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This paper illustrates the impact of the rising prices of the main natural resources used in the production process, oil, and natural gas, on the eurozone economy using a Vector Autoregressive (VAR) model. It also assesses the dependence of the eurozone economy on fossil fuels from Russia and the need to adopt green, renewable energy. The study compares the shocks administered pre/post the outbreak of the Covid-19 pandemic and the outbreak of the war in Ukraine. Finally, based on the futures trajectory of the Brent oil price, a conditional forecast for macroeconomic variables in the euro area is assessed.

This paper quantitatively investigated the historical transition of return transmission, volatility spillovers, and correlations between the US, UK, and Japanese stock markets. Applying a vector autoregressive (VAR)-dynamic conditional correlation (DCC)-multivariate exponential generalized autoregressive conditional heteroscedasticity (MEGARCH) model, we derived new evidence for four historical periods between 1984 and 2024. First, we found that the return transmission from the US to the other markets has historically become stronger, whereas recently, the return transmission from the UK to the US has disappeared. Second, we clarified that volatility spillovers from the US to the other markets have historically become stronger, whereas recently, volatility spillovers from the UK to the US have also disappeared. Third, our analyses of the historical constant correlations and DCCs revealed that stock market connectedness has gradually tightened between the US and Japan and between the UK and Japan, whereas recently, the connectedness between the US and UK has weakened. Fourth, our VAR-DCC analyses also revealed that volatility spillovers between the US, UK, and Japanese stock markets have been asymmetric. Fifth, we further showed that the skew-t errors incorporated into our VAR-DCC model are effective in estimating the dynamic stock return linkages between the US, the UK, and Japan. Finally, based on our findings, we derived many significant and beneficial interpretations and implications for historically and deeply considering return transmission, volatility spillovers, and DCCs between international stock markets.

This paper examines the factors influencing Kosovo's economic growth from 2009 to 2022, specifically investigating the relationship between export, capital formation, consumption, and economic growth using co-integration analysis and the Vector Autoregressive Model (VAR). The findings indicate that exports of goods and services, as well as household consumption, negatively affect economic growth. Conversely, gross capital formation positively impacts economic growth. The study underscores the complexity of economic growth, highlighting the varied significance of different determinants in different contexts. Key findings reveal that while export and gross capital formation are significant contributors to economic growth, household consumption shows an insignificant relationship to GDP. This research contributes to the ongoing debate on the critical factors influencing economic growth, providing empirical evidence from the context of Kosovo and enhancing our understanding of these dynamics, thus offering new insights for policymakers.

This study investigates the impact of inflation rate fluctuations on economic growth in China, with a particular focus on potential non-linear characteristics. The global economic impact of the COVID-19 pandemic notably heightens the study's relevance. The research that the unidirectional causal relationship from inflation to economic growth in China first strengthens and then weakens over time. Furthermore, there is an inflation rate threshold effect on economic growth, identified at 2%. Below this threshold, inflation positively influences economic growth, whereas above it, the impact turns negative. This finding underscores the importance of balancing economic growth with inflation control in the formulation of monetary policy.

In the present study, the effect of the alternative magnetic field (AMF) on the metal pool shape, primary carbides formation, and mechanical properties during vacuum arc remelting (VAR) process was investigated by experiments and numerical simulations. Without AMF, the original depth of the molten pool was 87.74 mm. The application of the AMF decreased the depth of the metal pool, to various degrees (86.46 mm at 40 Gs 0.5 Hz, 78.96 mm at 40 Gs 0.1 Hz, 69.82 mm at 40 Gs 0.05 Hz). As a consequence, the primary carbides became more refined average size reduced by 20.66%∼25.72%. After modification, uniformly distributed hardness and improved wear resistance was achieved. Meanwhile, compared with 0.5 and 0.1 Hz cases, the strengthening effect was more obvious at 0.05 Hz. The numerical simulation results showed that, the AMF and the melting current in the molten steel produced Lorentz force, which caused the horizontal circulation. Therefore, the temperature of metal pool became more homogenized under the horizontal circulation. When the frequency was 0.05 Hz, the horizontal circulation in liquid steel was more intense, which makes the temperature field more uniform. Horizontal circulation can also reduce the segregation of alloying elements, which restrains the precipitation condition and refines primary carbides. The hardness uniformity can also be attributed to the uniform distribution of alloying elements. The improvement in wear resistance is attributed to the refinement of the primary carbide, thereby reducing the occurrence of primary carbide spalling during friction.

