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|Title:||Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19||Authors:||Assaf, Ata
|Affiliations:||Faculty of Business and Management||Keywords:||Cryptocurrency markets
Multivariate Long memory
|Issue Date:||2022-07||Publisher:||Elsevier||Part of:||International Review of Financial Analysis||Volume:||82||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.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/5602||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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