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Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling
Information Sciences ( IF 8.1 ) Pub Date : 2020-04-03 , DOI: 10.1016/j.ins.2020.03.075
Roy Cerqueti , Massimiliano Giacalone , Raffaele Mattera

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.



中文翻译:

偏态非高斯GARCH模型用于加密货币波动率建模

最近,加密货币吸引了投资者,从业人员和研究人员越来越多的兴趣。然而,很少有研究集中在它们的可预测性上。在本文中,我们提出了一项关于加密货币市场的全新且全面的研究,从市场资本的角度评估了三种最重要的加密货币(比特币,以太坊和莱特币)的预测性能。为此,我们考虑了非高斯GARCH波动率模型,该模型形成了通常用于财务预测的一类随机递归系统。结果表明,当考虑比特币/美元和莱特币/美元汇率时,在偏斜广义误差分布下可获得最佳的规格和预测准确性,而在以太坊/美元汇率的情况下,偏斜分布可获得最佳性能。获得的结果表明,就预测性能而言,放宽正态性假设并考虑偏斜分布的有效性。

更新日期:2020-04-03
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