Detrended Cross-correlation Analysis Method Research Articles

Effect of NO2 on SO2–SO42- conversion in atmosphere: Evidence from long-term precipitation

Sulfate aerosol (SO42−) is an important factor leading to acid rain and aggravating atmospheric particulate pollution. SO42− is mainly converted from gaseous SO2, in which the oxidant gas NO2 may play a key role. In real atmosphere with complexity and nonlinearity, the impact of NO2 on SO2–SO42- conversion exhibits scale dependence due to the combined effect of meteorology, atmospheric chemistry, and pollution emission. Here, based on the long-term atmospheric precipitation data from 2014 to 2021 of Nanchong City in the Sichuan Basin, the Extended Ensemble Empirical Mode Decomposition (EEMD) and Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) methods have been employed to analyze the nonlinear correlations between gaseous SO2 and SO42− in rainwater at different time scales. A multifractal parameter Δh has been described as the scaling features of SO2–SO42- conversion efficiency in the real atmosphere. Then, the impact of NO2 on SO2–SO42- conversion in cold and warm seasons has been quantified, using sliding window analysis and Pearson correlation analysis. The results show that NO2 significantly promoted SO2–SO42- conversion in the cold season, but had little effect on it in the warm season. As direct evidence from atmospheric precipitation analysis, this finding has provided a new theoretical basis for evaluating the impact of NO2 on secondary aerosol generation.

Impact of atmospheric O3 and NO2 on the secondary sulfates in real atmosphere

As an important component of secondary aerosols, sulfate plays a crucial role in regulating atmospheric radiative balance and influencing the secondary formation of ozone (O3). In real atmosphere, atmospheric oxidants NO2 and O3 can promote the oxidation of SO2 to form sulfate (SO42−) through multiphase chemistry that occur at different time scales. Due to the combined impact of meteorology, pollution sources, atmospheric chemistry, etc., time-scale dependence of SO2-SO42− conversion makes the impact of NO2/O3 on it more complex. In this study, based on long-term time series (2013-2020) of air pollution variables from seven stations in Hong Kong, the Multifractal Detrended Cross-Correlation Analysis (MFDCCA) method has been employed to quantify the cross-correlations between SO2 and SO42− in real atmosphere at different time scales, for examining the time-scale dependence of SO2-SO42− conversion efficiency. Furthermore, the Pearson correlation analysis has been used to study the influence of NO2/O3 on SO2-SO42− conversion, and the regional and seasonal differences have been analyzed by considering factors such as meteorology, pollution sources, and regional transport. Changes in the main components of secondary aerosols are closely linked with the co-control of regional PM2.5 and O3. Therefore, the exploration of the impact of co-existing NO2/O3 gases on the secondary formation of sulfates in real atmosphere is significant.

Analysis of Risk Spillover and Asymmetry Between Three Crude Oil Markets and Chinese Financial Markets

Providing a scientific basis and method to ensure the smooth functioning of the Chinese crude oil market would be hugely significant to China’s future economic development and security. To this end, attempts have been made to both internationalize the Shanghai crude oil market and minimize financial risks. This paper selects three crude oil markets (INE, WTI and Brent) and five Chinese financial markets (the futures, bond, fund, stock and foreign exchange markets) as the research objects. The Diebold and Yilmaz spillover index model and the multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method are used to study the volatility spillover effect and the asymmetric cross-correlation between crude oil markets and financial markets. When the volatility spillover effect and the asymmetric relationship that exists between the financial markets are examined, the volatility spillover of the oil market to the financial market is found to be significantly higher than that of the financial market to the oil market. In particular, the spillover effect was even more significant from late-2019 to early-2020. Analysis demonstrates an asymmetric cross-correlation between crude oil markets and the abovementioned five Chinese financial markets. In particular, the impact of the Chinese crude oil market on the stock market is greatest, especially with respect to the Brent and WTI crude oil markets. Except for the bond market, when the INE and Brent markets are increasing, the risk exposure to financial markets is more significant. Among financial markets, INE-Bond market asymmetry is stronger than WTI-bond market asymmetry, but weaker than that of the Brent-Bond market when there are large fluctuations.