Abstract Investigating interplay between urbanization and carbon emissions is crucial for reaching carbon peak objective. This study employs a VAR model to examine correlation between the urbanization rate and carbon emissions specifically within Guizhou Province, VAR model has obvious advantages in studying the dynamic relationship between them. The findings indicate that: (1) In Guizhou Province, there is a nuanced interplay between the urbanization rate and carbon emissions, with the magnitude and direction of their influence varying across different time intervals. (2) Carbon emissions in Guizhou Province exhibit a notable self-propelling effect, while concurrently, the urbanization rate demonstrates an inertia effect, which also contributes to its own advancement. (3) The influence of the urbanization rate on carbon emissions in Guizhou Province experiences gradual rise before plateauing, suggesting that the high-quality advancement of new urbanization in the region facilitates the achievement of carbon reduction objectives. Finally, policy recommendations are put forward: (1) Conscientiously implement the central ecological environment zoning control policies, such as: Guizhou Province Ecological environment zoning control Plan and Guizhou Province Urban and Rural Construction Carbon peak Implementation Plan and other policies. (2) Pay attention to the quality of Guizhou’s urbanization process. Solve the relationship between urbanization and carbon emissions, and realize the coordination and unification of urbanization and the carrying capacity of resources and environment. (3) Develop a new type of urbanization rich in Guizhou’s mountainous characteristics and promote the construction of low-carbon cities. Give full play to the regional characteristics of Guizhou’s mountainous areas, build a new type of urbanization with Guizhou’s mountainous characteristics, promote the construction of low-carbon cities in the process of urbanization development, and strengthen the coordinated development of ecological environment construction and urbanization.

This study focuses on improving the FAST-ICA algorithm and GARCH model to more accurately analyze and predict the value at risk of financial stocks. Accurately measuring stock risk is crucial for investors and securities managers in today's financial markets, as it directly affects investment decisions and risk control. We have improved the FAST-ICA algorithm and proposed the TS-ICA algorithm, aiming to improve the separation performance and iteration efficiency of the algorithm. And we combine the TNA method for data preprocessing to eliminate noise and improve the robustness and prediction accuracy of the model. In terms of GARCH model, we constructed the TS-ICA-GARCH model using the independent decomposition results of the TS-ICA algorithm to more accurately predict the volatility and value at risk (VaR) of stocks. Through empirical analysis and backtesting, we have verified the superiority of the TS-ICA-GARCH model in measuring risk, especially in extreme market conditions with stronger coping ability. This study provides a more reliable risk assessment tool for financial market participants, which helps to develop more effective investment strategies and risk management measures. [ABSTRACT FROM AUTHOR]

The half-normal distribution is composited with the Pareto model to obtain a uni-parametric distribution with a heavy right tail, called the composite half-normal-Pareto distribution. This new distribution is useful for modeling positive data with atypical observations. We study the properties and the behavior of the right tail of this new distribution. We estimate the parameter using a method based on percentiles and the maximum likelihood method and assess the performance of the maximum likelihood estimator using Monte Carlo. We report three applications, one with simulated data and the others with income and expenditure data, in which the new distribution presents better performance than the Pareto distribution. [ABSTRACT FROM AUTHOR]

The Video Assistant Referee (VAR) was introduced into football in order to assist referees in their decision making during the match. The English Premier League (EPL) introduced VAR in the 2019-2020 season. The aim of this study was to explore the influence of the introduction of VAR on the English Premier League. The sample includes all 380 matches played in the 2018-2019 season without VAR and all 380 matches played in the 2019-2020 season with VAR. The following nine variables were collected for each match: the first half time, the second half time, total time, goals, penalties, fouls, yellow cards, red cards and offsides. These variables were compared pre and post the introduction of VAR using means comparison and Mixed Linear Model (MLM). The results demonstrated that after the introduction of VAR, the first half time, second half time, total time and fouls increased significantly (p< 0.01). In contrast, a significant decrease (p< 0.01) was found in the number of offsides after the implementation of VAR. The findings may help football practitioners and fans better understand the impact of VAR on European top professional football leagues, particularly on the English Premier League and also help to optimise referees' officiating strategies. [ABSTRACT FROM AUTHOR]

