Detrended Cross-correlation Analysis Coefficient Research Articles

Veza kamatne stope i deviznog kursa u Kini - koeficijent unakrsne korelacije dobijen DCCA analizom primenom pristupa "kliznog prozora"

This study aims to investigate the dynamics of the interest rates and exchange rates during the pandemic-induced crisis in the Chinese economy. In the study, rolling window detrended cross-correlation analysis (DCCA) was used. The DCCA coefficient was extracted based on detrended fluctuation analysis (DFA). The data used in the study are the daily data of the period from 2/1/2019 to 7/5/2021. The results obtained in the study suggest the presence of positive cross-correlation between China's interest rate and exchange rate after the COVID-19 pandemic, and they also report the existence of weak positive cross-correlation during the initial days of the pandemic. However, the weak positive cross-correlation became stronger over time. Higher interest rates are associated with higher exchange rates after the COVID-19 pandemic. The results of the research study have policy implications in that conventional higher interest rates introduced to defend the exchange rate might fail during pandemic-induced crises.

The Discovery of New Drug-Target Interactions for Breast Cancer Treatment.

Drug–target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds. In addition, the synthetic minority oversampling technique (SMOTE) is employed for dealing with the imbalanced data after Lasso dimensionality reduction. Then, the processed feature vectors are put into a random forest classifier to perform DTIs predictions on four gold standard datasets, including nuclear receptors (NR), G-protein-coupled receptors (GPCR), ion channels (IC), and enzymes (E). Furthermore, we explore new targets for breast cancer treatment using its risk genes identified from large-scale genome-wide genetic studies using PsePDC-DTIs. Through five-fold cross-validation, the average values of accuracy in NR, GPCR, IC, and E datasets are 95.28%, 96.19%, 96.74%, and 98.22%, respectively. The PsePDC-DTIs model provides us with 10 potential DTIs for breast cancer treatment, among which erlotinib (DB00530) and FGFR2 (hsa2263), caffeine (DB00201) and KCNN4 (hsa3783), as well as afatinib (DB08916) and FGFR2 (hsa2263) are found with direct or inferred evidence. The PsePDC-DTIs model has achieved good prediction results, establishing the validity and superiority of the proposed method.

Correlações em séries temporais de preços de frango, soja e milho

A evolução do mercado agrícola brasileiro alterou o processo de produção, exportação e consumo de commodities alimentares. Com isso, novos estudos acerca da relação entre o mercado de alimentos e outros mercados foram desenvolvidos, buscando explicar a ligação entre os preços de commodities agrícolas e não agrícolas. Visando contribuir para esse estudo, foram investigadas as correlações de longo prazo entre os preços de commodities agrícolas brasileiras, utilizando técnicas de Econofísica. Analisaram-se então as séries diárias de preços e retorno de preços da carne de frango, soja e milho, registrados entre 02/08/2004 e 16/06/2017 pelo Centro de Estudos Avançados em Economia Aplicada / Escola Superior de Agricultura Luiz de Queiroz / Universidade de São Paulo - CEPEA/ESALQ/USP. As correlações entre as séries temporais foram investigadas utilizando os métodos Detrended Fluctuation Analysis (DFA) e Detrended Cross Correlation Analysis (DCCA), para calcular o Detrended Cross Correlation Coefficient (DCCA Coefficient), que serve para quantificar correlações de longo prazo entre séries temporais não estacionárias. Os resultados obtidos apontam para a ausência de correlações nas escalas de até 30 dias e, para escalas maiores, acusam correlações mais fortes entre os preços de frango e milho que entre os preços de frango e soja. Após a crise alimentar de 2008, entretanto, as correlações entre as séries diárias de retorno de preços do frango e do milho diminuíram, enquanto que, entre as de frango e soja, aumentaram nas escalas menores e diminuíram nas escalas maiores.

The Exposure of European Union Productive Sectors to Oil Price Changes

Oil is one of the most important products in the world, being used for fuel production but also as an input in several industries. After the oil shocks of the 1970s, which caused great turbulence, the interest in the analysis of this particular product grew. The analysis of the comovements between oil and other assets became a hot topic. In this study, we propose an analysis of how oil price correlates with several industry indexes. The detrended cross-correlation analysis coefficient ( ρ DCCA ) is used, with data from 1992 to 2019, and we analyze not only the correlation between oil and several Euro Stoxx indexes during the whole sample, but also how that correlation evolved for the different decades (1990s, 2000s and 2010s). Naturally, oil and gas are the sectors that correlate the most with crude oil, with correlation coefficients reaching levels higher than 0.6 in some cases. However, the results also indicate that all sectors are now more exposed to oil price variations than in the past, with the financial sector as one of the sectors with the greatest increase in correlation.

