Search Results (261)

The global economic importance of green tech is rising. Yet the role of the green financial sector in the propagation of volatility is still unclear. Although the existing literature often characterizes green assets as stable, the new risks, particularly US–China trade tensions that target the green sector directly, may uncover potential vulnerabilities. As China’s green sector has attained global leadership, its interconnections with other major economies require a closer examination, especially within the BRICS block. Applying the Bayesian VAR with Minnesota Ridge prior and a TVP-VAR model-based connectedness approach on a dataset of 1880 observations spanning from 2016 to 2025, we identified that volatility in China’s green sector peaked during the COVID-19 pandemic and resurged in early 2025 amid trade tensions. Uniquely, this study also finds that, despite the intensification of political and economic relations between BRICS members, the interconnectedness of their financial markets has been weakening, suggesting their long-term decoupling and regionalization. From 2016 to 2024, green indices remained historically peripheral, with limited, stable ties to the Nasdaq and SSE. In 2025, short shock-driven transmitter episodes have emerged and indicate an incipient integration rather than a permanent regime change. Full article

The vulnerability of the global financial system to systemic risk-related adverse events has become more evident in recent years, as shown by the 2008 financial crisis and the global pandemic. This study examines systemic risk and its contributing factors using network analysis to understand how contagion occurs. To achieve this, a bibliometric analysis was conducted using a cluster analysis of publications from 2020 to 2025. The bibliometric analysis covered 1642 papers related to systemic risk and financial transmission networks. The CiteSpace software was used to identify seven thematic clusters. The results show the relevance of topological analysis in explaining the connection between institutions and the spread of risk. There is also a clear tradition in the literature of applying the DY spillover index, which captures the temporal dynamics of systemic connectivity. Multilayer networks stand out as a trend in recent studies, as they have the potential to represent different types of relationships simultaneously between nodes. Finally, the literature pays attention to systemic connectivity problems during crises, which can amplify volatility and generate forced asset sales, highlighting the need to use advanced VAR-type models to anticipate risk transmission and guide macroprudential management. Full article

This study analyzes how sectoral stock volatility in the GCC region responds to global financial uncertainty shocks originating from the U.S. (CBOE VIX), Europe (VSTOXX-50), Bitcoin investors’ Sentiment Indices (BSI), and disaggregated global Financial Stress Indicators (FSI) by using both the “Frequency” and “Time” domain TVP-VAR based connectivity approaches. The “Time” and “Frequency” domain TVP-VAR results indicate that the Energy, Financials, Materials and REIT sectors experience the highest shock spillover from the U.S. and European equity market uncertainty (VIX and VSTOXX-50) for the overall and long-term investment horizons. Whereas, all the five disaggregated global financial stress indicators and BSI transmit higher shocks spillovers towards the sectoral stock conditional volatility of Energy and Materials sectors for the overall and long-term investment horizons. Furthermore, the “Frequency” domain TVP-VAR approach shows that overall shocks spillovers are higher in long-term and intensified during the COVID-19 period. The Energy, Materials, and REIT sectors’ high sensitivity to U.S.VIX and Euro.VSTOXX-50 shocks calls for sector-specific hedging—such as sectors remain least susceptibility to long-term U.S. and European equity risk shocks such as Utility. Over the long-term and overall investment horizons, the Energy and Material sectors’ position as the main shock recipient from all five global financial stress components and the BSI underscores its role as a volatility hub. Policymakers should enforce stress tests and capital buffers for energy and material focused firms, while proactive liquidity management and commodity hedging are vital during global financial stress and BSI spikes to limit funding and operational risks. Full article

The urgency of transitioning to sustainable energy has accelerated amid climate change concerns and fossil fuel depletion. This study introduces a novel comparative framework that integrates Time-Varying Parameter Vector Autoregression (TVP-VAR) and Quantile Vector Autoregression (QVAR) models to examine both returns and realized volatility across renewable-energy tokens (Powerledger and Wepower), clean-energy stocks, and crude oil. This dual-method approach uniquely captures time-varying and tail-specific spillovers, extending previous studies that relied on a single model or ignored volatility interactions. Using daily data from February 2018 to January 2023, we reveal moderate but significant interconnectedness—about 30% on average—with stronger linkages during global crises such as COVID-19 and the Russia–Ukraine conflict. Renewable-energy tokens act mainly as net receivers of shocks, implying their role as protective diversification assets, while clean-energy stocks are net transmitters and oil alternates between both roles. These results highlight how digital assets interact with traditional energy markets under varying conditions. The study offers practical implications for portfolio diversification and emphasizes the need for transparent, supportive regulation to prevent tokens from amplifying systemic risk while promoting the stability of sustainable-energy investment markets. Full article

