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The study examines the relationship and linkages between spot and future prices of European Union Emission Trading Systems (EU-ETS) during 2019–2021. Dynamic conditional correlation specification of multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model was employed to examine the time-varying correlation between spot and future prices. Also, vector autoregression mean model and MGARCH-Baba-Engle-Kroner-Kraft volatility model was jointly estimated to model the spillover between EU ETS spot and future prices in the first and second moments. Lastly, we utilize the variance-covariance matrix of joint mean-variance model estimation to derive the optimal conditional hedge ratio as well as the hedge effectiveness of EU ETS future contracts. Our findings reveal a high conditional correlation and significant spillover between carbon spot and future markets in EU. Further, our study uncovers a high degree of hedge effectiveness for EU ETS future contracts. This is possibly the first study that examines the linkages between EU ETS spot and future prices pertaining to the recent transition stage of phase III and the initial stage of ongoing phase IV of the ETS market. Our findings pinpoint to ETS markets becoming more complete and in turn offering optimal hedging avenues. JEL Codes: G15, C58, Q38 [ABSTRACT FROM AUTHOR]

We assess the determinants of regional business cycle synchronization in Greece vis‐à‐vis the national reference business cycle, using NUTSII annual data. The computation of the time‐varying synchronization is based on the dynamic estimate of a conditional variance–covariance model and subsequently, a panel regression model is used to evaluate its determinants. The findings show that island regions, industrial structure, imports, savings and disposable income are the key determinants, based on the GVA business cycle synchronization vis‐à‐vis the national reference cycle. We also assess the determinants of employment synchronization (vis‐à‐vis the national employment level) and we find that regions with higher disposable income and public spending tend to drive the level of synchronization. Turning to the inter‐regional synchronization, we provide evidence that investments, disposable income and employment drive the GVA business cycle synchronization, whereas the employment synchronization is determined by the level of imports, disposable income and public spending, as well as by the status of regions as island economies. We further show that the Greek economic crisis during the period 2010–2018 has diminished or eliminated the effects of the aforementioned drivers, suggesting that during the said period, synchronization was mainly driven by the wider economic conditions. These findings lead to important policy implications, which are thoroughly discussed. [ABSTRACT FROM AUTHOR]

To stabilize hog price and promote hog industry chain development, this research adopted a time-varying copula model to analyze the data collected in the time period of January 8th 2021 to May 31th 2023, encompassing the period when hog futures were listed. The results indicated a significant positive correlation between hog futures and soybean meal and while there is a certain degree of dependency in the relationship with corn spot prices in China. The lower tail correlation between hog futures and hog spot prices was higher than the upper tail correlation, suggesting that hog futures served as a tool for price discovery and guidance, especially during the periods when prices were declined. Therefore, it is advisable to expand the influence and utilization of the hog futures market. Doing so will allow us to more effectively leverage the hog futures market for price discovery and hedging strategies, ultimately contributing to the stability of the hog industry chain and supporting the sustainable development of the sector. This heightened influence will also bring benefits to policy makers, farmers, traders, and consumers by providing them with a more reliable and transparent pricing mechanism. This study provided a new perspective for the optimization of policy making and risk management in hog industry, offering valuable insights for participants and policymakers in hog supply chain. [ABSTRACT FROM AUTHOR]

The dynamic structure of the global economy is one of many factors that affect the fluctuations in the global financial markets. COVID-19, like all other epidemics, has been an important period in human history. Exporting and importing oil are also important distinctions in economies. This study aims to investigate the dynamic relationship between stock market closing prices and Brent oil prices of six major oil-importing and oil-exporting economies during the COVID-19 period. These stock markets have a significant impact on the world economy and serve as reference points for evaluating industry performance. The Dynamic Conditional Correlation (DCC) method are used to model the complex interactions of the energy market and markets. It aims to reveal the relationship between fluctuations in energy prices and other macroeconomic indicators. According to the findings, it is concluded that fluctuations in oil prices have a non-negligible impact on stock markets due to the persistence of ARCH effects and volatility in each financial market. Assuming that volatility continues, it is seen that volatility continues to have an impact on the markets examined.

Rising fossil energy prices have sparked interest in biofuels, which has led to a closer link between biofuels and fossil energy markets. We apply a thermal optimal path approach based on daily returns to examine the lead‐lag correlation between three biomass futures and three energy futures for 2020–2022. The empirical results show that the biomass futures market first leads the energy futures market with a significant probability of 20–30 days. Second, the lead days decrease sharply after regional conflicts occur, and the link between the two markets strengthens. Third, the sequence of risk transmission between overseas and domestic energy markets changes, and domestic energy markets become more resilient. Finally, the epidemic disrupted the normal transmission of signals between markets, while regional conflicts reconstructed the links, and the transmission time was shorter. In the post‐Covid‐19 period, ongoing conflicts have synchronized risks between markets, further amplifying the possibility of crises. [ABSTRACT FROM AUTHOR]

This study presents a dynamic multi-factor correlation analysis method designed to predict provincial carbon dioxide emissions (CDE) within China's Bohai Rim region, including Tianjin, Hebei, Shandong, and Liaoning. By employing the sliding window technique, dynamic correlation curves are computed between various influencing factors and CDE at different time intervals, thereby facilitating the identification of key feature attributes. A novel metric, the Consistency Index of Influencing Factors (CIIF), is introduced to evaluate the consistency of these factors across regions. Furthermore, the Accurate Predictive Capability Indicator (APCI) is defined to measure the impact of different feature categories on the prediction accuracy. The findings reveal that models relying on a single influencing factor exhibit limited accuracy, whereas combining multiple factors with diverse correlation features significantly improves the prediction accuracy. This study introduces a refined analytical framework and a comprehensive indicator system for CDE prediction. It enhances the understanding of the complex factors that influence CDE and provides a scientific rationale for implementing effective emission reduction strategies. [ABSTRACT FROM AUTHOR]

