Modelling the volatility of Bitcoin returns using GARCH models

Samuel Asante Gyamerah; Samuel Asante Gyamerah

doi:10.3934/QFE.2019.4.739

Quantitative Finance and Economics

2019, Volume 3, Issue 4: 739-753. doi: 10.3934/QFE.2019.4.739

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Research article

Modelling the volatility of Bitcoin returns using GARCH models

Samuel Asante Gyamerah ^,

Department of Mathematics, Pan African University, Institute for Basic Sciences, Technology, and Innovation, Kenya

Received: 17 October 2019 Accepted: 26 September 2019 Published: 09 December 2019
JEL Codes: C01

Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. This paper evaluates the volatility of Bitcoin returns using three GARCH models (sGARCH, iGARCH, and tGARCH). The new development allows for the modeling of volatility clustering effects, the leptokurtic and the skewed distribution in the return series of Bitcoin. Comparative to the Students't-distribution and the Generalized error distribution, the Normal Inverse Gaussian (NIG) distribution captured adequately the leptokurtic and skewness in all the GARCH 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.
- cryptocurrency,
- Bitcoin,
- volatility,
- tGARCH,
- Normal Inverse Gaussian
Citation: Samuel Asante Gyamerah. Modelling the volatility of Bitcoin returns using GARCH models[J]. Quantitative Finance and Economics, 2019, 3(4): 739-753. doi: 10.3934/QFE.2019.4.739

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Abstract

Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. This paper evaluates the volatility of Bitcoin returns using three GARCH models (sGARCH, iGARCH, and tGARCH). The new development allows for the modeling of volatility clustering effects, the leptokurtic and the skewed distribution in the return series of Bitcoin. Comparative to the Students't-distribution and the Generalized error distribution, the Normal Inverse Gaussian (NIG) distribution captured adequately the leptokurtic and skewness in all the GARCH 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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