Dynamic connectedness and integration in cryptocurrency markets

被引:411
作者
Ji, Qiang [1 ,2 ]
Bouri, Elie [3 ]
Lau, Chi Keung Marco [4 ]
Roubaud, David [5 ]
机构
[1] Chinese Acad Sci, Ctr Energy & Environm Policy Res, Inst Sci & Dev, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Sch Publ Policy & Management, Beijing 100049, Peoples R China
[3] Holy Spirit Univ Kaslik, USEK Business Sch, Jounieh, Lebanon
[4] Univ Huddersfield, Huddersfield Business Sch, Dept Accountancy Finance & Econ, Huddersfield, W Yorkshire, England
[5] Montpellier Business Sch, Energy & Sustainable Dev, Montpellier, France
基金
中国国家自然科学基金;
关键词
Cryptocurrencies; Market integration; Return and volatility connectedness networks; Asymmetric spillover; IMPULSE-RESPONSE ANALYSIS; ASYMMETRIC CONNECTEDNESS; NETWORK TOPOLOGY; BITCOIN RETURNS; GOOD VOLATILITY; CRUDE-OIL; SPILLOVERS; COMMODITY; PREDICT; PRICES;
D O I
10.1016/j.irfa.2018.12.002
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
This study applies a set of measures developed by Diebold and Yilmaz (2012, 2016) to examine connectedness via return and volatility spillovers across six large cryptocurrencies from August 7, 2015 to February 22, 2018. Regardless of the sign of returns, the results show that Litecoin and Bitcoin are at the centre of the connected network of returns. This finding implies that return shocks arising from these two cryptocurrencies have the most effect on other cryptocurrencies. Further analysis shows that connectedness via negative returns is largely stronger than via positive ones. Ripple and Ethereum are the top recipients of negative-return shocks, whereas Ethereum and Dash exhibit very weak connectedness via positive returns. Regarding volatility spillovers, Bitcoin is the most influential, followed by Litecoin; Dash exhibits a very weak connectedness, suggesting its utility for hedging and diversification opportunities in the cryptocurrency market. Taken together, results imply that the importance of each cryptocurrency in return and volatility connectedness is not necessarily related to its market size. Further analyses reveal that trading volume and global financial and uncertainty effects as well as the investment-substitution effect are determinants of net directional spillovers. Interestingly, higher gold prices and US uncertainty increase the net directional negative-return spillovers, whereas they do the opposite for net directional positive-return spillovers. Furthermore, gold prices exhibit a negative sign for net directional-volatility spillovers, whereas US uncertainty shows a positive sign. Economic actors interested in the cryptocurrency market can build on our findings when weighing their decisions.
引用
收藏
页码:257 / 272
页数:16
相关论文
共 41 条
[1]   Financial connectedness of BRICS and global sovereign bond markets [J].
Ahmad, Wasim ;
Mishra, Anil V. ;
Daly, Kevin J. .
EMERGING MARKETS REVIEW, 2018, 37 :1-16
[2]   Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electricity markets? [J].
Apergis, Nicholas ;
Barunik, Jozef ;
Lau, Marco Chi Keung .
ENERGY ECONOMICS, 2017, 66 :108-115
[3]   Can volume predict Bitcoin returns and volatility? A quantiles-based approach [J].
Balcilar, Mehmet ;
Bouri, Elie ;
Gupta, Rangan ;
Roubaud, David .
ECONOMIC MODELLING, 2017, 64 :74-81
[4]   Asymmetric volatility connectedness on the forex market [J].
Barunik, Jozef ;
Kocenda, Evzen ;
Vacha, Lukas .
JOURNAL OF INTERNATIONAL MONEY AND FINANCE, 2017, 77 :39-56
[5]   Asymmetric connectedness on the US stock market: Bad and good volatility spillovers [J].
Barunik, Jozef ;
Kocenda, Evzen ;
Vacha, Lukas .
JOURNAL OF FINANCIAL MARKETS, 2016, 27 :55-78
[6]   Bitcoin: Medium of exchange or speculative assets? [J].
Baur, Dirk G. ;
Hong, KiHoon ;
Lee, Adrian D. .
JOURNAL OF INTERNATIONAL FINANCIAL MARKETS INSTITUTIONS & MONEY, 2018, 54 :177-189
[7]   Trading volume and the predictability of return and volatility in the cryptocurrency market [J].
Bouri, Elie ;
Lau, Chi Keung Marco ;
Lucey, Brian ;
Roubaud, David .
FINANCE RESEARCH LETTERS, 2019, 29 :340-346
[8]   Herding behaviour in cryptocurrencies [J].
Bouri, Elie ;
Gupta, Rangan ;
Roubaud, David .
FINANCE RESEARCH LETTERS, 2019, 29 :216-221
[9]   Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices [J].
Bouri, Elie ;
Gupta, Rangan ;
Lahiani, Amine ;
Shahbaz, Muhammad .
RESOURCES POLICY, 2018, 57 :224-235
[10]   Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions [J].
Bouri, Elie ;
Gupta, Rangan ;
Tiwari, Aviral Kumar ;
Roubaud, David .
FINANCE RESEARCH LETTERS, 2017, 23 :87-95