Roasting is essential for developing the characteristic aroma of flaxseed oil (FSO), yet its impact on oil quality remains underexplored. This study employed headspace‐gas chromatography‐mass spectrometry coupled with multivariate analysis to elucidate the dynamic changes in volatile compounds and quality characteristics of FSO subjected to varying roasting temperatures. Our findings revealed that seven key aroma compounds, identified through the variable importance in the projection scores of partial least square‐discrimination analysis models and relative aroma activity value, served as molecular markers indicative of distinct roasting temperatures. These compounds included 2,5‐dimethylpyrazine, 2‐pentylfuran, (E)‐2‐pentenal, 2‐ethyl‐3,6‐dimethylpyrazine, heptanal, octanal, and 2‐hexenal. Notably, roasting at 200°C was found to enhance oil stability and antioxidant capacity, with phenolic compounds and Maillard reaction products playing synergistic roles in bolstering these qualities. Network analysis further uncovered significant correlations between these key aroma compounds and quality characteristics, offering novel perspectives for assessing FSO quality under diverse roasting conditions. This research not only enriched our understanding of the roasting process's impact on FSO but also provided valuable guidance for the optimization of industrial roasting practices. This study would provide important practical applications in aroma regulation and process optimization of flaxseed oil.. [ABSTRACT FROM AUTHOR]

Based on the theory of new structural economics, this research aims to explore the dynamic correlation among high-quality development, environmental regulation structures, and property rights structures in China's construction industry. A panel vector autoregressive model (PVAR) is employed to conduct an empirical study of 30 provinces from 2008 to 2022. To further explore regional heterogeneity, K-means clustering is utilized to categorize the country into three types of regions. The results indicate that strict environmental regulation structures have a beneficial effect on the high-quality growth of the construction sector, which is most pronounced in Region III with a lower degree of construction development. Conversely, state-owned ownership structures are an impediment, and their influence is the greatest in Region I with a higher degree of construction development. Additionally, environmental regulation structures and property rights structures demonstrate a certain interactional effect. The dynamic correlation between these indicators varies in distinct regions. Various regions in China should combine their development characteristics and advantages to reasonably adjust environmental regulation structures and property rights structures. This research provides a direction for structural adjustments for the high-quality development of the construction industry. [ABSTRACT FROM AUTHOR]

Liquids and gases are distinct in their extent of dynamic atomic correlations; in gases, atoms are almost uncorrelated, whereas they are strongly correlated in liquids. This distinction applies also to electronic systems. Fermi liquids are actually gas-like, whereas strongly correlated electrons are liquid-like. Doped Mott insulators share characteristics with supercooled liquids. Such distinctions have important implications for superconductivity. We discuss the nature of dynamic atomic correlations in liquids and a possible effect of strong electron correlations and Bose–Einstein condensation on the high-temperature superconductivity of the cuprates. [ABSTRACT FROM AUTHOR]

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Two years ago, on 24 February 2022, Russia launched a multi-front attack against Ukraine, which was the largest military action against a European country since World War II. The aim of this paper is to compare the performance of safe haven assets after Russian aggression on Ukraine. We investigate the dynamic relationship between the countries: France, Germany, Great Britain, Japan, Poland, and the United States of America, and popular instruments: gold, silver, natural gas, crude oil, U.S. dollar (or Euro), Swiss franc, and Bitcoin. By choosing different countries we want to compare the influence of Russian aggression in Ukraine on the various market economies. We considered the period after Russian aggression in Ukraine, to be from 01.02.2022 – 01.03.2024. We estimate the parameters of DCC or CCC models to compare the dynamic relationship between the above-mentioned stock markets and financial instruments. The result shows that mostly, natural gas can be considered as a safe haven instrument during that time sample. For investors from Germany, Great Britain, and the USA we can observe that four out of seven instruments were able to act like safe haven assets. Surprisingly, only Bitcoin was not identified as a safe haven instrument for any country.

Based on the theory of new structural economics, this research aims to explore the dynamic correlation among high-quality development, environmental regulation structures, and property rights structures in China’s construction industry. A panel vector autoregressive model (PVAR) is employed to conduct an empirical study of 30 provinces from 2008 to 2022. To further explore regional heterogeneity, K-means clustering is utilized to categorize the country into three types of regions. The results indicate that strict environmental regulation structures have a beneficial effect on the high-quality growth of the construction sector, which is most pronounced in Region III with a lower degree of construction development. Conversely, state-owned ownership structures are an impediment, and their influence is the greatest in Region I with a higher degree of construction development. Additionally, environmental regulation structures and property rights structures demonstrate a certain interactional effect. The dynamic correlation between these indicators varies in distinct regions. Various regions in China should combine their development characteristics and advantages to reasonably adjust environmental regulation structures and property rights structures. This research provides a direction for structural adjustments for the high-quality development of the construction industry.

