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Long-term cross-correlational structures are examined for pairs of sea surface temperature anomalies (SSTAs) and advective forcing parameters and sea surface height anomalies (SSHAs) and current velocity anomalies (CVAs) in the East/Japan Sea (EJS); all these satellite datasets were collected between 1993 and 2023. By utilizing newly modified detrended cross-correlation analysis algorithms, incorporating local linear trend and local fluctuation level of an SSTA, the analyses were performed on timescales of 400–3000 days. Long-term cross-correlations between SSTAs and SSHAs are strongly persistent over nearly the entire EJS; the strength of persistence is stronger during rising trends and low fluctuations of SSTAs, while anti-persistent behavior appears during high fluctuations of SSTAs. SSTA-CVA pairs show high long-term persistence only along main current pathways: the zonal currents for the Subpolar Front and the meridional currents for the east coast of Korea. SSTA-CVA pairs also show negative long-term persistent behaviors in some spots located near the coasts of Korea and Japan: the zonal currents for the eastern coast of Korea and the meridional currents for the western coast of Japan; these behaviors seem to be related to the coastal upwelling phenomena. Further, these persistent characteristics are more conspicuous in the recent decades (2008~2023) rather than in the past (1993~2008). [ABSTRACT FROM AUTHOR]

Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in electroencephalography (EEG) data. Domain adaptation, however, is most often performed on sample covariance matrices (SCMs) obtained from EEG data, and thus might not fully account for components affecting covariance estimation itself, such as regional trends. Detrended cross-correlation analysis (DCCA) can be utilized to estimate the covariance structure of such signals, yet it is computationally expensive in its original form. A recently proposed online implementation of DCCA, however, allows for its fast computation and thus makes it possible to employ DCCA in real-time applications. In this study we propose to replace the SCM with the DCCA matrix as input to RGBC and assess its effect on offline and online BCI performance. First we evaluated the proposed decoding pipeline ofline on previously recorded EEG data from 18 individuals performing left and right hand motor imagery (MI), and benchmarked it against vanilla RGBC and popular MI-detection approaches. Subsequently, we recruited eight participants (with previous BCI experience) who operated an MI-based BCI (MI-BCI) online using the DCCA-enhanced Riemannian decoder. Finally, we tested the proposed method on a public, multi-class MI-BCI dataset. During offline evaluations the DCCA-based decoder consistently and significantly outperformed the other approaches. Online evaluation confirmed that the DCCA matrix could be computed in real-time even for 22-channel EEG, as well as subjects could control the MI-BCI with high command delivery (normalized Cohen's κ: 0.7409 ± 0.1515) and sample-wise MI detection (normalized Cohen's κ: 0.5200 ± 0.1610). Post-hoc analysis indicated characteristic connectivity patterns under both MI conditions, with stronger connectivity in the hemisphere contralateral to the MI task. Additionally, fractal scaling exponent of neural activity was found increased in the contralateral compared to the ipsilateral motor cortices (C4 and C3 for left and right MI, respectively) in both classes. Combining DCCA with Riemannian geometry-based decoding yields a robust and effective decoder, that not only improves upon the SCM-based approach but can also provide relevant information on the neurophysiological processes behind MI. [ABSTRACT FROM AUTHOR]

Correlations between exchange rates are valuable for illuminating the dynamics of international trade and the financial dynamics of countries. This paper explores the changing interactions of the US foreign exchange market based on detrended cross-correlation analysis. First, we propose an objective way to choose a time scale parameter appropriate for comparing different samples by maximizing the summed magnitude of all DCCA coefficients. We then build weighted signed networks under this optimized time scale, which can clearly display the complex relationships between different exchange rates. Our study shows negative cross-correlations have become pyramidally rare in the past three decades. Both the number and strength of positive cross-correlations have grown, paralleling the increase in global interconnectivity. The balanced strong triads are identified subsequently after the network centrality analysis. Generally, while the strong development links revealed by foreign exchange have begun to spread to Asia since 2010, Europe is still the center of world finance, with the euro and Danish krone consistently maintaining the closest balanced development relationship. Finally, we propose a fluctuation propagation algorithm to investigate the propagation pattern of fluctuations in the inferred exchange rate networks. The results show that, over time, fluctuation propagation patterns have become simpler and more predictable. [ABSTRACT FROM AUTHOR]

Analysis of brain functional connectivity (FC) could provide insight in how and why cognitive functions decline even in healthy aging (HA). Despite FC being established as fluctuating over time even in the resting state (RS), dynamic functional connectivity (DFC) studies involving healthy elderly individuals and assessing how these patterns relate to cognitive performance are yet scarce. In our recent study we showed that fractal temporal scaling of functional connections in RS is not only reduced in HA, but also predicts increased response latency and reduced task solving accuracy. However, in that work we did not address changes in the dynamics of fractal connectivity (FrC) strength itself and its plausible relationship with mental capabilities. Therefore, here we analyzed RS electroencephalography recordings of the same subject cohort as previously, consisting of 24 young and 19 healthy elderly individuals, who also completed 7 different cognitive tasks after data collection. Dynamic fractal connectivity (dFrC) analysis was carried out via sliding-window detrended cross-correlation analysis (DCCA). A machine learning method based on recursive feature elimination was employed to select the subset of connections most discriminative between the two age groups, identifying 56 connections that allowed for classifying participants with an accuracy surpassing 92%. Mean of DCCA was found generally increased, while temporal variability of FrC decreased in the elderly when compared to the young group. Finally, dFrC indices expressed an elaborate pattern of associations—assessed via Spearman correlation—with cognitive performance scores in both groups, linking fractal connectivity strength and variance to increased response latency and reduced accuracy in the elderly population. Our results provide further support for the relevance of FrC dynamics in understanding age-related cognitive decline and might help to identify potential targets for future intervention strategies. [ABSTRACT FROM AUTHOR]

Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in electroencephalography (EEG) data. Domain adaptation, however, is most often performed on sample covariance matrices (SCMs) obtained from EEG data, and thus might not fully account for components affecting covariance estimation itself, such as regional trends. Detrended cross-correlation analysis (DCCA) can be utilized to estimate the covariance structure of such signals, yet it is computationally expensive in its original form. A recently proposed online implementation of DCCA, however, allows for its fast computation and thus makes it possible to employ DCCA in real-time applications. In this study we propose to replace the SCM with the DCCA matrix as input to RGBC and assess its effect on offline and online BCI performance. First we evaluated the proposed decoding pipeline offline on previously recorded EEG data from 18 individuals performing left and right hand motor imagery (MI), and benchmarked it against vanilla RGBC and popular MI-detection approaches. Subsequently, we recruited eight participants (with previous BCI experience) who operated an MI-based BCI (MI-BCI) online using the DCCA-enhanced Riemannian decoder. Finally, we tested the proposed method on a public, multi-class MI-BCI dataset. During offline evaluations the DCCA-based decoder consistently and significantly outperformed the other approaches. Online evaluation confirmed that the DCCA matrix could be computed in real-time even for 22-channel EEG, as well as subjects could control the MI-BCI with high command delivery (normalized Cohen's κ: 0.7409 ± 0.1515) and sample-wise MI detection (normalized Cohen's κ: 0.5200 ± 0.1610). Post-hoc analysis indicated characteristic connectivity patterns under both MI conditions, with stronger connectivity in the hemisphere contralateral to the MI task. Additionally, fractal scaling exponent of neural activity was found increased in the contralateral compared to the ipsilateral motor cortices (C4 and C3 for left and right MI, respectively) in both classes. Combining DCCA with Riemannian geometry-based decoding yields a robust and effective decoder, that not only improves upon the SCM-based approach but can also provide relevant information on the neurophysiological processes behind MI.

Long-term cross-correlational structures are examined for pairs of sea surface temperature anomalies (SSTAs) and advective forcing parameters and sea surface height anomalies (SSHAs) and current velocity anomalies (CVAs) in the East/Japan Sea (EJS); all these satellite datasets were collected between 1993 and 2023. By utilizing newly modified detrended cross-correlation analysis algorithms, incorporating local linear trend and local fluctuation level of an SSTA, the analyses were performed on timescales of 400–3000 days. Long-term cross-correlations between SSTAs and SSHAs are strongly persistent over nearly the entire EJS; the strength of persistence is stronger during rising trends and low fluctuations of SSTAs, while anti-persistent behavior appears during high fluctuations of SSTAs. SSTA-CVA pairs show high long-term persistence only along main current pathways: the zonal currents for the Subpolar Front and the meridional currents for the east coast of Korea. SSTA-CVA pairs also show negative long-term persistent behaviors in some spots located near the coasts of Korea and Japan: the zonal currents for the eastern coast of Korea and the meridional currents for the western coast of Japan; these behaviors seem to be related to the coastal upwelling phenomena. Further, these persistent characteristics are more conspicuous in the recent decades (2008~2023) rather than in the past (1993~2008).

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Extraordinary events, regardless of their financial or non-financial nature, are a great challenge for financial stability. This study examines the impact of one such occurrence—the COVID-19 pandemic—on cryptocurrency markets. A detrended cross-correlation analysis was performed to evaluate how the links between 16 cryptocurrencies were changed by this event. Cross-correlation coefficients that were calculated before and after the onset of the pandemic were compared, and the statistical significance of their variation was assessed. The analysis results show that the markets of the assessed cryptocurrencies became more integrated. There is also evidence to suggest that the pandemic crisis promoted contagion, mainly across short timescales (with a few exceptions of non-contagion across long timescales). We conclude that, in spite of the distinct characteristics of cryptocurrencies, those in our sample offered no protection against the financial turbulence provoked by the COVID-19 pandemic, and thus, our study provided yet another example of ‘correlations breakdown’ in times of crisis.

