Cet article examine les répercussions macrofinancières entre les principales actions technologiques américaines et les principales cryptomonnaies en appliquant un cadre d’autorégression vectorielle quantile (QVAR). À l’aide de données quotidiennes couvrant la période de novembre 2017 à octobre 2023, nous examinons la connectivité dynamique entre les classes d’actifs à différents points de la distribution des rendements, en mettant particulièrement l’accent sur les extrémités, où le risque systémique s’amplifie généralement. Nos conclusions révèlent que, si la connectivité moyenne est importante, l’intensité des répercussions est nettement asymétrique et concentrée dans les quantiles extrêmes, ce qui indique une interdépendance accrue pendant les périodes de tension ou d’exubérance financières. Les cryptomonnaies agissent comme des transmetteurs nets de volatilité vers certaines actions technologiques dans des conditions de marché défavorables, bien qu’elles ne présentent que des retombées limitées en temps normal. Ces résultats soulignent l’importance macroéconomique croissante des actifs numériques et la nécessité d’intégrer des mesures basées sur les quantiles dans les cadres de surveillance macroprudentielle, suggérant que les modèles de tests de résistance et les cadres de stabilité financière doivent être accompagnés de ces modèles d’interconnexion des extrémités, visant à évaluer régulièrement les dépendances entre les extrémités des différents marchés. Enfin, le document fournit des informations importantes aux banques centrales, aux décideurs politiques et aux investisseurs institutionnels, en ce qui concerne la conception d’un suivi macrofinancier complexe, les techniques de gestion des risques et l’allocation des actifs. Classification JEL : C32, C58, D53, E44, E60, G10

In the wake of the digital revolution, this study examines the complex network of interrelations among the core semiconductor sector (semiconductors, technology), industrial supply chains (materials, energy, crude oil, transportation, metals & mining), and broad financial markets (US equity, emerging markets equity, US government bonds, US corporate high-yield bonds, gold). Utilizing the R2 decomposed connectedness approach and analyzing an extensive dataset spanning from November 1, 2012, to October 31, 2025, we investigate the evolution of market interdependencies across the pre- and post-pandemic eras. The findings show that total connectedness surged dramatically after the outbreak of the pandemic, suggesting heightened market synchronization. Simultaneously, a profound structural reconfiguration within the network is observed; semiconductors, initially functioning as the system’s primary net receiver, transitioned to a neutral status after 2020. Furthermore, we evaluate the effectiveness of semiconductors as a hedging tool within both bivariate and multivariate portfolios. While the sector exhibited minimal hedging potential prior to 2020, it emerged as a partial hedging asset in the post-pandemic regime. Conversely, portfolio optimization strategies reveal that the semiconductor sector, once the most efficient diversifier, saw its unique benefits eroded as it became deeply embedded into the post-2020 structure. These results provide crucial insights for investors and policymakers navigating modern financial markets, underscoring the sector’s progression from a cyclical tech input to a highly integrated systemic asset.

We propose an Artificial Intelligence (AI) model that provides a transparent, scalable tool for monitoring nonlinear production relations in corporate finance. Linear models can distort inferences about scale, productivity, and efficiency when production technologies are nonlinear. We address this by developing a parsimonious neural production model that uses a single-hidden-layer feedforward neural network with one hidden neuron and delivers observation-specific elasticities, returns to scale, technical efficiency, and total factor productivity via closed-form derivatives. Estimated on 174,190 Compustat North America firm-year (2000–2025) observations using within-year demeaned logs of labor, capital, and R&D to explain sales, the model reveals increasing returns to scale, high technical efficiency among adopters, and sizeable positive total factor productivity movements. A two-hidden-neuron specification yields only marginal improvements in fit and preserves these economic patterns. The architecture keeps the parameter count low, produces interpretable unit-level diagnostics, and is fully replicable for applied research and financial decision-making. Also, the model systematically outperforms the popular Cobb–Douglas production function. Operationally, the framework extends to supervisory and investment use, such as tracking nonlinear production, comparing units, and prioritizing interventions.

Examining inequality of carbon emissions sheds light on disparities in emission levels across different socioeconomic groups, highlighting the need for equitable and inclusive climate action to ensure environmental sustainability and social justice. Based on a country level dataset spanning 33 countries from 2001 to 2019, this paper empirically explores the impact of climate finance funds on carbon inequality, examining their asymmetric relationship, as well as the mediating and moderating effects. Our key findings are as follows. (1) Climate finance funds exhibit a negative impact on carbon inequality, with every 1% increase in climate finance funds leading to a decrease in carbon inequality by 0.0184%. (2) The relationship between climate finance funds and carbon inequality varies across quantiles, with its mitigating effect diminishing as carbon inequality levels increase. Moreover, the inhibiting effect of climate finance funds is markedly stronger in developing countries. (3) Artificial intelligence, particularly industrial robot installation and application, plays a crucial mediating role in the nexus between climate finance funds and carbon inequality. (4) The governance capacity of governments demonstrates a positive moderating effect on climate finance funds’ effectiveness in reducing carbon inequality, while climate change vulnerability exerts a negative moderating influence, rendering climate finance funds less effective in reducing carbon inequality. These findings enlighten us with some useful policy implications for narrowing carbon inequality and making better use of climate finance funds.