Financial contagion analysis in frontier markets: Evidence from the US subprime and the Eurozone debt crises

This study assesses the effects of the US financial and the Eurozone debt crises on a large set of frontier stock markets. Detrended Cross Correlation Analysis (DCCA) and Detrended Moving Cross Correlation Analysis (DMCA) are employed to investigate whether correlations between the crises-originating countries’ stock markets (US and Greece) and frontier stock markets increased from the calm to each crisis periods. Our results indicate that this was indeed the case and frontier markets were affected by both crises. DCCA and DMCA coefficients increased significantly for countries in Europe and also, although not so strongly, for Middle Eastern ones with the subprime crisis. In the case of the Eurozone debt crisis, the most affected countries were Slovenia, Romania, Nigeria, Kuwait, Oman and Vietnam. Evidence of contagion, using the test proposed by Guedes et al. (2018a, 2018b), is thus weaker in the case of the European debt crisis, leading to the conclusion that frontier stock markets were more affected by the US financial turmoil.

Neural network and regression methods for optimizations between two meteorological factors

This paper is concerned with the temporal variation characteristics of two meteorological factors (temperature and humidity) in four metropolitan cities (Seoul, Busan, Daegu, Daejeon) in South Korea. Data are extracted from seven years (2008 to 2014) of hourly time series data in meteorological offices of the Korea Meteorological Administration. Using the detrended cross-correlation analysis (DCCA) method, the DCCA coefficient of temperature is compared to that of humidity from daily time series data during four seasons in the four metropolitan cities. In particular, as window size s increases, the DCCA cross-correlation coefficient approaches 0.034 at a time lag of 14 days in the case of spring in Seoul. We find the weak cross-correlation between temperature and humidity at different time lags of 14, 21 and 28 days in spring in Seoul, and the errors ET in the ANN are relatively larger values than that of any other season in both the ANN and the MRM. Particularly, in the ANN model, there exist relatively a large error value of temperature as the characteristics of the non-stationary, deterministically chaotic and noisy meteorological data extracted for the short-term prediction rather than the long-term prediction.

Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction

BackgroundApoptosis is associated with some human diseases, including cancer, autoimmune disease, neurodegenerative disease and ischemic damage, etc. Apoptosis proteins subcellular localization information is very important for understanding the mechanism of programmed cell death and the development of drugs. Therefore, the prediction of subcellular localization of apoptosis protein is still a challenging task.ResultsIn this paper, we propose a novel method for predicting apoptosis protein subcellular localization, called PsePSSM-DCCA-LFDA. Firstly, the protein sequences are extracted by combining pseudo-position specific scoring matrix (PsePSSM) and detrended cross-correlation analysis coefficient (DCCA coefficient), then the extracted feature information is reduced dimensionality by LFDA (local Fisher discriminant analysis). Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of the apoptosis proteins. The overall prediction accuracy of 99.7, 99.6 and 100% are achieved respectively on the three benchmark datasets by the most rigorous jackknife test, which is better than other state-of-the-art methods.ConclusionThe experimental results indicate that our method can significantly improve the prediction accuracy of subcellular localization of apoptosis proteins, which is quite high to be able to become a promising tool for further proteomics studies. The source code and all datasets are available at https://github.com/QUST-BSBRC/PsePSSM-DCCA-LFDA/.

DCCA analysis of renewable and conventional energy prices

Here we investigate the inter-influence of oil prices and renewable energy sources. The non-stationary time series are scrutinized within the Detrended Cross-Correlation Analysis (DCCA) framework, where the resulting DCCA coefficient provides a useful and reliable index to the evaluate the cross correlation between events at the same time instant as well as at a suitably chosen time lags. The analysis is based on the quotient of two successive daily closing oil prices and composite indices of renewable energy sources in USA and Europe in the period 2006–2015, which was subject to several social and economic driving forces, as the increase of social pressure in favor of the use of non-fossil energy sources and the worldwide economic crisis that started in 2008. The DCCA coefficient is evaluated for different window sizes, extracting information for short and long term correlation between the indices. Particularly, strong correlation between the behavior of the two distinct economic sectors are observed for large time intervals during the worst period of the economic crisis (2008–2012), hinting at a very cautious behavior of the economic agents. Before and after this period, the behavior of two economic sectors are overwhelmingly uncorrelated or very weakly correlated. The results reported here may be useful to select proper strategies in future similar scenarios.