Modeling surface water quality has become crucial in providing better strategies for managing surface water resources, and adequate findings need accurate and geographically dispersed data. Hydrogeological modeling of these data sets is possible using empirically-based models. The other statistical models are also an alternative approach. In this study, a process with maximum probability is considered with the help of machine learning tools (MLT) to have optimized and valid output. The proposed method combines remote sensing and geographic information systems (RS and GIS) and MLT, which are appropriate for the predicament of neither small, large scale, nor long-term simulations. MLT methods such as VAR and ARIMA are developed in the Python programming with Jupyter notebook and tested according to the data in the spatial prediction for surface water quality parameters such as Tr, pH, Ec, TDS, AL, Ca++, NO−3, So, Cl, F−, Fe, and Mg2+ in the Krishna District, Andhra Pradesh, India—lower delta part. The delta with susceptible zones was identified using RS and GIS as those areas are prone to direct exposure to surface water contaminants from aquaculture, agricultural runoff, small- and medium-scale businesses, and household trash. Achieving effective surface water management for this ecosystem is critical for regional water management. The geographical information about the concentrations acquired via the RS and GIS was compared to the statistical modeling findings and verified using real-time measurements. MLT modeling seems more realistic than the experimental setting; data from the previous 20 years (2000–2020) were used for modeling, and the predicted values presented in the paper are predicted for the year 2021. The computed R2 value of ranges between 0.75 and 0.96% is recorded with ARIMA, and VAR posted range between 0.56 and 0.75% with the trained and tested data. The findings show the potential for MLT of geographically dispersed hydrogeological data to be used for pollution-free surface water management. From the surface water management perspective, combining RS and GIS and MLT offers an alternate data analysis approach for obtaining quick results utilizing a less laborious process that produces acceptable results. [ABSTRACT FROM AUTHOR]

This research investigates the causal relationship between innovation, financial development and economic growth in Brazil, Chile, Colombia, Mexico and Peru between 2000 and 2019. Based on quantitative analysis, including vector autoregressive (VAR) models, it can be concluded that bidirectional Granger-causalities are present in the trivariate nexus in the five Latin American countries over the investigated times. Consequently, the three variables support forecasting and policy implications focusing on one of the three sectors that impacts the other two in the future. The paper concludes that imitation and innovation policies focusing on intellectual property rights protection, education, knowledge, institutional change and technological catch-up are necessary to foster economic growth and financial development. [ABSTRACT FROM AUTHOR]

Dairy farming plays a crucial role in Saudi Arabia's agricultural industry. However, the intensive milk production process exerts pressure on local water and energy resources. This study aims to examine the impact of water stress and renewable energy consumption shocks on milk production in Saudi Arabia by using data from 2000 to 2021. The empirical analysis used the VAR model, Granger causality, forecast error variance decompositions (FEVDs), and impulse response functions (IRFs). The presence of a negative significant interdependence between total milk production and water stress levels in agriculture was observed. Significant bidirectional causality relationships among the variables were noted. The FEVD results show that water stress levels in agriculture are becoming a more dominant driver of variations in total milk production in Saudi Arabia, while the empirical evidence of the IRFs implies that milk production increases when both water stress levels and renewable energy are present. The adoption of water recycling and reuse systems on dairy farms can help farmers to improve water use efficiency. The encouragement of decision makers to formulate policies to support sustainable water resource management, reduce environmental impact, accelerate technological advancements, and initiate positive socioeconomic outcomes for the dairy industry is highly recommended. [ABSTRACT FROM AUTHOR]

This study investigates the effects of health care policy uncertainty (HCPU) on aggregate- and state-level employment in the USA. Using quarterly data during 1985Q1–2021Q4, we estimate a structural vector autoregressive model and find that HCPU has adverse effects on both aggregate- and state-level employment. The effects at the state level are heterogeneous in terms of both the magnitude and the impact persistence, lasting up to 5 quarters. The heterogeneity can be explained by state-specific structural factors, most notably the right-to-work legislation. The study suggests that uncertainty in health care policies has an economic cost that may be partially preventable. [ABSTRACT FROM AUTHOR]

