ABSTRACT
An approach based on fractal scaling analysis to characterize the organization of the Covid-19 genome sequences is presented in this work. The method is based on a multivariate version of the fractal rescaled range analysis implemented on a sliding window scheme to detect variations of long-range correlations over the genome sequence domains. As a preliminary step, the nucleotide sequence is mapped in a numerical sequence by following a Voss rule, resulting in a multichannel sequence represented as a binary matrix. Fractal correlations, quantified in terms of the Hurst exponent, depending on the region of the sequence, where the Covid-19 genome sequences are predominantly random, with some patches of weak long-range correlations. The analysis shows that the regions of randomness are more abundant in the Covid-19 sequences than in the primitive SARS sequence, which suggests that the Covid-19 virus possesses a more diverse genomic structure for replication and infection. The analysis constrained to the surface glycoprotein region shows that the Covid-19 sequence is less random as compared to the SARS sequence, which indicates that the Covid-19 virus can undergo more ordered replications of the spike protein. The Omicron variation exhibits an interesting pattern with some randomness similarities with the other SARS and the Covid-19 genome sequences. Overall, the results show that the multivariate rescaled range analysis provides a suitable framework to assess long-term correlations hidden in the internal organization of the Covid-19 genome sequence. © 2023
ABSTRACT
This work investigates the impact of the Covid-19 outbreak on crude oil market efficiency. The approach is based on the singular value decomposition (SVD) entropy. Iso-distributional surrogate data test was used to contrast the results against random patterns, and phase randomization based on Fourier transform was used to assess nonlinearities. The analysis considered the WTI market and focused on the Covid-19 pandemic period January 2020-November 2021 and contrasted with the long preceding period from January 2000 to date. It was found that the crude oil market was informationally efficient most of the time with small sporadic deviations from efficiency in the pre-Covid-19 years. The Covid-19 period exhibited the largest deviations from efficiency, mainly in the first months of the outbreak, accompanied by a marked reduction of nonlinear components. The analysis was conducted for different scales, and the results showed that the deviations from efficiency were more pronounced for quarterly scales. For the sake of comparison, the analysis was also carried out on the trading volume dynamics and the results showed that the market activity is not fully random. The dynamics of the trading volume exhibited significant deviations from the randomness behavior when the crude oil market was efficient, and a behavior that was consistent with nonlinear patterns. The opposite behavior was noted for stages when the crude oil market showed strong deviations from efficiency. Overall, the findings of this study suggest an increasing opportunity for crude oil price predictions and abnormal returns during the Covid-19 pandemic.
ABSTRACT
An approach based on fractal scaling analysis to characterize the organization of the SARS-CoV-2 genome sequence was used. The method is based on the detrended fluctuation analysis (DFA) implemented on a sliding window scheme to detect variations of long-range correlations over the genome sequence regions. The nucleotides sequence is mapped in a numerical sequence by using four different assignation rules: amino-keto, purine-pyrimidine, hydrogen-bond and hydrophobicity patterns. The originally reported sequence from Wuhan isolates (Wuhan Hu-1) was considered as a reference to contrast the structure of the 2002-2004 SARS-CoV-1 strain. Long-range correlations, quantified in terms of a scaling exponent, depended on both the mapping rule and the sequence region. Deviations from randomness were attributed to serial correlations or anti-correlations, which can be ascribed to ordered regions of the genome sequence. It was found that the Wuhan Hu-1 sequence was more random than the SARS-CoV-1 sequence, which suggests that the SARS-CoV-2 possesses a more efficient genomic structure for replication and infection. In general, the virus isolated in the early 2020 months showed slight correlation differences with the Wuhan Hu-1 sequence. However, early isolates from India and Italy presented visible differences that led to a more ordered sequence organization. It is apparent that the increased sequence order, particularly in the spike region, endowed some early variants with a more efficient mechanism to spreading, replicating and infecting. Overall, the results showed that the DFA provides a suitable framework to assess long-term correlations hidden in the internal organization of the SARS-CoV-2 genome sequence.
ABSTRACT
This letter revisits the informationally efficiency of the two major cryptocurrencies Bitcoin (2013–2021) and Ethereum (2016–2021). The analysis is based on the computation of the singular value decomposition (SVD) entropy of a matrix formed by lagged vectors of price returns. The computed entropy is compared with a reference obtained from uncorrelated time series to decide whether the rows of the lagged matrix are uncorrelated. The procedure was implemented over a sliding window to assess the time variations of the entropy. The results show that the markets are informationally efficient most of time over different scales, except for some short periods that are linked to the 2016–2017 price-boom period and the 2020 Covid-19 economic downturn. © 2021 Elsevier B.V.