Articles producció científica> Gestió d'Empreses

Wavelet Entropy and Complexity Analysis of Cryptocurrencies Dynamics

  • Identification data

    Identifier: imarina:9262034
    Authors:
    Vampa, VictoriaMartin, Maria T.Calderon, LucilaBariviera, Aurelio F.
    Abstract:
    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.
  • Others:

    Author, as appears in the article.: Vampa, Victoria; Martin, Maria T.; Calderon, Lucila; Bariviera, Aurelio F.;
    Department: Gestió d'Empreses
    URV's Author/s: Fernández Bariviera, Aurelio
    Keywords: Wavelet entropy Tomorrow Statistical complexity Prices Long memory Inefficiency Cryptocurrencies Bitcoin
    Abstract: 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.
    Thematic Areas: Signal processing Control and systems engineering Computer networks and communications
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: aurelio.fernandez@urv.cat
    Author identifier: 0000-0003-1014-1010
    Record's date: 2023-06-12
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://link.springer.com/chapter/10.1007/978-3-030-94485-8_2
    Papper original source: 384 25-35
    APA: Vampa, Victoria; Martin, Maria T.; Calderon, Lucila; Bariviera, Aurelio F.; (2022). Wavelet Entropy and Complexity Analysis of Cryptocurrencies Dynamics.
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.1007/978-3-030-94485-8_2
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Proceedings Paper
  • Keywords:

    Wavelet entropy
    Tomorrow
    Statistical complexity
    Prices
    Long memory
    Inefficiency
    Cryptocurrencies
    Bitcoin
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