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On long memory effects in the volatility measure of Cryptocurrencies

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  • Phillip, Andrew
  • Chan, Jennifer
  • Peiris, Shelton

Abstract

Cryptocurrencies as of late have commanded global attention on a number of fronts. Most notably, their variance properties are known for being notoriously wild, unlike their fiat counterparts. We highlight some stylized facts about the variance measures of Cryptocurrencies using the logarithm of daily return range and relate these results to their respective cryptographic designs such as intended transaction speed. The results favor oscillatory long run autocorrelations over standard long run autocorrelation filters to model the log daily return range. The overarching implication of this result is the volatility of Cryptocurrencies can be better understood and measured via the use of fast moving autocorrelation functions, as opposed to smoothly decaying functions for fiat currencies.

Suggested Citation

  • Phillip, Andrew & Chan, Jennifer & Peiris, Shelton, 2019. "On long memory effects in the volatility measure of Cryptocurrencies," Finance Research Letters, Elsevier, vol. 28(C), pages 95-100.
    • Handle: RePEc:eee:finlet:v:28:y:2019:i:c:p:95-100
    DOI: 10.1016/j.frl.2018.04.003
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Volatility measures; Cryptocurrencies; Long memory; Buffer threshold model; Jump diffusion;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G - Financial Economics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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