GARCH Model With Fat-Tailed Distributions and Bitcoin Exchange Rate Returns

  • Ruiping Liu School of Economics, Guizhou University, Guiyang, Guizhou, China.
  • Zhichao Shao Business School, Guangxi University, Nanning, Guangxi, China.
  • Guodong Wei Department of Economics, University of Utah, Salt Lake City, Utah, United States.
  • Wei Wang School of Economics, Guizhou University, Guiyang, Guizhou, China.

Abstract

In the era of diminishing power from US dollar and increasing competition among world currencies, Bitcoin, as a completely new concept as a medium of exchange, has received increasing attentions over the world. Nowadays, Bitcoin also becomes an investment vehicle, which carries attractive opportunities but also significant risks for the investment community. In this paper, we have compared the empirical performance of a newly-developed heavy-tailed distribution, the normal reciprocal inverse Gaussian (NRIG), with the most popular heavy-tailed distribution, the Student’s t distribution, under the GARCH framework in fitting the daily Bitcoin exchange rate returns. Our results indicate the heavy-tailed distribution has better performance in capture the daily Bitcoin exchange rate returns dynamics than the standard normal distribution. Our results also show the older fashioned Student’s t distribution still performs better than the new heavy-tailed distribution.

Keywords: Student’s t distribution, GARCH model, Bitcoin.

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Liu, R., Shao, Z., Wei, G., & Wang, W. (2017, December 18). GARCH Model With Fat-Tailed Distributions and Bitcoin Exchange Rate Returns. Journal of Accounting, Business and Finance Research, 1(1), 71-75. https://doi.org/https://doi.org/10.20448/2002.11.71.75
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