Autor según el artículo: Vampa, Victoria; Martin, Maria T.; Calderon, Lucila; Bariviera, Aurelio F.;
Departamento: Gestió d'Empreses
Autor/es de la URV: Fernández Bariviera, Aurelio
Palabras clave: Wavelet entropy Tomorrow Statistical complexity Prices Long memory Inefficiency Cryptocurrencies Bitcoin
Resumen: Cryptocurrencies emerged almost one decade ago, as an alternative peer-to-peer payment method. Even though their currency characteristics have been challenged by several researchers, they constitute an important speculative financial asset. This paper examines the long memory properties in high frequency (5 min) time series of eight important cryptocurrencies. We perform a statistical analysis of two key financial characteristics of time series: return and volatility. We compute information theory quantifiers using a wavelet decomposition of the time series: wavelet entropy and wavelet statistical complexity of returns and volatility of each time series. We find two important features in the time series: (i) high frequency returns exhibit a trend toward a more efficient behavior, and (ii) high frequency volatility reflects a strong persistence in volatility. Both findings have important implications for portfolio managers, and investors in general. The presence of persistent volatility validates the use of GARCH-type models. Thus, understanding volatility could create opportunities for short-term day traders.
Áreas temáticas: Signal processing Control and systems engineering Computer networks and communications
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: aurelio.fernandez@urv.cat
Identificador del autor: 0000-0003-1014-1010
Fecha de alta del registro: 2023-06-12
Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
Referencia al articulo segun fuente origial: 384 25-35
Referencia de l'ítem segons les normes APA: Vampa, Victoria; Martin, Maria T.; Calderon, Lucila; Bariviera, Aurelio F.; (2022). Wavelet Entropy and Complexity Analysis of Cryptocurrencies Dynamics.
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Proceedings Paper