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Wavelet Entropy and Complexity Analysis of Cryptocurrencies Dynamics

  • Datos identificativos

    Identificador: imarina:9262034
    Autores:
    Vampa, VictoriaMartin, Maria T.Calderon, LucilaBariviera, Aurelio F.
    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.
  • Otros:

    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
    Enlace a la fuente original: https://link.springer.com/chapter/10.1007/978-3-030-94485-8_2
    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/
    DOI del artículo: 10.1007/978-3-030-94485-8_2
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Proceedings Paper
  • Palabras clave:

    Wavelet entropy
    Tomorrow
    Statistical complexity
    Prices
    Long memory
    Inefficiency
    Cryptocurrencies
    Bitcoin
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