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In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

This investigation aims to determine the effect of trade uncertainty on domestic investment (DI) and foreign direct investment (FDI) for the Turkish economy from the first quarter of 2005 to the first quarter of 2020.

The authors adopt the vector autoregression (VAR) model augmented with Fourier terms. Using this methodology, the authors obtain the empirical results of the impulse-response functions and the variance decomposition analysis.

The empirical results demonstrate that a shock to trade uncertainty has a slight negative impact on DI for up to approximately 1.5 years, whereas its impact on FDI is negative but long-lasting. Moreover, the contribution of trade uncertainty to FDI is relatively higher than to DI in the error variance decomposition for the investigated period. These empirical results can be beneficial for shaping the Turkish authorities' trade policies in the following periods.

These findings have implications within the macroeconomic setting. Government authorities can provide tax exemptions for specified sectors and debureaucratize investment processes for both domestic and foreign entrepreneurs. Additionally, institutional quality and property rights should be protected strictly and developed gradually.

This study is the first to examine the impact of world trade uncertainty on Türkiye’s DI and FDI. Because trade uncertainty might act as fixed costs, this creates the option value of waiting and seeing the market, and firms hesitate to incur investment.

The purpose of this paper is to explore and analyse the dynamic relationship between remittances inflows of Egyptians working abroad and asymmetric oil price shocks.

This study uses a vector autoregressive (VAR) model to explain the impulse response functions (IRFs) and the forecast error variance decomposition (FEVD). The rationale behind using these tools is its ability to examine the dynamic effects of our variables of interest.

The impulse response functions confirmed that remittance inflows have various responses to asymmetric oil price shocks. For instance, inflowing remittances increase in response to positive oil price shocks, while it decreases in response to negative oil price shocks. Also, the results indicate that the responses are significant in the short and medium-run and insignificant in the long run. The magnitude of these responses reaches its peak or trough in the third year. Further, the variance decomposition reveals that oil price decreases are more influential than oil price increases.

This means that remittances inflows in Egypt are pro-cyclical with oil price shocks. That explained by the fact that more than one-half of those remittances sent from GCC countries where real economic growth is very pro-cyclical with the oil prices. This empirical assessment will help policymakers to determine the behaviour of remittances and highlights the impact of different kinds of oil prices shocks on remittances. Unlike the little existing literature, this study is the first study applied the VAR model using a novel dataset spanning 1960-2016.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

The literature mostly investigates the business cycle transmission of the United Kingdom (UK) and France as a part of a wider group (e.g. European Exchange Rate Mechanism or G7), despite their historical links and regional significance. Thus, herein paper aims to analyse the inter-dependence of these economies and how a shock from one of them affects the other for the data since 1978 to 2019.

In this paper, first, preliminary statistics were calculated in order to describe the historical relationship between these countries. The econometric part estimates the vector auto-regression model (VAR) to assess the inter-dependence of the economies. VAR model allows further to inspect the impulse response functions that shows the shock dynamics from one country to another. In order to verify if a shock from one of the economies is important to another, the study uses granger causality test.

The study establishes a strong link between these countries. A business cycle is transmitted significantly between the economies of France and UK, with a single standard deviation shock from France resulting in a long term effect of 0.4% change in gross domestic product (GDP) of UK and 1% vice versa. Additionally changes in GDP of both of the countries significantly Granger-cause change to GDP of the corresponding economy.

This is the first empirical study investigating the business cycle transmission between France and UK and providing a quantitative assessment of their inter-dependence.

A promising solution to meet the challenge of sustainability and ensure the protection of the environment consists in acting considerably on the adoption and use of new information and communication technologies. The latter can act on the protection of the environment; completely change manufacturing processes into energy-efficient, eco-friendly techniques or influence institutions and governance. The article attempts to cover shortcomings in the literature by providing a couple of theoretical frameworks and grounded empirical proofs for the dissemination of green technologies and the interaction of the latter with institutional quality.

The sample is made up of 43 African countries covering the period 2000–2020 and a panel VAR modeling approach is employed.

