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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

  • 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