In this paper, we propose multivariate stochastic volatility models with a spherical parameterization of a Cholesky decomposition to make a time-dependent correlation matrix be positive definite without any constraints. An attractive feature of our model is that it can be easily fit using the R package NIMBLE. In addition to the spherical transformation, we introduce a multivariate L measure as a Bayesian model comparison criterion to assess the fit of different models. We present extensive simulation studies to examine the empirical performance of the proposed method and illustrate the methodology on time series of energy usage in a science building on the main campus of the University of Connecticut.Please confirm if the inserted city and country name is correct. Amend if necessary.RightPlease confirm if the corresponding author is correctly identified. Amend if necessary.Right [ABSTRACT FROM AUTHOR]

We investigate the effectiveness of the Bank Recovery and Resolution Directive (BRRD) in mitigating the bank-sovereign nexus in the Euro Area. Using CDS spreads to measure bank and sovereign credit risk and a DCC-MIDAS model capturing the long-term component of bank-sovereign interconnectedness, we document that the dynamic correlation between banks and sovereigns has decreased in Euro Area countries since the introduction of the BRRD. Panel data analysis reveals that the decline in interconnectedness is not driven by the banks' capital adequacy, size or holdings of domestic sovereign securities. [ABSTRACT FROM AUTHOR]

We apply flexible multivariate dynamic models to capture the dependence structure of various US commodity futures across different sectors between 2004 and 2022; particular attention is paid to the 2008 financial crisis and the COVID-19 pandemic. Our copula-based models allow for time-varying nonlinear and asymmetric dependence by integrating elliptical and skewed copulas with dynamic conditional correlation (DCC) and block dynamic equicorrelation (Block DECO). Flexible copula models that allow for multivariate asymmetry and tail dependence are found to provide the best performance in characterizing co-movements of commodity returns. We also find that the connectedness between commodities has dramatically increased during the financial distress and the COVID-19 pandemic. The impacts of the financial crisis appear to be more persistent than those of the pandemic. We apply our models to some risk management tasks in the commodity markets. Our results suggest that optimal portfolio weights based on dynamic copulas have persistently outperformed the equal-weighted portfolio, demonstrating the practicality and usefulness of our proposed models. [ABSTRACT FROM AUTHOR]

Liquids and gases are distinct in their extent of dynamic atomic correlations; in gases, atoms are almost uncorrelated, whereas they are strongly correlated in liquids. This distinction applies also to electronic systems. Fermi liquids are actually gas-like, whereas strongly correlated electrons are liquid-like. Doped Mott insulators share characteristics with supercooled liquids. Such distinctions have important implications for superconductivity. We discuss the nature of dynamic atomic correlations in liquids and a possible effect of strong electron correlations and Bose–Einstein condensation on the high-temperature superconductivity of the cuprates.

Airports, as integral components of the global aviation industry, experience dynamic changes in air passenger traffic load, which are also related to the trends of local urban development and airspace restrictions of nearby airports. Using time-series data from 2006 to 2019, this study comprehensively applied the autoregressive distributed lag and vector auto-regression mode approaches to identify causal relationships between the urban development factors, including GDP, population, tourism industry, industrial structure, etc., of Tianjin city (China); the Tianjin airport passenger flow; and the Beijing Capital airport's airspace restriction factor, namely, airport aircraft sorties. The results show that the growth of Tianjin city's GDP, primary industry and disposable income per capita was accompanied by a long-term decline in the passenger flow at the Tianjin airport. In addition, increased aircraft sorties of Tianjin airport, as well as the growth of primary, secondary and tertiary industries in Tianjin city, led to a short-term decline in passenger flow at the Tianjin airport. In general, there is variability in the long- and short-term impacts of urban economic structure on airport passenger flow, and this variability applies to other airports. The increased aircraft sorties at the Beijing Capital airport had a short-term positive impact on passenger flow at the Tianjin airport but resulted in a long-term decline of the latter's aircraft sorties. This phenomenon indicates that there is interaction between airports and that this influence varies depending on the competition and cooperation mechanisms between airports. The findings of this study are considered instrumental in guiding the competitive and cooperative strategies of nearby airports and predicting the coupled trends of the airport and urban development.

Revisiting regulatory coherence: accounting for temporal bias in plant gene co-expression analyses Keywords: co-expression network; dynamic correlation; gene regulation; Oryza sativa; regulatory decoherence; stress response; temporal bias; wgcna EN co-expression network dynamic correlation gene regulation Oryza sativa regulatory decoherence stress response temporal bias wgcna 16 24 9 03/06/23 20230401 NES 230401 Introduction Plants evolve to maximize their individual performance under dynamically changing environments. Third, even within an isogenic homogeneous population, population-level correlations may be confounded by saturation in gene regulation, thereby failing to characterize the dynamic network rewiring and, thus, generating false negatives in tests for environmental responses of gene-gene interactions (Figs 2, 3). Our observation that co-expression increases - it becomes more coherent - with the onset of two stresses in rice is seemingly inconsistent with a recent study which used gene expression data collected from human monocytes to infer population correlation among transcripts (Lea I et al i ., [43]). [Extracted from the article]

In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly determines whether the marine time-series data prediction task can be completed efficiently. However, existing research has only focused on the correlation analysis of marine data in a certain area and has ignored the temporal and spatial characteristics of marine data in complex and changeable marine environments. Therefore, we constructed a spatio-temporal dynamic analysis model of marine water quality based on a cross-recurrence plot (CRP) and cross-recurrence quantitative analysis (CRQA). The time-series data of marine water quality were first mapped to high-dimensional space through phase space reconstruction, and then the dynamic relationship among various factors affecting water quality was visually displayed through CRP. Finally, their correlation was quantitatively explained by CRQA. The experimental results showed that our scheme demonstrated well the dynamic correlation of various factors affecting water quality in different locations, providing important data support for the spatio-temporal prediction of marine water quality. [ABSTRACT FROM AUTHOR]