Paradox between adequate sanitation and rainfall in dengue fever cases

BackgroundDengue fever is a tropical disease and a major public health concern, and almost half of the world's population lives in areas at risk of contracting this disease. Climate change is identified by WHO and other international health authorities as one of the primary factors that contribute to the rapid spread of dengue fever. MethodsWe evaluated the effect of sanitation on the cross-correlation between rainfall and the first symptoms of dengue in the city of Mato Grosso do Sul, which is in a state in the Midwest region of Brazil, and employed the time-lagged detrended cross-correlation analysis (DCCAC) method. ResultsCo–movements were obtained through the time-phased DCCAC to analyze the effects of climatic variables on arboviruses. The use of a time-lag analysis was more robust than DCCAC without lag to present the behavior of dengue cases in relation to accumulated precipitation. Our results show that the cross-correlation between rain and dengue increased as the city implemented actions to improve basic sanitation in the city. ConclusionWith climate change and the increase in the global average temperature, mosquitoes are advancing beyond the tropics, and our results show that cities with improved sanitation have a high correlation between dengue and annual precipitation. Public prevention and control policies can be targeted according to the period of time and the degree of correlation calculated to structure vector control and prevention work in places where sanitation conditions are adequate.

Multifractal detrended cross-correlation analysis of radioactivity borne radon, thoron and meteorological time series

Owing to the potential health threats due to sustained exposure from the radioactivity born radon and thoron gases and their applications, especially radon as earthquake precursor, the complex dynamic relationship between either of the radioactive gas with meteorological parameters has become the focus of research area. Several studies have reported that meteorological factors greatly influence the radon and thoron radioactive gases. In present study we investigated the scaling behavior of cross-correlations between simultaneously observed Radon and Thoron time series with temperature, pressure, and humidity time series. Data for radon, thoron and other meteorological parameters were obtained using RTM 1688-2 active detector from March 2017 to April 2018, with the total number of data points in each time series greater then fifteen thousands. Data obtained was investigated by Multi-fractal Detrended Cross-correlation Analysis (MFDXA) method through detrended covariance function. The computed cross-correlations were also investigated for multifractal or monofractal nature as well as for the degree of multifractality. Results of the study show that all correlations exhibit long range power law behavior. A nonlinear variation of scaling exponent ′hxy(q)′ vs ‘q’ (order of fluctuation) shows multifractal nature of cross-correlations with hxy(q)>0.5 for all correlations. Which means that all correlations are persistently positive correlated. The degree of multifractality was estimated by the width of singularity spectrum Δα. The results show that for each of the correlations width of singularity spectrum is nonzero confirming the multifractal nature of computed correlations. Finally, for Radon/Thoron correlation with temperature and humidity the width of singularity spectrum i.e., Δα>0.5 shows high multifractality when compared with Radon/Thoron vs pressure correlation with Δα<0.5 showing low multifractality.

Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things.

The present work aims to analyze the time-series data (TSD) from movies and support constructing the movie recommendation system. Referencing the Internet of Things (IoT) technology as the framework, a time-series data analysis system for movies is built based on the recurrent neural network (RNN) and multifractal detrended mobility cross-correlation analysis (MF-DCCA) method. First, the traditional RNN model is improved by replacing the conventional convolution operation with spatial adaptive convolution. Specifically, an additional convolution layer is used to obtain the position parameters required for adaptive convolution to improve the model performance to capture the characteristics of spatial-temporal transformation. Then, the MF-DCCA method is optimized to reduce the interference of noise signals to the analysis processing of TSD from movies. Finally, the TSD analysis system is tested for performance verification. The test results indicate that the method proposed here has outstanding stability and runs smoothly. When the prediction scheme is long short-term memory (LSTM) (L = 20), the similarity of the LSTM (L = 20) network under one frame is 0.977; the similarity of the LSTM (L = 20) network under nine frames is 0.727. This system provides a specific idea for applying the RNN model and MF-DCCA method in analyzing TSD from movies.

Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index

We applied the multifractal detrended cross-correlation analysis (MF-DCCA) method to investigate the cross-correlation behaviour between the USD/CNY exchange rate (UCER) and the Baidu index (Chines netizens’ behaviour big data, BI). We observed a significant anti-persistent cross-correlation between UCER and BI through MF-DCCA testing. Based on the cross-correlation between UCER and BI, a hybrid deep learning model WOA-STL-BI-LSTM combining network big data, decomposition and integration techniques, optimisation algorithms and LSTM is constructed to predict UCER returns, where BI is used as a potential predictor to predict UCER, and the predictability of BI on UCER was discussed. The experimental results show that BI can improve the forecasting accuracy of UCER returns, but not significantly. WOA-STL-BI-LSTM has a significant improvement in forecasting performance compared with the benchmark model and can be used as an advanced model for UCER returns forecasting. This paper provides a new approach and evidence for the study of UCER.