Investigating interplay between urbanization and carbon emissions is crucial for reaching carbon peak objective. This study employs a VAR model to examine correlation between the urbanization rate and carbon emissions specifically within Guizhou Province, VAR model has obvious advantages in studying the dynamic relationship between them. The findings indicate that: (1) In Guizhou Province, there is a nuanced interplay between the urbanization rate and carbon emissions, with the magnitude and direction of their influence varying across different time intervals. (2) Carbon emissions in Guizhou Province exhibit a notable self-propelling effect, while concurrently, the urbanization rate demonstrates an inertia effect, which also contributes to its own advancement. (3) The influence of the urbanization rate on carbon emissions in Guizhou Province experiences gradual rise before plateauing, suggesting that the high-quality advancement of new urbanization in the region facilitates the achievement of carbon reduction objectives. Finally, policy recommendations are put forward: (1) Conscientiously implement the central ecological environment zoning control policies, such as: Guizhou Province Ecological environment zoning control Plan and Guizhou Province Urban and Rural Construction Carbon peak Implementation Plan and other policies. (2) Pay attention to the quality of Guizhou's urbanization process. Solve the relationship between urbanization and carbon emissions, and realize the coordination and unification of urbanization and the carrying capacity of resources and environment. (3) Develop a new type of urbanization rich in Guizhou's mountainous characteristics and promote the construction of low-carbon cities. Give full play to the regional characteristics of Guizhou's mountainous areas, build a new type of urbanization with Guizhou's mountainous characteristics, promote the construction of low-carbon cities in the process of urbanization development, and strengthen the coordinated development of ecological environment construction and urbanization. [ABSTRACT FROM AUTHOR]

For fulfilling the objectives of the economic growth, policy makers mostly rely on the effects of fiscal policy measures and instruments, to strive for its improvement and harmonization. In this regard, the main aim of this paper is investigation of the interrelationship and connection between the fiscal policy and its impact on the unemployment rate and economic growth, focusing the analysis in the Republic of North Macedonia. Since there is no universal model for the application of measures and instruments of fiscal policy and their combination, this paper highlights the application of the measures of these policies in developing countries such as the Republic of North Macedonia, in order to achieve financial stability. By applying the VAR technique, this paper analyzes the effects of the public revenues and expenditures on the unemployment rate and GDP growth in the Republic of North Macedonia for the time period 2000q1 - 2022q4. Finally, the results imply that fiscal policy has a significant effect on the economic growth of the Republic of North Macedonia, while there is no evidence for a significant nexus between unemployment rate and public revenues and expenditures in the Republic of North Macedonia. [ABSTRACT FROM AUTHOR]

Should the South African Reserve Bank (SARB) lower the inflation target (IT) band? Does lowering the IT band impact the relationship between GDP growth and inflation? This paper explores these questions considering the SARB Governor, Lesetja Kganyago statements that there is a need to lower the IT band from 3–6% to a point target of 3%. We estimate the VAR model to determine whether the passthrough of positive GDP growth shocks to inflation is nonlinear in South Africa. The inflation effects are delineated into bands (i) above 6% (ii) between 4.5% and 6% (iii) between 3% and 4.5% (iv) between 0% and 3% and (v) when there are no IT bands. Evidence reveals that the passthrough is elevated when inflation exceeds 6% and is lower when inflation is within the (i) 3 to 4.5% and (ii) 0 to 3% IT bands. The passthrough from positive GDP growth shocks is more than halved when inflation is less than 3%. The policy implication is that lowering the IT band from 3 to 6% to 0 to 3% will reduce the passthrough of GDP growth shocks to inflation. It allows expansionary monetary to have more real effects as prices are more rigid in the low inflation environment. [ABSTRACT FROM AUTHOR]

This paper illustrates the impact of the rising prices of the main natural resources used in the production process, oil, and natural gas, on the eurozone economy using a Vector Autoregressive (VAR) model. It also assesses the dependence of the eurozone economy on fossil fuels from Russia and the need to adopt green, renewable energy. The study compares the shocks administered pre/post the outbreak of the Covid-19 pandemic and the outbreak of the war in Ukraine. Finally, based on the futures trajectory of the Brent oil price, a conditional forecast for macroeconomic variables in the euro area is assessed. [ABSTRACT FROM AUTHOR]

