Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19 br

【Author】 Assaf, Ata; Bhandari, Avishek; Charif, Husni; Demir, Ender

【Source】INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS

【影响因子】8.235

【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 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

【Keywords】Multivariate Long memory; Fractal connectivity; Hurst exponent; Cryptocurrency markets; Wavelet

【发表时间】2022 JUL

【收录时间】2022-07-30

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-数字货币