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On fitting cryptocurrency log-return exchange rates

Author

Listed:
  • Ayman Alzaatreh

    (American University of Sharjah)

  • Hana Sulieman

    (American University of Sharjah)

Abstract

Cryptocurrency has become the leading method for peer-to-peer electronic cash system. It uses cryptography to secure financial transactions. Recently, several researchers have attempted to understand the behaviors of cryptocurrency exchange rates. In this paper, we introduce a new location–scale family of distributions to understand the distributional properties of the log-return exchange rates of cryptocurrencies. We use quantile kurtosis measures to show that the proposed family of distributions provides a desirable level of flexibility in terms of skewness and tail heaviness. Several recent data on US dollar-based cryptocurrency exchange rates have been analyzed, and the results are compared against those of the generalized hypergeometric distribution. It is shown that the proposed family of distributions possesses desired features including model simplicity, shape flexibility and quality of distribution fit.

Suggested Citation

  • Ayman Alzaatreh & Hana Sulieman, 2021. "On fitting cryptocurrency log-return exchange rates," Empirical Economics, Springer, vol. 60(3), pages 1157-1174, March.
    • Handle: RePEc:spr:empeco:v:60:y:2021:i:3:d:10.1007_s00181-019-01782-6
    DOI: 10.1007/s00181-019-01782-6
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    References listed on IDEAS

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    1. Samuel Kotz & Edith Seier, 2009. "An analysis of quantile measures of kurtosis: center and tails," Statistical Papers, Springer, vol. 50(3), pages 553-568, June.
    2. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    3. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    4. Jeffrey Chu & Saralees Nadarajah & Stephen Chan, 2015. "Statistical Analysis of the Exchange Rate of Bitcoin," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-27, July.
    5. Canan G. Corlu & Alper Corlu, 2015. "Modelling exchange rate returns: which flexible distribution to use?," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1851-1864, November.
    6. Ayman Alzaatreh & Carl Lee & Felix Famoye, 2013. "A new method for generating families of continuous distributions," METRON, Springer;Sapienza Università di Roma, vol. 71(1), pages 63-79, June.
    7. Kim, Tae-Hwan & White, Halbert, 2003. "On More Robust Estimation of Skewness and Kurtosis: Simulation and Application to the S&P500 Index," University of California at San Diego, Economics Working Paper Series qt7b52v07p, Department of Economics, UC San Diego.
    8. Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2015. "A note on "Modelling exchange rate returns: which flexible distribution to use?"," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1777-1785, November.
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