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A Statistical Analysis of Cryptocurrencies

Author

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  • Stephen Chan
  • Jeffrey Chu
  • Saralees Nadarajah
  • Joerg Osterrieder

Abstract

We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes.

Suggested Citation

  • Stephen Chan & Jeffrey Chu & Saralees Nadarajah & Joerg Osterrieder, 2017. "A Statistical Analysis of Cryptocurrencies," JRFM, MDPI, vol. 10(2), pages 1-23, May.
  • Handle: RePEc:gam:jjrfmx:v:10:y:2017:i:2:p:12-:d:100126
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    References listed on IDEAS

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    1. Gabriel Bruneau & Kevin Moran, 2017. "Exchange rate fluctuations and labour market adjustments in Canadian manufacturing industries," Canadian Journal of Economics, Canadian Economics Association, vol. 50(1), pages 72-93, February.
    2. Zhiguo He & Arvind Krishnamurthy, 2019. "A Macroeconomic Framework for Quantifying Systemic Risk," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(4), pages 1-37, October.
    3. 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.
    4. Marie Briere & Kim Oosterlinck & Ariane Szafarz, 2015. "Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoins," Post-Print CEB, ULB -- Universite Libre de Bruxelles, vol. 16(6), pages 365-373.
    5. Zhu, Dongming & Galbraith, John W., 2010. "A generalized asymmetric Student-t distribution with application to financial econometrics," Journal of Econometrics, Elsevier, vol. 157(2), pages 297-305, August.
    6. Linden, Mikael, 2001. "A Model for Stock Return Distribution," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(2), pages 159-169, April.
    7. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    8. McDonald, James B. & Newey, Whitney K., 1988. "Partially Adaptive Estimation of Regression Models via the Generalized T Distribution," Econometric Theory, Cambridge University Press, vol. 4(3), pages 428-457, December.
    9. Harald Kinateder, 2015. "What drives tail risk in aggregate European equity markets?," Journal of Risk Finance, Emerald Group Publishing, vol. 16(4), pages 395-406, August.
    10. Fabio Parlapiano & Vitali Alexeev & Mardi Dungey, 2017. "Exchange rate risk exposure and the value of European firms," The European Journal of Finance, Taylor & Francis Journals, vol. 23(2), pages 111-129, January.
    11. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    12. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    13. Marcel Schröder, 2017. "The equilibrium real exchange rate and macroeconomic performance in developing countries," Applied Economics Letters, Taylor & Francis Journals, vol. 24(7), pages 506-509, April.
    14. Pham Van Dai & Sarath Delpachitra & Simon Cottrell, 2017. "Real Exchange Rate And Economic Growth In East Asian Countries: The Role Of Financial Integration," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 62(01), pages 163-177, March.
    15. 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.
    16. Linden, Mikael, 2005. "Estimating the distribution of volatility of realized stock returns and exchange rate changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(2), pages 573-583.
    17. Seyyedsajjad Seyyedi, 2017. "Analysis of the Interactive Linkages Between Gold Prices, Oil Prices, and Exchange Rate in India," Global Economic Review, Taylor & Francis Journals, vol. 46(1), pages 65-79, January.
    18. 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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