In addressing the challenges of energy security and climate change, ongoing efforts involve the development of innovative technologies to support the shift from conventional to clean energy sources. Concurrently, the establishment of carbon markets aims to facilitate a decrease in carbon emissions by enabling the trade of carbon credits. This study adds to the energy-climate discussion by examining the evolving dynamics of returns on investments in renewable energy, technology, and carbon markets. Specifically, the time-varying correlation among renewable energy, technology, and carbon markets in the European Union is examined from September 18, 2017 to December 14, 2022. The techniques employed for empirical analyses are the nonparametric time-frequency, Benjamini-Hochberg, and Benjamini-Yekutieli correlation techniques, as well as the time-varying Granger causality. Highlights from the results are as follows. Renewable energy, technology, and carbon markets exhibit time and frequency variations in their relationships, with significant correlations notably present during periods marked by major shifts in energy policy. Secondly, renewable energy and carbon markets exhibit very weak or even absent correlation, indicating their suitability for portfolio diversification. Furthermore, investors could benefit significantly by including both technology and carbon markets in their portfolios to reduce overall risk. Several additional recommendations are also provided in this study. [ABSTRACT FROM AUTHOR]

Airports, as integral components of the global aviation industry, experience dynamic changes in air passenger traffic load, which are also related to the trends of local urban development and airspace restrictions of nearby airports. Using time-series data from 2006 to 2019, this study comprehensively applied the autoregressive distributed lag and vector auto-regression mode approaches to identify causal relationships between the urban development factors, including GDP, population, tourism industry, industrial structure, etc., of Tianjin city (China); the Tianjin airport passenger flow; and the Beijing Capital airport's airspace restriction factor, namely, airport aircraft sorties. The results show that the growth of Tianjin city's GDP, primary industry and disposable income per capita was accompanied by a long-term decline in the passenger flow at the Tianjin airport. In addition, increased aircraft sorties of Tianjin airport, as well as the growth of primary, secondary and tertiary industries in Tianjin city, led to a short-term decline in passenger flow at the Tianjin airport. In general, there is variability in the long- and short-term impacts of urban economic structure on airport passenger flow, and this variability applies to other airports. The increased aircraft sorties at the Beijing Capital airport had a short-term positive impact on passenger flow at the Tianjin airport but resulted in a long-term decline of the latter's aircraft sorties. This phenomenon indicates that there is interaction between airports and that this influence varies depending on the competition and cooperation mechanisms between airports. The findings of this study are considered instrumental in guiding the competitive and cooperative strategies of nearby airports and predicting the coupled trends of the airport and urban development. [ABSTRACT FROM AUTHOR]

Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the large number of latent quantities, their efficient estimation is non-trivial and software that allows to easily fit SV models to data is rare. We aim to alleviate this issue by presenting novel implementations of five SV models delivered in two R packages. Several unique features are included and documented. As opposed to previous versions, stochvol is now capable of handling linear mean models, conditionally heavy tails, and the leverage effect in combination with SV. Moreover, we newly introduce factorstochvol which caters for multivariate SV. Both packages offer a user-friendly interface through the conventional R generics and a range of tailor-made methods. Computational efficiency is achieved via interfacing R to C++ and doing the heavy work in the latter. In the paper at hand, we provide a detailed discussion on Bayesian SV estimation and showcase the use of the new software through various examples.

In recent years, the relationship between agricultural commodities and crude oil has become increasingly close with the promotion of biofuel policies. This study examines the dynamic correlation between global crude oil futures and seven agricultural commodity futures by applying the consistent dynamic conditional correlation and dynamic equicorrelation models. The empirical results show that the dynamic correlation between the global crude oil futures market and China's agricultural futures market is weak compared to the global agricultural futures market. In particular, soybean oil has the strongest correlation with crude oil, while Dalian Commodity Exchange (DCE) corn and Zhengzhou Commodity Exchange wheat have the weakest correlation with crude oil. There is an indirect linkage between crude oil futures and DCE soybean meal and DCE soybean oil. Moreover, the dynamic correlation between crude oil and agricultural commodities increased during the financial crisis, the novel coronavirus (COVID‐19) epidemic, and the crude oil crash crisis. Brent crude oil has a stronger co‐movement with China's agricultural commodities than West Texas Intermediate crude oil and can better hedge the risk of agricultural commodities. The findings of this study provide some insights into the contagion risk management of crude oil futures and agricultural futures markets. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, like the Russian aggression against Ukraine, had a significant impact on many financial markets and asset prices. The latter additionally led to large fluctuations on financial markets. In our paper, we try to compare the performance of 'safe haven' assets during turbulent times, such as the recent global financial crisis, the eurozone debt crisis, the COVID-19 pandemic and the Russian aggression against Ukraine. We investigate the dynamic relationship between indices from several European countries (Czech Republic, France, Germany, Great Britain, Poland, Slovakia and Spain), and popular financial instruments (gold, silver, Brent and WTI crude oil, US dollar, Swiss franc and Bitcoin). The study further estimates the parameters of DCC or CCC models to compare dynamic relationships between the above-mentioned stock markets and financial instruments. The results demonstrate that in most cases, the US dollar and Swiss franc were able to protect investors from stock market losses during turbulent times. In addition, investors from France, Poland, the Czech Republic and Slovakia saw gold as a 'safe haven' asset throughout all the abovementioned crises. Our findings are in line with other literature which points out that 'safe haven' instruments can change over time and across countries. In the literature, we can find research performed for the USA, China, Canada, and Great Britain, but there is no such research for Poland, France, the Czech Republic or Slovakia. The purpose of this paper is to try to fill this research gap. [ABSTRACT FROM AUTHOR]

The trading market of carbon emission permits is not only an effective tool for tackling climate change, but it also reinforces the connection between carbon and energy markets by risk transmission. This paper investigates the volatility spillover effects and dynamic correlation between fossil energy, clean energy and the European Union carbon markets by applying the asymmetric BEKK- and DCC-GARCH models in a time-frequency framework and designs the portfolio strategies. Empirical results display that fossil energy market has a significantly positive impact on the clean energy market at most scales, but the feedback transmission is relatively weak. Two indirect volatility spillover effects exist between the three markets, which are 'fossil energy→carbon→clean energy' and 'clean energy→carbon→fossil energy', suggesting that the carbon market plays a vital role in bridging the clean and fossil energy markets. Significant time-varying asymmetric effects are identified between the three markets, and the carbon market is confirmed to offer the least expensive hedge to an investment in oil. Our findings can provide meaningful implications for policymakers to design market mechanism and investors to adjust hedging strategies and diversify their portfolios. [ABSTRACT FROM AUTHOR]

