Articles producció científica> Gestió d'Empreses

Wavelet Entropy and Complexity Analysis of Cryptocurrencies Dynamics

  • Dades identificatives

    Identificador: imarina:9262034
    Autors:
    Vampa, VictoriaMartin, Maria T.Calderon, LucilaBariviera, Aurelio F.
    Resum:
    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.
  • Altres:

    Autor segons l'article: Vampa, Victoria; Martin, Maria T.; Calderon, Lucila; Bariviera, Aurelio F.;
    Departament: Gestió d'Empreses
    Autor/s de la URV: Fernández Bariviera, Aurelio
    Paraules clau: Wavelet entropy Tomorrow Statistical complexity Prices Long memory Inefficiency Cryptocurrencies Bitcoin
    Resum: 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.
    Àrees temàtiques: Signal processing Control and systems engineering Computer networks and communications
    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: 2023-06-12
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://link.springer.com/chapter/10.1007/978-3-030-94485-8_2
    Referència a l'article segons font original: 384 25-35
    Referència 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 Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.1007/978-3-030-94485-8_2
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2022
    Tipus de publicació: Proceedings Paper
  • Paraules clau:

    Wavelet entropy
    Tomorrow
    Statistical complexity
    Prices
    Long memory
    Inefficiency
    Cryptocurrencies
    Bitcoin
  • Documents:

  • Cerca a google

    Search to google scholar