Abstract:
Cryptocurrencies have become increasingly popular in recent years at-
tracting the attention of the media, academia, investors, speculators, regu-
lators, and governments worldwide. This paper focuses on modelling the
volatility dynamics of eight most popular cryptocurrencies in terms of
their market capitalization for the period starting from 7th August 2015 to
1st August 2018. In particular, we consider the following cryptocurrencies;
Bitcoin, Ethereum, Litecoin, Ripple, Moreno, Dash, Stellar and NEM. The
GARCH-type models assuming different distributions for the innovations
term are fitted to cryptocurrencies data and their adequacy is evaluated us-
ing diagnostic tests. The selected optimal GARCH-type models are then used
to simulate out-of-sample volatility forecasts which are in turn utilized to es-
timate the one-day-ahead VaR forecasts. The empirical results demonstrate
that the optimal in-sample GARCH-type specifications vary from the selected
out-of-sample VaR forecasts models for all cryptocurrencies. Whilst the em-
pirical results do not guarantee a straightforward preference among
GARCH-type models, the asymmetric GARCH models with long memory
property and heavy-tailed innovations distributions overall perform better for
all cryptocurrencies.