Stable and efficient food markets are crucial for global food security, yet international staple food markets are increasingly exposed to complex risks, including intensified risk contagion and escalating external uncertainties. This paper systematically investigates risk spillovers in global staple food markets and explores the key determinants of these spillover effects, combining innovative decomposition-reconstruction techniques, risk connectedness analysis, and random forest models. The findings reveal that short-term components exhibit the highest volatility, with futures components generally more volatile than spot components. Further analysis identifies two main risk transmission patterns, namely cross-grain and cross-timescale transmission, and clarifies the distinct roles of each component in various net risk spillover networks. Additionally, price drivers, external uncertainties, and core supply-demand indicators significantly influence these spillover effects, with heterogeneous importance of varying factors in explaining different risk spillovers. This study provides valuable insights into the risk dynamics of staple food markets, offers evidence-based guidance for policymakers and market participants to enhance risk warning and mitigation efforts, and supports the stabilization of international food markets and the safeguarding of global food security.

This study investigates the application of Multiple Criteria Decision Analysis (MCDA) methods for portfolio selection in the Vietnamese stock market using daily stock price data from the VN100 index spanning January 2015 to November 2023. Creating 150 criteria based on stock returns, volatility, and correlation, we employ five popular MCDA methods and four weighting methods to compare the performance of up to 500 portfolios. While MCDA methods may not effectively differentiate between stocks with higher and lower future returns, they consistently excel in selecting portfolios with superior risk-adjusted returns and lower drawdown compared to both the benchmark and the traditional Mean-Variance (MV) method. The PROMETHEE II (Preference Ranking Organization Method for Enrichment of Evaluations) method stands out as a robust performer, and the CRITIC (Criteria Importance Through Intercriteria Correlation) weighting method emerges as a reliable choice for constructing portfolios with favorable risk-adjusted returns. Moreover, the MCDA methods demonstrate potential computational efficiency over the tested MV implementation, enhancing their practicality. These findings highlight the practical utility of MCDA, particularly PROMETHEE II with CRITIC weighting, for navigating the complexities of portfolio optimization in dynamic emerging markets like Vietnam, offering a compelling alternative to traditional approaches.

The energy transition from fossil fuel to renewable energy relies upon developing clean energy technologies that are mineral intensive. Critical minerals for the energy transition are subject to geopolitical risk due to increased competition over access to minerals supply. Thus, geopolitical risk may hinder the performance of clean energy markets. This study investigates the interplay between clean energy markets and geopolitical risk by focusing on rare earth elements that are key components of clean technologies, including wind turbines and electric vehicles. Using the TVP - VAR model, we study the connectedness between four clean energy indices, the rare earth market and the Geopolitical risk index from Caldara and Iacoviello (2022). We then conduct a Wavelet Coherence analysis for complementary results. Overall, the results show that geopolitical risk has a significant negative impact on the four clean energy markets during periods of increased geopolitical tensions. The magnitude of connectedness between clean energy markets, rare earth markets and geopolitical risk also increases during these periods. As a result, geopolitical risk impacts on the rare earths market and clean technology markets might hinder investment in clean energy.

Urban economies are increasingly shaped by climate variability, making it essential to understand how environmental factors interact with economic drivers to influence output growth. This study examines the determinants of urban economic performance in Athens, Greece, focusing on labor growth, gross fixed capital formation, and climate variables such as temperature and precipitation. Using an extensive dataset spanning the period 2000–2022, we employ robust econometric techniques to quantify the impact of these factors on regional output growth. Our findings highlight the critical role of climate conditions (temperature and precipitation) in shaping economic trajectories, stressing the necessity for climate-conscious urban policies. The results emphasize the importance of integrating climate resilience strategies into economic planning, as cities face mounting environmental uncertainties. By bridging economic modeling with climate science, this study provides actionable insights for policymakers, urban planners, and economists, contributing to the broader discourse on sustainable urban development in the face of climate change.