Our results show that an attenuation of CO2 emissions amplifies the diffusion of digital technologies (mobile telephones and Internet). Efficiency in the institutional quality of African countries is mandatory for environmental preservation. Moreover, the provision of a favorable institutional framework in favor of renewable energy helps to stimulate environmental performance in African states.

This study complements the extant literature by assessing nexuses between green technology and CO2 emissions in environmental sustainability.

This paper aims to establish the determinants of production in the Spanish Designation of Origin (DO) area for Cava wine and forecasts sales to establish vineyard area variations that maintain market equilibrium.

By applying a vector autoregressive (VAR) model, the authors forecast demand and the consequent requirements for base wine production.

The results show that Cava sales determine the base wine supply. After forecasting demand and the consequent requirements for base wine, the authors’ results show that, to avoid oversupply, the vineyard area for Cava wine should not be increased.

The paper develops a simple and effective method for DOs affected by the current European wine plantation regulations to forecast from a supply and demand perspective and their surface needs in response to market changes.

This study contributes to the literature because, to the best of the authors’ knowledge, no other study has investigated the determinants of Cava supply and demand or defines a model to assess the effects of changes in growing areas. The model is applicable to other European protected designations of origin wines and would help policymakers to accurately establish vine planting authorizations.

This paper aims to determine causal relationships between the level of productive credit, real deposits and money demand – all of them in real terms – and Gross National Product between 2006 and 2020.

The vector autoregressive technique (VAR) was used, where data from real macroeconomic aggregates published by the Central Bank of Ecuador (BCE) are correlated, such as productive credit, gross domestic product (GDP) per capita, deposits and money demand.

The results indicate that there is no causal relationship, in the Granger sense, between GDP and financial activity, but there is between the growth rate of real money demand per capita and the growth rate of total real deposits per capita.

The study shows that bank credit mainly finances the operations of current assets and/or liabilities. In addition, economic agents use the banking system mainly to carry out transactional and precautionary activities.

This paper aims to examine the influence of big data (BD) on human resource management (HRM). It defines how these data can be a useful tool in the decision-making process of companies’ human resources to obtain a sustainable competitive advantage.

This paper emphasizes the need to develop a holistic approach to emphasize these relations. Starting from these observations, the document proposes empirical research employing Eurostat data to test the benefits of BD in HRM decisions that optimize the relationship between training, productivity, and well-being.

The findings estimate HRM decisions and their impact in a broader macroeconomic and microeconomic perspective.

BD research is emerging as a crucial discipline in human resources. To overcome this problem, the paper develops an analysis of the literature on cleaner production and sustainability context; it creates a conceptual framework to clarify whether the existing studies consider the growing intensity of BD on human resources.

Using composite indices, the paper examines the nexus between militarization, globalization and liberal democracy. The democratic peace theory, the conflict inhibiting effects of international trade – a key and dominant facet of globalization – and the democracy promoting globalization hypothesis form the theoretical underpinnings of the empirical investigation.

To probe into the issue at hand, the paper adopts a dynamic panel VAR estimation procedure. Given the usual data constraints, the sample consists of 113 countries, and the estimations span the period 1995–2019.

The findings from the dynamic panel VAR estimations suggest the presence of a negative and statistically significant nexus between the level of globalization and the level of militarization. No statistically traceable nexus between globalization and liberal democracy was found.

The findings offer empirical support to the hypothesis that the strong links of interdependence shaped by globalization reduce the need for military preparedness. The results lead to a tentative inference in favor of the doux commerce thesis. Nonetheless, given that the estimations span a historically specific period – the entire post-bipolar era – the inferences that stem from the findings should be treated with caution.

To the best of the authors’ knowledge, the composite indices Bonn International Centre for Conflict Studies (BICC) militarization index, the globalization index of the Swiss Economic Institute (Konjunkturforschungsstelle) (KOF), LibDem, polyarchy have not hitherto been jointly used in previous studies to examine the nexus between militarization, globalization and liberal democracy.