Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/6508
Title: Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19
Authors: Assaf, Ata 
Mokni, Khaled
Yousaf, Imran
Bhandari, Avishek
Affiliations: Department of Business Administration 
Keywords: Cryptocurrency markets
Fractal connectivity
Multivariate Long memory
Multivariate long memory test
Wavelet
Issue Date: 2023-01
Publisher: Elsevier
Part of: Research in International Business and Finance
Volume: 64
Abstract: 
In this paper, we study the long memory behavior of the hourly cryptocurrency returns during the COVID-19 pandemic period. Initially, we apply different tests against the spurious long memory, with the results indicating the presence of true long memory for most cryptocurrencies. Yet, using the multivariate test, the series are found to be contaminated by level shifts or smooth trends. Then, we adopt the wavelet-based multivariate long memory approach suggested by Achard and Gannaz (2016) to model their long memory connectivity. The findings indicate a change in persistence for all series during the sample period. The fractal connectivity clustering indicates a similarity among Ethereum (ETH) and Litecoin (LTC), Monero (XMR), Bitcoin (BTC), and EOC token (EOS), while Stellar (XLM) is clustered away from the remaining series, indicating the absence of any interdependence with other crypto returns. Overall, shocks arising from COVID-19 crisis have led to changes in long-run correlation structure.
URI: https://scholarhub.balamand.edu.lb/handle/uob/6508
ISSN: 02755319
DOI: 10.1016/j.ribaf.2022.101821
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Business Administration

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