Accurate prediction of carbon prices is crucial for policymakers, investors, and other participants in emissions trading schemes (ETS) and during regulatory transitions. In this work, carbon price movements are forecasted using a nonlinear ARIMA model as the baseline, alongside XGBoost and LSTM as competing models. The widely adopted XGBoost model is a machine learning (ML) technique, while the LSTM model belongs to the class of Recurrent Neural Network (RNN) models. To harness the predictive strengths of both approaches, we also employ a hybrid model that averages forecasts from the LSTM and XGBoost models. The dataset used in this study is in daily format, ranging from December 1, 2010, to January 10, 2025. The results show that both XGBoost and LSTM outperform the baseline ARIMA model. Furthermore, the hybrid model demonstrates statistically significant improvements in forecasting accuracy compared to the baseline model. These findings suggest that ML- and RNN-based approaches can serve as effective alternatives to traditional statistical and econometric models in carbon pricing forecasting.

This study contributes to forecasting financial time series, focusing on the social Sustainable Development Goals (SDGs) to enhance investment strategies. By analyzing stock portfolios across the United States, Europe, and Vietnam, we explore whether companies with strong social commitments are rewarded in the stock market. Our conservative approach, which involves systematic portfolio rebalancing, ensures a thorough examination of the relationship between social responsibility and financial performance, contributing valuable insights to sustainable finance. Our findings reveal that long portfolios, particularly those based on social SDGs, consistently outperform traditional benchmarks. This outperformance is especially pronounced in the European and Vietnamese markets. In contrast, the U.S. market shows less pronounced gain. A standout result is observed in the Vietnamese market, where portfolios focused on the 'Gender Equality' SDG exhibit notably low drawdowns, suggesting greater stability and resilience. Additionally, market-neutral portfolios, which balance long positions against index benchmarks, perform exceptionally well in Vietnam, reflecting the unique dynamics of this market and its growing emphasis on social responsibility. These results highlight the dual benefits of social SDG-based portfolio management in achieving both financial outperformance and positive social impact.

The energy transition is highly mineral-intensive, leading to substantial dependencies on a select group of producing countries, which may, in turn, spark new geopolitical tensions. Prominent among these critical minerals are rare earth elements (REEs), with China being the predominant producer. REEs serve as indispensable components in cutting-edge low-carbon technologies, particularly in the realm of offshore wind turbines and electric vehicles, through their crucial role in rare earth permanent magnets (REPM). This paper delves deep into the ramifications of geopolitical risk on the export of rare earth permanent magnets from China, the world’s leading supplier. Utilizing multiple linear regression on Chinese national export figures spanning from 2017 to 2024, the study uncovers that geopolitical risk exerts a markedly positive influence on the supply of rare earth permanent magnets. The results highlight that elevated geopolitical risks in the United States and Australia — China’s primary competitors in the upstream rare earth industry — positively influence REPM exports. Furthermore, the insights reveal that escalating geopolitical risks might significantly shape China’s strategic export volumes.

Climate change has become one of the principal global concerns, with an expected increase in the frequency of extreme weather events, rising temperatures, and shifts in precipitation patterns, all of which are likely to profoundly impact agriculture, human health, and the economy. In China, the swift process of urbanization, coupled with climate change, has heightened the complexity of migration patterns. The intricate interplay between socio-economic and environmental changes compels people to consider migration as a strategy for adaptation. Our research reveals a complex interconnection between climate change and the patterns of population influx and outflow. This study aims to explore the effects of climate change on migratory dynamics to inform policy-making for nations grappling with the dual challenges of economic growth and environmental sustainability.

Eco-city policy is one of the cornerstones of sustainable urban development strategies, integrating climate policy objectives with social and economic considerations. In recent years, numerous eco-city projects worldwide have emphasised green construction, renewable energy deployment, efficient water management, and sustainable lifestyles. But their role in promoting economic resilience and mitigating the socio-economic impacts of climate variability remains underexplored. Using the Sino-Singapore Eco-city in Tianjin, China, as a case, this study examines how climate variability is associated with property prices in eco-city and non-eco-city areas. The findings disclose that eco-city enhances economic resilience, stabilising local real estate markets through the integration of green development goals. However, limitations in industrial diversity, constrained economic scale, and concerns over social equity underscore the need to refine eco-city policies-ensuring not only environmental sustainability, but also economic inclusiveness and social justice. By bridging climate policy and energy justice, this research offers valuable insights into promoting equitable and resilient urban transitions under climate stress.

Investors and risk managers looking to manage or diversify risks by investing in volatility derivatives continue to face the challenge of accurately valuing variance and volatility swaps. This article proposes an alternative approach for the valuation of variance and volatility derivatives on discrete samples by developing an enlarged Markov switching stochastic volatility model with jumps. Merging these three economic and financial properties enables our model to account for both endogenous and exogenous factors. A key theoretical contribution is the derivation of partial integro-differential equations (PIDEs) that characterize the fair delivery price of discretely sampled variance swaps within this framework. We perform comprehensive numerical simulations that demonstrate the model's ability to capture important market features, including regime shifts, jumps, and volatility clustering. Findings suggest that the model provides an enhanced framework for pricing discretely sampled variance swaps.

