Katsiampa (2017) shows that, among different GARCH models, the optimal conditional heteroskedasticity model regarding the goodness-of-fit to Bitcoin price data is the AR-Component GARCH (AR-CGARCH) model. However, in that paper the author does not take into account for statistical proprieties of Bitcoin’s return distribution, and even showing both skewness and non-normality of the data, we consider a standardized normal distribution for all studied GARCH models. This paper represents an improvement of the previous literature about GARCH model for Bitcoin. In particular, this paper examines different distributional assumptions about innovations distribution for some GARCH models, showing that it is possible to obtain better estimates through the AR(1)-APARCH(1,1) model assuming that innovations follow a t-student distribution.
Alternative distribution based GARCH models for Bitcoin volatility estimation
Mattera R.;Giacalone M.
2018
Abstract
Katsiampa (2017) shows that, among different GARCH models, the optimal conditional heteroskedasticity model regarding the goodness-of-fit to Bitcoin price data is the AR-Component GARCH (AR-CGARCH) model. However, in that paper the author does not take into account for statistical proprieties of Bitcoin’s return distribution, and even showing both skewness and non-normality of the data, we consider a standardized normal distribution for all studied GARCH models. This paper represents an improvement of the previous literature about GARCH model for Bitcoin. In particular, this paper examines different distributional assumptions about innovations distribution for some GARCH models, showing that it is possible to obtain better estimates through the AR(1)-APARCH(1,1) model assuming that innovations follow a t-student distribution.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
