Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19 |
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| Institution: | 1. Faculty of Business and Management, University of Balamand, P.O. Box 100, Tripoli, Lebanon;2. Cyprus International Institute of Management (CIIM), P.O. Box 20378, Nicosia 2151, Cyprus;3. Higher Institute of Management of Gabès, University of Gabès, Tunisia;4. College of Business and Public Management, Wenzhou-Kean University, China;5. SHSSM, Indian Institute of Technology Bhubaneswar |
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| 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. |
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| Keywords: | Multivariate Long memory Fractal connectivity Multivariate long memory test Cryptocurrency markets Wavelet |
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