Search Results (309)

This study examines the impact of geopolitical risk (GPR) on the connectedness dynamics among the sovereign bonds of the emerging seven (E7) and the Group of Seven (G7) countries. Initially, a quantile-based vector-autoregressive (Q-VAR) connectedness approach is used to calculate the total connectedness index (TCI) among sovereign bonds under different market states. Then, the impact of GPR on the TCI at the median and tails is estimated to examine if GPR affects the TCI among sovereign bonds. Using daily yields from 30 January 2012, to 17 June 2024, the findings show that the GPR is one of the significant determinants of the TCI among sovereign bonds during normal and extreme market conditions. Other determinants of the TCI include yields on Treasury bills (T-bills), the exchange rate, and the financial market volatility index. The impact of GPR on the TCI varies significantly during different GPR episodes and bond market conditions. The effect of GPR on the TCI among sovereign bonds yields is higher during war times and when bond yields are average. These findings can be utilized by investors seeking to achieve international diversification and policymakers aiming to mitigate the effects of heightened geopolitical risk on financial stability. Furthermore, GPR can be used as an early signal tool for systematic tail risk spillovers among sovereign bonds. Full article

Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets during the Trump (2017–2020) and Biden (2021–2024) governments. The 48-week return forecast of the Bitcoin–Gold correlation was also conducted by using the Bayesian Structural Time Series (BSTS) model. Results indicate that Bitcoin was the most volatile asset, while the U.S. Dollar remained the least volatile under both regimes. Under Trump, U.S. Dollar significantly influenced Oil and Bitcoin while Bitcoin and Gold were negatively linked to Oil and positively associated with U.S. Dollar. An inverse relationship between Bitcoin and Gold also emerged. Under Biden, Bitcoin, Gold, and U.S. Dollar all significantly affected Oil with Bitcoin showing a positive impact. Bitcoin and Gold remained negatively correlated though not significantly, and the Dollar maintained positive ties with both. Forecasts show a positive link between Bitcoin and Gold in the coming year. However, Bitcoin does not exhibit consistent characteristics of a safe-haven asset during the U.S. presidential transitions examined, largely due to its high volatility and unstable correlations with a traditional safe-haven asset, Gold. This study contributes to the understanding of shifting relationships between digital and traditional assets across political regimes. Full article

This study investigates the dynamic relationship between maritime trade and cultural convergence between China and Saudi Arabia, with a particular focus on the roles of creative goods and information and communication technology (ICT) exports as proxies for sociocultural integration. Utilizing quarterly data from 2012 to 2021, the analysis employs the Toda–Yamamoto Granger causality approach within a Vector Autoregression (VAR) framework. This methodology offers a robust means of testing causality without requiring data stationarity or cointegration, thereby reducing estimation bias and enhancing applicability to real-world economic data. The empirical model examines causal interactions among maritime trade, creative goods exports, ICT exports, and population, the latter serving as a control variable to account for demographic scale effects on trade dynamics. The results indicate statistically significant bidirectional causality between maritime trade and both creative goods and ICT exports, suggesting a reciprocal reinforcement between trade and cultural–technological exchange. In contrast, the relationship between maritime trade and population is found to be unidirectional. These findings underscore the strategic importance of cultural and technological flows in shaping maritime trade patterns. Furthermore, the study contextualizes its results within broader policy initiatives, notably China’s Belt and Road Initiative and Saudi Arabia’s Vision 2030, both of which aim to promote mutual economic diversification and regional integration. The study contributes to the literature on international trade and cultural economics by demonstrating how cultural convergence can serve as a catalyst for strengthening bilateral trade relations. Policy implications include the promotion of cultural and technological collaboration, investment in maritime infrastructure, and the incorporation of cultural dimensions into trade policy formulation. Full article

