Autor segons l'article: Arouxet MB; Bariviera AF; Pastor VE; Vampa V
Departament: Gestió d'Empreses
Autor/s de la URV: Fernández Bariviera, Aurelio
Paraules clau: Wavelet transform Long-range dependence Hurst exponent Cryptocurrencies Covid-19 weak wavelet transform time-series rates noises market informational efficiency inefficiency hurst exponent covid-19
Resum: 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.
Àrees temàtiques: Statistics and probability Statistical and nonlinear physics Química Psicología Physics, multidisciplinary Physics Nutrição Medicina veterinaria Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Linguística e literatura Interdisciplinar Geociências Farmacia Ensino Engenharias iv Engenharias iii Engenharias ii Engenharias i Educação física Economia Direito Condensed matter physics Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Biodiversidade Astronomia / física Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: aurelio.fernandez@urv.cat
Identificador de l'autor: 0000-0003-1014-1010
Data d'alta del registre: 2024-09-07
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Physica A-Statistical Mechanics And Its Applications. 596
Referència de l'ítem segons les normes APA: Arouxet MB; Bariviera AF; Pastor VE; Vampa V (2022). Covid-19 impact on cryptocurrencies: Evidence from a wavelet-based Hurst exponent. Physica A-Statistical Mechanics And Its Applications, 596(), -. DOI: 10.1016/j.physa.2022.127170
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2022
Tipus de publicació: Journal Publications
Repositori URV