In the context of a complex and volatile domestic and global environment, Chinese enterprises face frequent risks of supply chain disruption that seriously hinder their operations. The rise of the digital economy offers new opportunities to strengthen supply chain resilience. Building on supply chain collaboration and value co-creation theories, this study conceptualizes supply chain collaboration through three dimensions, namely information collaboration, governance collaboration, and innovation collaboration, and explores their role in enhancing resilience. Using panel data of Chinese A-share listed firms from 2011 to 2023, this study investigates the impact of focal firm digitalization on supply chain resilience and its underlying mechanisms. The results indicate that focal firm digitalization generates significant backward spillover effects, enhancing the resilience of its upstream suppliers. Although its positive influence on supply chain stability (measured by supply chain demand and supply fluctuations) is not statistically significant, it substantially enhances recovery (measured by supply chain efficiency) and adaptability (measured by supplier innovation). Mechanism analysis further reveals that digitalization strengthens supply chain collaboration through information, governance, and innovation channels, thereby reinforcing resilience. Moreover, the positive effects are heterogeneous, varying with industry competition intensity, the closeness of upstream–downstream relationships, and suppliers’ regional resource endowments. These findings highlight the need to design digitalization strategies centered on focal firm leadership and upstream–downstream collaboration, thereby advancing both resilience improvement and collaborative mechanism development through differentiated and targeted approaches. Full article

This study examines risk transmission between the United States and China using integrated economic policy uncertainty (EPU) and geopolitical risk (GPR) indices. We employ a dual methodology that combines Vector Autoregressive (VAR) and Granger causality in quantiles tests to analyze interactions during systemic leadership transitions, a dimension that is currently under-explored. Our dataset covers the period from June 2000 to June 2023. Results indicate that China is narrowing the economic influence gap and strengthening its role as a regional anchor. The U.S., however, maintains predominant global leadership. This dynamic reframes bilateral tensions as a “status dilemma” rather than a security conflict. Crucially, we identify asymmetric spillover effects: the U.S. uncertainty shocks spread globally, while China’s volatility remains regional. Our findings contribute to the understanding of financial stability by demonstrating that leadership asymmetries are critical determinants, providing valuable insights for designing systemic risk monitoring tools and contagion mitigation policies during periods of heightened uncertainty. Full article

This paper investigates the short-term predictability of daily crude oil price movements by employing a multi-method analytical framework that incorporates both econometric and machine learning techniques. Utilizing a dataset of 21 financial and commodity time series spanning ten years of trading days (2015–2024), we explore the dynamics of oil price volatility and its key determinants. In the forecasting phase, we applied seven models. The meta-learner model, which consists of three base learners (Random Forest, gradient boosting, and support vector regression), achieved the highest R² value of 0.532, providing evidence that our complex model structure can successfully outperform existing approaches. This ensemble demonstrated that the most influential predictors of next-day oil prices are VIX, OVX, and MOVE (volatility indices for equities, oil, and bonds, respectively), and lagged oil returns. The results underscore the critical role of volatility spillovers and nonlinear dependencies in forecasting oil returns and suggest future directions for integrating macroeconomic signals and advanced volatility models. Moreover, we show that combining multiple machine learning procedures into a single meta-model yields superior predictive performance. Full article

Reducing carbon emissions in the industrial sector is a critical component of achieving green and sustainable development. We employ quantile vector autoregressive methods to analyze the dynamic interactions of industrial carbon emissions across various countries. Initially, we observe that, under normal conditions, developed countries led by the EU exhibit a significant total spillover effect. Secondly, during extreme quantile conditions, the spillover effects of EU-led developed countries shift from positive to negative, whereas in the UK, the opposite trend is observed. This highlights the importance of considering carbon transfer’s role in emission reduction during extreme quantile scenarios. Thirdly, we find that China’s industrial carbon emissions spillover effects remain relatively stable at all times. Lastly, total spillover effects are highly volatile during extreme market conditions, such as the COVID-19 pandemic. These findings will help clarify each country’s emission reduction responsibilities within the international industrial system and facilitate a more equitable allocation of emission reduction tasks. Full article

