencies, VIX, Gold Bullion Price, the S&P500 composite index, and the S&P400 commodity chemicals index Risk spillover is significant; a steady rise in the overall connectedness among cryptocurrencies over time; Bitcoin (Ethereum) is the largest receiver (transmitter) of systemic risk
7 Borri and Shakhnov (2019) Panel Regression 3 January 2017 to 27 April 2017 Bitcoin price listed at several exchanges Domestic regulatory changes bring about significant spillovers among cryptocurrencies; relative Bitcoin prices and trading volume rise in countries sharing borders
8 Moratis (2020) Bayesian Vector Autoregressive Mode October 2016 to May 2020, 30 largest-cap cryptocurrencies Spillovers among cryptocurrencies are not solely determined by size; increased spillovers combine with greater market integration; internal factors are more critical than external ones
9 Luu Duc Huynh (2019) VAR-SVAR Granger Causality; Student' s-t Copulas 8 September 2015 to 4 January 2019 Bitcoin, Litecoin Ethereum, Stellar, and XRP Ethereum exhibits the potential to decouple from other cryptocurrencies, whereas Bitcoin seems to be a spillover recipient
10 Baumöhl (2019) Detrended Moving-Average Cross-Correlation; Quantile Cross-Spectral Approach (Baruník and Kley 2019) 1 September 2015 to 29 December 2017 Bitcoin, Litecoin, Ethereum, Stellar Lumens, Ripple, and NEM; Japanese Yen, Euro, Swiss Franc, British Pound, Chinese Yuan, and Canadian Dollar Cryptocurrencies are not as tightly interconnected as they appear; intra-group (inter-group) interactions under extreme lower quantiles are positive (negative)
11 Ji et al. (2019) Diebold and Yilmaz (2012, 2015) 7 August 2015 to 22 February 2018 Bitcoin, Litecoin, Ethereum, Stellar, Ripple, and Dash Return connectedness network is centered around Bitcoin (Litecoin); negative returns are more tightly connected than positive ones; global financial uncertainty effects and trading volume drive spillovers
12 Antonakakis et al. (2019) TVP-FAVAR Connectedness Framework; DCC-GARCH t-Copula; Dynamic Optimal Portfolio Weights; Dynamic Hedge Ratios; Hedge Effectiveness 7 August 2015 to 31 May 2018 Bitcoin, Bitshares, Ethereum, Ripple, Litecoin, Dash, Monero, Nem, and Stellar Overall, cryptocurrency connectedness shows huge dynamic changes; amplified prospects for heightened connectedness over time; the magnitude of connectedness is associated with cryptocurrency uncertainty; Ethereum transfer shocks to Bitcoin after the recent hyper-volatility episode of Bitcoin
13 Bouri et al. (2019a) Time–Frequency Granger-causality Test (Bodart and Candelon 2009) 8 August 2015 to 18 February 2019 Bitcoin, Ethereum, Litecoin, Monero, Ripple, Dash, Stellar, and Nem In some cryptocurrencies, short- and long-run causalities differ from each other; permanent (transitory) shocks dominate over shorter (longer) horizons
14 Bouri et al. (2019b) GSADF Test (Phillips et al. 2015); Logistic Regression 7 August 2015 to 31 December 2017 Bitcoin, Litecoin, Ripple, Ethereum, Nem, Stellar, and Dash Multiple explosivity periods are found in all cases, while explosivity transfers across cryptocurrencies; co-explosivity does not necessarily transfer from bigger to smaller cryptocurrencies
15 Bouri et al. (2019c) Semi-Parametric Approach (Laurent et al. 2016); Co-Jumping Method (Ma et al. 2019); Logistic Regression 8 August 2015 to 28 February 2019 Bitcoin, Bytecoin, Bitshares, Dash, Dogecoin, Digibyte, Litecoin, Ethereum, Nem, Monero, Stellar, and Ripple While all cryptocurrencies undergo jumps, some experience co-jumping coinciding with the jumping of the trading volume. This confirms the trading volume's importance for cryptocurrency volatility
16 Fousekis and Tzaferi (2021) Diebold and Yilmaz (2012); Baruník and Křehlík (2018) January 2018 to March 2020 Bitcoin, Litecoin, Ethereum, and Ripple Volume data improves the profitability of technical trading. Rational but uninformed traders can benefit from trend analysis. Positive returns may lead to changes in investor expectations
17 Bouri et al. (2021b) Diebold and Yilmaz (2012) based on quantile VAR 8 August 2015 to 31 December 2020 Bitcoin, Ethereum, Litecoin, Dash, Monero, Ripple, and Stellar Connectedness becomes stronger with the magnitude of positive and negative shocks. Return connectedness over extreme market conditions is asymmetric
18 Luu Duc Huynh (2019) SVAR; Granger causality; Student’s-t Copulas 8 September 2015 to 4 January 2019 Bitcoin, Litecoin, Ethereum, Xrp, and Stellar Ethereum is disentangled from the spillover network, whereas Bitcoin is the spillover recipient
19 Caporale et al. (2021) Trivariate GARCH-BEKK 12 August 2015 to 15 January 2020 Bitcoin, Ethereum, and Litecoin Cyber-attacks influence the spillover transmission between cryptocurrency return and volatility, strengthening the connection and thus reducing opportunities for portfolio diversification
20 Huynh et al. (2020) Transfer Entropy April 2013 to April 2019 14 Cryptocurrencies Cryptocurrencies with smaller market capitalization turn out to be shock transmitters than the larger ones
  1. VAR, Vector Auto-Regression; GARCH, Generalized Autoregressive Conditional Heteroskedasticity; MGARCH, Multivariate Generalized Autoregressive Conditional Heteroskedasticity; DCC, Dynamic Conditional Correlation; SVAR, Structural Vector Auto-Regression; TVP-FAVAR, Time-Varying Parameter Factor Augmented VAR; BEKK, Baba, Engle, Kraft, and Kroner; GJR, Glosten-Jagannathan-Runkle; TENET, Tail-Event driven NETwork; GSADF, Generalized Supremum Augmented Dickey-Fuller; LASSO, Least Absolute Selection and Shrinkage Operator