Search Results (254)

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

This paper examines the interconnection and wavelet coherence between the green cryptocurrency market and the green conventional market, utilizing daily data. The research period covers 1 July 2020 to 30 September 2024. Employing the time-varying parametric vector autoregression (TVP-VAR) model and wavelet coherence analysis, we capture both short- and long-term spillovers across markets. The results show that cryptocurrencies, particularly Binance and Litecoin, act as dominant transmitters of volatility and return shocks, while green conventional indices function mainly as receivers with strong self-dependence. Spillover intensity is highly time-varying, with peaks during periods of systemic stress, particularly during the COVID-19 pandemic, and troughs indicating diversification opportunities. These findings advance the literature on systemic risk and portfolio design by showing that crypto assets can simultaneously amplify vulnerabilities and enhance diversification when combined with green finance instruments. For policy, the results highlight the need for regulatory frameworks that integrate sustainability taxonomies, mandate environmental disclosures for digital assets, and incentivize energy-efficient blockchain adoption to align crypto markets with sustainable finance objectives. This research enhances our understanding of the interrelationship between green investments and cryptocurrencies, providing valuable insights for investors and policymakers on risk management and diversification strategies in an increasingly sustainable financial landscape. Full article

Using high-frequency financial data, this study investigates volatility spillovers between five renewable energy subsectors (wind, solar, geothermal, bioenergy, and fuel cells), five conventional energy markets (oil, gas, coal, uranium, and gasoline), and carbon emissions for five industrial sectors (power, industry, ground transportation, domestic aviation, and residential) based on a Diebold–Yilmaz VAR-based spillover framework. The results document that the industry and power sectors are the key players in the transmission effects of carbon shocks. In contrast, the reverse is true for the residential and aviation sectors. For renewable energy, fuel cells, and geothermal power, strong forward linkages appear to significantly reduce carbon emissions, while reverse linkages that increase carbon emissions in response to shocks in clean-energy and carbon-intensive industries are relatively high for coal and oil. We also find that the total volatility connectedness exceeds 84%, indicating significant systemic risk transmission. The clean-energy subsectors, particularly wind and solar, now compete in fossil-fuel markets during geopolitical crises. Applying the DCC-GARCH t-copula method to assess portfolio hedging strategies, we find that fuel cell and geothermal assets are the most effective in hedging against volatility in fossil-fuel prices. In contrast, nuclear and gas assets provide benefits from diversification. These results underscore the growing strategic importance of clean energy in mitigating sector-specific emission risks and fostering resilient energy systems in alignment with the United States’ net-zero carbon goals. Full article

This study investigates the nonlinear, regime-dependent, and frequency-specific interdependencies between rare earth element (REE) markets and key global critical sectors, including artificial intelligence, semiconductors, clean energy, defense, and advanced manufacturing, under varying levels of geopolitical and financial uncertainty. The main objective is to assess how REE markets transmit and absorb systemic risks across these critical domains. Using a mixed-methods approach combining Quantile-on-Quantile Regression (QQR), Continuous Wavelet Transform (CWT), and Wavelet Transform Coherence (WTC), we examine the dynamic connections between two REE proxies, SOLLIT (Solactive Rare Earth Elements Total Return) and MVREMXTR (MVIS Global Rare Earth Metals Total Return), and major sectoral indices based on a dataset of daily observations from 2018 to 2025. Our results reveal strong evidence of asymmetric, regime-specific risk transmission, with REE markets acting as systemic amplifiers during periods of extreme uncertainty and as sensitive receptors under moderate or localized geopolitical stress. High co-volatility and persistent low-frequency coherence with critical sectors, especially defense, technology, and clean energy, indicate deeply embedded structural linkages and a heightened potential for cross-sectoral contagion. These findings confirm the systemic relevance of REEs and underscore the importance of integrating critical resource exposure into global supply chain risk strategies, sector-specific stress testing, and national security frameworks. This study offers relevant insights for policymakers, risk managers, and institutional investors aiming to anticipate disruptions and strengthen resilience in critical industries. Full article