Multifractal Analysis of the Impact of COVID-19 on NASDAQ, CIOPI, and WTI Crude Oil Market

In this paper, we explore the impact of COVID-19 on auto-correlations and cross-correlations among NASDAQ stock index of the USA, China iron ore price index (CIOPI), and West Texas Intermediate Crude Oil price (WTI). To find out the effect of COVID-19 on financial markets, we divide the investigated data series into two sub-periods, i.e., pre-COVID19 period and post-COVID19 period. First, multifractal detrended fluctuation analysis (MF-DFA) of those series shows a general trend of strong multifractality after COVID-19, indicating lower market efficiency after the pandemic shock. Second, multifractal detrended cross-correlation analysis (MF-DCCA) method is employed to examine cross-correlations among NASDAQ, CIOPI, and WTI. The three cross-correlations all increase in post-COVID19. The correlation between NASDAQ and CIOPI increases the most, becoming the strongest correlation among the three cross-correlations in post-COVID19. The surrogate procedure shows that the post-COVID19 cross-correlation multifractalities are mostly due to fat-tail distribution. Third, we use multi-scale multifractal analysis (MMA) to visualize the dynamic behaviors of correlations among the series. The Hurst surfaces of the three cross-correlations have more fluctuation, both at small and large scale in post-COVID19 than that of pre-COVID19. Particularly, the Hurst surface of cross-correlation between NASDAQ and CIOPI exhibits stronger multifractality during the outbreak of COVID-19 than that in both pre-COVID19 and post-COVID19. The above investigations provide helpful insights of relevant market trends.

Robustness of Detrended Cross-correlation Analysis Method Under Outliers Observations

The computation of the bivariate Hurst exponent constitutes an important technique to test the power-law cross-correlation of time series. For this objective, the detrended cross-correlation analysis (DCCA) method represents the most used one. In this paper, we prove the robustness of the DCCA method, where the trend is estimated using the polynomial fitting, to estimate the bivariate Hurst exponent when time series are corrupted by outliers observations. On the other hand, we give the exact polynomial order and a regression region for computing a detrended cross-correlation function to obtain a least square estimator of bivariate Hurst exponent. Our theoretical results are shown by a simulation study on a two-fractional Gaussian noise process corrupted by outliers observations. Additionally, our results are applied to financial time series. The empirical findings results are accompanied by interpretations.

Detrended multiple moving average cross-correlation analysis and its application in the correlation measurement of stock market in Shanghai, Shenzhen, and Hong Kong

Based on the traditional detrended moving average cross-correlation analysis (DMCA) method and the detrended multiple cross-correlation analysis (DMC) method, we propose the detrended multiple moving average cross-correlation analysis method (DMMC) to quantify the correlation between non-stationary signals on different time scales. It will be the generalization of the correlation between pairwise time series and can be used to measure the multivariate correlation between the dependent variable y with the others i independent variable x. To validate the DMMC method, we determined the DMMC coefficient into numerical simulations on the standard Gaussian distribution, Lognormal distribution t distribution and ARFIMA long memory random sequences. Then we apply the proposed method to explore the multiple correlations between China’s Shanghai, Shenzhen, and Hong Kong stock markets and analyze the dynamic time-varying characteristics of the correlation with the sliding window approach. The results show that DMMC is suitable to measure multiple cross-correlations. This new method can be further applied to multiple cross-correlation in different areas of knowledge, such as energy, meteorology, medicine, transportation, etc.

Non-Linear Impact of Chinese Treasury Bond Futures on the Information Content of IRS

This paper investigates the impact of Chinese Treasury bond (CTB) futures on the information content of interest rate swap (IRS) from a multifractality perspective. We first use multifractal detrended fluctuation analysis (MF-DFA) method and show that the swap rate and the CTB yield exhibit strong multifractality. In addition, employing multifractal detrended cross-correlation analysis (MF-DCCA) method, we find that cross-correlations between the swap rate and the CTB yield are multifractally persistent. Moreover, after the reintroduction of Treasury bond futures, the persistence of cross-correlation between the series is weaker. Our results indicate that the information content of IRS decreased after the re-launch of CTB futures.

TIME-DELAY MULTISCALE MULTIFRACTAL DETRENDED PARTIAL CROSS-CORRELATION ANALYSIS OF HIGH-FREQUENCY STOCK SERIES

To study the time-delay multiscale partial cross-correlation between the high-frequency stock series SSEC and SZSE in the Chinese stock market, this paper proposes a new partial cross-correlation exponent, namely the [Formula: see text]-order time-delay multiscale partial cross-correlation exponent (denoted by [Formula: see text]). We integrate the time-delay factor and the multiscale range fluctuation function into partial cross-correlation analysis. Numerical experiments show that the newly proposed exponent [Formula: see text] can be used to detect the partial cross-correlation of two time series with time delay. After excluding the influence of other stock indexes, we found that the persistence and multifractal characteristics between SSEC and SZSE have become stronger. Among them, the American high-frequency stock series has a greater impact on the two Chinese stocks than the Asian high-frequency stock series. Comparing the methods of time-delay multiscale multifractal detrended partial cross-correlation analysis method (time-delay MM-DPXA) and time-delay multiscale multifractal detrended cross-correlation analysis method (time-delay MM-DCCA), we find that cross-correlation and partial cross-correlation have different properties on different time scales and different time delays.

