Kontaktujte nás | Jazyk: čeština English
Název: | Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19 | ||||||||||
Autor: | Assaf, Ata; Bhandari, Avishek; Charif, Husni; Demir, Ender | ||||||||||
Typ dokumentu: | Recenzovaný odborný článek (English) | ||||||||||
Zdrojový dok.: | International Review of Financial Analysis. 2022, vol. 82 | ||||||||||
ISSN: | 1057-5219 (Sherpa/RoMEO, JCR) | ||||||||||
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DOI: | https://doi.org/10.1016/j.irfa.2022.102132 | ||||||||||
Abstrakt: | 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 con-nectivity, 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 dy-namics of the crypto prices over time | ||||||||||
Plný text: | https://www.sciencedirect.com/science/article/pii/S1057521922001004 | ||||||||||
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