  In this paper, an application of three GARCH-type models (sGARCH, iGARCH, and
tGARCH) with Student t-distribution, Generalized Error distribution (GED), and
Normal Inverse Gaussian (NIG) distribution are examined. The new development
allows for the modeling of volatility clustering effects, the leptokurtic and
the skewed distributions in the return series of Bitcoin. Comparative to the
two distributions, the normal inverse Gaussian distribution captured adequately
the fat tails and skewness in all the GARCH type models. The tGARCH model was
the best model as it described the asymmetric occurrence of shocks in the
Bitcoin market. That is, the response of investors to the same amount of good
and bad news are distinct. From the empirical results, it can be concluded that
tGARCH-NIG was the best model to estimate the volatility in the return series
of Bitcoin. Generally, it would be optimal to use the NIG distribution in GARCH
type models since time series of most cryptocurrency are leptokurtic.
