A Statistical Classification of Cryptocurrencies

38 Pages Posted: 30 Mar 2020 Last revised: 13 Apr 2020

See all articles by Daniel Traian Pele

Daniel Traian Pele

The Bucharest University of Economic Studies, Department of Statistics and Econometrics

Niels Wesselhöfft

Humboldt Universität zu Berlin | IRTG 1792

Wolfgang K. Härdle

Blockchain Research Center; Xiamen University - Wang Yanan Institute for Studies in Economics (WISE); Charles University; National Chiao Tung University; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Michalis Kolossiatis

University of Cyprus; Central Bank of Cyprus

Yannis G. Yatracos

Tsinghua University - Yau Mathematical Sciences Center

Date Written: March 4, 2020

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.

Keywords: Cryptocurrency, Classification, Multivariate Analysis, Factor Models, Synchronicity

JEL Classification: C1, G1

Suggested Citation

Pele, Daniel Traian and Wesselhöfft, Niels and Härdle, Wolfgang K. and Kolossiatis, Michalis and Yatracos, Yannis G., A Statistical Classification of Cryptocurrencies (March 4, 2020). Available at SSRN: https://ssrn.com/abstract=3548462 or http://dx.doi.org/10.2139/ssrn.3548462

Daniel Traian Pele (Contact Author)

The Bucharest University of Economic Studies, Department of Statistics and Econometrics ( email )

Romania

Niels Wesselhöfft

Humboldt Universität zu Berlin | IRTG 1792 ( email )

Unter den Linden 6
Berlin, AK Berlin 10099
Germany

Wolfgang K. Härdle

Blockchain Research Center ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Xiamen University - Wang Yanan Institute for Studies in Economics (WISE) ( email )

A 307, Economics Building
Xiamen, Fujian 10246
China

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

National Chiao Tung University ( email )

No. 1001 Ta Hsueh Road
Hsinchu 300
Taiwan

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

Michalis Kolossiatis

University of Cyprus ( email )

1 Panepistimiou Avenue
Nicosia, Nicosia 2109
Cyprus

Central Bank of Cyprus ( email )

80 Kennedy Ave
1076 Nicosia
Cyprus

Yannis G. Yatracos

Tsinghua University - Yau Mathematical Sciences Center ( email )

Beijing
China

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