DCCA cross-correlation coefficients reveals the change of both synchronization and oscillation in EEG of Alzheimer disease patients

Alzheimer’s disease (AD) is a degenerative disorder of neural system that affects mainly the older population. Recently, many researches show that the EEG of AD patients can be characterized by EEG slowing, enhanced complexity of the EEG signals, and EEG synchrony. In order to examine the neural synchrony at multi scales, and to find a biomarker that help detecting AD in diagnosis, detrended cross-correlation analysis (DCCA) of EEG signals is applied in this paper. Several parameters, namely DCCA coefficients in the whole brain, DCCA coefficients at a specific scale, maximum DCCA coefficient over the span of all time scales and the corresponding scale of such coefficients, were extracted to examine the synchronization, respectively. The results show that DCCA coefficients have a trend of increase as scale increases, and decreases as electrode distance increases. Comparing DCCA coefficients in AD patients with healthy controls, a decrease of synchronization in the whole brain, and a bigger scale corresponding to maximum correlation is discovered in AD patients. The change of max-correlation scale may relate to the slowing of oscillatory activities. Linear combination of max DCCA coefficient and max-correlation scale reaches a classification accuracy of 90%. From the above results, it is reasonable to conclude that DCCA coefficient reveals the change of both oscillation and synchrony in AD, and thus is a powerful tool to differentiate AD patients from healthy elderly individuals.

Dynamical behavior of the correlation between meteorological factors

We study the temporal and spatial variation characteristics of meteorological factors (temperature, humidity, and wind velocity) at a meteorological tower located on Bosung-gun of South Korea. We employ the detrended cross-correlation analysis (DCCA) method to extract the overall tendency of the hourly variation from data of meteorological factors. The relationships between meteorological factors are identified and quantified by using DCCA coefficients. From our results, we ascertain that the DCCA coefficient between temperature and humidity at time lag m = 24 has the smallest value at the height of 10 m of the measuring tower. Particularly, the DCCA coefficient between temperature and wind speed at time lag m = 24 has the largest value at a height of 10 m of the measuring tower

Effect of Weather on Agricultural Futures Markets on the Basis of DCCA Cross-Correlation Coefficient Analysis

This study investigates the correlation between weather and agricultural futures markets on the basis of detrended cross-correlation analysis (DCCA) cross-correlation coefficients and [Formula: see text]-dependent cross-correlation coefficients. In addition, detrended fluctuation analysis (DFA) is used to measure extreme weather and thus analyze further the effect of this condition on agricultural futures markets. Cross-correlation exists between weather and agricultural futures markets on certain time scales. There are some correlations between temperature and soybean return associated with medium amplitudes. Under extreme weather conditions, weather exerts different influences on different agricultural products; for instance, soybean return is greatly influenced by temperature, and weather variables exhibit no effect on corn return. Based on the detrending moving-average cross-correlation analysis (DMCA) coefficient and DFA regression results are similar to that of DCCA coefficient.

Optimal portfolio strategy with cross-correlation matrix composed by DCCA coefficients: Evidence from the Chinese stock market

In this paper, a new estimator of correlation matrix is proposed, which is composed of the detrended cross-correlation coefficients (DCCA coefficients), to improve portfolio optimization. In contrast to Pearson’s correlation coefficients (PCC), DCCA coefficients acquired by the detrended cross-correlation analysis (DCCA) method can describe the nonlinear correlation between assets, and can be decomposed in different time scales. These properties of DCCA make it possible to improve the investment effect and more valuable to investigate the scale behaviors of portfolios. The minimum variance portfolio (MVP) model and the Mean–Variance (MV) model are used to evaluate the effectiveness of this improvement. Stability analysis shows the effect of two kinds of correlation matrices on the estimation error of portfolio weights. The observed scale behaviors are significant to risk management and could be used to optimize the portfolio selection.

Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces.

When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multiscale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross correlation between crude oil and gold futures by taking into consideration the impact of the U.S. dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the multifractal DCCA method fails.

Cross-Correlations and Structures of Aero-Engine Gas Path System Based on DCCA Coefficient and Rooted Tree

Identifying the mutual interaction is a crucial problem that facilitates the understanding of emerging structures in complex system. We here focus on aero-engine dynamic as an example of complex system. By applying the detrended cross-correlation analysis (DCCA) coefficient method to aero-engine gas path system, we find that the low-spool rotor speed (N1) and high-spool rotor speed (N2) fluctuation series exhibit cross-correlation characteristic. Further, we employ detrended cross-correlation coefficient matrix and rooted tree to investigate the mutual interactions of other gas path variables. The results can infer that the exhaust gas temperature (EGT), N1, N2, fuel flow (WF) and engine pressure ratio (EPR) are main gas path parameters.