论文标题
偏斜的非高斯GARCH型号用于加密货币的波动性建模
Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling
论文作者
论文摘要
最近，加密货币引起了投资者，从业者和研究人员的兴趣日益增长的兴趣。然而，很少有研究集中在它们的可预测性上。在本文中，我们提出了一项有关加密货币市场的新的全面研究，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。在此目标中，我们考虑了非高斯GARCH波动率模型，该模型构成了通常用于财务预测的一类随机递归系统。结果表明，当考虑到比特币/USD和Litecoin/USD汇率时，在偏斜的广义误差分布下实现了最佳的规范和预测精度，而在以太坊/USD汇率的情况下，获得了最佳性能。获得的发现指出，就预测性能而言，有效性是放宽正态性假设并考虑偏斜分布的有效性。
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.
