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Title: Bitcoin and cryptocurrencies - not for the faint-hearted
Authors : Osterrieder, Jörg
Strika, Martin
Lorenz, Julian
Published in : International Finance and Banking
Volume(Issue) : 4
Issue : 1
Pages : 56
Pages to: 94
Publisher / Ed. Institution : Macrothink Institute
Issue Date: 2017
License (according to publishing contract) : CC BY 3.0: Attribution 3.0 Unported
Type of review: Peer review (publication)
Language : English
Subjects : Risk; Bitcoin; Cryptocurrency; Currency
Subject (DDC) : 332: Financial economics
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 both empirical and Gaussian copulas). For investors - especially institutional ones - as well as regulators, an understanding of the risk and tail characteristics are 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.
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Publication type: Article in scientific journal
DOI : 10.21256/zhaw-5509
ISSN: 2374-2089
Appears in Collections:Publikationen School of Engineering

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