论文标题
$α$稳定的方法，用于建模高投机资产和加密货币
An $α$-Stable Approach to Modelling Highly Speculative Assets and Cryptocurrencies
论文作者
论文摘要
我们使用比特币，以太坊和波纹的数据调查了加密货币的行为，这些数据占加密货币市场的70％以上。我们证明，$α$稳定的分布是高度投机的加密货币的适当模型，它的表现优于金融计量经济学中使用的其他重型尾部分布。我们发现，Dumouchel（1971）提出的最大似然方法比McCulloch（1986）的基于分位数的方法和Koutrouvelis（1980）的样本特征方法的估算值要好得多。经验结果表明，加密货币返回数据中呈现的LeptOkurtic特征可以通过$α$稳定的分布来捕获。调查结果表明，$α$稳定的发行版不仅与其四个免费参数相比，而且是一个接近现实的创意模型。本文涵盖了有关加密货币和稳定分布的早期报告和文献。
We investigate the behaviour of cryptocurrencies using data for bitcoin, ethereum and ripple which account for over 70% of the cryptocurrency market. We demonstrate that $α$-stable distribution is an appropriately sufficient model for highly speculative cryptocurrencies which outperforms other heavy tailed distributions that are used in financial econometrics. We find that the maximum likelihood method proposed by DuMouchel (1971) produces estimates that fit the cryptocurrency return data much better than the quantile based approach of McCulloch (1986) and sample characteristic method by Koutrouvelis (1980). The empirical results show that the leptokurtic feature presented in cryptocurrency return data can be captured by an $α$-stable distribution. The findings highlight that $α$-stable distribution is not only parsimonious with its four free parameters but also a creative model that is close to reality. This paper covers early reports and literature on cryptocurrencies and stable distributions.