This research investigates the dynamic correlation between China's policy uncertainty and the crude oil markets (i.e. domestic and international markets) using monthly time-series data from May 2003 to December 2018. To consider the non-linear and dynamic properties, we adopt the time-varying parameter structural vector autoregression model (TVP-SVAR) to estimate the dynamic correlations between the time series. By employing four categorical policy uncertainty indices, i.e. monetary policy uncertainty index (MYPU), fiscal policy uncertainty index (FLPU), exchange rate policy uncertainty index (EXER), and trade policy uncertainty index (TEPU), our results reveal that the correlation between China's policy uncertainty and real crude oil returns is time-varying and non-linear. Specifically, the independence between policy uncertainty and oil returns varies more constantly and at a high degree while the time-varying interaction between policy uncertainty and global oil production (global economic activity) varies at a lower frequency and at a low level. Furthermore, regarding the associations between categorical policy uncertainty and global oil returns, the average correlation of EXER (negative) is the strongest one, followed by FLPU (negative), then MYPU (positive), and finally TEPU (negative). Moreover, it is noteworthy that WTI (Daqing) crude oil prices are utilized to increase the robustness of our conclusions. Finally, we generally confirm the dynamic and negative correlation between China's geopolitical risk and crude oil returns. It is evidently clear from the results that investors should pay close attention to the policy uncertainty to avoid the adverse impact of specific policy uncertainty on crude oil returns. [ABSTRACT FROM AUTHOR]

This study analyzes the spillover effect and dynamic correlation of the China-US bean futures markets and discusses the relationship between the dynamic correlation of the bean futures price index and investor sentiment. First, the spillover effect of the China-US bean futures markets is analyzed through the BEKK-GARCH model. Then, the DCC-GARCH model is used for obtaining the dynamic correlation coefficients of the China-US bean futures markets. Next, the principal component analysis method is chosen to construct a comprehensive investor sentiment index. Lastly, the dynamic impacts between the change in investor sentiment and the correlation of the China-US bean futures price index are discussed through the ensemble empirical mode decomposition and impulse response analysis. The results show that the spillover effect of different degrees and directions exists between the China-US bean futures markets, and the dynamic correlation coefficients among different bean futures are also different. Besides, a certain degree of interactions exists between the high-frequency and low-frequency components of the comprehensive investor sentiment index and the dynamic correlations of bean futures price indexes.

In recent years, with the frequency of marine disasters, water quality has become an important environmental problem for researchers, and much effort has been put into the prediction of marine water quality. The temporal and spatial correlation of marine water quality parameters directly determines whether the marine time-series data prediction task can be completed efficiently. However, existing research has only focused on the correlation analysis of marine data in a certain area and has ignored the temporal and spatial characteristics of marine data in complex and changeable marine environments. Therefore, we constructed a spatio-temporal dynamic analysis model of marine water quality based on a cross-recurrence plot (CRP) and cross-recurrence quantitative analysis (CRQA). The time-series data of marine water quality were first mapped to high-dimensional space through phase space reconstruction, and then the dynamic relationship among various factors affecting water quality was visually displayed through CRP. Finally, their correlation was quantitatively explained by CRQA. The experimental results showed that our scheme demonstrated well the dynamic correlation of various factors affecting water quality in different locations, providing important data support for the spatio-temporal prediction of marine water quality.

This paper proposes study framework that investigates the globalization–regionalization debate using both the time domain and frequency domain measures of connectedness. Seven international global crude oil benchmarks are used to analyse whether crude oil market is globalized or regionalized. To this end, we first utilize the Bayesian inference of dynamic correlation in multivariate factor stochastic framework proposed by Kastner et al. (Sparse Bayesian time-varying covariance estimation many dimensions, 2017. arXiv:1608.08468). Next, we employ the Diebold and Yilmaz (Int J Forecast 28:57–66, 2012) and Barunik and Krehlik (J Financ Econom, 2018. http://doi.org/10.1093/jjfinec/nby001Barunik) spillover indices to analyse the connectedness among the set of oil prices under consideration. The period of the study is 15/05/1996 to 07/03/2018. The dynamic correlation results reveal persistent correlation between different pairs of crude oil market over the whole sample period. The overall connectedness results indicate that crude oil market is well integrated. The volatility spillover results show that Forcado is the most affected by shocks from other markets. The return series of Brent at all frequencies appear to be the main source of volatility transmission in crude oil market. The results of overall connectedness show that connectedness is similar across frequencies. These findings are very important from the perspective of understanding the connectedness of global oil market. [ABSTRACT FROM AUTHOR]

