Multivariate long memory structure in the cryptocurrency markets: The impact of COVID-19
International Review of Financial Analysis
; : 102132, 2022.
Article
in English
| ScienceDirect | ID: covidwho-1768217
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 connectivity, 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 dynamics of the crypto prices over time.
Full text:
Available
Collection:
Databases of international organizations
Database:
ScienceDirect
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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