This study examines the dynamics between treasury and market capitalization in two Decentralized Autonomous Organization (DAO) projects: OlympusDAO and KlimaDAO. This research examines the relationship between market capitalization and treasuries in these projects using vector autoregression (VAR), Granger causality, and Vector Error Correction models (VECM), incorporating an exogenous variable to account for the comovement of decentralized finance assets. Additionally, a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is employed to assess the impact of carbon offset tokens on KlimaDAO's market capitalization returns' conditional variance. The findings suggest a connection between market capitalization and treasuries in the analyzed projects, underscoring the importance of the treasury and carbon offset tokens in impacting a DAO's market capitalization and variance. Additionally, the results suggest significant implications for predictive modeling, highlighting the distinct behaviors observed in OlympusDAO and KlimaDAO. Investors and policymakers can leverage these results to refine investment strategies and adjust treasury allocation strategies to align with market trends. Furthermore, this study addresses the importance of responsible investing, advocating for including sustainable investment assets alongside a foundational framework for informed investment decisions and future studies in the field, offering novel insights into decentralized finance dynamics and tokenized assets' role within the crypto-asset ecosystem. [ABSTRACT FROM AUTHOR]

Copyright of Latin American Journal of Economic Developement (LAJED) is the property of Universidad Catolica Boliviana San Pablo 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.)

AbstractBackground: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing. [ABSTRACT FROM AUTHOR]

The media, speculators, investors, and governments throughout the world have all become increasingly interested in cryptocurrencies in recent years. The price swings of cryptocurrencies are notoriously unstable and have a high level of volatility. This study focused on modeling that volatility of cryptocurrencies, the purpose of this study is to identify the most suitable or appropriate innovation distribution and different GARCH Models to model the returns of the most popular cryptocurrencies. The majority of our work was focused on the top ten cryptocurrencies, but we also extended our analysis to 377 cryptocurrencies. To describe the time dependent volatility of the cryptos, we utilize eleven different GARCH models, including the sGARCH, iGARCH, GJRGARCH, eGARCH, tGARCH, AVGARCH, CSGARCH, ALLGARCH, NGARCH, APARCH, and NAGARCH. For the research period of September 14, 2014 to November 10, 2022, the daily closing prices of cryptocurrencies are collected. The underlying innovation(error) distribution are assumed to be from one of the following eight distributions of Normal, Student's t, Generalized Error, Skew Normal, Skew Student's t, Skew Generalized error, Normal Inverse Gaussian and Generalized Hyperbolic Distribution. Each GARCH-type model was fitted with this eight innovations. [ABSTRACT FROM AUTHOR]

The implementation of Video Assistant Referees (VARs) in 2018 has had a significant impact on the multi-billion-dollar soccer industry. As the most popular and watched sport globally, soccer's financial stakes are high, with clubs, leagues, broadcasters, sponsors, and fans heavily invested in the game. The ongoing debate surrounding the VAR system brings to light the intricate balance between preserving the authenticity of football (soccer) and harnessing technology to improve accuracy. It is crucial to strike the right equilibrium in order to uphold football's metaphorical power and sustain the timeless joy it has brought to fans throughout generations. In this context, Immanuel Kant's philosophy can offer valuable insights into the utilization of VARs in soccer. According to Kantian ethics, using VARs can be justified if it serves to enhance fairness and accuracy, aligning with the moral duties of referees. Nevertheless, it is important to consider the potential dehumanizing effects and the necessity of preserving the value of human judgment in the game. Therefore, this paper aims to explore in-depth the intricate dynamics that arise when technology is integrated into traditional practices, emphasizing the significance of critical reflection on the implications of such advancements. [ABSTRACT FROM AUTHOR]

