On long memory effects in the volatility measure of Cryptocurrencies

Andrew Phillip, Jennifer Chan and Shelton Peiris

Finance Research Letters, 2019, vol. 28, issue C, 95-100

Abstract: Cryptocurrencies as of late have commanded global attention on a number of fronts. Most notably, their variance properties are known for being notoriously wild, unlike their fiat counterparts. We highlight some stylized facts about the variance measures of Cryptocurrencies using the logarithm of daily return range and relate these results to their respective cryptographic designs such as intended transaction speed. The results favor oscillatory long run autocorrelations over standard long run autocorrelation filters to model the log daily return range. The overarching implication of this result is the volatility of Cryptocurrencies can be better understood and measured via the use of fast moving autocorrelation functions, as opposed to smoothly decaying functions for fiat currencies.

Keywords: Volatility measures; Cryptocurrencies; Long memory; Buffer threshold model; Jump diffusion (search for similar items in EconPapers)
JEL-codes: C22 C5 G (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (36)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612318301120
Full text for ScienceDirect subscribers only

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:eee:finlet:v:28:y:2019:i:c:p:95-100

DOI: 10.1016/j.frl.2018.04.003

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().