This study examines the dynamic relationship between the gold markets of India and the United States from 2005 to 2025. Recognising gold’s role as a hedge and safe-haven during market uncertainty, we employ the Autoregressive Distributed Lag (ARDL) model to assess long-term co-integration and apply the Toda–Yamamoto causality test to evaluate directional influences. Additionally, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1) model is applied to examine volatility spillovers. Results reveal no long-term co-integration between the two markets, suggesting they function independently over time. However, unidirectional causality is observed from the U.S. to the Indian gold market, and the GARCH model confirms bidirectional volatility transmission, indicating interconnected short-run dynamics. These findings imply that gold market shocks in one country may affect short-term pricing in the other, but not long-term trends. From a portfolio diversification and risk management perspective, investors may benefit from allocating assets across both markets. This study contributes a novel empirical framework by integrating ARDL, Toda–Yamamoto Granger causality, and GARCH(1, 1) models over a two-decade period (2005–2025), incorporating post-COVID market dynamics. The combination of these methods, applied to both an emerging (India) and developed (U.S.) economy, provides a comprehensive understanding of gold market interdependence. In doing this, the paper offers valuable insights into causality, volatility transmission, and diversification potential. The econometric rigour of the study is enhanced through residual diagnostic tests, including tests of normality, autocorrelation, and other heteroscedasticity tests, as well as VAR stability tests. These ensure strong inference and model validity; more specifically, they are pertinent to the analysis of financial time series. Full article

Global enterprises face increasingly volatile market conditions, with foreign exchange (FX) movements often forcing executives to make rapid pricing and strategy decisions under uncertainty. While artificial intelligence (AI) has transformed operational decision-making, its role in supporting board-level strategic choices remains underexplored. This paper examines how AI and advanced analytics can serve as a ‘decision companion’ for management teams and executives confronted with global shocks. Using Roblox Corporation as a case study, we apply a Long Short-Term Memory (LSTM) neural network to forecast bookings and simulate counterfactual scenarios involving euro depreciation and European price adjustments. The analysis reveals that a ten percent depreciation of the euro reduces consolidated bookings and profits by approximately six percent, and that raising European prices does not offset these losses due to demand elasticity. Regional attribution shows that the majority of the decline is concentrated in Europe, with only minor spillovers elsewhere. The findings demonstrate that AI enhances strategic agility by clarifying risks, quantifying trade-offs, and isolating regional effects, while ensuring that ultimate decisions remain with human executives. Full article

This study explores the new insights into the integration and dynamic asymmetric volatility risk spillovers between Bitcoin, currency pairs (USD/ZAR, GBP/ZAR and EUR/ZAR), and traditional financial assets (ALSI, Bond, and Gold) in South Africa using daily data spanning the period from 2010 to 2024 and employing Time-Varying Parameter Vector Autoregression (TVP-VAR) and wavelet coherence. The findings revealed strengthened integration between traditional financial assets and currency pairs, as well as weak integration with BTC/ZAR. Furthermore, BTC/ZAR and traditional financial assets were receivers of shocks, while the currency pairs were transmitters of spillovers. Gold emerged as an attractive investment during periods of inflation or currency devaluation. However, the assets have a total connectedness index of 28.37%, offering a reduced systemic risk. Distinct patterns were observed in the short, medium, and long term in time scales and frequency. There is a diversification benefit and potential hedging strategies due to gold’s negative influence on BTC/ZAR. Bitcoin’s high volatility and lack of regulatory oversight continue to be deterrents for institutional investors. This study lays a solid foundation for understanding the financial dynamics in South Africa, offering valuable insights for investors and policymakers interested in the intricate linkages between BTC/ZAR, currency pairs, and traditional financial assets, allowing for more targeted policy measures. Full article

