This study provides a foundational theoretical investigation into the mathematical existence and asymptotic properties of Ulanowicz's structural resilience. While ecological evidence suggests that sustainable systems gravitate toward an optimal efficiency-redundancy balance at alpha = 1/e, the mathematical attainability of this configuration across broader network topologies remains unverified. We rigorously prove that while optimal resilience is structurally unattainable in two-node networks, there exists at least one optimal flow configuration within the feasible probability space for any weighted and directed network with the network size N-V >= 3 and no self-loops. To make the derivations analytically tractable, we introduce a parameterized symmetric network model with uniform marginal distributions. Using this stylized ansatz, our analytical and numerical results reveal that maintaining the optimal state requires distinct asymptotic scaling behaviors as Nv increases: adjacent primary links scale as O(N-V(-1)), whereas non-adjacent background links exhibit a steeper quadratic decay of O(N-V(-2)) with specific logarithmic corrections. Rather than serving as an immediate engineering tool, this work establishes a rigorous mathematical boundary for the optimal resilience framework, demonstrating analytically how an optimally resilient system differentiates into high-throughput primary channels and sparse redundancy pathways.

Understanding how contextual factors are associated with the spatial organization of football passing remains a key challenge in performance analysis. While network approaches reveal tactical structures, their interpretability is often limited by a lack of statistical validation and unclear links to match context. This study bridges these gaps by first establishing the statistical robustness of spatial passing communities and then quantifying their association with contextual variables. We analyzed 532 FC Barcelona matches, constructing directed passing networks on a discretized pitch. Community structures were detected using the Infomap algorithm and validated against null models and the Directed Degree-Corrected Stochastic Block Model. The association between communities and contextual factors was measured using Normalized Mutual Information (NMI). Our results, validated by permutation tests, demonstrate that Barcelona’s own coach exhibits the most robust statistical associations with passing community structure, while opponent coach shows only marginal significance. In contrast, match venue and scoreline showed negligible association. Barcelona’s networks consistently exhibited high connectivity, clustering, and a tendency to form two dominant spatial modules, reflecting a stable, possession-based core adaptable to specific threats. This work provides a scalable, context-aware framework for football analytics, advancing from descriptive mapping to diagnostic modeling. Metrics like clustering coefficient and module size offer structure-sensitive tools for tactical evaluation, linking network architecture to actionable insights.

Resilience of a financial market reflects its capacity to withstand external shocks and to recover its essential functions. To operationalize its measurement, this paper employs a dual-metric framework quantifying the adaptability and recoverability of China’s financial markets using a stress-test paradigm driven by global volatility (the VIX). Furthermore, we analyze the impacts of five China-related uncertainties on this structural resilience. Baseline estimates demonstrate that geopolitical risks, economic and trade policy uncertainties, and U.S.–China tensions significantly reduce aggregate market resilience, whereas the effect of climate policy uncertainty remains limited. In contrast, robust domestic consumer confidence plays a vital role in accelerating the post-shock recovery process. Furthermore, we find that market vulnerability to uncertainties is highly state-dependent, becoming significantly amplified during major historical crises characterized by domestic liquidity stress. Finally, the impacts display substantial cross-market heterogeneity, highlighting that the bond market acts as a safe haven during crises. Our findings enrich the resilience measurement literature and provide fresh evidence to inform targeted, counter-cyclical policy design.

Food supply shocks in major producing economies can propagate through trade networks and generate uneven impacts across the global food system. This study examines the robustness of economies' food supply under production shocks to major producers in the global staple food system. Using 2023 production, reserve, and bilateral trade data for wheat, rice, maize, and soybean, we construct a calorie-based global food supply network across economies. We extend a dynamic shock propagation framework and then simulate production shocks to major producing economies, tracing how supply losses propagate. The results show substantial heterogeneity in robustness across crops and economies. Wheat exhibits the highest overall robustness, whereas soybean shows the lowest. Economies with high robustness tend to be either relatively isolated from the trade network or actively engaged in trade while maintaining strong and stable domestic production, whereas low-robustness economies are predominantly those with high import dependence. Import dependence and per capita production emerge as the most important determinants of robustness. Based on these findings, we design two counterfactual policies targeting highly import-dependent economies: increasing reserve availability and adjusting trade linkages. Counterfactual experiments show that the two policies yield only modest overall improvements, with effects varying substantially across crops. Both policies improve robustness in the aggregated system and wheat, trade adjustment is more effective for rice, and it brings limited or even negative effects for maize and soybean.