Extraordinary events, regardless of their financial or non-financial nature, are a great challenge for financial stability. This study examines the impact of one such occurrence—the COVID-19 pandemic—on cryptocurrency markets. A detrended cross-correlation analysis was performed to evaluate how the links between 16 cryptocurrencies were changed by this event. Cross-correlation coefficients that were calculated before and after the onset of the pandemic were compared, and the statistical significance of their variation was assessed. The analysis results show that the markets of the assessed cryptocurrencies became more integrated. There is also evidence to suggest that the pandemic crisis promoted contagion, mainly across short timescales (with a few exceptions of non-contagion across long timescales). We conclude that, in spite of the distinct characteristics of cryptocurrencies, those in our sample offered no protection against the financial turbulence provoked by the COVID-19 pandemic, and thus, our study provided yet another example of 'correlations breakdown' in times of crisis. [ABSTRACT FROM AUTHOR]

Investor sentiment and herding, as two important indicators of investors' beliefs, both have great impacts on stock market volatility. Based on the daily transaction data in the Chinese stock market, we measure investor sentiment and herding on monthly basis from 2005 to 2020. Then, two indicators are, respectively, introduced to GARCH-MIDAS model for volatility forecasting. Moreover, their forecasting abilities are compared with five GARCH-type models in four crisis periods and two up markets through the model confidence set (MCS) test. We find that herding can better explain the long-term component of volatility than investor sentiment in GARCH-MIDAS model. What is more, both investor sentiment and herding possess excellent out-of-sample forecasting performances. Finally, the forecasting ability of herding is overwhelming compared with investor sentiment in crisis periods, while investor sentiment presents better prediction accuracy than herding in up markets. Our research shows that the forecasting abilities of investor sentiment and herding on volatility may be related to the market states. [ABSTRACT FROM AUTHOR]

Correlations between exchange rates are valuable for illuminating the dynamics of international trade and the financial dynamics of countries. This paper explores the changing interactions of the US foreign exchange market based on detrended cross-correlation analysis. First, we propose an objective way to choose a time scale parameter appropriate for comparing different samples by maximizing the summed magnitude of all DCCA coefficients. We then build weighted signed networks under this optimized time scale, which can clearly display the complex relationships between different exchange rates. Our study shows negative cross-correlations have become pyramidally rare in the past three decades. Both the number and strength of positive cross-correlations have grown, paralleling the increase in global interconnectivity. The balanced strong triads are identified subsequently after the network centrality analysis. Generally, while the strong development links revealed by foreign exchange have begun to spread to Asia since 2010, Europe is still the center of world finance, with the euro and Danish krone consistently maintaining the closest balanced development relationship. Finally, we propose a fluctuation propagation algorithm to investigate the propagation pattern of fluctuations in the inferred exchange rate networks. The results show that, over time, fluctuation propagation patterns have become simpler and more predictable.

Non-fungible tokens (NFTs) are a type of digital record of ownership used in a unique way: ensuring authenticity and uniqueness. Due to these characteristics, NFTs have been used in several markets: games, arts, and sports, among others. In 2020, the volume of negotiations of the NFTs was about USD 200 million. Despite the strong interest of economic agents in operating with NFTs, there are still gaps in the literature, regarding their dynamics and price interrelation with other potentially related assets, which deserve to be studied. In this sense, the main purpose in this paper is to analyze the cross-correlation between NFTs and larger cryptocurrencies. To this end, our methodological approach is based on a Detrended Cross-Correlation Analysis correlation coefficient, with a sliding windows approach. Our main finding is that the cross-correlations are not significant, except for a few cryptocurrencies, with weak significance at some moments of time. We also carried out an analysis of the long-term memory of NFTs, which demonstrated the antipersistence of these assets, with results seemingly corroborating the market inefficiency hypothesis. Our results are particularly important for different classes of investors, due to the analysis on different time scales. [ABSTRACT FROM AUTHOR]

In this work, we evaluated the evolution of some atmospheric pollutants (O3, NOx and PM10) over time and their relationship with four different climate variables (solar irradiation, air temperature, relative humidity and wind speed). To this end, we assessed the long-range dependence of those concentrations with a Detrended Fluctuation Analysis (DFA) and analyzed the cross-correlation of such dependence with the climate variables through a Detrended Cross-Correlation Coefficient Analysis ( ρ D C C A ). The results show that air pollution tends to increase over time, impairing air quality and likely affecting human health. The results indicate a cross-correlation between air pollution and the climatic variables, which persisted for a certain period, with a greater correlation between O3 concentration and wind, mainly temperature, and a negative correlation with humidity for all monitoring stations. Moreover, unlike O3 and PM10, NOx concentrations always had a persistent behavior in the region of study for the entire analyzed period. [ABSTRACT FROM AUTHOR]

• The interaction between financial structure and financial risk is explored. • The market-based financial structure increases financial risk. • The interaction between them has the characteristics of multifractal, asymmetry and bidirectional conduction. • There is a nonlinear Granger causality between them. The evolution of financial structure is not only a persistent issue but also a key factor affecting the financial risk. Concerns over the financial structure evolution and risk prevention have been voiced, but studies documented the interaction between financial risk and financial structure are still in their infancy. Therefore, this study investigates it by using detrended cross-correlation analysis, its multifractal asymmetric version, and detrended cross-correlation based on time-delay. Empirical research finds that the market-based financial structure increases financial risk in China. The characteristics of financial risk and financial structure are asymmetry, multifractal, bidirectional transmission, and nonlinear causality. The financial structure's influence on financial risk is strengthened within 15 months, while financial risk's influence on the financial structure strengths within 7 months. These findings support financial supervisory authorities to seek effective strategies to facilitate the evolution of the financial structure and establish a more stable financial system. There is an interaction between financial structure and financial risk. The interaction between them is multifractal, asymmetrical and bidirectional. Market-based financial structures increase financial risk, while bank-based financial structures reduce it. [Display omitted] [ABSTRACT FROM AUTHOR]

