Skewed non-Gaussian GARCH models for cryptocurrencies volatility\n modelling

Abstract

Recently, cryptocurrencies have attracted a growing interest from investors,\npractitioners and researchers. Nevertheless, few studies have focused on the\npredictability of them. In this paper we propose a new and comprehensive study\nabout cryptocurrency market, evaluating the forecasting performance for three\nof the most important cryptocurrencies (Bitcoin, Ethereum and Litecoin) in\nterms of market capitalization. At this aim, we consider non-Gaussian GARCH\nvolatility models, which form a class of stochastic recursive systems commonly\nadopted for financial predictions. Results show that the best specification and\nforecasting accuracy are achieved under the Skewed Generalized Error\nDistribution when Bitcoin/USD and Litecoin/USD exchange rates are considered,\nwhile the best performances are obtained for skewed Distribution in the case of\nEthereum/USD exchange rate. The obtain findings state the effectiveness -- in\nterms of prediction performance -- of relaxing the normality assumption and\nconsidering skewed distributions.\n