RESUMO
Given the importance of the financial markets in the global context, data analysis and new statistical approach are always welcome, especially if we are referring to G-20 group (the world's richest countries). As we know, the pandemic outbreak of COVID-19 has affected the global economy, and its impact seems to be inevitable (as it was in 2020). From the perspective of what was raised above, this paper aims to analyze the stock market efficiency in 21 indexes of G-20. We are going to do our analysis with intraday scale (of hour), from May 2019 to May 2020. In order to be successful in this analysis, we applied the DFA and the DCCA methods, to identify or not two points:i)Are G-20 stock market efficient in their weak form?ii)With open/close values, it is possible to identify some type of memory in G-20 group? The answer to these points will be given throughout this paper. For this purpose, the entire analysis will be divided into two different time-scale: Period I, time-scale less than five days and Period II, with time-scale greater than ten days. In the pandemic times of COVID-19, our results show that taking into account the DFA method, for time-scale shorter than 5 days, the stock markets tend to be efficient, whereas for time-scale longer than 10 days, the stock market tend to be inefficient. But, with DCCA method for cross-correlation analysis, the results for open/close indexes show different types of behaviors for each stock market index separately.
RESUMO
In this paper the algorithm for ΔρDCCA statistical test (Guedes et al., 2018) [1] is presented. Our test begins with the simulation of four time series pairs, by an ARFIMA process. These time series has N=250 , 500, 1000, and 2000 points, see Guedes et al. (2018) [1]. The probability distribution function (PDF) is made available for all 10,000 samples, that start from the original time series, in supplementary material.
RESUMO
The hydrophobic effect is the major factor that drives a protein toward collapse and folding. As a consequence of the folding process a hydrophobic core is shielded by the solvent-accessible surface area of the protein. We analyze the solvent-accessible surface area of 1825 nonhomolog protein chains deposited in the Brookhaven Protein Data Bank. This solvent-accessible surface area presents an intrinsic self-similarity behavior. The comparison between the accessible surface area as function of the number of amino acids and the accessible surface area as function of gyration radius supplies a measure of the scaling exponent close to the one observed by volume as function of radius of gyration or by mass-size exponent. The present finding indicates that the fractal analysis describes the protein compactness as an object packing between random spheres in percolation threshold and crumpled wires.
Assuntos
Proteínas/química , Bases de Dados de Proteínas , Interações Hidrofóbicas e Hidrofílicas , Dobramento de Proteína , Solventes/química , Propriedades de Superfície , Água/químicaRESUMO
It is well known that the hydrophobic effect is the major factor that drives a protein toward collapse and folding. We analyze the variation of the solvent-accessible surface area of amino acids in small fragments of protein (3N45) . In this way, we look into 5526 protein chains deposited in the Brookhaven Protein Data Bank. The accessible surface area behaves as a power law for N9 . The comparison between the loss of accessible area and the self-similar behavior gives us a measure of the possibility of an amino acid to have apolar or polar side chain. It is therefore possible to infer about amino acid hydrophobicity, i.e., if one amino acid has a hydrophobic side chain or if it has a hydrophilic one. Furthermore, the present findings indicate that the variation of the accessible surface area describes an alternative hydrophobicity scale.
Assuntos
Aminoácidos/química , Biofísica/métodos , Interações Hidrofóbicas e Hidrofílicas , Proteínas/química , Solventes/química , Modelos Biológicos , Conformação Molecular , Polímeros/química , Conformação Proteica , Dobramento de Proteína , Propriedades de Superfície , ÁguaRESUMO
Fractal properties of 5526 different protein chains are investigated. Characteristic fractal behavior for different molecular systems is obtained from the fractal dimension analysis, which shows that the dimension is delta=2.47 . This dimension gives a measure of the protein compactness. The present finding indicates that the fractal analysis describes some structural properties of proteins and corroborates the explanation about multifractality in the energy hypersurface.
Assuntos
Cristalografia/métodos , Fractais , Modelos Químicos , Modelos Moleculares , Proteínas/química , Proteínas/ultraestrutura , Sequência de Aminoácidos , Simulação por Computador , Dados de Sequência Molecular , Peso Molecular , Conformação Proteica , Proteínas/análise , Relação Estrutura-AtividadeRESUMO
We study time series of x-ray sources of 129 stellar binary systems present in the public data collected by the instrument All Sky Monitor on board of the satellite Rossi X-Ray Timing Explorer. The light time series was analyzed by applying detrended fluctuation analysis to estimate the long-range power-law correlation exponents alpha. The scaling exponent was calculated for all systems and its value indicated a signature of each kind of system, i.e., whether flare takes place (with alpha=1.22) or not (with alpha=0.64). As a consequence, our results may identify the stability of the systems from the scaling exponent alpha value, for instance, if alpha approximately 0.5 (white noise) the system is stable and unstable when alpha not equal to 0.5 (long-range power-law correlation).