Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19 br
International Review of Financial Analysis
; 82:17, 2022.
Article
in English
| Web of Science | ID: covidwho-1914518
ABSTRACT
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
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Type of study:
Experimental Studies
Topics:
Long Covid
Language:
English
Journal:
International Review of Financial Analysis
Year:
2022
Document Type:
Article
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