As the stablecoin market has expanded rapidly, the linkages between stablecoins and the U.S. traditional money market have grown increasingly close. This paper examines the dynamic effects of stablecoin depegging on liquidity in the U.S. money market. We develop an analytical framework for money market liquidity that incorporates stablecoins, and demonstrate how depegging events transmit liquidity stress to the traditional money market by tightening the balance sheet constraints of financial intermediaries. Empirical analysis based on local projections reveals significant market heterogeneity: liquidity in the Treasury bill market follows a non-monotonic path, improving initially before subsequently reversing; liquidity in the commercial paper market deteriorates significantly on the day of the shock; and liquidity deterioration in the repo market exhibits a delayed peak. Further analysis indicates that the impact of depegging on intermediary balance sheets is primarily reflected in a rise in the marginal cost of expanding balance sheet capacity, rather than a substantial drawdown of capital. When intermediary capital is under stress, liquidity deterioration across all three markets is significantly amplified. These findings provide empirical evidence of risk coupling between crypto-asset markets and traditional money markets, with policy implications for the design of macroprudential regulatory frameworks for stablecoins.

This paper investigates dynamic risk spillovers and portfolio linkages among artificial intelligence (AI) exchange-traded funds (ETFs), AI tokens, and green financial markets using daily data from 2021 to 2025. Using an R2 decomposition of connectedness, we separate contemporaneous co-movements from lagged spillovers to identify cross-market risk transmission. The results show that AI ETFs and clean energy assets act as major risk transmitters, while AI tokens and green bonds are primary risk receivers. Spillovers occur mainly in real time rather than with delay. Green assets, particularly green bonds, provide more effective cross-hedging than AI tokens, which offer limited diversification. Portfolio analysis shows that minimum correlation portfolios deliver superior performance, highlighting asymmetric but globally integrated relationships between AI and green assets.

Online donation platforms are instrumental in facilitating individual charitable giving by leveraging the confluence of financial technology and mobile social networks. Despite the impact of these technological advancements, the role of culture in shaping the motivations and behaviors of donors remains an open area for exploration. By analyzing three different datasets, we elucidate the relationship between individualism/collectivism and charitable donations at the national, regional, and personal levels. A remarkable U-shaped pattern is revealed between individualism/collectivism and charitable donations. It is also found that the personal reputation strengthens the positive effect of individualism/collectivism on the donation amount, with a notably stronger influence on individualists than on collectivists. Furthermore, collectivists prefer to allocate donations to acquaintances, whereas individualists tend to direct their contributions towards strangers. Our results not only provide an understanding of the psychological and social mechanisms that underlie cultural influences on charitable giving but also highlight the importance of considering cultural contexts when designing strategies to encourage and enhance charitable donations.

This study investigates the relationships between agricultural spot markets and external uncertainties through multifractal detrending moving-average cross-correlation analysis (MF-X-DMA). The dataset contains the Grains Oilseeds Index (GOI) and its five subindices for wheat, maize, soyabeans, rice, and barley. Moreover, we use three uncertainty proxies, namely, economic policy uncertainty (EPU), geopolitical risk (GPR), and Volatility Index (VIX). We observe multifractal cross-correlations between agricultural markets and uncertainties. Furthermore, statistical tests reveal that maize has intrinsic joint multifractality with all the uncertainty proxies, highly sensitive to external shocks. Additionally, intrinsic multifractality among GOI-GPR, wheat-GPR, and soyabeans-VIX is illustrated. However, other series have apparent multifractal cross-correlations with high probabilities. Moreover, our analysis suggests that among the three types of external uncertainties, GPR has the strongest association with grain prices, excluding maize and soyabeans.

