Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F22%3A63553978" target="_blank" >RIV/70883521:28120/22:63553978 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.sciencedirect.com/science/article/pii/S1057521922001004" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1057521922001004</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.irfa.2022.102132" target="_blank" >10.1016/j.irfa.2022.102132</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
50206 - Finance
Návaznosti výsledku
Projekt
—
Návaznosti
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
International Review of Financial Analysis
ISSN
1057-5219
e-ISSN
—
Svazek periodika
82
Číslo periodika v rámci svazku
Neuveden
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
17
Strana od-do
—
Kód UT WoS článku
000806724700003
EID výsledku v databázi Scopus
2-s2.0-85128278696