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Title: Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19
Authors: Assaf, Ata 
Bhandari, Avishek
Charif, Husni 
Demir, Ender
Affiliations: Faculty of Business and Management 
Keywords: Cryptocurrency markets
Fractal connectivity
Hurst exponent
Multivariate Long memory
Issue Date: 2022-07
Publisher: Elsevier
Part of: International Review of Financial Analysis
Volume: 82
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.
ISSN: 10575219
DOI: 10.1016/j.irfa.2022.102132
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Business Administration

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