  We extend the application and test the performance of a recently introduced
volatility prediction framework encompassing LSTM and rough volatility. Our
asset class of interest is cryptocurrencies, at the beginning of the
"crypto-winter" in 2022. We first show that to forecast volatility, a universal
LSTM approach trained on a pool of assets outperforms traditional models. We
then consider a parsimonious parametric model based on rough volatility and
Zumbach effect. We obtain similar prediction performances with only five
parameters whose values are non-asset-dependent. Our findings provide further
evidence on the universality of the mechanisms underlying the volatility
formation process.
