Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19

被引：21
|
作者：
Assaf, Ata [1 ,2 ]
Bhandari, Avishek [3 ]
Charif, Husni [1 ]
Demir, Ender [4 ,5 ]
机构：
[1] Univ Balamand, Fac Business & Management, POB 100, Tripoli, Lebanon
[2] Cyprus Int Inst Management CIIM, BPB 20378, CY-2151 Nicosia, Cyprus
[3] Inst Management Technol, Hyderabad, India
[4] Reykjavik Univ, Sch Social Sci, Dept Business Adm, Reykjavik, Iceland
[5] Tomas Bata Univ Zlin, Zlin, Czech Republic
来源：
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS | 2022年 / 82卷
关键词：
Multivariate Long memory; Fractal connectivity; Hurst exponent; Cryptocurrency markets; Wavelet; WHITTLE ESTIMATION; TIME-SERIES; BITCOIN; TESTS; INEFFICIENCY; PERSISTENCE; STATIONARY; PRICES;
D O I：
10.1016/j.irfa.2022.102132
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
In this paper, we study the long memory behavior of Bitcoin, Litecoin, Ethereum, Ripple, Monero, and Dash with a focus on the COVID-19 period. Initially, we apply a time-varying Lifting method to estimate the Hurst exponent for each cryptocurrency. Then we test for a change in persistence over time. To model the multivariate con-nectivity, the wavelet-based multivariate long memory approach proposed by Achard and Gannaz (2016) is implemented. Our results indicate a change in the long-range dependence for the majority of cryptocurrencies, with a noticeable downward trend in persistence after the 2017 bubble and then a dramatic drop after the outbreak of COVID-19. The drop in persistence after COVID-19 is further illustrated by the Fractal connectivity matrix obtained from the Wavelet long-memory model. Our findings provide important implications regarding the evolution of market efficiency in the cryptocurrency market and the associated fractal structure and dy-namics of the crypto prices over time
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页数：17
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