Volatility modelling and VAR: The case of Bitcoin, ether and ripple
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F20%3A82476" target="_blank" >RIV/60460709:41110/20:82476 - isvavai.cz</a>
Result on the web
<a href="https://content.sciendo.com/view/journals/danb/11/3/article-p253.xml?language=en" target="_blank" >https://content.sciendo.com/view/journals/danb/11/3/article-p253.xml?language=en</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.2478/danb-2020-0015" target="_blank" >10.2478/danb-2020-0015</a>
Alternative languages
Result language
čeština
Original language name
Volatility modelling and VAR: The case of Bitcoin, ether and ripple
Original language description
Since Bitcoin introduction in 2008, the cryptocurrency market has grown into hundreds-of-billion-dollar market. The cryptocurrency market is well known as very volatile, mainly for the fact that the cryptocurrencies have not the price to fall back upon and that anybody can join the trading (no license or approval is required). Since empirical literature suggests that GARCH-type models dominate as VaR estimators the overall objective of this paper is to perform comprehensive volatility and VaR estimation for three major digital assets and conclude which method gives the best results in terms of risk management. The methods we used are parametric (GARCH and EWMA model), non-parametric (historical VaR) and Monte Carlo simulation (given by Geometric Brownian Motion). We conclude that the best method for value-at-risk estimation for cryptocurrencies is the Monte Carlo simulation due to the heavy diffusion (stochastic) process and robustness of the results.
Czech name
Volatility modelling and VAR: The case of Bitcoin, ether and ripple
Czech description
Since Bitcoin introduction in 2008, the cryptocurrency market has grown into hundreds-of-billion-dollar market. The cryptocurrency market is well known as very volatile, mainly for the fact that the cryptocurrencies have not the price to fall back upon and that anybody can join the trading (no license or approval is required). Since empirical literature suggests that GARCH-type models dominate as VaR estimators the overall objective of this paper is to perform comprehensive volatility and VaR estimation for three major digital assets and conclude which method gives the best results in terms of risk management. The methods we used are parametric (GARCH and EWMA model), non-parametric (historical VaR) and Monte Carlo simulation (given by Geometric Brownian Motion). We conclude that the best method for value-at-risk estimation for cryptocurrencies is the Monte Carlo simulation due to the heavy diffusion (stochastic) process and robustness of the results.
Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Danube: Law and Economics Review
ISSN
1804-8285
e-ISSN
1804-8285
Volume of the periodical
11
Issue of the periodical within the volume
3
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
17
Pages from-to
253-269
UT code for WoS article
—
EID of the result in the Scopus database
2-s2.0-85095680453