The persistently high inflation since the COVID-19 pandemic, including its strong upward trend in 2020-2023 in Europe and the USA, raises many questions as to the causes for such a situation. Evidently, the problem lies in the persistent inflation expectations of enterprises (in light of the overlapping effects of the energy crises and the outbreak of the war in Ukraine) and increased markups as the response of enterprises to future cost increases. Empirical data indicates that the dynamics of markups in individual economic sections are diversified like never before. All this creates a research gap, which this paper aims to fill. Therefore, the aim of this study is to diagnose the impact of markups on changes in the consumer price index (CPI) in Poland in 2008-2023, identify the markups with the strongest impact, and determine changes in the competitiveness of the economy compared to EU countries. Markups were divided into nine main groups as per NACE classification of economic activity. The impact of markups on CPI changes was assessed using the VAR model. The results indicate that markups in the mining and quarrying (B) and the real estate market service (L) had the greatest pro-inflationary impact on CPI changes and explained about 30% of all CPI changes in Poland. The research results are useful to those formulating the monetary policy, as identifying key sectors whose markup policies explain the changes in CPI is crucial to determining the optimal actions and policy measures. [ABSTRACT FROM AUTHOR]

This paper quantitatively investigated the historical transition of return transmission, volatility spillovers, and correlations between the US, UK, and Japanese stock markets. Applying a vector autoregressive (VAR)-dynamic conditional correlation (DCC)-multivariate exponential generalized autoregressive conditional heteroscedasticity (MEGARCH) model, we derived new evidence for four historical periods between 1984 and 2024. First, we found that the return transmission from the US to the other markets has historically become stronger, whereas recently, the return transmission from the UK to the US has disappeared. Second, we clarified that volatility spillovers from the US to the other markets have historically become stronger, whereas recently, volatility spillovers from the UK to the US have also disappeared. Third, our analyses of the historical constant correlations and DCCs revealed that stock market connectedness has gradually tightened between the US and Japan and between the UK and Japan, whereas recently, the connectedness between the US and UK has weakened. Fourth, our VAR-DCC analyses also revealed that volatility spillovers between the US, UK, and Japanese stock markets have been asymmetric. Fifth, we further showed that the skew- t errors incorporated into our VAR-DCC model are effective in estimating the dynamic stock return linkages between the US, the UK, and Japan. Finally, based on our findings, we derived many significant and beneficial interpretations and implications for historically and deeply considering return transmission, volatility spillovers, and DCCs between international stock markets. [ABSTRACT FROM AUTHOR]

The research is intended to determine factors affecting Investors at Pakistan Stock exchange, and for this purpose four macroeconomic variables; interest rate, inflation rate, exchange rate and FDI were selected as predictors and return of KSE 100 was included as a dependent variable. Meanwhile, the time frame of the study was from January 2001 to December 2015 and the frequency of the data was monthly. The preliminary testing and diagnostic analysis suggested towards Johansen Cointegration which further suggested VECM and Granger causality. Empirical findings of the study suggest that there are cointegration equations within the variables, implying that variables can be used to perform estimation and prediction of another variable. Meanwhile, VECM model reveals no significant effect of the macroeconomic variables on the stock market in long-run at lag 1; but at lag 2 interest rate and FDI shows a negative and significant effect on the KSE 100 returns. In addition to this, granger causality also shows no short-run bi-directional interrelation of macroeconomic variables with KSE 100 returns. Therefore, it is concluded that in Pakistan macroeconomic variables does not influence investor's behaviors and do not affect investing trend in KSE 100 index. Meanwhile, implications for policymakers, investors, regulatory authorities, government and for researcher have also been discussed in the paper. [ABSTRACT FROM AUTHOR]

This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The bivariate VAR system comprises asset returns and a further prediction variable, such as the dividend-price ratio, and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004. "Predictive Regressions: A Reduced-Bias Estimation Method." Journal of Financial and Quantitative Analysis 39: 813–41). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to a system comprising annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021, and the logarithmic dividend-price ratio. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed. Then, instead of the dividend-price ratio, some prediction variables proposed in Welch and Goyal (2008. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction." Review of Financial Studies 21: 1455–508) are used. Also with these prediction variables, only weak evidence for return predictability is supported by Bayesian testing. These results are corroborated with an out-of-sample forecasting analysis. [ABSTRACT FROM AUTHOR]