Asymmetric multifractal features of the price–volume correlation in China’s gold futures market based on MF-ADCCA

In recent years, fractal theory has become a recognized research direction for explaining various complex phenomena that are difficult to constrain in the conventional efficient market hypothesis for financial markets. Moreover, because the gold futures prices are crucial to the futures market, research on the relationship between quantity and price is important for understanding market fluctuations. Therefore, this paper conducts an empirical analysis of the multifractal features and asymmetry in the price–volume correlation of China’s gold futures market based on the multifractal asymmetric detrended cross-correlation analysis method11Multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method. . Results show that the cross-correlation between market price and volume is asymmetric and multifractal and that multifractal features are stronger when the price increases compared with when it declines. Moreover, the multifractal features vary over time. These findings indicate that the risk of China’s gold futures market will change with the price trend over time.

Dollar’s Influence on Crude Oil and Gold Based on MF-DPCCA Method

This paper analyzed the influence of dollar on crude oil and gold based on the multifractal detrended partial cross-correlation analysis method. It showed that affected by the dollar, the crude oil and gold markets have a partial cross-correlation relationship which is stronger than their own cross-correlation. The partial cross-correlation is long-term and has multifractal characteristics. Through shuffled and Fourier-phase randomization, it is found that this multifractal feature is caused by the combined effect of the long-term cross-correlation between the returns and the fluctuation fat-tailed distribution, where the influence of the fat-tailed distribution is slightly greater than that of the long-term cross-correlation between the returns.

The multifractal evaluation of PM2.5-O3 coordinated control capability in China

Under combined air pollution conditions, the coordinated control of PM2.5 and O3 has significant implications for environmental management. To better understand the coordinated control capability of different metropolises, a prime consideration is how to develop a scientific and objective index for quantifying it. In this paper, based on the multifractal detrended cross-correlation analysis (MF-DCCA) method, a novel dynamical index η is developed to quantitatively determine the capability of metropolis to control PM2.5 and O3 pollution cooperatively. Furthermore, combing with the sliding window method, the high temporal scale resolution of η values of four metropolises (Beijing, Shanghai, Guangzhou, and Chengdu) in China is conducted in order to determine the temporal evolution of η. Then, the monthly variation of η values in the four metropolises is investigated. The U-shaped curves in the monthly mean η values indicate that PM2.5-O3 coordinated control capability of the four metropolises is best in summer. The further discussions about the relations between index η and meteorological conditions have been made. Next, long-term trends of η series of four metropolises are studied by Mann–Kendall statistical test. Result shows that the trends of the η series of four metropolises are similar from 2015 to 2017. After 2017, the η series of Beijing, Shanghai and Chengdu show an increasing trend, while that of Guangzhou show a decreasing trend. This suggests that PM2.5-O3 coordinated control capability of Guangzhou is gradually strengthened after 2017. This may be caused by the combined effect of targeted strategies and special climatic conditions in Guangzhou. The η index could directly quantify the two-pollutant coordinated control capability of different metropolises based on field observation, rather than rely on any subjective factor. It can provide guidance for policy-making of pollution emission reduction, so as to control the combined air pollution.

Research on the correlation between electricity consumption and pollutant emission concentration based on DCCA and the prediction of pollutant emission concentration

Based on the big data of electricity consumption in industrial enterprises, this paper uses the DCCA (Detrended cross-correlation analysis) method to study the correlation between electricity consumption and pollutant emission concentration. The BP neural network method is also used to model and predict the pollutant emission trends, in order to monitoring of pollutant emissions in industrial enterprises reasonably. The research results show that there is an obvious correlation between electricity consumption data and pollutant concentration data, and the pollutant emission concentration can be predicted to a certain extent by building a BP neural network model. and the pollutant emission concentration can be predicted to a certain extent by building a BP neural network model. The prediction of pollutants through electricity consumption data helps enterprises to actively respond to the national environmental protection policy and promote national economic development, while allowing more enterprises to enjoy the convenience brought by big data analysis.

