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用阿尔法稳定分布建模加密货币收益

文章 arXiv papers · 作者: Taurai Muvunza
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总结

本研究评估阿尔法稳定分布作为比特币、以太坊和瑞波币收益模型的效果；在所考察的数据中，这些资产合计占加密货币市场的大部分。研究关注这种灵活的厚尾分布能否捕捉加密货币收益中观察到的尖峰厚尾特征，而较简单的高斯模型可能无法体现这一特征。

本文比较了三种参数估计方法：与杜穆舍尔相关的最大似然估计、与麦卡洛克相关的基于分位数的估计，以及与库特鲁维利斯相关的样本特征法。研究报告称，最大似然估计对所考察收益数据的拟合优于另外两种方法，阿尔法稳定模型也捕捉到了其厚尾特征。该分布有四个自由参数，作者称其较为简约。证据仅限于所考察的资产和样本；摘要没有给出日期、详细拟合统计、预测检验，也没有证据表明该分布能改善投资组合决策或交易表现。

核心观点

  • 本文评估阿尔法稳定分布作为加密货币厚尾收益模型的效果。
  • 实证研究考察了比特币、以太坊和瑞波币的收益。
  • 作者报告称，最大似然估计对所考察数据的拟合优于基于分位数和样本特征的方法。
  • 该模型捕捉到了加密货币收益中报告的尖峰厚尾特征。
  • 拟合优度本身并不能证明预测效果或交易盈利能力有所提升。

标签

加密资产统计学波动率
全文
# An $α$-Stable Approach to Modelling Highly Speculative Assets and Cryptocurrencies


# An $α$-Stable Approach to Modelling Highly Speculative Assets and Cryptocurrencies









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。