Title: Investments in cryptocurrencies: how risky are they?
Authors: Málek, Jiří
Tran, Van Quang
Citation: Trendy v podnikání = Business trends : vědecký časopis Fakulty ekonomické ZČU v Plzni. 2018, č. 1, roč. 8, s. 3-11.
Issue Date: 2018
Publisher: Západočeská univerzita v Plzni
Document type: článek
URI: http://hdl.handle.net/11025/31015
ISSN: 1805-0603
Keywords: kryptoměny;alfa-stabilní distribuce;normální inverzní gaussovská distribuce;silné konce
Keywords in different language: cryptocurrencies;alpha-stable distribution;normal inverse Gaussian distribution;fat tails
Abstract in different language: The article analyzes the probability distribution of returns of the daily data of four cryptocurrencies (Bitcoin, Ethereum, Ripple,Litecoin). Alpha-stable distribution and normal inverse Gaussian distribution (NIG) are used as approximation of the empirical distribution of log-returns as they allow to capture the "power" tails. First basic information about all four cryptocurrencies are given, followed by definition of alpha-stable distribution and normal inverse Gaussian distributions which is special case of generalized hyperbolic distribution. These distributions are used to approximate empirical distributions of these cryptocurrencies. The difference between these two distributions is that the stable distribution can model heavier ends than the NIG (NIG has so called semi-heavy tails). The parameters are estimated using MLE (Maximum Likelihood Estimation) method, which has proved to be the most accurate one. First, we compare the empirical distribution of Bitcoin with NIG and alpha-stable distribution (the stable distribution appears to be much more accurate than the NIG). Then the only stable distribution is used and its parameters are searched for all four cryptocurrencies. α of all cryptocurrencies is close to one, which means that the probability distribution is similar to Cauchy one. The smallest α (and therefore the fattest tail) has Litecoin, followed by Ripple, Bitcoin, and the highest α of Ethereum. On the other hand, Ethereum has the highest sample volatility.
Rights: © Západočeská univerzita v Plzni
Appears in Collections:Číslo 1 (2018)
Číslo 1 (2018)

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