Cryptocurrencies: Dust in the Wind?

Physica A: Statistical Mechanics and its Applications, Volume 525, pp. 1063-1079, July 2019, DOI: 10.1016/j.physa.2019.03.123

43 Pages Posted: 22 Oct 2018 Last revised: 12 Apr 2019

See all articles by Min Luo

Min Luo

Shanghai University

Vasileios Kontosakos

Monash University - Department of Econometrics & Business Statistics

Athanasios A. Pantelous

Monash University - Department of Econometrics & Business Statistics

Jian Zhou

Shanghai University, School of Management

Date Written: April 2, 2019

Abstract

Analogous to the way wind blows single grains of sand and the subsequent settling back atop sand dunes, we find statistical evidence to claim that the prices of cryptocurrencies exhibit similar unpredicted patterns, characterized by positive or negative jumps. Motivated by extant evidence of asset returns' non-normality, we capture distributional properties of the log-returns of the Bitcoin and the following three cryptocurrencies in terms of market capitalization (Ethereum, Ripple and Bitcoin cash). The total error induced by the fitted distribution is remarkably decreased when the generalized hyperbolic distribution is used, a finding further validated by a series of goodness-of-fit type statistical tests. A complementary analysis for the foreign exchange market is conducted with inherent similarities to that of cryptocurrencies. We reveal that the generalized hyperbolic distribution can also be used to model very widely traded currency pairs significantly more accurately than the log-normal.

Keywords: Generalized Hyperbolic Distributions; Distribution Fitting; Cryptocurrency; Bitcoin; Foreign Exchange Market

JEL Classification: C10; G14; E40; F39

Suggested Citation

Luo, Min and Kontosakos, Vasileios and Pantelous, Athanasios A. and Zhou, Jian, Cryptocurrencies: Dust in the Wind? (April 2, 2019). Physica A: Statistical Mechanics and its Applications, Volume 525, pp. 1063-1079, July 2019, DOI: 10.1016/j.physa.2019.03.123. Available at SSRN: https://ssrn.com/abstract=3262530 or http://dx.doi.org/10.2139/ssrn.3262530

Min Luo

Shanghai University ( email )

149 Yanchang Road
SHANGDA ROAD 99
Shanghai 200072, SHANGHAI 200444
China

Vasileios Kontosakos

Monash University - Department of Econometrics & Business Statistics ( email )

Wellington Road
Clayton, Victoria 3168
Australia

Athanasios A. Pantelous (Contact Author)

Monash University - Department of Econometrics & Business Statistics ( email )

Wellington Road
Clayton, Victoria 3168
Australia

Jian Zhou

Shanghai University, School of Management ( email )

149 Yanchang Road
SHANGDA ROAD 99
Shanghai 200072, SHANGHAI 200444
China

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