Open AccessVolatility of cryptocurrencies
Author(s) -
Branimir Cvitko Cicvarić
Publication year - 2020
Publication title -
notitia
Language(s) - English
Resource type - Journals
ISSN - 1849-9066
DOI - 10.32676/n.6.1.2
Subject(s) - autoregressive conditional heteroskedasticity , heteroscedasticity , autoregressive model , econometrics , volatility (finance) , cryptocurrency , economics , mathematics , computer science , computer security
Many models have been developed to model, estimate and forecast financial time series volatility, amongst which are the most popular autoregressive conditional heteroscedasticity (ARCH) model introduced by Engle (1982) and generalized autoregressive conditional heteroscedasticity (GARCH) model introduced by Bollerslev (1986). The aim of this paper is to determine which type of ARCH/GARCH models can fit the best following cryptocurrencies: Ethereum, Neo, Ripple, Litecoin, Dash, Zcash and Dogecoin. It is found that the EGARCH model is the best fitted model for Ethereum, Zcash and Neo, PARCH model is the best fitted model for Ripple, while for Litecoin, Dash and Dogecoin it depends on the selected distribution and information criterion.