TY - JOUR
T1 - Structural breaks and double long memory of cryptocurrency prices
T2 - A comparative analysis from Bitcoin and Ethereum
AU - Mensi, Walid
AU - Al-Yahyaee, Khamis Hamed
AU - Kang, Sang Hoon
N1 - Funding Information:
We would like to thank the editor and the two reviewers for their constructive suggestions, which helped improve the quality of our manuscript. The corresponding author acknowledges the financial support from the National Research Foundation of Korea (grant number NRF-2017S1A5A8019204). This work was supported by a Humanities ∙ Social-Science ∙ Arts Journal Research Promotion of Pusan National University.
Funding Information:
The last author (Sang Hoon Kang) acknowledges receiving financial support from the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A5B8057488).
Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2019/6
Y1 - 2019/6
N2 - This study explores the impacts of structural breaks (SB) on the dual long memory levels of Bitcoin and Ethereum price returns. We identify dual long memory and structural changes on cryptocurrency markets using four different generalized autoregressive conditional heteroskedasticity models (e.g., GARCH, FIGARCH, FIAPARCH, and HYGARCH). Furthermore, the persistence level of both returns and volatility decreases after accounting for long memory and switching states. Finally, the FIGARCH model with SB variables provides a comparatively superior forecasting accuracy performance. These findings have significant implications for both cryptocurrency allocations and portfolio management.
AB - This study explores the impacts of structural breaks (SB) on the dual long memory levels of Bitcoin and Ethereum price returns. We identify dual long memory and structural changes on cryptocurrency markets using four different generalized autoregressive conditional heteroskedasticity models (e.g., GARCH, FIGARCH, FIAPARCH, and HYGARCH). Furthermore, the persistence level of both returns and volatility decreases after accounting for long memory and switching states. Finally, the FIGARCH model with SB variables provides a comparatively superior forecasting accuracy performance. These findings have significant implications for both cryptocurrency allocations and portfolio management.
KW - Bitcoin
KW - Ethereum
KW - GARCH family models
KW - Long memory
KW - Structural breaks
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U2 - 10.1016/j.frl.2018.07.011
DO - 10.1016/j.frl.2018.07.011
M3 - Article
AN - SCOPUS:85050800068
SN - 1544-6123
VL - 29
SP - 222
EP - 230
JO - Finance Research Letters
JF - Finance Research Letters
ER -