This research evaluates the suitability of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) for improving financial return predictions across 15 major worldwide stock indices. The proposed method uses graph modeling to represent financial index relationships which enables the detection of symmetric market dependencies including mutual spillover effects and bidirectional influence patterns. The symmetric network structures become most important during financial instability because market interdependencies strengthen at such times. The evaluation process compares these models against XGBoost and Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) traditional forecasting approaches. The results of 30 time-series cross-validation experiments show that GNN models produce lower RMSE and MAE values, especially during financial crises and recovery phases and volatile market periods. The models show reduced advantages when markets remain stable. The research demonstrates that graph-based forecasting models which incorporate symmetry effectively detect complex financial relationships which leads to important implications for investment strategies and financial risk management and global economic forecasting. Full article

As a strategic material second only to grain, cotton serves both as a vital agricultural commodity and a key industrial crop. With the increasing frequency of global shocks and the deepening financialization of commodity markets, price linkages among major international cotton futures markets have strengthened. Consequently, in addition to fundamental supply and demand factors, cross-border price transmission has become a significant determinant of cotton pricing. This study employs daily closing prices of China’s cotton futures, cotton yarn futures, and U.S. cotton futures from 1 September 2017 to 31 March 2025 to examine the spillover effects among these three futures markets using time series models. The results reveal that U.S. cotton futures have dominated the Chinese cotton-related futures markets even prior to the onset of trade tensions, with strong domestic market comovements. However, both the U.S.-China trade war and the COVID-19 pandemic significantly weakened price co-movements while intensifying volatility spillovers. Although these external shocks enhanced the relative independence of China’s cotton yarn futures and modestly increased China’s pricing influence, U.S. cotton futures have consistently maintained their central role in price discovery. Full article

This paper analyzes the volatility relationship between the Romanian BET index and the U.S. SFT-500 index during the period 2019–2024, with a particular focus on the impact of political and geopolitical shocks. The study investigates whether financial markets in emerging economies react symmetrically or asymmetrically to external shocks originating from mature markets, especially during periods of political uncertainty. The research period includes four major systemic events: the COVID-19 pandemic, the military conflict in Ukraine, the 2024 U.S. presidential elections, and the 2024 Romanian elections, all of which generated significant volatility in global markets. The methodological approach combines time series econometrics with the Impulse Indicator Saturation (IIS) technique to identify structural breaks and outliers, without imposing exogenous assumptions about the timing of events. The econometric model includes autoregressive and lagged exogenous variables to estimate the influence of the SFT-500 index on the BET index, while IIS variables capture unanticipated political and economic shocks. Additionally, a Fractionally Integrated GARCH (FIGARCH) specification is applied to model the persistence of volatility over time, capturing the long-memory behavior often observed in emerging markets like Romania. The results confirm a statistically significant but partial synchronization between the two markets, with lagged and contemporaneous effects from the SFT-500 index on the BET index. Volatility in Romania is markedly higher and longer-lasting during domestic political episodes, confirming that local factors are a primary source of market instability. For investors, this underscores the need to embed political risk metrics into emerging market portfolios. For policymakers, it highlights how stronger institutions and transparent governance can dampen election- and crisis-related turbulence. Full article

This paper investigates the dynamics of volatility spillovers in the South African foreign exchange market across calm and crisis periods, with particular attention paid to the pre- and post-COVID-19 eras. Employing daily exchange rate returns from 2015 to 2025, we apply a Quantile Vector Autoregression (QVAR) model to uncover asymmetries in spillover transmission across the distribution of returns. We evaluate the implications of these spillovers for portfolio performance under three canonical strategies: risk parity, tangency, and naïve equal-weighting. Our findings indicate that the COVID-19 shock intensified volatility spillovers and exacerbated their asymmetry, especially in the lower tail, while the pre-COVID period portrayed higher volatility compared to the post-COVID period under calm market conditions. While risk-based strategies dominate in tranquil markets, equal-weighted portfolios exhibit superior downside resilience under stress, although they ignore risk exposure. These results underscore the importance of accounting for tail-risk-driven interconnectedness in portfolio construction and risk management. This study contributes to the growing literature on volatility spillovers and offers practical insights for managing currency exposure in emerging markets under nonlinear dependence structures. Full article