一种建模高投机性资产与加密货币的 $α$-稳定方法
An $α$-Stable Approach to Modelling Highly Speculative Assets and Cryptocurrencies
AI总结：
本文基于占市场70%以上的三大加密货币数据，证明 $α$-stable 分布能有效拟合高投机性加密货币收益的尖峰重尾特征，且优于其他重尾分布，其中 DuMouchel (1971) 的极大似然法拟合效果最佳。
摘要：
我们使用占加密货币市场总量 70% 以上的 bitcoin、ethereum 和 ripple 数据研究了加密货币的行为。我们证明，$α$-stable 分布是高投机性加密货币的适当充分模型，其表现优于金融计量经济学中使用的其他重尾分布。我们发现，DuMouchel (1971) 提出的极大似然法产生的估计值在拟合加密货币收益数据方面，远优于 McCulloch (1986) 的基于分位数的方法和 Koutrouvelis (1980) 的样本特征方法。实证结果表明，加密货币收益数据中呈现的尖峰特征可由 $α$-stable 分布捕捉。研究结果强调，$α$-stable 分布不仅因其四个自由参数而简约，而且是一个贴近现实的创新模型。本文涵盖了关于加密货币和稳定分布的早期报告与文献。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
