2019
DOI: 10.1016/j.ribaf.2019.02.001
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Bitcoin return: Impacts from the introduction of new altcoins

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Cited by 39 publications
(19 citation statements)
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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%
“…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%
“…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%
“…Our focus is solely on blockchain adoption in banking and not cryptocurrencies. These literature focus on many cryptocurrency aspects such as price bubbles (Cheah and Fry 2015;Fry and Cheah 2016;Cheung et al 2015;Corbet et al 2018a;Gandal et al 2018;Bianchetti et al 2018;Chaim and Laurini 2019;Geuder et al 2019;Kallinterakis and Wang 2019;Sifat et al 2019;Xiong et al 2019;Shu and Zhu 2020), Bitcoin price determinants and characteristics (Akyildirim et al 2020;Ammous 2018;Beneki et al 2019;Bianchetti et al 2018;Bouoiyour et al 2016;Cagli 2019;Caporale et al 2018;De Sousa and Pinto 2019;Dwyer 2015;Corbet et al 2018b;Corbet et al 2019aCorbet et al , 2019bCorbet et al , 2019cFlori 2019;Corbet et al 2020aCorbet et al , 2020bHandika et al 2019;Hayes 2019;Fry 2018;Mensi et al 2019;Ma and Tanizaki 2019;Nadler and Guo 2020;Nguyen et al 2019aNguyen et al , 2019bPanagiotidis et al 2018;Phillips and Gorse 2018;Puljiz et al 2018;Pyo a...…”
Section: Buterinmentioning
confidence: 99%