Co-movement in crypto-currency markets: evidences from wavelet analysis

Kumar, Anoop; Ajaz, Taufeeq

doi:10.1186/s40854-019-0143-3

Cited by 43 publications

(30 citation statements)

References 41 publications

(32 reference statements)

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“…Specifically, it is evident that XRP, XLM, LTC, and ETH are more strongly connected to others in the low volatility regime, implying that these cryptocurrencies dominate the spillover in stable periods. Notably, the role of BTC is less important, which contradicts with Kumar and Ajaz ( 2019 ). However, the result is generally in line with Zięba et al ( 2019 ) who highlight the importance of smaller cryptocurrencies to the network of return shocks due to the specificity of the supply mechanism of those cryptocurrencies.…”

Section: Resultsmentioning

confidence: 65%

“…They show that Bitcoin is not the dominant cryptocurrency although its shocks on other cryptocurrencies are the longest lasting. 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.…”

Section: Literature Reviewsmentioning

confidence: 99%

“…They mainly focus on price formation by examining factors such as attractiveness (Kristoufek 2013 ), trading volume (Balcilar et al 2017 ), and economic and financial variables. 1 However, if cryptocurrencies represent a separate asset class as it is often argued, it is informative to study their inter-price dynamics for the sake of traders and portfolio managers who can exploit evidence concerning how the price of one cryptocurrency can affect the prices of other cryptocurrencies while making inferences about price predictability in the highly controversial cryptocurrency markets (Koutmos 2018 ; Corbet et al 2018 ; Kumar and Ajaz 2019 ; Zięba et al 2019 ; Ji et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 65%

Section: Literature Reviewsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…There is also a branch of recent literature that studies the interdependences among cryptocurrencies following different methodologies such as the quantile regression approach ( Jareño et al., 2020 ), ARDL models ( Ciaian et al., 2018 and Nguyen et al., 2019 ), NARDL models ( González et al., 2020 and 2021 ; Jareño et al., 2020 ), wavelet-based models ( Kumar and Ajaz, 2019 ; Omane-Adjepong and Alagidede, 2019 ; Mensi et al., 2019 ; Sharif et al., 2020 ), VAR models ( Bação et al., 2018 ), GARCH models ( Corbet et al., 2020 ), VAR-GARCH models ( Symitsi and Chalvatzis, 2019 ), the bivariate diagonal BEKK model ( Katsiampa, 2019 ; Katsiampa et al., 2019 ), BEKK-GARCH models ( Beneki et al., 2019 ), BEKK-MGARCH models ( Tu and Xue, 2019 ), the GARCH-MIDAS model ( Walther et al., 2019 ), DCC models ( Charfeddine et al., 2020 ; Kumar and Anandarao, 2019 ), the Diebold and Yilmaz (2009) approach ( Koutmos, 2018 ) and Diebold and Yilmaz (2012) indices ( Ji et al., 2019 ; Umar et al., 2021 b), among others. In this paper, we use an extension and improvement of the two previous models, Diebold and Yilmaz's ( 2009 and 2012 ) approach, which is a time-varying parameter vector autoregression (TVP-VAR) model developed by Antonakakis and Gabauer (2017) .…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of COVID-19-related media coverage on the return and volatility connectedness of cryptocurrencies and fiat currencies

Umar

Jareño

González

2021

Technological Forecasting and Social Change

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This research explores the impact of COVID-19-related media coverage on the dynamic return and volatility connectedness of the three dominant cryptocurrencies (Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP)) and the fiat currencies of the euro, GBP and Chinese yuan. The sample period covers the first and second devasting waves of the COVID-19 pandemic crisis and ranges from January 1, 2020, to December 31, 2020. The dynamic return and volatility connectedness measures are estimated using the time varying parameter-VAR approach. Our return connectedness analysis shows that the media coverage index (only before the first wave) and the cryptocurrencies are the net transmitters of shocks while the fiat currencies are the net receivers of shocks. Similar results are obtained in terms of volatility, except for the euro, which shows a clear net receiver profile in January and February. This fiat currency (the euro) became a net transmitter in March and during the first wave of the COVID-19 crisis, which possibly shows the virulence of the pandemic on the European continent. Moreover, the most relevant differences between the net dynamic (return and volatility) connectedness of these two groups of currencies are focused on the beginning of the sample period, just before the first wave of the SARS-CoV-2 pandemic crisis, although some differences are observed during the first and second waves of the coronavirus outbreak.

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“…It seems that there are yet opportunities to get benefits from Bitcoin volatilities and its market inefficiencies (Bouri et al 2018). It is important to highlight that this inefficiency is getting weaker over time since liquidity seems to have a positive effect on the informational efficiency of Bitcoin prices (S Kumar and Ajaz 2019).…”

Section: Introductionmentioning

confidence: 99%

Bitcoin pricing: impact of attractiveness variables

Neves¹

2020

Financ Innov

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The research seeks to contribute to Bitcoin pricing analysis based on the dynamics between variables of attractiveness and the value of the digital currency. Using the error correction model, the relationship between the price of the virtual currency, Bitcoin, and the number of Google searches that used the terms bitcoin, bitcoin crash and crisis between December 2012 and February 2018 is analyzed. The study also applied the same analysis to prices of Bitcoin denominated in different sovereign currencies traded during the same period. The Johansen (J Econ Dyn Control 12:231-254, 1988) test demonstrates that the price and number of searches on Google for the first two terms are cointegrated. This research indicates that there are strong short-term and long-term dynamics among attractiveness factors, suggesting that an increase in worldwide interest in Bitcoin is usually preceded by a price increase. In contrast, an increase in market mistrust over a collapse of the currency, as measured by the term bitcoin crash, is followed by a fall in price. Intense world economic crisis events appear to have a strong impact on interest in the virtual currency. This study demonstrates that during a worldwide crisis Bitcoin becomes an alternative investment, increasing its price. Based on it, bitcoin may be used as a safe haven by the financial market and its intrinsic characteristics might help the investors and governments to find new mechanisms to deal with monetary transactions.

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Co-movement in crypto-currency markets: evidences from wavelet analysis

Cited by 43 publications

References 41 publications

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

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

The impact of COVID-19-related media coverage on the return and volatility connectedness of cryptocurrencies and fiat currencies

Bitcoin pricing: impact of attractiveness variables

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