In this review we outline the theory and recent progress of the restricted active space configuration interaction (RASCI) methodology within the hole and particle approximation. The RASCI is a single reference approach based on the splitting of the orbital space in different subsets, in which the target CI space is expressed by the concomitant number of electrons and empty orbitals in each subspace. Initially, the method was born as a spin complete version of the spin‐flip ansatz able to deal with any number of unpaired electrons. Since then, the method has experienced several improvements related to its theoretical foundations, its efficient implementation, in the characterization of RASCI wave functions, and in the calculation of molecular properties. It has shown to be especially suitable for the characterization of medium to large molecular diradicals and polyradicals, and to the study of photophysical processes involving open‐shell species and/or multiexcitonic states, like in the singlet fission reaction. But, despite this success, several limitations emerge from the sever truncation of the excitation operator, which might translate into inaccurate relative energies between different regions of the potential energy surface, and overestimated (or underestimated) excitation energies. These errors are associated with the lack of dynamic correlation, which has motivated the development and implementation of various improvements based on multi‐reference perturbation theory and to blend the RASCI wave function with density functional theory through range separation of electron–electron interactions. Finally, we discuss some of the properties available from RASCI wave functions and give potential future developments. This article is categorized under:Electronic Structure Theory > Ab Initio Electronic Structure MethodsSoftware > Quantum ChemistryQuantum Computing > Theory Development [ABSTRACT FROM AUTHOR]

Using a long span of data (from 1751 to 2016), this paper empirically investigates the relationship between carbon dioxide (CO2) emissions and economic growth (real GDP per capita) in the United Kingdom. The empirical results provide strong support for the existence of an Environmental Kuznets Curve (EKC) in the UK, i.e., an inverted U-shaped relationship between CO2 emissions and economic growth, with turning point estimated around the mid-twentieth century. This turning point corresponds with the introduction of changes in environmental standards and policies, which reflects the regulatory efforts to limit pollution by reducing the discharge of grit into the atmosphere, as well as the decline in the use of coal as source of energy, which reflects the country willingness to ensure the energy transition necessary for sustainable development. [ABSTRACT FROM AUTHOR]

Multivariate functional data from a complex system are naturally high-dimensional and have a complex cross-correlation structure. The complexity of data structure can be observed as that (1) some functions are strongly correlated with similar features, while some others may have almost no cross-correlations with quite diverse features; and (2) the cross-correlation structure may also change over time due to the system evolution. With this regard, this article presents a dynamic subspace learning method for multivariate functional data modeling. In particular, we consider that different functions come from different subspaces, and only functions of the same subspace have cross-correlations with each other. The subspaces can be automatically formulated and learned by reformatting the problem as a sparse regression. By allowing but regularizing the regression change over time, we can describe the cross-correlation dynamics. The model can be efficiently estimated by the fast iterative shrinkage-thresholding algorithm, and the features of each subspace can be extracted using the smooth multi-channel functional principal component analysis. Some theoretical properties of the model are presented. Numerical studies, together with case studies, demonstrate the efficiency and applicability of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper examines the correlation and connectedness across global base metal futures markets in the periods before and during the COVID-19 pandemic. We find that the impacts of the pandemic shock on the dynamic correlations between global commodity exchanges vary across futures markets, with significant structural breaks being detected in the underlying dynamic correlations at different stages of the pandemic. We document that stronger price signals were generated in the aggregate markets due to the pandemic shock, and the roles of copper and zinc futures as the pricing center in the global base metal markets increased during the pandemic period. Moreover, our findings show that the leading position of the London Metal Exchange in the global base metal futures pricing declined, while the pricing authority of the Shanghai Futures Exchange improved significantly during the pandemic time. Leveraging post-pandemic data, we identify interdependence structures amid some crucial geopolitical conflicts. [Display omitted] • The effects of the pandemic shock on the correlations and connectedness differ across global base metal futures markets. • The pandemic shifted the relationships and interdependencies among the global base metal futures markets. • The pricing dynamics became more pronounced during the pandemic period. • LME experienced a decline in its leading position in global base metal futures pricing during the pandemic. • The pricing authority of SHFE significantly improved during the pandemic. [ABSTRACT FROM AUTHOR]

The certified emission reduction (CER) carbon trading market promoted by the clean development mechanism (CDM) has become an important platform for the development of the international carbon market. However, the CER carbon market has shown unsteady development with the present phenomena of price decrease, transaction inactivity, and recession. Against this backdrop, this study aims to explore the intuition behind CER price volatility from the new perspective of internal and external market dynamic linkages. By introducing three homogeneous carbon products of CER futures, namely, the daily dataset of CER spot, EUA (European Union Allowance) spot and EUA futures, and taking five heterogeneous market drivers comprising stock, exchange rates, coal, crude oil, and natural gas into account, we analyze the dynamic correlations and volatility spillovers between CER futures returns and these influencing factors using the DGC-MSV model. With sample data from January 2013 to May 2019, our empirical results show a persistent dynamic dependence between CER futures price and its factors. The homogeneous and heterogeneous markets have significant positive and negative spillover effects, respectively, on the CER futures market. The decline of CER futures price in the post-Kyoto era is due to two aspects: fluctuation of the exchange rate market, which is closely connected to the settlement of currency, and coal price volatility in energy markets. However, the CER futures market has no obvious spillover effect on other markets, except for its strong impact on the CER spot market and weak information spillover to the exchange rate market. Overall, this finding indicates the feeblest financial property of CER carbon futures market.

The stochastic characteristics of wind power and photovoltaic (PV) make the resource allocation of power system difficult. Therefore, it is necessary to consider the correlation between the power generation of wind and PV power stations to avoid resource waste and guarantee system power supply, while the traditional correlation analysis method cannot accurately describe the multiscale and time-varying characteristics of the correlation. In this article, based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the time series of the wind and PV power generation was decomposed into multiscale components. Moreover, the time-dependent intrinsic correlation (TDIC) is introduced to excavate the local correlation of the power generation time series under the framework of a multi-time scale, the dynamic change of a correlation is captured by analyzing the TDIC plots. The analysis shows that the strength and nature of the association between wind and PV vary with time scales and time spells, reflecting rich, dynamic characteristics. The correlation variation of different scale components in local time is of great significance to power system operation, planning, and resource optimal allocation.

