 Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19Ata Assaf, Avishek Bhandari, Husni Charif and Ender Demir International Review of Financial Analysis, 2022, vol. 82, issue C 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. Keywords: Multivariate Long memory; Fractal connectivity; Hurst exponent; Cryptocurrency markets; Wavelet (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:82:y:2022:i:c:s1057521922001004 DOI: 10.1016/j.irfa.2022.102132 Access Statistics for this article International Review of Financial Analysis is currently edited by B.M. Lucey More articles in International Review of Financial Analysis from Elsevier |
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