Preliminary communication
https://doi.org/10.32676/n.6.1.2
Volatility of Cryptocurrencies
Branimir Cvitko Cicvarić
orcid.org/0000-0002-2519-4097
; Addiko Bank d.d.
Abstract
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.
Keywords
cryptocurrency returns; heteroscedasticity; ARCH/GARCH models
Hrčak ID:
247232
URI
Publication date:
30.12.2020.
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