Indonesia’s dependence on fossil fuel exports, particularly coal and crude oil, presents a dual challenge: sustaining economic growth while addressing rising CO₂ emissions. Despite significant attention to domestic energy consumption, the environmental implications of export activities remain underexplored. This study examines the dynamic relationship between energy exports, crude oil prices, and CO₂ emissions in Indonesia using a Vector Autoregressive (VAR) model with annual data from 2002 to 2022. The analysis incorporates Impulse Response Functions (IRFs) and Forecast Error Variance Decomposition (FEVD) to trace short- and long-term interactions among variables. Findings reveal that coal exports are strongly persistent and positively linked to past emission levels, while oil exports respond negatively to both coal and emission shocks—suggesting internal trade-offs. CO₂ emissions are primarily self-driven yet increasingly influenced by oil export fluctuations over time. Crude oil prices, in contrast, have limited impact on domestic emissions. This study contributes a novel export-based perspective to Indonesia’s emission profile and demonstrates the value of dynamic modeling in policy analysis. Results underscore the importance of integrated strategies that balance trade objectives with climate commitments, offering evidence-based insights for refining Indonesia’s nationally determined contributions (NDCs) and sustainable energy policies. Full article

Time series data are fundamental for analyzing temporal dynamics and patterns, enabling researchers and practitioners to model, forecast, and support decision-making across a wide range of domains, such as finance, climate science, environmental studies, and signal processing. In the context of high-dimensional time series, the Vector Autoregressive model (VAR) is widely used, wherein each variable is modeled as a linear combination of lagged values of all variables in the system. However, the traditional VAR framework relies on the assumption of stationarity, which states that the autoregressive coefficients remain constant over time. Unfortunately, this assumption often fails in practice, especially in systems subject to structural breaks or evolving temporal dynamics. The Time-Varying Vector Autoregressive (TV-VAR) model has been developed to address this limitation, allowing model parameters to vary over time and thereby offering greater flexibility in capturing non-stationary behavior. In this study, we propose an enhanced modeling approach for the TV-VAR framework by incorporating minimization solvers in generalized additive models and one-sided kernel smoothing techniques. The effectiveness of the proposed methodology is assessed using simulations based on non-homogeneous Markov chains, accompanied by a detailed discussion of its advantages and limitations. Finally, we illustrate the practical utility of our approach using an application to real-world financial data. Full article

Vegetation growth is influenced not only by current climatic conditions but also by growth-enhancing signals and preceding climate factors. Taking the dominant species, Juniperus seravschanica Kom, in Tajikistan as the research subject, this study combines tree-ring width data with early xylem growth season data (from the start of xylem growth to the first day of the NDVI peak month), simulated using the Vaganov–Shashkin (V-S) model, a process-based tree-ring growth model. This study aims to explore the effects of vegetation growth carryover (VGC) and lagged climatic effects (LCE) on tree rings and the early xylem growth season at two different scales by integrating tree-ring width data and xylem phenology simulations. A vector autoregression (VAR) model was employed to analyze the response intensity and duration of VGC and LCE. The results show that the VGC response intensity in the early xylem growth season is higher than that of tree-ring width. The LCE duration for both the early xylem growth season and tree-ring width ranges from 0 to 11 (years or seasons), with peak LCE response intensity observed at a lag of 2–3 (years or seasons). The persistence of the climate lag effect on vegetation growth has been underestimated, supporting the use of a lag of 0–3 (years or seasons) to study the long-term impacts of climate. The influence of VGC on vegetation growth is significantly stronger than that of LCEs; ultimately indicating that J. seravschanica adapts to harsh environments by modulating its growth strategy through VGC and LCE. Investigating the VGC and LCE of multi-scale xylem growth indicators enhances our understanding of forest ecosystem dynamics. Full article

Complex networks of stochastic processes are crucial for modeling the dynamics of interacting systems, particularly those involving high-order interactions (HOIs) among three or more components. Traditional measures—such as mutual information (MI), interaction information (II), the redundancy-synergy index (RSI), and O-information (OI)—are typically limited to static analyses not accounting for temporal correlations and become computationally unfeasible in large networks due to the exponential growth of the number of interactions to be analyzed. To address these challenges, first a framework is introduced to extend these information-theoretic measures to dynamic processes. This includes the II rate (IIR), RSI rate (RSIR), and the OI rate gradient ($\Delta \mathrm{OIR}$), enabling the dynamic analysis of HOIs. Moreover, a stepwise strategy identifying groups of nodes (multiplets) that maximize either redundant or synergistic HOIs is devised, offering deeper insights into complex interdependencies. The framework is validated through simulations of networks composed of cascade, common drive, and common target mechanisms, modelled using vector autoregressive (VAR) processes. The feasibility of the proposed approach is demonstrated through its application in climatology, specifically by analyzing the relationships between climate variables that govern El Niño and the Southern Oscillation (ENSO) using historical climate data. Full article