The fractal characteristics of the security market were considered in portfolio strategy optimization. First, the detrended cross-correlation analysis was adopted to measure the fractal correlation of different securities. Second, the fractal correlation was embedded into the mean-variance criterion of the modern portfolio theory. Third, the mean-detrended cross-correlation analysis portfolio strategy of multiple risk assets was constructed, and the analytic solution of the strategy was given. Finally, the evaluation results revealed that the constructed the mean-detrended cross-correlation analysis portfolio strategy clearly improved investment performance, thus achieving the goal of optimizing the multiple risk asset portfolio strategy. [ABSTRACT FROM AUTHOR]

Assessing power-law cross-correlations between a pair – or among a set – of processes is of great significance in diverse fields of analyses ranging from neuroscience to financial markets. In most cases such analyses are computationally expensive and thus carried out offline once the entire signal is obtained. However, many applications – such as mental state monitoring or financial forecasting – call for fast algorithms capable of estimating scale-free coupling in real time. Detrended cross-correlation analysis (DCCA), a generalization of the detrended fluctuation analysis (DFA) to the bivariate domain, has been introduced as a method designed to quantify power-law cross-correlations between a pair of non-stationary signals. Later, in analogy with the Pearson cross-correlation coefficient, DCCA was adapted to the detrended cross-correlation coefficient (DCCC), however as of now no online algorithms were provided for either of these analysis techniques. Here we introduce a new formula for obtaining the scaling functions in real time for DCCA. Moreover, the formula can be generalized via matrix notation to obtain the scaling relationship between not only a pair of signals, but also all possible pairs among a set of signals at the same time. This includes parallel estimation of the DFA scaling function of each individual process as well, thus allowing also for real-time acquisition of DCCC. The proposed algorithm matches its offline variants in precision, while being substantially more efficient in terms of execution time. We demonstrate that the method can be utilized for mental state monitoring on multi-channel electroencephalographic recordings obtained in eyes-closed and eyes-open resting conditions. [ABSTRACT FROM AUTHOR]

Assessing power-law cross-correlations between a pair – or among a set – of processes is of great significance in diverse fields of analyses ranging from neuroscience to financial markets. In most cases such analyses are computationally expensive and thus carried out offline once the entire signal is obtained. However, many applications – such as mental state monitoring or financial forecasting – call for fast algorithms capable of estimating scale-free coupling in real time. Detrended cross-correlation analysis (DCCA), a generalization of the detrended fluctuation analysis (DFA) to the bivariate domain, has been introduced as a method designed to quantify power-law cross-correlations between a pair of non-stationary signals. Later, in analogy with the Pearson cross-correlation coefficient, DCCA was adapted to the detrended cross-correlation coefficient (DCCC), however as of now no online algorithms were provided for either of these analysis techniques. Here we introduce a new formula for obtaining the scaling functions in real time for DCCA. Moreover, the formula can be generalized via matrix notation to obtain the scaling relationship between not only a pair of signals, but also all possible pairs among a set of signals at the same time. This includes parallel estimation of the DFA scaling function of each individual process as well, thus allowing also for real-time acquisition of DCCC. The proposed algorithm matches its offline variants in precision, while being substantially more efficient in terms of execution time. We demonstrate that the method can be utilized for mental state monitoring on multi-channel electroencephalographic recordings obtained in eyes-closed and eyes-open resting conditions.

Two different fractal regression methodologies were employed with the aim of understanding the relationship between prices of ethanol produced in the USA and Brazil, the most important producers of ethanol. These methodologies, which have the advantage of giving us information about the relationships between different timescales, could also provide interesting and important information about this particular market. Our results suggest, first, some differences in the basic price in these markets, and second, a positive relationship between prices (as expected) but not a one‐to‐one relationship. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd [ABSTRACT FROM AUTHOR]

This paper aims to analyse the connectivity of 13 stock markets, between 1998 and 2019, with a time-varying proposal, to evaluate evolution of the linkage between these markets over time. To do so, we propose to use a network built based on the correlation coefficients from the Detrended Cross-Correlation Analysis, using a sliding windows approach. Besides allowing for analysis over time, our approach also enables us to verify how the network behaves for different time scales, which enriches the analysis. We use two different properties of networks: global efficiency and average grade, to measure the network’s connectivity over time. We find that the markets under analysis became more connected before the subprime crisis, with this behavior extending even after the Eurozone crisis, showing that during extreme events there is an increase in financial risk, as found in the international literature.