We investigate the key factors that shape the dynamic evolution of Day-Ahead spot prices of seven European interconnected electricity markets of the Core Capacity Calculation Region, Core CCR (Austria AT, Hungary HU, Slovenia SI, Romania RO), the Southeast CCR (Bulgaria BG, Greece GR) and the Greece-Italy CCR (GRIT CCR), with emphasis on price surges and discrepancies observed in SEE CCR markets, during the period 2022–2024. The high differences in the prices of the two groups have generated political reactions from the countries that ‘suffer’ from these price discrepancies. By applying Machine Learning (ML) approaches, as Markov Blanket (MB) and Local, causal structures learning (LCSL), we are able of ‘revealing’ the entire path of volatility spillover of both spot price and the Cross-Border Transfer Availabilities (CBTA) between the countries involved, from north to south, thus uncovering i.e., ‘lifting the blanket’, to discover the ‘true’ structure’ of the path of causalities, responsible for the price disparity. The above methods are supported by the ‘mainstream’ approach of computing the correlation of the spot price and CBTA’s volatility curves of all markets, to detect volatility spillover effects across markets. The main findings of this hybrid approach are (a) the volatility of some Core CCRs (AT, HU, RO) markets’ spot price and CBTAs with neighboring countries, ‘uncovered’ to be pivotal, operating as a ‘transmitter’ of volatility ‘disturbances’, over its entire connection and causal path from Core CCR to SEE CCR markets, partially contributing to their price surge, (b) reduced available capacity for cross-border trading of some Core and SEE CCRs (they have not satisfied the minimum 70% requirement margin available for cross-zonal trade, MACZT), combined with local weather and geopolitical conditions, could have exacerbated the impact of the Flow-based Market coupling method (FBMC) used in the Core CCRs, on the prices’ surge of SEE CCR’s countries, e.g., via induced non-intuitive flows. This phenomenon questions the efficiency and reliability of the European Target’s model (TM) in securing ‘homogeneous’ power prices across all interconnected markets, core and peripheral. Full article

The FTX collapse marked a significant shock to global crypto markets, prompting concerns about systemic contagion. This paper investigates the dynamic connectedness between cryptocurrencies, DeFi tokens, and tech stocks, focusing on the systemic impact of the FTX collapse. We decompose total, internal, and external connectedness across asset groups using a time-varying parameter VAR model. The results show that post-FTX, Bitcoin and Ethereum intensified their roles as core shock transmitters, while Tether consistently acted as a volatility absorber. DeFi tokens exhibited heightened intra-group spillovers and occasional external influence, reflecting structural fragility. Tech stocks remained largely insulated, with reduced cross-market linkages. Network visualizations confirm a post-crisis fragmentation, characterized by denser internal crypto-DeFi ties and weaker inter-group contagion. These findings have important policy implications for regulators, investors, and system designers, indicating the need for targeted risk monitoring and governance within decentralized finance. Full article

Food price volatility is a persistent challenge in Indonesia, where agriculture is central to food security and rural livelihoods. While price transmission has been studied, little is known about how volatility spreads sub-nationally in archipelagic economies with fragmented infrastructure. This study applies a Dynamic Conditional Correlation GARCH (DCC-GARCH) model to monthly rural producer price data from 2009 to 2022 for six commodities: rice, chicken, eggs, chili, cayenne, and shallots. Results show that Java functions as the core volatility transmitter, with long-run conditional correlations exceeding 0.92 in Sumatra, 0.91 in Kalimantan, and 0.90 in Papua, reflecting strong and persistent co-movements. Even in low-production regions such as Maluku, significant volatility linkages reveal structural dependence on Java. Volatility clustering is particularly intense for perishables like chili and shallots. The findings highlight the need for spatially differentiated stabilization policies, including upstream interventions in Java and cooperative-based storage systems in outer islands. This study is the first to apply a DCC-GARCH framework to rural producer price data in an archipelagic context, capturing volatility transmission across regions. Its novelty lies in linking these spillovers with regional market dependence, offering new empirical evidence and actionable insights for designing inclusive and geographically responsive food security strategies. Full article

Agricultural markets are increasingly exposed to global risks as climate change intensifies and macro-financial volatility becomes more prevalent. This study examines the dynamic interconnection between major agricultural commodities—soybeans, corn, wheat, rough rice, and sugar—and key uncertainty indicators, including climate policy uncertainty, global economic policy uncertainty, geopolitical risk, financial market volatility, oil price volatility, and the U.S. Dollar Index. Using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with monthly data, we assess both internal spillovers within the commodity system and external spillovers from macro-level uncertainties. On average, the external shock from the VIX to corn reaches 12.4%, and the spillover from RGEPU to wheat exceeds 10%, while internal links like corn to wheat remain below 8%. The results show that external uncertainty consistently dominates the connectedness structure, particularly during periods of geopolitical or financial stress, while internal interactions remain relatively subdued. Unexpectedly, recent global disruptions such as the COVID-19 pandemic and the Russia–Ukraine conflict do not exhibit strong or persistent effects on the connectedness patterns, likely due to model smoothing, stockpiling policies, and supply chain adaptations. These findings highlight the importance of strengthening international macro-financial and climate policy coordination to mitigate the propagation of external shocks. By distinguishing between internal and external connectedness under climate stress, this study contributes new insights into how systemic risks affect agri-food systems and offers a methodological framework for future risk monitoring. Full article