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...

The iid property of the model’s residuals is a crucial criterion for assessing the fit of the model to the data. GARCH-class models are the most commonly used nonlinear models in financial econometrics. In this paper various uni- and multivariate GARCH-class models were applied to selected...

Construction, estimation and application of the mutual information measure have been presented in this paper. The simulations have been carried out to verify its usefulness to detect nonlinear serial dependencies. Moreover, the mutual information measure has been applied to the indices and the...

A concept of fractal dimension as a measure of risk in securities trading is presented in this paper. The two methods of calculating fractal dimension of time series – R/S analysis and segment-variation method are described and applied to indices of the Warsaw Stock Exchange.