The present study investigates the changes in G20 stock market dynamics and their interlinkages in the aftermath of COVID-19. It utilizes the Detrended Cross-Correlation Analysis (DCCA) along with the network and complexity theories for detecting the contagion effect of the pandemic on the stock markets. We find that both advanced and emerging stock markets, departing from their pre-crisis structures, form a tightly coupled close community after the disease outbreak – except for China that distanced itself from the rest of the cohort. This indicates that COVID-19 has caused contagion in the global equity market, which has increased the risk to international portfolio diversification benefits. The adoption of the Network theory for contagion detection technique augments the detailed understanding of the structural changes and their interlinkages among the stock markets in a more comprehensive way. [ABSTRACT FROM AUTHOR]

How stock markets relate to each other is very important because this could have positive effects (such as enhancing economic growth) but also negative effects (possible contagion risks). Considering this issue, this study proposes continuous evaluation of the cross-correlations between markets, applying a sliding windows approach based on the detrended cross-correlation analysis correlation coefficient. Measuring the cross-correlations between the USA and other eight stock markets (the remainder of the G7 plus China and Russia), this allows dynamic analysis of the evolution of cross-correlations and also continuous analysis of the contagion effect. The results show that in the period before the crisis the correlation levels with the US stock market decreased, while post-crisis the results point to a contagion effect. As the proposed approach could be used for continuous monitoring of cross-correlations, this kind of information could be important for the different agents involved in stock markets. [ABSTRACT FROM AUTHOR]

Considering the importance of continuously analysing stock market integration, and based on an earlier study, this paper adopts a sliding windows approach, jointly with the Detrended Cross-Correlation Analysis correlation coefficient, in order to assess the evolution of integration in Central and Eastern European stock markets. With this approach, we are able to analyse stock market integration in a dynamic way. Our results show that the stock markets of the Czech Republic, Hungary, Croatia, Poland and Romania are most integrated, while those of Bosnia, Montenegro, Serbia and Slovakia are less so. Moreover, we found that during crises, levels of integration increased, while the Brexit referendum seems to have had the contrary effect on markets. This is important information for investors, for example, when wanting to build portfolios, but also for authorities, for whom the information about correlations could be important in detecting potential price crashes. [ABSTRACT FROM AUTHOR]

Brazil is one of the world’s largest producers and exporters of cattle, chicken and swine. Therefore, co-movements of Brazilian meat prices are important for both domestic and foreign stakeholders. We propose to analyse the cross-correlation between meat prices in Brazil, namely, cattle, swine and chicken, including also in the analysis information from some commodities, namely maize, soya beans, oil, and the Brazilian exchange rate. Our sample covers the recent period which coincided with extensive macroeconomic and institutional changes in Brazil, from 2011 to 2020, and is divided in two periods: (i) presidential pre-impeachment (P1), occurring in August 2016, and; (ii) post-impeachment (P2). Our results indicate that in P1, only the prices of swine and chicken showed a positive and strong correlation over time, and that cattle showed some positive correlation with chicken only in the short run. In P2, there was also a positive and consistent correlation between swine and chicken, and only a positive association with swine and cattle in the long run. For more spaced time scales (days), the changes in the degree of correlation were significant only in the long run for swine and cattle.

Finding the critical factor and possible “Newton’s laws” in financial markets has been an important issue. However, with the development of information and communication technologies, financial models are becoming more realistic but complex, contradicting the objective law “Greatest truths are the simplest.” Therefore, this paper presents an evolutionary model independent of micro features and attempts to discover the most critical factor. In the model, information is the only critical factor, and stock price is the emergence of collective behavior. The statistical properties of the model are significantly similar to the real market. It also explains the correlations of stocks within an industry, which provides a new idea for studying critical factors and core structures in the financial markets.

In this research work, we propose to assess the dynamic correlation between different mobility indices, measured on a daily basis, and the new cases of COVID-19 in the different Portuguese districts. The analysis is based on global correlation measures, which capture linear and non-linear relationships in time series, in a robust and dynamic way, in a period without significant changes of non-pharmacological measures. The results show that mobility in retail and recreation, grocery and pharmacy, and public transport shows a higher correlation with new COVID-19 cases than mobility in parks, workplaces or residences. It should also be noted that this relationship is lower in districts with lower population density, which leads to the need for differentiated confinement policies in order to minimize the impacts of a terrible economic and social crisis.

The rapid development of cryptocurrencies has drawn attention to this particular market, with investors trying to understand its behaviour and researchers trying to explain it. The evolution of cryptocurrencies' prices showed a kind of bubble and a crash at the end of 2017. Based on this event, and on the fact that Bitcoin is the most recognized cryptocurrency, we propose to evaluate the contagion effect between Bitcoin and other major cryptocurrencies. Using the Detrended Cross-Correlation Analysis correlation coefficient (ΔρDCCA) and comparing the period after and before the crash, we found evidence of a contagion effect, with this particular market being more integrated now than in the past--something that should be taken into account by current and potential investors [ABSTRACT FROM AUTHOR]

Wind energy is a weather and climate-dependent energy resource with natural spatio-temporal variabilities at time scales ranging from fraction of seconds to seasons and years, while at spatial scales it is strongly affected by the terrain and vegetation. To optimize wind energy systems and maximize the energy extraction, wind measurements on various time scales as well as wind energy forecasts are required and needed. This study focuses on spatio-temporal characteristics of the wind velocity in complex terrain, relevant to wind energy assessment, operation, and grid integration, using data collected at 11 towers ranging from 40 to 80 m tall over a 12-year period in complex terrain of westerncentral and northern Nevada, USA. The autocorrelation analysis, Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA) showed strong coherence between the wind speed and direction with slowly decreasing amplitude of the multi-day periodicity with increasing lag periods. Besides pronounced diurnal periodicity at all locations, statistical analysis and DFA also showed significant seasonal and annual periodicities, long-memory persistence with similar characteristics at all sites and towers with a relatively narrow range of the Weibull parameters. The DCCA indicates similar wind patterns at each tower, and strong correlations between measurement sites in spite of separations of about 300 km across the towers' setup. The northern Nevada area exhibits higher wind resource potential and higher wind persistence compared to the western-central region. Overall, the DFA and DCCA results suggest higher degree of complementarity among wind data at measurement sites compared to previous standard statistical analysis. [ABSTRACT FROM AUTHOR]