This paper investigates the impact of repatriated export proceeds on exchange rate volatility in Indonesia. By applying a time-varying parameter vector autoregression (TVP-VAR) model with stochastic volatility, we assess whether the impact of repatriated export proceeds can dampen the effect of short-term capital flows. Our findings indicate that the influence of export proceeds on exchange rate volatility varies over time, with no evidence supporting its ability to dampen the impact of short-term capital flows in the short and intermediate terms. Furthermore, we identify a reversal pattern in the impacts of both repatriated export proceeds and short-term foreign capital flows after 3–5 days, suggesting a potential need to evaluate policies aimed at dampening short-term capital flow impacts on exchange rate volatility. Our results are robust across a range of sensitivity and robustness checks, confirming the reliability of our findings. Full article

This study investigates the impact of digital transactions on sustainable economic growth in Turkey, utilizing a vector autoregressive (VAR) model and quarterly data from 2006 to 2023. The results indicate a positive long-term association between digital payments and GDP. Granger causality tests and impulse response functions suggest a bidirectional relationship, highlighting mutual reinforcement between economic activity and digital financial adoption. The study also investigates three potential transmission channels linking digital payments to economic performance: household consumption, productivity, and financial intermediation. Evidence shows that digital payments are associated with increased consumption and financial sector activity, while the link to productivity is less conclusive. These findings imply that policymakers should prioritize digital financial infrastructure development and enhance regulatory frameworks to promote inclusive and sustainable economic growth. Full article

Understanding a learner’s resources as a system of interacting components, the success of a learning process is determined by the effectiveness of their interactions. Theoretical assumptions and empirical findings clearly show the importance of resource availability in learning systems but do not sufficiently consider the individuality or the temporal and situational aspects of resource regulation. Therefore, the current study addresses the complex interplay between learning resources (educational and learning capitals) in an individual learner (N = 1) by utilizing multivariate time series data of a 50-day vocabulary learning process with daily assessments of learning resource availability, performance, learning duration, and stress. We draw on methods of psychometric network analysis, modeling all variables in simultaneous interaction and allowing predictions between all variables from measuring point to measuring point (temporal dynamics). Specifically, using a Graphical Vector Autoregressive (graphicalVAR) model, yielding a contemporaneous and a temporal dynamics network model, we identified pivotal resources in regulating the student’s learning processes and outcomes, including resources with strong connections to other variables, intermediary resources, and resources maintaining the system’s homeostasis. This innovative approach has possible applications as a diagnostic tool that lays the foundation for tailored interventions. Full article

Guided by China’s “Dual Carbon” targets, the construction of its carbon market advances steadily. As a key policy mechanism for promoting emissions reduction and sustainable development, the emissions trading system plays a vital role in the national green transition strategy. Nonetheless, significant regional disparities exist in carbon emissions, and carbon allowance prices are subject to considerable fluctuations. This study examines the spatiotemporal evolution of China’s carbon emissions, investigating their distribution patterns across different regions. Furthermore, it analyzes the spatiotemporal changes in carbon allowance prices, focusing on their fluctuation patterns and spatial distribution, particularly regional differences in carbon market prices. This study focuses on the interplay between carbon emissions and carbon allowance prices, conducting an in-depth investigation into their interaction mechanisms. Using Shanghai as a case study, we construct a Vector Autoregression (VAR) model to empirically assess the dynamic impact of carbon emissions on carbon prices and their associated feedback effects. Subsequently, we propose policy recommendations for optimizing carbon market operations. This study enhances carbon markets’ functionality as climate governance tools, providing empirical and theoretical foundations for advancing low-carbon transitions and Sustainable Development Goals (SDGs). Full article

