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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]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

This study aims to analyze the effect of oil price volatility, gold price volatility and exchange rate volatility on the dynamic relationship between Indonesian and United States capital market. The data used in this study are daily closing prices of oil, gold and exchange rates (USD/IDR) as well as Indonesian capital market (JKSE) and United States capital market (DJIA) composite indices during period of January 2005 to October 2020. This study uses DCC-GARCH method to calculate the dynamic correlation between two capital markets and GARCH with the GED parameter to analyze oil volatility, gold volatility and exchange rate volatility on the integration of Indonesian capital market and United States capital market. The results of this study show positive and strong results on the integration of Indonesian and United States capital markets, thus proving that the movements of Indonesian market and American market tend to be strong and mutually influence the two capital markets. Moreover, the oil, gold and exchange rates volatilities have a negative effect on the integration of the Indonesia capital market and the US capital market. This finding implies investors should take oil, gold and exchange rates volatilities in their investment consideration.

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]

Copyright of China Forestry Economy is the property of China Forestry Economy Magazine Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Using the monthly data of the returns of 5 assets during 05/31/2011 to 02/28/2021, the volatilities of Iranian asset markets have been modeled in this paper. Factor multivariate stochastic volatility model in the framework of space-state approach is the basis for decomposing the asset market volatility into two components, “volatility rooted in latent factors” and “idiosyncratic volatility” and estimating time-varying covariance matrix and dynamic pair-wise correlation of time series. The findings reveal that: first, there are two latent factors. Second idiosyncratic volatilities of 3 assets, including stock, dollar and gold have increased since mid-2017, and emerges evidence of clustering behavior. Third, the volatility of inflation is explained by the hidden factors, and consequently the idiosyncratic volatility is almost smooth. Fourth, the volatility of stock return is highly correlated with the volatilities of inflation and dollar. In addition, there is a significant pairwise correlation between inflation-dollar and inflation-interest rate.

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.

Copyright of Investigación Económica is the property of Universidad Nacional Autonoma de Mexico, Facultad de Economia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

Objective: Modelling dynamic nature of covariance of assets return almost always is a challenging area of finance. Econometrics models just pay attention to variance behavior longitudinally, however, core of numerous finance models need the analysis of the total covariance structure of returns. Method: Among the first models that analyze covariance behavior are multivariate GARCH models which were criticized for the need to estimate a large number of parameters. This paper is aimed to investigate the effect of stock return dynamic correlation structure on systematic risk, idiosyncratic risk and average stock return. To this end, a sample of 148 listed companies in Tehran Stock Exchange is examined during 2003 to 2014. GARCH framework is used for testing this claim. Results: According to the results, securities that were highly correlated with market wide risk factors in the past are likely to have low systematic risk, idiosyncratic risk and average return at present. It can be expected there is significant relationship between idiosyncrstic risk and correlation for lower turnover stock (information transparency proxy) although there is no relationship for smaller firms.