Abstract

Cryptocurrencies became popular with the emergence of Bitcoin and have shown an unprecedented growth over the last few years. As of November 2016, more than 720 cryptocurrencies exist, with Bitcoin still being the most popular one. We provide both a statistical analysis as well as an extreme value analysis of the returns of the most important cryptocurrencies. A particular focus is on the tail risk characteristics and we will provide an in-depth univariate and multivariate extreme value analysis. The tail dependence of cryptocurrencies is investigated (using empirical copulas). For investors - especially institutional ones - as well as regulators, an understanding of the risk and tail characteristics is of utmost importance. For cryptocurrencies to become a mainstream investable asset class, studying these properties is necessary. Our findings show that cryptocurrencies exhibit strong non-normal characteristics, large tail dependencies, depending on the particular cryptocurrencies and heavy tails. Statistical similarities can be observed for cryptocurrencies that share the same underlying technology. This has implications for risk management, financial engineering (such as derivatives on cryptocurrencies) - both from an investor's as well as from a regulator's point of view. To our knowledge, this is the first detailed study looking at the extreme value behaviour of cryptocurrencies, their correlations and tail dependencies as well as their statistical properties.

Highlights

  • Introduction and MotivationSince Bitcoin became the first decentralized cryptocurrency in 2009, numerous cryptocurrencies have been created

  • There are more than 720 cryptocurrencies with the top ten of them (Bitcoin, Ethereum, Ripple, Litecoin, Ethereum Classic, Monero, Dash, Augur, MaidSafeCoin, Waves) representing about 95% of the market (CoinMarketCap, 2016)

  • We are describing the statistical properties of cryptocurrencies and we focus in particular on extreme value characteristics

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Summary

Macrothink

Jörg Osterrieder School of Engineering, Zurich University of Applied Sciences, Winterthur, Switzerland. Received: Dec. 12, 2016 Accepted: Dec. 22, 2016 Published: Jan. 23, 2017 doi:10.5 296/ifb.v4i1.10451

Introduction and Motivation
What are Cryptocurrencies?
A Short Description of the Individual Cryptocurrencies
Distribution of Returns
Volatility
Correlations
Extreme Value Analysis of Cryptocurrencies
Copulas
Empirical Copula
Gaussian Copula
Tail Dependence Coefficient
Value-at-risk and Expected Shortfall
Extremal Index
Extreme Value Distributions
Cryptocurrencies and the Generalized Pareto Distribution
Cryptocurrencies and the Generalized Extreme Value Distribution
Findings
Conclusion
Full Text
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