Risk management of Bitcoin futures with GARCH models

【Author】 Guo, Zi-Yi

【Source】FINANCE RESEARCH LETTERS

【影响因子】9.848

【Abstract】In this article, we investigate the quantitative risk management of Bitcoin futures by using the GARCH models. We first found that it is crucial to introduce a heavy-tailed distribution into the GARCH models to explain return volatilities of Bitcoin futures. Then, we compare the VaR estimates based on the parametric methods, namely the GARCH model with the normal distribution (GARCH-Normal) and the GARCH model with the normal inverse Gaussian distribution (GARCHNIG), and the nonparametric method. Our results illustrate that although the VaR estimates based on the nonparametric method are overall accurate and even more accurate than the VaR estimates based on the GARCH-Normal model, the VaR estimates based on the GARCH-NIG model perform the best. Overall, we conclude that the GARCH-NIG model could generate accurate VaR estimates for the Bitcoin futures return series. In addition, we found that in contrast to Bitcoin cash, the return volatilities of the Bitcoin futures do not increase by more in response to positive shocks than in response to negative shocks.

【Keywords】Bitcoin; Value-at-risk; Heavy-tailed distribution

【发表时间】2022 MAR

【收录时间】2022-03-17

【文献类型】期刊

【主题类别】

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