Table 1 Summary of literature
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) | 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) | 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 |