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

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.

Bitcoin monthly return forecast: A comparison of ARIMA and multi layer Perceptron Artificial Neural Network

“…which can affect price behaviour. In the case of cryptocurrencies, we have seen that the price volatility of key cryptocurrencies has been slightly impacted by the COVID-19 crisis [50]. The current Russian-Ukrainian war also does not significantly affect the occurrence of extreme cryptocurrency volatility and liquidity decline [51] [52] [53].…”

Exploring the relationship between digital trails of social signals and bitcoin returns

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