Herding behavior and contagion in the cryptocurrency market

Silva, Paulo Vitor Jordão da Gama; Klötzle, Marcelo Cabús; Pinto, Antônio Carlos Figueiredo; Gomes, Leonardo Lima

doi:10.1016/j.jbef.2019.01.006

Cited by 159 publications

(63 citation statements)

References 37 publications

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“…A significant portion of the embryonic academic research on herding behaviour in cryptocurrency markets provides evidence that strong herding phenomena exist. To be more precise, Ballis and Drakos (2020) , da Gama Silva et al. (2019) , Kaiser and Stöckl (2020) , and Kallinterakis and Wang (2019) support this perspective.…”

Section: Studies Revealing Herding Behaviour In Digital Currency Markmentioning

confidence: 92%

“…Thereby, asymmetric herding behaviour exists. In a partly similar mentality, da Gama Silva et al. (2019) analyze herding behaviour of 50 very liquid and capitalized digital currencies spanning the period from March 2015 to November 2018.…”

Section: Studies Revealing Herding Behaviour In Digital Currency Markmentioning

confidence: 99%

“…(2019) FRL Bitcoin Ethereum Ripple Litecoin Stellar Dash Nem Monero Bytecoin Verge Siacoin BitShares Decred Dogecoin Coinmarketcap.com 28 April 2013–2 May 2018 Cross-sectional absolute deviations (CSAD) by Chang et al. (2000) Significant herding during 4 periods: (24 April 2016–28 November 2016, 5 January 2017–1 April 2017, 21 May 2017–29 May 2017, 20 July 2017–13 September 2017), especially from April 2016 to September 2017 da Gama Silva et al. (2019) JBEF CRIX Bitcoin Ethereum Ripple Stellar Lumens Litecoin Monero Tether Dollar Dash Dogecoin BitShares Bytecoin DigiByte Verge MaidSafeCoin Monacoin Reddcoin Nxt Syscoin Peercoin Nexus Groestlcoin VertCoin Einsteinium Ubiq Blocknet NavCoin BitCNY Novacoin DigitalNote ViaCoin BitBay Burst WhiteCoin CloakCoin Boolberry Unobtanium Gulden BitUSD GameCredits CassinoCoin Counterparty Namecoin Feathercoin PrimeCoin Crown FlorinCoin BlackCoin ECC Diamond PotCoin Crix.hu-berlin.de Coinmarketcap.com March 2015–November 2018 Cross-sectional absolute deviations (CSAD) by Chang et al.…”

Section: Figure A1mentioning

confidence: 99%

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Herding behaviour in digital currency markets: An integrated survey and empirical estimation

Kyriazis

2020

Heliyon

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This paper reviews the empirical literature on the highly popular phenomenon of herding behaviour in the markets of digital currencies. Furthermore, a comparison takes place with outcomes from earlier studies about traditional financial assets. Moreover, we empirically investigate herding behaviour of 240 cryptocurrencies during bull and bear markets. The present survey suggests that empirical findings about whether herding phenomena have made a significant appearance or not in cryptocurrency markets are split. The Cross-sectional absolute deviations (CSAD) and Cross-sectional standard deviations (CSSD) approaches for measuring herding tendencies are found to be the most popular. Different behaviour is detected in bull periods compared to bear markets. Nevertheless, evidence from primary studies indicates that herding is stronger during extreme situations rather than in normal conditions. However, our empirical estimations reveal that herding behaviour is evident only in bull markets. These findings cast light on and provide a roadmap for investment decisions with modern forms of liquidity.

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Section: Studies Revealing Herding Behaviour In Digital Currency Markmentioning

confidence: 92%

Section: Studies Revealing Herding Behaviour In Digital Currency Markmentioning

confidence: 99%

Section: Figure A1mentioning

confidence: 99%

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Herding behaviour in digital currency markets: An integrated survey and empirical estimation

Kyriazis

2020

Heliyon

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“…For example, Antonakakis et al (2019) investigated the transmission mechanism among the top nine cryptocurrencies between 15 August 2015 and 31 May 2018, finding that periods of high (low) uncertainty were characterized by strong (low) connectivity, with Bitcoin affecting the market's connectivity the most. In another analysis, Silva et al (2019) analyzed 50 cryptocurrencies with greater liquidity, identifying the contagion effect of Bitcoin in almost all cases.…”

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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“…In the beginning, correlation analysis used essentially linear approaches, but with the development of methodologies and the evolution of computational capacity, other different approaches were created, such as the one that will be used here. Regarding the particular case of the cryptocurrency market, Antonakakis et al (2019) and Silva et al (2019) are some examples of studies analysing the behaviour between different cryptocurrencies.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies

Costa

Silva

Ferreira

2019

IJFS

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In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

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Herding behavior and contagion in the cryptocurrency market

Cited by 159 publications

References 37 publications

Herding behaviour in digital currency markets: An integrated survey and empirical estimation

Herding behaviour in digital currency markets: An integrated survey and empirical estimation

Contagion Effect in Cryptocurrency Market

Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies

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