This paper presents a comprehensive study of statistical and machine learning methods for predicting daily and weekly volatility of the following four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Monero. Several methods, i.e., HAR, ARFIMA, GARCH, LASSO, ridge regression, SVR, MLP, fuzzy...

We analyze the effects of the outbreak of the Russian-Ukrainian war on the volatility in commodity and financial markets. The Russian invasion sharply raised the volatility of most assets, however, the scale of reactions was market-specific and some markets exhibited a strong tendency to return...

The dynamic conditional correlation (DCC) model by Engle (2002) is one of the most popular multivariate volatility models. This model is based solely on closing prices. It has been documented in the literature that the high and low prices of a given day can be used to obtain an efficient...