Aim: To evaluate cerebral autoregulation changes in preterm infants receiving a loading dose of caffeine base.Methods: In a cohort of 30 preterm infants, we extracted measures of cerebral autoregulation using time and frequency domain techniques to determine the correlation between mean arterial pressure (MAP) and tissue oxygenation index (TOI) signals. These measures included the cerebral oximetry index (COx), cross-correlation and coherence measures, and were extracted prior to caffeine loading and in the 2 hours following administration of 10 mg/kg caffeine base.Results: We observed acute reductions in time domain correlation measures, including the cerebral oximetry index (linear mixed model coefficient -0.093, standard error 0.04; p = 0.028) and the detrended cross-correlation coefficient (ρ5 coefficient -0.13, standard error 0.055; p = 0.025). These reductions suggested an acute improvement in cerebral autoregulation. Features from detrended cross-correlation analysis also showed greater discriminative value than other methods in identifying changes prior to and following caffeine administration.Conclusion: We observed a reduced correlation between MAP and TOI from near-infrared spectroscopy following caffeine administration. These findings suggest an acute enhanced capacity for cerebral autoregulation following a loading dose of caffeine in preterm infants, contributing to our understanding of the physiological impact of caffeine therapy. [ABSTRACT FROM AUTHOR]

Abstract The relationship between oil prices and stock markets is commonly studied, in order to understand how financial markets are influenced by this important real asset. The evidence of any kind of interdependence is important, because investors could have additional information about the evolution of those kinds of assets. In this paper, we analyse the detrended cross-correlation coefficient between oil price and 20 different stock markets. We split our sample, in order to analyse the behaviour of that correlation before and after the 2008 crisis, allowing us to use the Δ ρ DCCA , searching for a possible increase in the connection between both variables. Based on the existing literature, which used simulated and real time series to define the critical values for Δ ρ DCCA , we study the statistical significance of this variable Our results show some evidence that before the crisis the correlations were low, but increased after the crisis, which could be understood as an increase in the relationship between oil and stock markets. This is an interesting result because it shows that stock markets are now more exposed to oil price fluctuation than before the crisis. Highlights • We analyse the relationship between oil prices and 20 different stock markets. • We use the Δ ρ DCCA to analyse the long-range correlation before and after the 2008 crisis. • Results show that stock markets are now more exposed to oil price fluctuation. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we investigated the cross correlations among non-stationary passive scalars series. Based on the detrended cross correlation analysis (DCCA), we found that there existed long-range cross correlations among these series. The DCCA exponents for each pair of passive scalars are all greater than 0.5. This implies that the cross correlation is persistent. Moreover, the degree of cross correlation between H 2 O and PM 2.5 is greater compared to CO 2 and PM 2.5 as well as CO 2 and H 2 O. The same analysis is also performed on randomly shuffled series. The calculated DCCA exponents fluctuate slightly over 0.5, which verifies that there indeed exist cross correlations among original series. Highlights • Diffusion conditions drive the concerted variability of passive scalars in turbulent boundary layer. • There exist long-range persistent correlations among observed passive scalars in the turbulent boundary layer during air pollution. • The existence of long-range correlations is conducive to understand the interdependent mechanism of air pollutants and serves as an effective way of evaluating numerical air pollution models. [ABSTRACT FROM AUTHOR]

Abstract We present a novel approach in the research field of price–trading in stock markets. We consider the effect of trading volume (related with liquidity) on the dynamic dependence of time series, using data from the Portuguese stock market, with both listed and non-listed firms in the main index. We use detrended fluctuation analysis, with a sliding windows approach, to estimate the dynamic dependence of shares. We found two interesting and related features: firms listed in the main index have results more centred at the 0.5 level; exponents' volatility is less in the second half of the sample, after the most severe period of the Eurodebt crisis. Then, we relate this dependence with trading volume, with detrended cross-correlation analysis and the respective correlation coefficient. We found that for firms listed in the main index, some show evidence of negative correlations while for non-listed firms, most of the shares have the same results. We conclude that the lack of liquidity could be related to increased dependence. An interesting feature found is that some firms have a positive correlationship between the variables, but all those firms work with natural resources in their activities, which could mean that other factors influence their dependence patterns. Highlights • A novel approach is presented to analyse the relationship between price and trading. • We used a sliding windows approach, to evaluate dependence evolution. • We make the correlation between (dynamical) dependence and trading volume. • Results show the importance of lack of liquidity in stock market dependence. [ABSTRACT FROM AUTHOR]

