ABSTRACT
This paper performs the asymmetric multifractal cross-correlation analysis to examine the COVID-19 effects on three relevant high-frequency fiat currencies, namely euro (EUR), yen (YEN) and the Great Britain pound (GBP), and two cryptocurrencies with the highest market capitalization and traded volume (Bitcoin and Ethereum) considering two periods (Pre-COVID-19 and during COVID-19). For both periods, we find that all pairs of these financial assets are characterized by overall persistent cross-correlation behavior (alpha xy(0) > 0.5). Moreover, COVID-19 promoted an increase in the multifractal spectrum's width, which implies an increase in the complexity for all pairs considered here. We also studied the Generalized Cross-correlation Exponent, which allows us to verify that there is no asymmetric behavior between Bitcoin and fiat currencies and between Ethereum and fiat currencies. We conclude that investing simultaneously in major fiat currencies and leading cryptocurrencies can reduce the portfolio risk, leading to improvement in the investment results.
ABSTRACT
This research explores the multifractal dynamics of time series of the daily number of vaccinees for COVID-19, considering six European countries (Belgium, Denmark, France, Germany, Greece and Italy) using the Multifractal Detrended Fluctuations Analysis (MF-DFA). We calculate the multifractal spectrum f(alpha) and apply a fourth-degree polynomial regression fit to estimate the complexity parameters that describe the degree of multifractality of the underlying process. We found that the multifractal dynamics of all these countries are characterized by strongly anti-persistent behavior (alpha (0) < 0.5) a lower degree of multifractality, and small fluctuations are dominant in the multifractal spectrum. From an immunization perspective, it means that a panorama that encompasses the population's behaviour is marked by the dynamics of anti-persistent adherence to COVID-19 vaccines. Our findings confirm that the period of immunization of the population that adhered to the vaccination campaigns is short and that the application of new doses of vaccines must obey this phenomenology to keep people safe. In addition, we used the multifractal efficiency coefficient to rank countries that are most proactive in developing campaigns that promote greater adherence and loyalty to COVID-19 vaccines. Our findings indicate that Germany, Belgium and France were more efficient than Greece, Denmark and Italy.
ABSTRACT
In this paper, we presented an overview diagnosis consider the time series of daily deaths by COVID-19 in the Brazilian States using Bandt &Pompe method (BPM) to estimate the Information Theory quantifiers, more specifically the Permutation entropy (Hs) and the Fisher information measure (Fs). Based on the Information Theory quantifiers, we build up the Shannon-Fisher causality plane (SFCP) to promote insights into the COVID-19 temporal evolution inherent in the phenomenology associated with the number of daily deaths well as their respective locations along the SFCP. Moreover, we apply Hs and Fs to elaborate on the rank of the Brazilian States’ real situation, considering the number of daily death due to COVID-19 based on the complexity hierarchy. The Brazilian States that are located in the middle region of the two-dimensional plane (Hs x Fs), such as Amapá (AP), Roraima (RO), Acre (AC), and Tocantins (TO) are characterized by a less entropic and low disorder, which implies in high predictability of the COVID-19 lethality. While, the Brazilian States that are located in the lower-right region, such as Ceará (CE), Bahia (BA), Pernambuco (PE), and Rio de Janeiro (RJ), are characterized by high entropy and high disorder, which leads to low predictability of the COVID-19 lethality. Given this, our results provide empirical evidence that the permutation entropy is a powerful approach to predicting infectious diseases. Dynamic monitoring of permutation entropy can help policymakers to take more or less restrictive measures to combat COVID-19. © 2021 IOP Publishing Ltd.