This research investigates the spillover effects between assets of the Fourth Industrial Revolution (4IR), focusing on the role of climate policy uncertainty in shaping these interactions. Using a time-varying parameter vector autoregressive (TVP-VAR) approach and a joint connectedness method, the analysis incorporates five global indices representing key 4IR domains: the internet, cybersecurity, artificial intelligence and robotics, fintech, and blockchain. The findings reveal significant interdependencies among 4IR assets and evaluate the effect of risk factors, including climate policy uncertainty, as a critical driver of the determinants of returns. The results indicate the growing impact of climate-related risks on the structure of connectedness between 4IR assets, highlighting their implications for portfolio diversification and risk management. These insights are vital for investors and policymakers navigating the intersection of technological innovation and environmental challenges in a rapidly changing global economy. Full article

This research examines the challenges of issuing green bonds due to a lack of established benchmarks. We compare regional differences between the U.S. and the E.U., hypothesizing that issuers of green bonds stand to benefit from comparing them to conventional (black) bonds. As most investors prioritize net positive returns as opposed to intangible sustainability metrics, the existence of a “green premium”, defined as the opportunity to price green bonds differently, remains to be proven. To this end, we employ a time-varying parameter vector autoregression (TVP-VAR), first deriving dynamic variance–covariance matrices and then conducting variance decomposition analysis to gauge connectedness and spillover effects of various bond benchmarks. Implementing multivariate portfolio construction strategies, we investigate the hedging capabilities of green and black bonds. Our findings show that both green and black bonds contribute to portfolio diversification as a risk management strategy. The paper highlights the role played by green bonds in promoting financial stability. Full article

Background: The growing demand for innovative biomaterials with antimicrobial properties has driven research into natural and synthetic compounds, such as propolis and nanosilver, known for their antimicrobial efficacy. Methods: This study uses Google Trends data to analyze global search interest in five key terms—propolis, antimicrobial, antibacterial, nanosilver, and biomaterials—over a ten-year period (starting November 2014). The objective is to evaluate temporal variations, quantify correlations between the terms, and explore how external events, such as the COVID-19 pandemic, have influenced public and clinical interest in these topics. Search data were extracted, normalized, and analyzed using multivariate time series methods, including vector autoregression (VAR) modeling, Impulse Response Function (IRF) analysis, and forecast error variance decomposition (FEVD). Stability, causality, and inter-period relationships were assessed using statistical analysis, with results visualized through time series plots and impulse response coefficients. Results: Key findings reveal significant interdependencies between search terms, with surges in one often resulting in immediate or short-term increases in others. Notable trends include a marked increase in COVID-19 interest for nanosilver, propolis, and antibacterial, followed by a return to baseline levels, while antimicrobial maintained a sustained upward trajectory. Biomaterials experienced initial declines but later stabilized at elevated levels. Conclusions: These findings underscore the oscillating nature of public interest in antimicrobial and biomaterial innovations, highlighting opportunities for targeted research and commercialization. By adapting future material development to emerging trends and clinical needs, dentistry can use these insights to develop infection control strategies, improve restorative materials, and deal with persistent challenges such as antimicrobial resistance, peri-implantitis, and tooth caries treatment. Full article

This paper employs a Time-Varying Parameter Vector Autoregression Directional–Spillover (TVP-VAR-DY) model to investigate the dynamic spillover effects among China’s energy, real estate, and stock markets from 2013 to 2023, with a focus on the impact of extreme events. The findings show that the total conditional spillover index (TCI) typically remains below 40% in the absence of extreme events, but significantly increases during such events, reaching 51.09% during the 2015 stock market crisis and nearing 60% during the COVID-19 pandemic in 2020. Specifically, the oil and gas market exhibited a net spillover index of 4.61%, emerging as a major source of risk transmission. In contrast, the real estate market, which had a net spillover index of −9.38%, became a net risk absorber. The net spillover index indicates that the risk transmission role of different markets towards other markets is dynamically changing over time and is closely related to significant global or domestic economic events. These results indicate that extreme events not only directly impact specific markets but also rapidly propagate risks through complex inter-market linkages, exacerbating systemic risks. Therefore, it is recommended to enhance market monitoring, improve transparency, and optimize risk management strategies to cope with uncertainties in the global economy and financial markets. Full article