Abstract We forecast the Range Value at Risk (RVaR) of main cryptocurrencies using the
GARCH model with different error distributions. We compare the performance of the different …

With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling
of cryptocurrencies. This paper provides the first GARCH modelling of the seven most …

Several procedures to forecast daily risk measures in cryptocurrency markets have been
recently implemented in the literature. Among them, long‐memory processes, procedures …

Abstract We estimate the Expected Shortfall (ES) of four major cryptocurrencies using
various error distributions and GARCH-type models for conditional variance. Our aim is to …

We perform a large-scale analysis to evaluate the performance of traditional and Markov-
switching GARCH models for the volatility of 292 cryptocurrencies. For each cryptocurrency …

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 aims to select the best model or set of models for modelling volatility of the four
most popular cryptocurrencies, ie Bitcoin, Ethereum, Ripple and Litecoin. More than 1000 …

This study aims to jointly predict conditional quantiles and tail expectations for the returns of
the most popular cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin and Litecoin) using …

Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in
existence and even more on the way. This study implements some statistical and machine …

This paper evaluates the presence of regime changes in the log‐returns volatility dynamics
of cryptocurrencies using Markov‐Switching GARCH (MS‐GARCH) models. The empirical …