Spectral analysis of global-mean precipitation, P, evaporation, E, precipitable water, W, and surface temperature, Ts, revealed significant variability from sub-daily to multi-decadal time-scales, superposed on high-amplitude diurnal and yearly peaks. Two distinct regimes emerged from a transition in the spectral exponents, β. The weather regime covering time-scales < ~ 10 days with β ≥ 1; and the macroweather regime extending from a few months to a few decades with 0 <β <1. Additionally, the spectra showed a generally good statistical agreement amongst several different model- and satellite-based datasets. Detrended cross-correlation analysis (DCCA) revealed three important results which are robust across all datasets: (1) Clausius-Clapeyron (C-C) relationship is the dominant mechanism of W non-periodic variability at multi-year time-scales; (2) C-C is not the dominant control of W, P or E non-periodic variability at time-scales below about 6 months, where the weather regime is approached and other mechanisms become important; (3) C-C is not a dominant control for P or E over land throughout the entire time-scale range considered. Furthermore, it is suggested that the atmosphere and oceans start to act as a single coupled system at time-scales > ~ 1-2 years, while at time-scales < ~ 6 months they are not the dominant drivers of each other. For global-ocean and full-globe averages, ρDCCA showed large spread of the C-C importance for P and E variability amongst different datasets at multi-year time-scales, ranging from negligible (< 0.3) to high (~ 0.6-0.8) values. Hence, state-of-the-art climate datasets have significant uncertainties in the representation of macroweather precipitation and evaporation variability and its governing mechanisms. [ABSTRACT FROM AUTHOR]

Abstract When considering an investment, agents are interested in obtaining returns but always considering the risk of that investment. Obviously, they intend to manage their risk, preferring less risky assets. Still, there are other factors, such as the case of corporative image and the international position regarding environmentally friendly policies that might influence this selection. Retrieving information for 26 shares of firms producing renewable energy, in this paper we use the DCCA correlation coefficient to see how those shares behave with the main indices. If shares and indices are not correlated, different factors affect them, which could be seen as a possibility of risk diversification. The results show that in the short run correlation exists (in scales under 100 days) but in the long run most shares are uncorrelated with the indices of their countries (in scales greater than 200), meaning that those shares, associated with more sustainable principles, could be used to manage portfolio risks. Highlights • The DCCA correlation coefficient among renewable energy firms and main indices is calculated. • In the short run the shares are related with the main indices. • In the long run, correlations are not statistically significant. • The use of shares of renewable energy firms could be interesting for portfolio diversification. [ABSTRACT FROM AUTHOR]

The multifractal detrended fluctuation analysis（ MFDFA） is developed only for processing one- dimension time series. Consequently,when applied to analyze complex vibration data from a defective gearbox,the MFDFA frequently produces poor results due to disturbance from noise and other factors. As a result,high- dimension data analysis seems effective in solving this problem. To this end,multifractal detrended cross- correlation analysis（ MF- DCCA） is introduced to examine gearbox vibration data and then a novel method for fault diagnosis of gearboxes is proposed based on MF- DCCA. In the proposed method,a cross- correlation function of two- dimension signals is firstly estimated and then the multifractal feature hidden in the cross- correlation function are analyzed. Application to probing realist gearbox vibration data suggested that the proposed method can clearly distinguish between similar and close fault patterns of gearboxes and performs better than the method based on MFDFA in fault diagnosis of gearbox.

We examine risk profiles of the Portuguese stock market index component stocks using a novel approach to the classical capital asset pricing model (CAPM). Specifically, we estimate the CAPM via fractal regressions that allow studying the marginal effects at selected scales. In this way, we can reveal whether the risk is perceived differently by market participants with different investment horizons. Apart from the analysis itself, we provide new statistical insights into the issue of separating and comparing the scale-specific effects with statistical validity. We find several stocks deviating from an expected risk perception homogeneity across investment horizons. This is true for both analysed periods, i.e. before and after the global financial crisis. There are also several stocks that changed their relationship to the market portfolio in between, which has strong implications for possible portfolio construction. The proposed methodology is not limited to financial topics but can be used in any discipline where the scale-specific marginal effects might be of interest. [ABSTRACT FROM AUTHOR]

The geopolitical and economic landscape of Central and Eastern European (CEE) countries has changed dramatically since the 1990s. The fragmentation of some countries and the change from communist regimes to capitalist ones transformed those countries. In this context, we propose to study 16 CEE stock markets, in relation to China, Germany, Russia and the UK, using the correlation coefficient of DCCA and DMCA. The results show that most stock markets (about 60%) are more related to the Russian one than to other European markets, even in the case of European Union members. The Bosnian, Slovakian and Latvian stock markets show evidence of segmentation, which could be interesting for portfolio managers, in terms of diversification. [ABSTRACT FROM AUTHOR]

