The volatility surprise of leading cryptocurrencies: Transitory and permanent linkages

Bouri, Elie; Lucey, Brian M.; Roubaud, David

doi:10.1016/j.frl.2019.05.006

Cited by 58 publications

(37 citation statements)

References 19 publications

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“…Kumar and Ajaz ( 2019 ) apply wavelet-based methods and conclude that Bitcoin is the main driver of cryptocurrency prices. Using a Granger causality framework, Bouri et al ( 2019a ) study volatility linkages in the frequency domain and highlight the importance of large cryptocurrencies, other than Bitcoin. In another study, Bouri et al ( 2019b ) test for jumps in GARCH models and indicate that Bitcoin and 11 large and small altcoins exhibit jumps and co-jumps in their price process.…”

Section: Literature Reviewsmentioning

confidence: 99%

Regime specific spillover across cryptocurrencies and the role of COVID-19

et al. 2021

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The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.

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Section: Literature Reviewsmentioning

confidence: 99%

Regime specific spillover across cryptocurrencies and the role of COVID-19

et al. 2021

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“…Katsiampa et al (2019) report evidence of two-way volatility flows between all the pairs of cryptocurrencies under study, and that the pairwise conditional correlations are positive and vary with time. Bouri et al (2019a) apply a Granger causality approach in the frequency domain and find that Bitcoin is not the only transmitter of volatility, highlighting the importance of other large cryptocurrencies to the network of volatility spillovers. In another study, Bouri et al (2019b) apply a jump-analysis to the price process of 12 leading cryptocurrencies based on GARCH models.…”

Section: Literature Reviewmentioning

confidence: 99%

Volatility connectedness of major cryptocurrencies: The role of investor happiness

Bouri

Gabauer

Gupta

et al. 2021

Journal of Behavioral and Experimental Finance

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In this paper, we first obtain a time-varying measure of volatility connectedness involving fifteen major cryptocurrencies based on a dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model, and then analyze the role of investor sentiment in explaining the movement of the connectedness metric within a quantile-on-quantile framework. Our findings show that lower quantiles of investor happiness, built on Twitter feed data as a proxy for investor sentiment, is positively associated with the entire conditional distribution of connectedness, but the opposite is observed at higher values of investor happiness. In addition, when we look at the effect of sentiment on the common market volatility, we are able to deduce that as investors become exceedingly unhappy, overall market volatility increases and this is associated with high market connectedness. The heightened volatility possibly due to higher trading, seems to suggest that cryptocurrencies are used for hedging when investor sentiment is weak, with evidence in favor of this behavior being relatively stronger than the possible speculative motive associated with happy investors, as low total connectedness is coupled with high common volatility. Our results tend to suggest that, relatively more diversification opportunities are available when investors are happy rather than when sentiment is weak.

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“…Ji et al (2019a) applied network methodologies to analyze linkages between cryptocurrencies and other commodities, finding connections with some of those commodities. Bouri et al (2019a) analyzed linkages between cryptocurrencies, focusing on the relationship between volatility measures and discriminating between transitory and permanent causalities. The authors concluded that permanent shocks are more important.…”

Section: Literature Reviewmentioning

confidence: 99%

Contagion Effect in Cryptocurrency Market

Ferreira

Pereira

2019

JRFM

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The rapid development of cryptocurrencies has drawn attention to this particular market, with investors trying to understand its behaviour and researchers trying to explain it. The evolution of cryptocurrencies’ prices showed a kind of bubble and a crash at the end of 2017. Based on this event, and on the fact that Bitcoin is the most recognized cryptocurrency, we propose to evaluate the contagion effect between Bitcoin and other major cryptocurrencies. Using the Detrended Cross-Correlation Analysis correlation coefficient (ΔρDCCA) and comparing the period after and before the crash, we found evidence of a contagion effect, with this particular market being more integrated now than in the past—something that should be taken into account by current and potential investors.

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The volatility surprise of leading cryptocurrencies: Transitory and permanent linkages

Cited by 58 publications

References 19 publications

Regime specific spillover across cryptocurrencies and the role of COVID-19

Regime specific spillover across cryptocurrencies and the role of COVID-19

Volatility connectedness of major cryptocurrencies: The role of investor happiness

Contagion Effect in Cryptocurrency Market

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