This study examines the static and dynamic correlations in the ASEAN equity markets. The importance of this research appears from the fact that practitioners can get the benefit if their investments yield the same or higher returns given lower or the same risk in their portfolio. Firstly, this advantage comes from including the assets that decrease volatility of the portfolio. Hence, the correlation between the ASEAN markets should be examined. Secondly, co-movements in market realizations may increase global financial instability. Its existence is important for international investors, financial institutions, and policy makers. The study locates the relationship between ASEAN and its major trading partners, including Japanese, US, and UK markets, in order to find more rational results. This study utilizes alternative multivariate GARCH forms to provide useful information on the dynamic evolution and implications of return volatilities. The results show that the volatilities of all the equity markets under study are persistent over time. The estimates from VEC model indicate that the movements of the US and UK equity market returns have some degree of influence on several of the ASEAN equity markets. The results imply that, first, most of the developing ASEAN equity markets work by its own information with small relation to the developed world. Second, it is still convincing to state that investing in ASEAN equity markets should provide investors a better mean-variance portfolio. And, third, buy-and-hold strategy seems to be more beneficial than readjusting the ASEAN equities portfolio.

The Bitcoin market has become a research hotspot after the outbreak of Covid-19. In this paper, we focus on the relationships between the Bitcoin spot and futures. Specifically, we adopt the vector autoregression-dynamic correlation coefficient-generalized autoregressive conditional heteroskedasticity (VAR-DCC-GARCH) model and vector autoregression-Baba, Engle, Kraft, and Kroner-generalized autoregressive conditional heteroskedasticity (VAR-BEKK-GARCH) models and calculate the hedging effectiveness (HE) value to investigate the dynamic correlation and volatility spillover and assess the risk reduction of the Bitcoin futures to spot. The empirical results show that the Bitcoin spot and futures markets are highly connected; second, there exists a bi-directional volatility spillover between the spot and futures market; third, the HE value is equal to 0.6446, which indicates that Bitcoin futures can indeed hedge the risks in the Bitcoin spot market. Furthermore, we update the data to the post-Covid-19 period to do the robustness checks. The results do not change our conclusion that Bitcoin futures can hedge the risks in the Bitcoin spot market, and besides, the post-Covid-19 results indicate that the hedging ability of Bitcoin futures increased. Finally, we test whether the gold futures can be used as a Bitcoin spot market hedge, and we further control other cryptocurrencies to illustrate the hedging ability of the Bitcoin futures to the Bitcoin spot. Overall, the empirical results in this paper will surely benefit the related investors in the Bitcoin market.

The present study investigates the price (co)volatility of four dairy commodities—skim milk powder, whole milk powder, butter, and cheddar cheese—in three major dairy markets. It uses a multivariate factor stochastic volatility model for estimating the time‐varying covariance and correlation matrices by imposing a low‐dimensional latent dynamic factor structure. The empirical results support four factors representing the European Union and Oceania dairy sectors as well as the milk powder markets. Factor volatilities and marginal posterior volatilities of each dairy commodity increase after the 2006/07 global (food) crisis, which also coincides with the free trade agreements enacted from 2007 onward and EU and US liberalization policy changes. The model‐implied correlation matrices show increasing dependence during the second half of 2006, throughout the first half of 2007, as well as during 2008 and 2014, which can be attributed to various regional agricultural dairy policies. Furthermore, in‐sample value at risk measures (VaRs and CoVaRs) are provided for each dairy commodity under consideration. [ABSTRACT FROM AUTHOR]

This paper investigates the extent of volatility or risk spillovers between the currency carry trade and asset markets, namely the equity and bond markets, in South Africa to infer the extent of the connectivity between the two markets. The carry trade operation examined in this paper involves two strategies, both of which use the South African rand as the investment currency, with the U.S. dollar and the Japanese yen as the funding currencies. The vector autoregressive BEKK-Generalised Autoregressive Conditional Heteroscedastic (multivariate VAR-BEKK-GARCH) model is used to this end. Moreover, the paper assesses the dynamic correlation between each currency carry trade and asset markets to infer the time-varying dependence between the two markets. The results of the empirical analysis show evidence of volatility spillover between the carry trade returns and the two asset market returns. The extent of the spillover depends on the choice of the funding currency, with the U.S. dollar-funded strategy transmitting more shocks to the South African equity market compared to the bond market. Moreover, the synchronisation of the dynamic correlation between each asset market and the currency carry trade returns shows that any possibility of arbitrage is precluded in the currency carry trade market. [ABSTRACT FROM AUTHOR]

The current literature concentrates its attention to the interactions between oil and exchange rates, focusing only on the first moments. Extending this line of research, we investigate the time‐varying correlation between the volatilities of two oil benchmarks (Brent and WTI) and six currencies of the major oil‐importers and oil‐exporters, for the period from February 1, 1999 to May 30, 2016, using a Diag‐BEKK model. The optimal portfolio weights and hedge ratios for portfolios comprised of the aforementioned volatilities, are also examined. The analysis reveals that oil and currency volatilities exhibit positive correlations during major global or region‐specific economic events (such as the Global Financial Crisis of 2007–2009 and the EU debt crisis period). By contrast, country‐specific events yield heterogeneous time‐varying correlations between oil and the different currencies in our sample. Both the optimal portfolio weights and optimal hedge ratios estimations demonstrate a time‐varying behaviour, suggesting that continuous portfolio rebalancing is necessary for diversification purposes. The findings also show that risk reduction based on the optimal portfolio weight strategy is primarily beneficial for oil volatility investors, whereas currency volatility investors achieve better hedging using the optimal hedge ratio strategy. [ABSTRACT FROM AUTHOR]

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In this paper, we examine the dynamic correlations between mass-media news and new-media news as well as their reprints in the Chinese stock market. We mainly find that: (1) there is a significant positive correlation between the four types of news and the correlation between reprints and their congener media news is stronger; (2) both the mass-media news and new-media news have significant positive autocorrelation. The new-media news and the reprints rely more on mass-media news. (3) both mass-media news and new-media news will make a positive response to the corporate events, but the reaction of new-media news including the new-media reprints is stronger than mass-media; (4) the results of dynamic correlation analysis indicate that there is a significant correlation between the four types of news over time. The correlation between mass-media news and new-media news is stronger when the stock market performs well. We attribute these results to the symbiotic relationship between mass media and new media in the Chinese stock market, where mass media devotes to provide original news with high quality and new media transfers these numbers of news to more readers to attract more attention and discussion.

