Correlations among cryptocurrencies: Evidence from multivariate factor stochastic volatility model

Shi, Yongjing; Tiwari, Aviral Kumar; Gözgör, Giray; Lu, Zhou

doi:10.1016/j.ribaf.2020.101231

Cited by 56 publications

(24 citation statements)

References 21 publications

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“…(2019) study the structure of the cryptocurrency market and highlight the leadership of Bitcoin and Ethereum. Shi et al. (2020) find correlations between six cryptocurrencies and state that it is necessary to possess knowledge on them in order to implement trading strategies.…”

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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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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“…Previous literature analysing the relationship among cryptocurrencies, as well as between cryptocurrencies and other asset classes follows different methodologic approaches, such as VAR models ( Bação et al, 2018 and Conlon and McGee, 2020 ), GARCH models ( Corbet et al, 2020b ), VAR-GARCH models ( Symitsi and Chalvatzis, 2019 ), bivariate Diagonal BEKK model ( Katsiampa, 2019 ; Katsiampa et al, 2019 ), BEKK-GARCH models ( Beneki et al, 2019 ; Klein et al, 2018 ), BEKK-MGARCH models ( Tu and Xue, 2019 ), GARCH-MIDAS model ( Walther et al, 2019 ), DCC models ( Charfeddine et al, 2020 and Kumar and Anandarao, 2019 ), DCC-MGARCH models ( Canh et al, 2019 ), VARMA-DCC-GARCH models ( Guesmi et al, 2019 ), Multivariate factor stochastic volatility models (MFSVM) ( Shi et al, 2020 ), wavelet-based models ( Kumar and Ajaz, 2019 ; Omane-Adjepong and Alagidede, 2019 ; Mensi et al, 2019 ; Sharif et al, 2020 ), Diebold and Yilmaz (2009) approach ( Koutmos, 2018 ), the Quantile Regression approach ( Jareño et al, 2020 ), Quantile cross-spectral approach ( Rehman and Vo, 2020 ), ARDL models ( Ciaian et al, 2018 ; Nguyen et al, 2019 ) and NARDL models ( Bouri et al, 2018 ; Demir et al, 2021 ; González et al, 2020b ). This paper aims to analyse the interdependencies between major cryptocurrencies and oil price shocks by applying the NARDL approach to simultaneously capture long- and short-run asymmetric interdependencies between these variables in a sample period that includes the devastating first wave of the COVID-19 pandemic.…”

Section: Literature Reviewmentioning

confidence: 99%

Cryptocurrencies and oil price shocks: A NARDL analysis in the COVID-19 pandemic

et al. 2021

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This study explores potential non-linear and asymmetric interdependencies between oil price shocks and leading cryptocurrency returns. In addition, this research splits changes in crude oil prices into three relevant components: risk, demand, and supply shocks. By applying the NARDL methodology, this paper examines the connection between oil and cryptocurrencies in the period between November 20, 2018 and June 30, 2020, conducting a study of the first wave of the COVID-19 pandemic. Our results confirm that demand shocks show the greatest connection with the returns of the cryptocurrencies analysed. In addition, both short-term and long-term results show a greater interdependence between oil and cryptocurrencies in periods of economic turbulence, such as the SARS-CoV-2 coronavirus crisis.

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“…Therefore, it is important to analyze volatility and its transmission. Shi et al (2020) found that the price volatility of Ethereum, Ripple, Dash, Stellar, Bitcoin, and Litecoin are related. Aslanidis et al (2021) assessed market linkages across seventeen major cryptocurrencies by employing the daily returns from August 2015 to July 2020 using principal component analysis and a vector autoregression framework.…”

Section: Introductionmentioning

confidence: 98%

A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission

Joshi

Wang

Busler

2022

JRFM

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This study analyzes the volatility spillover effects in the US stock market (SP500) and cryptocurrency market (BGCI) using intraday data during the COVID-19 pandemic. As the potential drivers of portfolio diversification, we measure the asymmetric volatility transmission on both markets. We apply MGARCH-BEKK and the algorithm-based GA2M machine learning model. The negative shocks to returns impact the SP500 and the cryptocurrency market more than the positive shocks on both markets. This study also indicates evidence of unidirectional cross-market asymmetric volatility transmission from the cryptocurrency market to the SP500 during the COVID-19 pandemic. The research findings show the potential benefit of portfolio diversification between the SP500 and BGCI.

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Correlations among cryptocurrencies: Evidence from multivariate factor stochastic volatility model

Cited by 56 publications

References 21 publications

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

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

Cryptocurrencies and oil price shocks: A NARDL analysis in the COVID-19 pandemic

A Study of the Machine Learning Approach and the MGARCH-BEKK Model in Volatility Transmission

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