The depletion of conventional energy sources has led to an increase in interest in renewable energy across the globe. The usage of renewable energy has lowered economic risk in the electricity markets. This study presents an approach to utilize solar photovoltaic as a renewable energy source, fuel cells as the energy storage system, and Flexible AC Transmission networks (FACTS) to reduce system risk in deregulated networks. The difference between real and expected renewable energy data is the primary cause of disequilibrium pricing (DP) in the renewable energy-integrated system. Integration of the FCs with a Unified Power Flow Controller (UPFC) can play an important role in coping with the disequilibrium pricing, emphasizing optimizing profitability and societal welfare in a deregulated environment. The paper also evaluates the system voltage outline and LBMP (location-based marginal pricing) scenarios, both with and without the integration of solar power. Two distinct factors, i.e., Bus Sensitivity Index (BSI) and Line Congestion Factor (LCF), have been proposed to identify the key buses and lines for solar power and Unified Power Flow Controller installation in the system. The study also employs conditional-value-at-risk (CVaR) and value-at-risk (VaR) to assess the system’s risk. Using a real-time IEEE 39-bus New England system, multiple optimization algorithms including Sequential Quadratic Programming and the Slime Mould Algorithm (SMA) are employed to estimate the financial risk of the considered system. This analysis demonstrates that the risk coefficient values improve with the placement of UPFC and fuel cells in the renewable incorporated system.

This study investigates the VAR time series data of the overall expenditures and income in the Kurdistan ‎Region of Iraq. It applies multivariate wavelet shrinkage within the VAR model, comparing it to ‎traditional methods to identify the most appropriate model. The chosen model will then be used to ‎predict general expenditures and revenues for the years 2022-2026. The analysis involved assessing the ‎stationarity of the expenditure and revenue time series, which are interrelated variables during the ‎interval 1997-2021, and identifying the overall trend through differencing to achieve stationarity. The ‎proposed method incorporated multivariate wavelet shrinkage in the VAR model to address data ‎contamination in expenditures and revenue using various wavelets like Coiflets, Daubechies, Symlets, ‎and Fejér–Korovkin at different orders. Threshold levels were estimated using the SURE method and soft ‎thresholding rules to denoise the data for the following analysis within the VAR model. Model selection ‎was based on Akaike and Bayes information criteria. The analysis, conducted using MATLAB, indicated ‎the superiority of the proposed method over traditional methods, forecasting a continued rise in ‎expenditures and revenues for the Iraqi Kurdistan region from 2022 to 2026. The findings suggest that ‎advanced techniques can offer more accurate economic forecasts, benefiting regional planning and ‎policy-making.‎

This paper examines medium-term inflationary risks in the wake of the energy crisis. Firstly, the inflation spillovers between five major EU economies, viz. Germany, France, Italy, Spain, and Poland are analyzed using the Diebold and Yilmaz VAR framework. This analysis reveals that the interconnection between increases in inflation was stronger after the outbreak of the energy crisis. Poland and Spain have been transmitting inflation to the other countries under consideration. This impact is strongest when prices are “sticky”, i.e. when they are changed least frequently. Secondly, the impact of wage pressures in the Eurozone was analyzed with a special emphasis on the Netherlands on account of its historically high frequency of wage strikes. The data show that wage pressures from that country precede similar changes elsewhere in the eurozone. These two factors suggest that returning inflation to central bank targets in Europe is going to be a slow process.

Abstract The structural modeling of open-high-low-close (OHLC) data contained within the candlestick chart is crucial to financial practice. However, the inherent constraints in OHLC data pose immense challenges to its structural modeling. Models that fail to process these constraints may yield results deviating from those of the original OHLC data structure. To address this issue, a novel unconstrained transformation method, along with its explicit inverse transformation, is proposed to properly handle the inherent constraints of OHLC data. A flexible and effective framework for structurally modeling OHLC data is designed, and the detailed procedure for modeling OHLC data through the vector autoregression and vector error correction model are provided as an example of multivariate time-series analysis. Extensive simulations and three authentic financial datasets from the Kweichow Moutai, CSI 100 index, and 50 ETF of the Chinese stock market demonstrate the effectiveness and stability of the proposed modeling approach. The modeling results of support vector regression provide further evidence that the proposed unconstrained transformation not only ensures structural forecasting of OHLC data but also is an effective feature-extraction method that can effectively improve the forecasting accuracy of machine-learning models for close prices.