The Effect of “Wuhan Closure” on the COVID-19 Pandemic in China

In this study, we analyzed daily records of newly diagnosed cases in Wuhan, Hubei excluding Wuhan (HEW), and China excluding Hubei (CEH) to investigate the impact of the new coronavirus outbreak in Wuhan on cities around it and throughout China. We used multifractal detrended cross-correlation analysis (MF-DXA) method to investigate the correlations between the daily number of patients in Wuhan and HEW as well as in Wuhan and CEH. We concluded that the cross-correlations between the daily number of patients in Wuhan and HEW were higher than those between the daily number of patients in Wuhan and CEH because the multifractal features of Wuhan and HEW are greater than those of Wuhan and CEH. We also found that the “Wuhan closure” conducted on January 23 resulted in a decrease in cross-correlations between Wuhan and CEH.

Dynamical Characteristics in Time Series Between PM10 and Wind Speed

We study the temporal variation characteristics of PM10 and wind velocity in eight South Korean cities. We employ the detrended cross-correlation analysis (DCCA) method to extract the overall tendency of the hourly variation. We ascertain from three-daily and one-weekly intervals that Busan has the negative largest, while Donghae has the positive largest in the DCCA cross-correlation coefficient between PM10 and wind velocity. As a result of Asian dust events, the cross-correlation is statistically significant for the hourly time series data less than two days. Particularly, we discuss whether a cross-correlation is statistically significant or not from random number surrogation and shuffled time series surrogation

Multiscale multifractal detrended partial cross-correlation analysis of Chinese and American stock markets

• We analyzed multifractal binomial measures contaminated with strong white noises. • We investigated the cross correlations between stock markets using MM DPXA methods. • New conclusions regarding multiscale correlation structures were obtained. • We studied the effect of financial crisis on the cross correlations of stock markets. In this paper, a new method called multiscale multifractal detrended partial cross-correlation analysis (MM-DPXA) method is proposed, which combines multifractal detrended partial cross-correlation analysis (MF-DPXA) with multiscale multifractal analysis (MMA). To demonstrate the advantages of this method, we analyze multifractal binomial measures contaminated with strong white noises and compare the performance of MM-DPXA method to traditional cross-correlation techniques. It is found that MM-DPXA method can not only eliminate the influence of other variables, but also provide more valuable information from multiscale perspective. Moreover, this method is able to characterize monofractality or multifractality of the time series in a wide range of scales simultaneously and without assuming any presumed time scale. To further show the utility of MM-DPXA method in complex systems, we provide new evidence on the financial time series. By comparing Hurst surfaces before and after removing common influences, we conclude that cross-correlations and intrinsic cross-correlations show different properties in different scales. Furthermore, we also study the effect of financial crisis on the cross-correlations between Chinese and American stock markets using MF-DCCA and MF-DPXA methods.

Cross-correlations between price and volume in China's crude oil futures market: A study based on multifractal approaches

Abstract In this paper, we investigate the multifractal characteristics of the cross-correlations between price and volume in China's first crude oil futures market (INE), and compared the INE market with Brent and WTI market. Multifractal detrended cross-correlation analysis (MF-DCCA) was applied to comprehensively research the multifractal characteristics of the three different crude oil futures markets based on the data of the past five years. The results show that the cross-correlations between price change and volume change have significant multifractal characteristics in three markets, and the degree of multifractality is stronger in the WTI market for the full sample. Due to the INE market appeared late, the sample size of INE is not consistent with that of the other two markets. In order to solve this influence, we further compare the multifractal characteristics of the three crude oil futures markets in the same period (March 26, 2018 to November 20, 2020) and compares the results with those of the full sample period. The empirical results show that the degree of multifractality in both Brent and WTI markets is increased. To further verify whether this result is due to the emergence of the INE crude oil market, the entire sample of the Brent and WTI markets are divided into two periods according to the launch time of INE market. The result shows that the degree of multifractality and the risk of these two markets are all stronger in the last two years. Thus, there are reasons to believe that the emergence of INE market has a certain impact on the traditional Brent and WTI market, and a greater impact on Brent market. However, MFDCCA only consider the overall situation and cannot describe the multifractal cross-correlation between the upward and downward trends of the market. Thus, it probably won't accurately reflect the nonlinear cross-correlation. Furthermore, we study the characteristics of the asymmetric multifractal cross-correlations in different trends (upwards or downwards) by using the multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method. Empirical results indicate that the cross-correlation between price and volume in three markets shows obvious asymmetry. Specifically, when the price changes series is on an upward trend, or when the volume changes series is on a downward trend, the degree of asymmetry in three markets is stronger, and the risk situation of the market is more complex. Therefore, for a portfolio of assets with different markets, investors should fully observe and understand different market trends and make reasonable investment based on their own risk preference.