Establishing a resilient food trade system is an international consensus on safeguarding food security amid growing disruptions. We employ an entropy-based approach to quantify the dynamic structural resilience of international trade networks for maize, rice, soybean, and wheat from 1986 to 2022. Using index decomposition analysis, we also investigate the relative contributions of several internal components to changes in resilience. Within this framework, despite heterogeneity across different food commodities, we find that current trade networks are relatively redundant, with improvements in efficiency being the dominant driver of changes in resilience. In addition, we reveal a historically pronounced impact of flow concentrations on resilience, while trade interactions have become increasingly important in recent years. Furthermore, following the leave-one-out approach, we identify critical economies and trade relationships that disproportionately affect the overall resilience, many of which are overlooked by conventional volume-based or centrality-based metrics. Moreover, we highlight that the overconcentration of flows along core trade relationships may undermine the overall efficiency and resilience, whereas peripheral trade networks play an essential role in sustaining resilience, underscoring the importance of promoting more equitable trade relations. These findings not only provide new insights for assessing the resilience of international food trade systems but also propose directions for making them more resilient.

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.

Traditional sparse factor models (e.g., Fama–French) struggle to explain cross-sectional returns in high-dimensional settings due to the ‘factor zoo’—a proliferation of anomalies with overlapping or noisy signals. We show that a principal component (PC)-based stochastic discount factor (SDF) using regularization techniques can aggregate characteristics into dominant risk sources, balancing parsimony and robustness. First, the SDF is estimated using a small sample of 25 portfolios double-sorted by size/book-to-market ratio, and it is found that only 2 principal component factors are needed to predict the cross-sectional returns well, which is consistent with the classical size premium and value premium. Then, the sample is further extended to 72 anomalous characteristics. The results show that the sparse PC-based SDF predicts the cross-sectional returns better than the sparse original characteristic-based SDF. We verify that sparse PC-based models outperform traditional sparse factor models even in emerging markets like China, where retail-driven trading and regulatory shifts amplify idiosyncratic risks.

With the frequent occurrence of black swan events, global energy security situation has become increasingly complex and severe. Assessing the resilience of the international oil trade network (iOTN) is crucial for evaluating its ability to withstand extreme shocks and recover thereafter, ensuring energy security. We overcomes the limitations of discrete historical data by developing a simulation model for extreme event shock-recovery in the iOTNs. We introduce network efficiency indicator to measure oil resource allocation efficiency and evaluate network performance. Then, construct a resilience index to explore the resilience of the iOTNs from dimensions of resistance and recoverability. Our findings indicate that extreme events can lead to sharp declines in performance of the iOTNs, especially when economies with significant trading positions and relations suffer shocks. The upward trend in recoverability and resilience reflects the self-organizing nature of the iOTNs, demonstrating its capacity for optimizing its own structure and functionality. Unlike traditional energy security research based solely on discrete historical data or resistance indicators, our model evaluates resilience from multiple dimensions, offering insights for global energy governance systems while providing diverse perspectives for various economies to mitigate risks and uphold energy security.

We investigate the extreme return connectedness between the food, fossil energy, and clean energy markets using the quantile connectedness approach, which combines the traditional spillover index with quantile regression. Our results show that return connectedness at the tails (57.91 and 61.47 (23.02 over time, with notable increases during extreme events. Among these markets, fossil energy market consistently acts as the net receiver, while clean energy market primarily serves as the net transmitter. Additionally, we use linear and nonlinear ARDL models to examine the role of external uncertainties on return connectedness. We find that climate policy uncertainty (CPU), geopolitical risk (GPR), and the COVID-19pandemic significantly impact median connectedness, while economic policy uncertainty (EPU),GPR, and trade policy uncertainty (TPU) are crucial drivers of extreme connectedness. Our findings provide valuable insights for investors and policymakers on risk spillover effects between food and energy markets under both normal and extreme market conditions.

Foreign direct investment (FDI) affects the economic development of an economy. We explore the characteristics and evolutionary patterns of intrinsic communities in the international foreign direct investment networks (iFDINs) by analyzing their backbones. Using bilateral FDI data from 2009 to 2022, we construct the iFDINs, extract their backbones, and then apply the Louvain community detection algorithm to identify communities. The evolutionary process of these communities is then analyzed over time. With the recovery from the financial crisis in 2008, the iFDIN gradually expanded, leading to the formation of several longterm stable community structures in the backbone. After 2018, however, the network began to contract, influenced by international events such as the US tax reform, the US-China trade conflict, rising investment protectionism, and the COVID-19 pandemic. As a result, several community structures in the backbone underwent fragmentation and reorganization. By extracting the intrinsic community blocks of the network, we find that most economies in the same intrinsic community block are geographically clustered. Moreover, free trade zones and economic cooperation organizations can effectively facilitate the FDI relationships between member economies. Additionally, historical and cultural ties not only reduce the transaction costs of FDI, but also enable economies to maintain stable FDI relationships across geographical boundaries.

