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

IF 2.3 Q2 BUSINESS, FINANCE

Studies in Economics and Finance Pub Date : 2022-12-01 DOI:10.1108/sef-05-2022-0251

Miriam Sosa, E. Ortiz, Alejandra Cabello-Rosales

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引用次数: 2

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.

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对比特币和以太币回报、波动性和Covid-19大流行的长期记忆

本研究的目的是分析比特币(BTC)和以太坊(ETH)的长记忆和条件波动。设计/方法/方法实证方法包括ARFIMA-HYGARCH和ARFIMA-FIGARCH，这两个模型都是学生t分布下的模型，期间(ETH: 2017年11月9日至2021年11月25日，BTC: 2014年9月17日至2021年11月25日)。研究结果表明，ARFIMA-HYGARCH是分析BTC波动率的最佳模型，ARFIMA-FIGARCH是模拟ETH波动率的最佳方法。经验证据也证实了长期记忆在回报和比特币波动参数上的存在。结果表明，所提出的模型不适合模拟ETH的波动性，因为它们适合于BTC。原创性/价值研究结果证实了比特币市场的分形市场假说。数据证实，尽管受到Covid-19危机的影响，但比特币的回报动态和波动性保持了其模式，即它们相对于大流行前时代的演变方式没有改变，而是得到了肯定。然而，ETH有条件波动率受到的影响更大，因为它在Covid-19期间明显更高。本研究的独创性在于分析的重点、提出的研究方法、研究的变量和研究的时期。

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来源期刊

Studies in Economics and Finance BUSINESS, FINANCE-

CiteScore

4.30

自引率

10.50%

发文量

期刊介绍： Topics addressed in the journal include: ■corporate finance, ■financial markets, ■money and banking, ■international finance and economics, ■investments, ■risk management, ■theory of the firm, ■competition policy, ■corporate governance.