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Long memory in Bitcoin and ether returns and volatility and Covid-19 pandemic

Miriam Sosa (Economics Department, Metropolitan Autonomous University, Tlalpan, Mexico)
Edgar Ortiz (Faculty of Social and Political Sciences, National Autonomous University of Mexico, Mexico City, Mexico)
Alejandra Cabello-Rosales (Graduate Program in Administrative Sciences, National Autonomous University of Mexico, Mexico City, Mexico)

Studies in Economics and Finance

ISSN: 1086-7376

Article publication date: 1 December 2022

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Abstract

Purpose

The purpose of this research is to analyze the Bitcoin (BTC) and Ether (ETH) long memory and conditional volatility.

Design/methodology/approach

The empirical approach includes ARFIMA-HYGARCH and ARFIMA-FIGARCH, both models under Student‘s t-distribution, during the period (ETH: November 9, 2017 to November 25, 2021 and BTC: September 17, 2014 to November 25, 2021).

Findings

Findings suggest that ARFIMA-HYGARCH is the best model to analyze BTC volatility, and ARFIMA-FIGARCH is the best approach to model ETH volatility. Empirical evidence also confirms the existence of long memory on returns and on BTC volatility parameters. Results evidence that the models proposed are not as suitable for modeling ETH volatility as they are for the BTC.

Originality/value

Findings allow to confirm the fractal market hypothesis in BTC market. The data confirm that, despite the impact of the Covid-19 crisis, the dynamics of BTC returns, and volatility maintained their patterns, i.e. the way in which they evolve, in relation to the prepandemic era, did not change, but it is rather reaffirmed. Yet, ETH conditional volatility was more affected, as it is apparently higher during Covid-19. The originality of the research lies in the focus of the analysis, the proposed methodology and the variables and periods of study.

Keywords

Citation

Sosa, M., Ortiz, E. and Cabello-Rosales, A. (2022), "Long memory in Bitcoin and ether returns and volatility and Covid-19 pandemic", Studies in Economics and Finance, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SEF-05-2022-0251

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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