Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling

【Author】 Cerqueti, Roy; Giacalone, Massimiliano; Mattera, Raffaele

【Source】INFORMATION SCIENCES

【影响因子】8.233

【Abstract】Recently, cryptocurrencies have attracted a growing interest from investors, practitioners and researchers. Nevertheless, few studies have focused on the predictability of them. In this paper we propose a new and comprehensive study about cryptocurrency market, evaluating the forecasting performance for three of the most important cryptocurrencies (Bitcoin, Ethereum and Litecoin) in terms of market capitalization. At this aim, we consider non-Gaussian GARCH volatility models, which form a class of stochastic recursive systems commonly adopted for financial predictions. Results show that the best specification and forecasting accuracy are achieved under the Skewed Generalized Error Distribution when Bitcoin/USD and Litecoin/USD exchange rates are considered, while the best performances are obtained for skewed Distribution in the case of Ethereum/USD exchange rate. The obtain findings state the effectiveness - in terms of prediction performance - of relaxing the normality assumption and considering skewed distributions. (C) 2020 Elsevier Inc. All rights reserved.

【Keywords】Generalized error distribution; GARCH models; Skewed distributions; Volatility forecasting; Non linear GARCH

【发表时间】2020 JUL

【收录时间】2022-01-02

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