The resilience of cryptocurrency market efficiency to COVID-19 shock.

We examine the price disorder and informational efficiency of five cryptocurrencies (Bitcoin, BNB, Cardano, Ethereum, and XRP) before and during the COVID-19 pandemic. In this sense, we estimate the permutation entropy and Fisher information measure (FIM). We use these complexity measures to construct the Shannon-Fisher causality plane (SFCP) to map these cryptocurrencies and their respective locations in a two-dimensional plane and then apply the sliding time window approach to study the temporal evolution of informational efficiency. All cryptocurrencies exhibit high but slightly varying informational efficiency during both periods. Cardano was the most efficient cryptocurrency. These results might point to the increasing maturity and lower potential for price predictability, which matter to cryptocurrencies' usage for liquidity risk diversification strategy.

Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers.

This paper examines the predictability of COVID-19 worldwide lethality considering 43 countries. Based on the values inherent to Permutation entropy ( H s ) and Fisher information measure ( F s ), we apply the Shannon-Fisher causality plane (SFCP), which allows us to quantify the disorder an evaluate randomness present in the time series of daily death cases related to COVID-19 in each country. We also use Hs and Fs to rank the COVID-19 lethality in these countries based on the complexity hierarchy. Our results suggest that the most proactive countries implemented measures such as facemasks, social distancing, quarantine, massive population testing, and hygienic (sanitary) orientations to limit the impacts of COVID-19, which implied lower entropy (higher predictability) to the COVID-19 lethality. In contrast, the most reactive countries implementing these measures depicted higher entropy (lower predictability) to the COVID-19 lethality. Given this, our findings shed light that these preventive measures are efficient to combat the COVID-19 lethality.

Insights from the (in)efficiency of Chinese sectoral indices during COVID-19.

This article evaluates the effects of the crisis caused by the new Coronavirus (COVID-19) on the Chinese sectoral indices. Using the complexity-entropy plane methodology, we find that the COVID-19 crisis caused increased inefficiency in most of China's equity sectors. We also find heterogeneous effects depending on the economic sector. Our results are useful for a better understanding the effect of global shocks on the stock markets and how their effects are distributed across economic sectors.