EconPapers    
Economics at your fingertips  
 

Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple

Pınar Kaya Soylu (), Mustafa Okur (), Özgür Çatıkkaş () and Z. Ayca Altintig ()
Additional contact information
Pınar Kaya Soylu: Department of Business Informatics, Faculty of Business Administration, Marmara University, Istanbul 34722, Turkey
Mustafa Okur: Department of Capital markets, School of Banking & Insurance, Marmara University, Istanbul 34722, Turkey
Özgür Çatıkkaş: Department of Insurance, School of Banking & Insurance, Marmara University, Istanbul 34722, Turkey
Z. Ayca Altintig: Peter F. Drucker and Masatoshi Ito Graduate School of Management, Claremont Graduate University, Claremont, CA 91711, USA

Journal of Risk and Financial Management, 2020, vol. 13, issue 6, 1-21

Abstract: This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable modelling technique. Our findings indicate that the Hyperbolic GARCH (HYGARCH) model appears to be the best fitted model for Bitcoin. On the other hand, the Fractional Integrated GARCH (FIGARCH) model with skewed student distribution produces better estimations for Ethereum. Finally, FIGARCH model with student distribution appears to give a good fit for Ripple return. Based on Kupieck’s tests for Value at Risk (VaR) back-testing and expected shortfalls we can conclude that our models perform correctly in most of the cases for both the negative and positive returns.

Keywords: volatility modelling; cryptocurrency; value at risk; expected shortfall; long memory (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
https://www.mdpi.com/1911-8074/13/6/107/pdf (application/pdf)
https://www.mdpi.com/1911-8074/13/6/107/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:13:y:2020:i:6:p:107-:d:364466

Access Statistics for this article

Journal of Risk and Financial Management is currently edited by Prof. Dr. Michael McAleer

More articles in Journal of Risk and Financial Management from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().

 
Page updated 2021-07-22
Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:6:p:107-:d:364466