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In comparison with other respiratory viruses, the current COVID-19 pandemic's rapid seizing the world can be attributed to indirect (contact) way of transmission of SARS-CoV-2 virus in addition to the regular airborne way. A significant part of indirect transmission is made through cash bank notes. SARS-CoV-2 remains on cash paper money for period around four times larger than influenza A virus and is absorbed by cash notes two and a half times more effectively than influenza A (our model). During the pandemic, cryptocurrencies have gained attractiveness as an "epidemiologically safe" means of transactions. On the basis of the authors' gallop polls performed online with social networks users in 44 countries in 2020–2021 (the total number of clear responses after the set repair 32,115), around 14.7% of surveyed participants engaged in cryptocurrency-based transactions during the pandemic. This may be one of the reasons of significant rise of cryptocurrencies rates since mid-March 2020 till the end of 2021. The paper discusses the reasons for cryptocurrency attractiveness during the COVID-19 pandemic. Among them, there are fear of SARS-CoV-2 spread via cash contacts and the ability of the general population to mine cryptocurrencies. The article also provides a breakdown of the polled audience profile to determine the nationalities that have maximal level of trust to saving and transacting money as cryptocurrencies. [ABSTRACT FROM AUTHOR]

Bitcoin has attracted attention from different market participants due to unpredictable price patterns. Sometimes, the price has exhibited big jumps. Bitcoin prices have also had extreme, unexpected crashes. We test the predictive power of a wide range of determinants on bitcoins' price direction under the continuous transfer entropy approach as a feature selection criterion. Accordingly, the statistically significant assets in the sense of permutation test on the nearest neighbour estimation of local transfer entropy are used as features or explanatory variables in a deep learning classification model to predict the price direction of bitcoin. The proposed variable selection do not find significative the explanatory power of NASDAQ and Tesla. Under different scenarios and metrics, the best results are obtained using the significant drivers during the pandemic as validation. In the test, the accuracy increased in the post-pandemic scenario of July 2020 to January 2021 without drivers. In other words, our results indicate that in times of high volatility, Bitcoin seems to self-regulate and does not need additional drivers to improve the accuracy of the price direction. [ABSTRACT FROM AUTHOR]

This paper examines the impact of COVID-19 lockdowns on Bitcoin trading volume. Using data from Apple mobility trends and several time-series econometric models, we find that investors became active participants during the COVID-19 pandemic period and traded more bitcoins on days with low mobility associated with lockdown mandates. These results remain robust after controlling for stocks and gold returns, the VIX index, and the level of attention and sentiment toward Bitcoin, as measured by Google search frequencies and the tone of Tweets discussing Bitcoin. These results suggest that when individual investors have ample free time on their hands, they trade cryptocurrencies as a pastime and use the Bitcoin market as a form of entertainment. Moreover, our results have important implications concerning investors' herding behavior and overconfidence leading to noise trader risks and bubbles typically accompanied by high trading volume in cryptocurrency markets. [ABSTRACT FROM AUTHOR]

This study examines the volatility of nine leading cryptocurrencies by market capitalization—Bitcoin, XRP, Ethereum, Bitcoin Cash, Stellar, Litecoin, TRON, Cardano, and IOTA-by using a Bayesian Stochastic Volatility (SV) model and several GARCH models. We find that when we deal with extremely volatile financial data, such as cryptocurrencies, the SV model performs better than the GARCH family models. Moreover, the forecasting errors of the SV model, compared with the GARCH models, tend to be more accurate as forecast time horizons are longer. This deepens our insight into volatility forecast models in the complex market of cryptocurrencies. [ABSTRACT FROM AUTHOR]

This study examines the interaction of Bitcoin with fiat currencies of three developed (euro, pound sterling and yen) and three emerging (yuan, rupee and ruble) market economies. Empirical investigations are executed through symmetric, asymmetric and non-linear causality tests, and Markov regime-switching regression (MRSR) analysis. Results show that Bitcoin has a causal nexus with Chinese yuan and Indian rupee for price and various return components. The MRSR analysis justifies these findings by demonstrating the presence of interaction in contractionary regimes. Accordingly, it can be stated that when markets display a downward trend, appreciation of the Chinese yuan and Indian rupee positively and strongly affects the value of Bitcoin, possibly due to the market timing. The MRSR analysis also exhibits a transition from a tranquil to a crisis regime in March 2020 because of the pandemic. However, a shorter duration spent in the crisis regime in 2020 indicates the limited and relatively less harmful effect of the pandemic on the cryptocurrency market when compared to the turmoil that occurred in 2018. [ABSTRACT FROM AUTHOR]

The study of connectedness is key to assess spillover effects and identify lead-lag relationships among market exchanges trading the same asset. By means of an extension of Diebold and Yilmaz (2012) econometric connectedness measures, we examined the relationships of five major Bitcoin exchange platforms during two periods of main interest: the 2017 surge in prices and the 2018 decline. We concluded that Bitfinex and Gemini are leading exchanges in terms of return spillover transmission during the analyzed time-frame, while Bittrexs act as a follower. We also found that connectedness of overall returns fell substantially right before the Bitcoin price hype, whereas it leveled out during the period the down market period. We confirmed that the results are robust with regards to the modeling strategies. [ABSTRACT FROM AUTHOR]

This paper studies the causal relationship between Bitcoin and other investment assets. We first test Granger causality and then calculate transfer entropy as an information-theoretic approach. Unlike the Granger causality test, we discover that transfer entropy clearly identifies causal interdependency between Bitcoin and other assets, including gold, stocks, and the U.S. dollar. However, for symbolic transfer entropy, the dynamic rise–fall pattern in return series shows an asymmetric information flow from other assets to Bitcoin. Our results imply that the Bitcoin market actively interacts with major asset markets, and its long-term equilibrium, as a nascent market, gradually synchronizes with that of other investment assets. [ABSTRACT FROM AUTHOR]