It is a crucial topic to identify the cross-correlations between time series in multivariate systems. In this paper, we extend the detrended cross-correlation analysis (DCCA) into the multivariate systems, assigned multivariate detrended cross-correlation analysis (MVDCCA). Numerical simulations of synthetic multivariate time series generated by two-exponent and mix-correlated ARFIMA processes are applied to illustrate the validity of the proposed MVDCCA. Results show that the external coupling parameter determines the strength of cross-correlation no matter that it is inter-independent or correlated among channels in a certain multivariate time series. The MVDCCA method is robust enough to detect the scale properties of time series by estimating the Hurst exponent. And we use cross-correlation coefficient to quantify the level of cross-correlations clearly. Furthermore, the MVDCCA method performs well when applied to the stock markets combining the stock daily price returns and trading volume of stock indices. By comparing results only using stock daily price returns in published literatures, we find that the higher recognizability between the pair stock indices can be observed whatever from the same regions or different regions in multivariate situations and the conclusions are more comprehensive. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

The purpose of this paper is to verify the long-range correlation between the stock markets of the largest economies in the world and the respective exchange rate with the USD. According to theory, a negative correlation is expected, meaning that an increase in the return of one of the assets will cause a decrease in the return of the other. Using detrended cross-correlation and detrended moving average cross-correlation analyses and the respective correlation coefficients, we analysed this possibility, analysing behaviour according to different time scales. Our main results showed that in European markets, the exchange rate does not have a significant effect. This significant effect just occurs in the case of the Indian stock market, while in the case of the Japanese one, the relationship is positive. Japanese authorities’ monetary policy could be the reason for this different result.

In this paper, Stock market comovements are examined using cointegration, Granger causality tests and nonlinear approaches in context of mutual information and correlations. Since underlying data sets are affected by non-stationarities and trends, we also apply Adaptive Multifractal Detrended Fluctuation Analysis (AMF-DFA) and Adaptive Multifractal Detrended Cross-Correlation Analysis (AMF-DXA). We find only 170 pair of Stock markets cointegrated, and according to the Granger causality and mutual information, we realize that the strongest relations lies between emerging markets, and between emerging and frontier markets. According to scaling exponent given by AMF-DFA, h ( q = 2 ) > 1 , we find that all underlying data sets belong to non-stationary process. According to Efficient Market Hypothesis (EMH), only 8 markets are classified in uncorrelated processes at 2 σ confidence interval. 6 Stock markets belong to anti-correlated class and dominant part of markets has memory in corresponding daily index prices during January 1995 to February 2014. New-Zealand with H = 0 . 457 ± 0 . 004 and Jordan with H = 0 . 602 ± 0 . 006 are far from EMH. The nature of cross-correlation exponents based on AMF-DXA is almost multifractal for all pair of Stock markets. The empirical relation, H x y ≤ [ H x x + H y y ] ∕ 2 , is confirmed. Mentioned relation for q > 0 is also satisfied while for q < 0 there is a deviation from this relation confirming behavior of markets for small fluctuations is affected by contribution of major pair. For larger fluctuations, the cross-correlation contains information from both local (internal) and global (external) conditions. Width of singularity spectrum for auto-correlation and cross-correlation are Δ α x x ∈ [ 0 . 304 , 0 . 905 ] and Δ α x y ∈ [ 0 . 246 , 1 . 178 ] , respectively. The wide range of singularity spectrum for cross-correlation confirms that the bilateral relation between Stock markets is more complex. The value of σ D C C A indicates that all pairs of stock market studied in this time interval belong to cross-correlated processes. [ABSTRACT FROM AUTHOR]

We analyse the covered interest parity (CIP) using two novel regression frameworks based on cross-correlation analysis (detrended cross-correlation analysis and detrending moving-average cross-correlation analysis), which allow for studying the relationships at different scales and work well under non-stationarity and heavy tails. CIP is a measure of capital mobility commonly used to analyse financial integration, which remains an interesting feature of study in the context of the European Union. The importance of this features is related to the fact that the adoption of a common currency is associated with some benefits for countries, but also involves some risks such as the loss of economic instruments to face possible asymmetric shocks. While studying the Eurozone members could explain some problems in the common currency, studying the non-Euro countries is important to analyse if they are fit to take the possible benefits. Our results point to the CIP verification mainly in the Central European countries while in the remaining countries, the verification of the parity is only residual. [ABSTRACT FROM AUTHOR]

In rural area, due to the reduction of NOx and CO emitted from vehicle exhausts, the ozone photochemical reaction exhibits relatively weak effect and ozone formation presents different pattern with its precursors in contrast to urban situation. Hence, in this study, we apply detrended cross-correlation analysis to investigate the multifractal properties between ozone and its precursors in a rural area in Hong Kong. The observed databases of ozone, NO 2 , NOx and CO levels during 2005–2014 are obtained from a rural monitoring station in Hong Kong. Based on the collected database, the cross-correlation analysis is carried out firstly to examine the cross-correlation patterns and the results indicate that close interactive relations exist between them. Then the detrended cross-correlation analysis is performed for further analysis. The multifractal characters occur between ozone and its precursors. The long-term cross-correlations behaviors in winter are verified to be stronger than that in other seasons. Additionally, the method is extended on daily averaged data to explore the multifractal property on various time scales. The long-term cross-correlation behavior of ozone vs NO 2 and NOx on daily basis becomes weaker while that of ozone vs CO becomes stronger. The multifractal properties for all pairs in summer are found to be the strongest among the whole year. These findings successfully illustrate that the multifractal analysis is a useful tool for describing the temporal scaling behaviors of ozone trends in different time series in rural areas. [ABSTRACT FROM AUTHOR]