arXiv 11 Sep 2019 · Finance — Statistical Finance
arXiv:1909.04903 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | M. Gronwald (2014) The economics of bitcoins–market characteristics and price jumps | 0.511 | 2 | 1 | 100% |
| 2 | T. W. Anderson, D. A. Darling (1954) A test of goodness of fit | 0.405 | 1 | 1 | 100% |
| 3 | O. Barndorff-Nielsen (1977) Exponentially decreasing distributions for the logarithm of particle size | 0.405 | 1 | 1 | 100% |
| 4 | T. Bollerslev (1986) Generalized autoregressive conditional heteroskedasticity | 0.405 | 1 | 1 | 100% |
| 5 | G. E. Box, D. A. Pierce (1970) Distribution of residual autocorrelations in autoregressive-integrated moving average time series models | 0.405 | 1 | 1 | 100% |
| 6 | J. Chu, S. Chan, S. Nadarajah, J. Osterrieder (2017) Garch modelling of cryptocurrencies | 0.405 | 1 | 1 | 100% |
| 7 | D. A. Dickey, W. A. Fuller (1979) Distribution of the estimators for autoregressive time series with a unit root | 0.405 | 1 | 1 | 100% |
| 8 | R. Engle (2001) Garch 101: The use of arch/garch models in applied econometrics | 0.405 | 1 | 1 | 100% |
| 9 | L. R. Glosten, R. Jagannathan, D. E. Runkle (1993) On the relation between the expected value and the volatility of the nominal excess return on stocks | 0.405 | 1 | 1 | 100% |
| 10 | C. M. Jarque, A. K. Bera (1987) A test for normality of observations and regression residuals | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 13 scored citations.