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A new look at Cryptocurrencies

Author

Listed:
  • Phillip, Andrew
  • Chan, Jennifer S.K.
  • Peiris, Shelton
Abstract
The complexities of Cryptocurrencies are yet to be fully explored. New evidence suggests the most popular Cryptocurrency, Bitcoin, displays many diverse stylized facts including long memory and heteroskedasticity. This note combines many of these attributes into a single model to conditionally measure the varied nature of Cryptocurrencies. Understanding these properties helps us to evaluate their investability. We fit our model to 224 different Cryptocurrencies in order to determine which of these properties exist. It is found that Cryptocurrencies in general have several unique properties including leverage effects and Student-t error distributions.

Suggested Citation

  • Phillip, Andrew & Chan, Jennifer S.K. & Peiris, Shelton, 2018. "A new look at Cryptocurrencies," Economics Letters, Elsevier, vol. 163(C), pages 6-9.
    • Handle: RePEc:eee:ecolet:v:163:y:2018:i:c:p:6-9
    DOI: 10.1016/j.econlet.2017.11.020
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    References listed on IDEAS

    as
    1. Urquhart, Andrew, 2017. "Price clustering in Bitcoin," Economics Letters, Elsevier, vol. 159(C), pages 145-148.
    2. Omori, Yasuhiro & Watanabe, Toshiaki, 2008. "Block sampler and posterior mode estimation for asymmetric stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2892-2910, February.
    3. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    4. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    5. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    6. Henry L. Gray & Nien‐Fan Zhang & Wayne A. Woodward, 1989. "On Generalized Fractional Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(3), pages 233-257, May.
    7. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
    8. Shephard, Neil (ed.), 2005. "Stochastic Volatility: Selected Readings," OUP Catalogue, Oxford University Press, number 9780199257201.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Long memory; Stochastic volatility; Leverage; Heavy tails; Cryptocurrency; Bitcoin;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G1 - Financial Economics - - General Financial Markets

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