A Statistical Classification of Cryptocurrencies

D. Pele, Niels Wesselhöfft, W. Härdle, M. Kolossiatis, Y. Yatracos
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引用次数: 4

Abstract

The aim of this paper is to derive the main factors that separate cryptocurrencies from the classical assets, by using various classification techniques applied to the daily time series of log-returns. In this sense, a daily time series of asset returns (either cryptocurrencies or classical assets) can be characterized by a multidimensional vector with statistical components like variance, skewness, kurtosis, tail probability, quantiles, conditional tail expectation or GARCH parameters. By using dimension reduction techniques (Factor Analysis) and classification models (Binary Logistic Regression, Discriminant Analysis, Support Vector Machines, K-means clustering, Variance Components Split methods) for a representative sample of cryptocurrencies, stocks, exchange rates and commodities, we are able to classify cryptocurrencies as a new asset class with unique features in the tails of the log-returns distribution. The main result of our paper is the complete separation of the cryptocurrencies from the other type of assets, by using the Maximum Variance Components Split method. In addition, we observe a synchronicity in the evolution of of the cryptocurrencies, compared to the classical assets, mainly due to the tails behaviour of the log-return distribution.
加密货币的统计分类
本文的目的是通过使用应用于日志收益的每日时间序列的各种分类技术,得出将加密货币与经典资产分开的主要因素。从这个意义上说,资产回报的每日时间序列(无论是加密货币还是经典资产)都可以用一个多维向量来表征,其中包含方差、偏度、峰度、尾部概率、分位数、条件尾部期望或GARCH参数等统计成分。通过对加密货币、股票、汇率和商品的代表性样本使用降维技术(因子分析)和分类模型(二元逻辑回归、判别分析、支持向量机、k均值聚类、方差成分分割方法),我们能够将加密货币分类为一种新的资产类别,在对数回报分布的尾部具有独特的特征。我们论文的主要结果是通过使用最大方差成分分割方法将加密货币与其他类型的资产完全分离。此外,与经典资产相比,我们观察到加密货币的演变具有同步性,这主要是由于对数回报分布的尾部行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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