We investigate whether the inclusion of Cat Bonds in portfolios composed of traditional assets and common factors is beneficial to investors. Various mean‐variance spanning tests performed for the period of 2002 to 2017 show that under different market conditions, the addition of Cat Bonds gives rise to previously unattainable portfolios. Using the Engle (2002) Dynamic Conditional Correlation (DCC) model, we find that including Cat bonds increases significantly the time‐varying Sharpe ratio and the Choueifaty and Coignard (2008) maximum diversification ratio. Cat Bonds provide needed diversification during critical times particularly during episodes of crisis and of high volatility. Under the second‐order stochastic dominance efficiency (SDE) tests, the null hypothesis that portfolios without Cat Bonds are efficient cannot be rejected. Out‐of‐sample analyses indicate that the performance of portfolios with Cat Bonds included varies depending on the performance measures employed, the portfolio construction techniques used and the assets or factors considered. [ABSTRACT FROM AUTHOR]

The variational determination of the second‐order reduced density matrices arising from N‐electron doubly occupied configuration interaction wave functions has recently been proposed as a method to describe systems possessing static correlation in their ground states. In this work, we propose to combine this approach with the on‐top pair density functional theory in order to incorporate dynamic correlations and improve the description of systems possessing both types of electron correlation. The procedure requires ensuring that the second‐order reduced density matrix elements satisfy determined N‐representability variational conditions, as well as other constraints arising from the doubly‐occupied configuration interaction wave function features. An analysis of the results obtained through this method, in studies of strongly correlated many‐electron systems, shows the usefulness of our proposals. [ABSTRACT FROM AUTHOR]

The existing studies on environmental efficiency evaluation generally have the problem of efficiency overestimation. To solve this problem, a new data envelopment analysis (DEA) cross‐efficiency approach with undesirable outputs is developed to evaluate environmental efficiency from the perspectives of both self‐evaluation and peer evaluation. Then, three new evaluation strategies, namely, economic development strategy, environmental protection strategy, and win–win strategy, are proposed to reflect the needs of decision makers under different policy objectives. The proposed cross‐efficiency approach with different evaluation strategies not only realizes the cross evaluation of environmental efficiency, but also guarantees the relative uniqueness of the optimal solution on the basis of the preferences of decision makers. Combining the metafrontier DEA approach and DEA window analysis, a new cross‐efficiency analytical framework is constructed to gradually analyse the influences of policy objectives, technology heterogeneity, and dynamic correlation on the environmental efficiency. Subsequently, the environmental efficiency of China's economic development during 2006–2015 is in‐depth analysed on the basis of the proposed analytical framework, and some interesting conclusions, and some useful suggestions are obtained. [ABSTRACT FROM AUTHOR]

In this paper, a two-product newsvendor problem is taken into consideration, where the demands of products are correlated random variables and the buyer is risk-averse. Some important qualitative properties of the constructed model are analyzed, particularly the gradient information of the model is obtained and incorporated into solution method of the model. Based on the theory of Copulas, an efficient algorithm, called the feasible-direction based BFGS algorithm, is developed for solution of the constrained optimization model. Case study shows the efficiency of model and algorithm, and numerical results demonstrate that compared with the situation of independent demands, the total order quantity reduces against the high dynamic interrelation coefficient of two demands with the same degree of risk-aversion, and the optimal order quantity decreases as the degree of risk-aversion becomes greater. [ABSTRACT FROM AUTHOR]

This paper explores the dynamic correlation and risk contagion between "black" futures in China at various time horizons. We employ the DCC-GARCH-t model and Granger causality in risk test jointly with variational modal decomposition. Our study covers the period from October 2013, to January 2018. The paper's three key findings are as follows: first, a positive dynamic correlation exists between "black" futures across most sample period at each time scale. Secondly, dynamic correlation differs between "black" futures, which is largest during the medium-term time scale. What's more, the correlation of coking coal futures and coke futures is consistently higher than other pairs at each time scale. Thirdly, the direction and the time lag of risk contagion varies across different time scales. The complexity of contagion will increase as the length of the time scale increases. We have also discovered some synchronic contagions between "black" futures. [ABSTRACT FROM AUTHOR]

This study quantifies the dynamic interrelationship between the KOSPI index return and search query data derived from the Naver DataLab. The empirical estimation using a bivariate GARCH model reveals that negative contemporaneous correlations between the stock return and the search frequency prevail during the sample period. Meanwhile, the search frequency has a negative association with the one-week- ahead stock return but not vice versa. In addition to identifying dynamic correlations, the paper also aims to serve as a test bed in which the existence of profitable trading strategies based on big data is explored. Specifically, the strategy interpreting the heightened investor attention as a negative signal for future returns appears to have been superior to the benchmark strategy in terms of the expected utility over wealth. This paper also demonstrates that the big data-based option trading strategy might be able to beat the market under certain conditions. These results highlight the possibility of big data as a potential source-which has been left largely untapped-for establishing profitable trading strategies as well as developing insights on stock market dynamics.