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True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods

Author

Listed:
  • Ata Assaf

    (University of Balamand
    Cyprus International Institute of Management (CIIM))

  • Luis Alberiko Gil-Alana

    (University of Navara)

  • Khaled Mokni

    (Northern Border University
    Gabès University)

Abstract

This paper applies a new proposed multivariate score-type test against spurious long memory to a group of cryptocurrency market returns. The test statistic developed by Sibbertsen et al. (J Econ 203(1): 33–49, 2018) is based on the multivariate local Whittle likelihood function and is proven to be consistent against the alternative two cases of random level shifts and smooth trends. We apply the test to the returns, absolute returns, and modified absolute returns. Overall, the recently developed test statistic fails to reject the null hypothesis of true long memory for most cryptocurrencies, except for the Stellar market. Therefore, applying the new test statistic supports the argument that the long memory in the cryptocurrency markets is real and is not a spurious one. Our results are further supported by applying other consistent local Whittle methods that allow for the estimation of the memory parameter by accounting for the presence of perturbations or low-frequency contaminations.

Suggested Citation

  • Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    • Handle: RePEc:spr:empeco:v:63:y:2022:i:3:d:10.1007_s00181-021-02165-6
    DOI: 10.1007/s00181-021-02165-6
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    Keywords

    Cryptocurrency markets; Multivariate long-memory tests; Spurious long memory; Cryptocurrency volatility;
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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