When the issues of gender equality and environmental protection become hot topics in society, exploring the influence of corporate gender diversity culture on corporate greenwashing can satisfy societal demands and corporate development. This paper examines the impact of corporate executive gender diversity culture (CEGDC) on corporate greenwashing based on 833 listed Chinese companies covering 2010-2020. Besides, considering the importance of informal institutions and green innovation behavior on enterprise development and construction, we further explore the mediating role of both, thus deepening the research on corporate informal institutions and green innovation. The results show that, first, CEGDC can reduce corporate greenwashing, and these findings remain valid even after robustness tests using the instrumental variable method. Second, informal institutions and green innovation behaviors play an important role in reducing corporate greenwashing. Specifically, female executives can reduce corporate greenwashing behaviors by enhancing executives' green cognition and promoting green innovation. Finally, the heterogeneity study found that when faced with strict external environmental regulation, the CEGDC can play a more obvious effect. In addition, CEGDC has mainly decreased greenwashing in corporations with small-sized and higher loan capacities.

With escalating macroeconomic uncertainty, the risk interlinkages between energy and food markets have become increasingly complex, posing serious challenges to global energy and food security. This paper proposes an integrated framework combining the GJRSK model, the time-frequency connectedness analysis, and the random forest method to systematically investigate the moment connectedness within the energy-food nexus and explore the key drivers of various spillover effects. The results reveal significant multidimensional risk spillovers with pronounced time variation, heterogeneity, and crisis sensitivity. Return and skewness connectedness are primarily driven by short-term spillovers, kurtosis connectedness is more prominent over the medium term, while volatility connectedness is dominated by long-term dynamics. Notably, crude oil consistently serves as a central transmitter in diverse connectedness networks. Furthermore, the spillover effects are influenced by multiple factors, including macro-financial conditions, oil supply-demand fundamentals, policy uncertainties, and climate-related shocks, with the core drivers of connectedness varying considerably across different moments and timescales. These findings provide valuable insights for the coordinated governance of energy and food markets, the improvement of multilayered risk early-warning systems, and the optimization of investment strategies.

Balancing development and climate sustainability is a critical issue confronting nations worldwide. This is the core focus of our new journal, Development and Sustainability in Economics and Finance (DSEF). Herein, we dive into the primary economic considerations and financial challenges related to achieving this double objective of economic development in a sustainable way. DSEF contributions will examine key aspects of this challenge, particularly, economic development, financial stability, environmental sustainability, and social development in the context of a changing climate, increasing international polarization and limited resources. The journal will provide valuable insights for researchers and practitioners worldwide on several topics related to this crucial issue. Through in-depth analysis and evaluation, DSEF will highlight pathways towards ensuring long-term economic prosperity alongside with sustainable development. This article provides an initial overview of the type of research DSEF seeks to publish.

The world continues to face major economic challenges due to crises that have a considerable impact on energy consumption. Since the global energy crisis, energy commodity costs have risen, and economic slowdowns in various regions continue to influence electricity market trends.Clean energy markets, propelled by the growing interest of economic entities and investors, are now interconnected with several commodity markets. In this context, energy metals play a crucial role in influencing the dynamics of the clean energy and particularly electricity markets. This study takes a close look at the complex interconnections between clean energy indices and the metals market, focusing on volatility dynamics. Drawing on advanced methodologies, including the Diebold and Yilmaz method, we unveil the complex relationships that link these markets. Our results reveal distinct patterns and interactions, highlighting the nuanced nature of the connection between clean energy indices and metal prices. The insights revealed have significant implications for policymakers and investors seeking to align their strategies with sustainable energy transitions and ensure financial stability. By enhancing our understanding of the interdependencies between clean energy indices and metals markets, this research provides valuable guidance for navigating the changing clean energy investment landscape.

Climate change presents challenges to policy and economic stability, necessitating effective trading strategies to reduce environmental risks. This article addresses gaps in existing studies by using a Markov-switching model to consider climate risk. Backward stochastic differential equations are used to optimize utility with three hedging strategies based on the concept of risk aversion. Numerical scenarios confirm the model's superiority in incorporating exogenous events, with our risk-averse strategy outperforming classical approaches. Our strategy outperforms classical strategies by taking a flexible risk trading when investors face risk-averse behavior due to climate risk events. The findings presented in this article have important implications for the development of more resilient investment portfolios and can contribute to climate policy.