Covid-19 impact on cryptocurrencies: Evidence from a wavelet-based Hurst exponent

被引:24
|
作者
Belen Arouxet, M. [1 ]
Bariviera, Aurelio F. [4 ]
Pastor, Veronica E. [2 ,3 ]
Vampa, Victoria [3 ]
机构
[1] Univ Nacl La Plata, Fac Ciencias Exactas, Ctr Matemat La Plata, La Plata, Argentina
[2] Univ Buenos Aires, Fac Ingn, Dept Matemat, Buenos Aires, DF, Argentina
[3] Univ Nacl La Plata, Dept Ciencias Basicas, Fac Ingn, La Plata, Argentina
[4] Univ Rovira & Virgili, Dept Business, Av Univ 1, Reus 43204, Spain
关键词
Cryptocurrencies; Hurst exponent; Wavelet transform; Covid-19; LONG-RANGE DEPENDENCE; TIME-SERIES; INFORMATIONAL EFFICIENCY; INEFFICIENCY; NOISES; MARKET; RATES; WEAK;
D O I
10.1016/j.physa.2022.127170
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Cryptocurrency history begins in 2008 as a means of payment proposal. However, cryptocurrencies evolved into a complex ecosystem of high yield speculative assets. Contrary to traditional financial instruments, they are not (mostly) traded in organized, law-abiding venues, but on online platforms, where anonymity reigns. This paper examines the long term memory in return and volatility, using high frequency time series of seven important coins. Our study covers the pre-Covid-19 and the subsequent pandemic period. We use a recently developed method, based on the wavelet transform, which provides more robust estimators of the Hurst exponent. We detect that, during the peak of Covid-19 pandemic (around March 2020), the long memory of returns was only mildly affected. However, volatility suffered a temporary impact in its long range correlation structure. Our results could be of interest for both academics and practitioners.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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页数:12
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