It is important to maintain the resilient international food trade network for food security. We have constructed the international trade networks of maize, rice, soybean, and wheat based on bilateral flows data between economies. Drawing on information theory, we have measured their dynamic resilience based on efficiency and redundancy during 1986 to 2022. We have also investigated the impact of economies and relationships on their resilience. Overall, we argue that rice and soybean trade networks deserve more attention while resilience in maize and wheat shows a steady upward trend. Meanwhile, our findings emphasize the importance of diversity of trade flows and partners for enhancing resilience. Currently, for example, excessively high monopolization of soybean trade may not be beneficial for its resilience. Also, we have found that major exporters and relationships between geographically bordering economies have greater impact on the resilience. Moreover, we have confirmed the existence of different network structures with the optimal resilience as relationships are removed cumulatively, which may be an informative guide for the international food trade.

The current international landscape is turbulent and unstable, with geopolitical risk having emerged as a significant threat. Focusing on the grain futures market, this paper builds different geopolitical risk measures by random matrix theory and constructs GJR-GARCH-MIDAS models to investigate the impact of geopolitical risk on grain market volatility. The findings indicate that rolling-window modeling performs better in describing the overall volatility of wheat, corn, soybean, and rice markets, and two-factor models generally exhibit stronger explanatory power in most cases. Short-term volatility demonstrates obvious volatility clustering and high volatility persistence, without significant asymmetry. Additionally, realized volatility of wheat, corn, and soybean significantly exacerbates their long-run volatility, while geopolitical risks of different dimensions show varying directions and degrees of effects in explaining long-term volatility of the four submarkets. This study offers valuable insights into grain market volatility and geopolitical risk, contributing to agricultural futures investment and global food security.

This paper investigates the risk spillovers among AI ETFs, AI tokens, and green markets using the R2 decomposition method. We reveal several key insights. First, the overall transmission connectedness index (TCI) closely aligns with the contemporaneous TCI, while the lagged TCI is significantly lower. Second, AI ETFs and clean energy act as risk transmitters, whereas AI tokens and green bond function as risk receivers. Third, AI tokens are difficult to hedge and provide limited hedging ability compared to AI ETFs and green assets. However, multivariate portfolios effectively reduce AI tokens investment risk. Among them, the minimum correlation portfolio outperforms the minimum variance and minimum connectedness portfolios.

The study examines the return connectedness between climate policy uncertainty (CPU), clean energy, fossil energy, and food markets. Using the time-domain method of Diebold and Yilmaz (2012) and frequency-domain methods of Baruník and Křhlík (2018), we find substantial spillover effects between these markets. Furthermore, high frequency domain is the primary driver of overall connectedness. In addition, CPU is a net contributor of return shocks in the short term, whereas it turns to be a net recipient in the medium and long terms. Across all frequencies, clean energy and oils are consistent net recipients, while meat is a dominant net contributor.

The stability of the global food supply network is critical for ensuring food security. This study constructs an aggregated international food supply network based on the trade data of four staple crops and evaluates its structural robustness through network integrity under accumulating external shocks. Network integrity is typically quantified in network science by the relative size of the largest connected component, and we propose a new robustness metric that incorporates both the broadness p and severity q of external shocks. Our findings reveal that the robustness of the network has gradually increased over the past decades, punctuated by temporary declines that can be explained by major historical events. While the aggregated network remains robust under moderate disruptions, extreme shocks targeting key suppliers such as the United States and India can trigger systemic collapse. When the shock broadness p is less than about 0.3 and the shock severity q is close to 1, the structural robustness curves S(p,q) decrease linearly with respect to the shock broadness p, suggesting that the most critical economies have relatively even influence on network integrity. Comparing the robustness curves of the four individual staple foods, we find that the soybean supply network is the least robust. Furthermore, regression and machine learning analyses show that increaseing food (particularly rice and soybean) production enhances network robustness, while rising food prices significantly weaken it.