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Wei-Xing Zhou

Publications and source records attributed to Wei-Xing Zhou.

At least 19 recordsLinked to original sources

Import dependence and per capita production are main determinants of economies' food supply robustness under production shocks

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.

econ.GN

On the existence of Ulanowicz's optimal structural resilience in complex networks

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 $α= 1/\mathrm{e}$, 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 $N_\mathcal{V} \geq 3$ 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 $N_\mathcal{V}$ increases: adjacent primary links scale as $O(N_\mathcal{V}^{-1})$, whereas non-adjacent background links exhibit a steeper quadratic decay of $O(N_\mathcal{V}^{-2})$ 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.

physics.soc-ph

Uncertainty and financial market resilience: Evidence from China

Financial market resilience reflects the ability of a financial market to withstand external shocks and to recover from them, while its measurement has yet to be standardized. Accordingly, this paper quantifies the adaptability and recoverability of China's total financial market and five key sub-markets as both proxy indicators of their resilience. The results highlight the event-driven nature of China's financial market resilience and reveal a strong correlation between the two indicators, which is more pronounced in the stock and bond markets. Using the Diebold-Yilmaz connectedness approach, we further examine volatility spillovers among resilience of sub-markets and identify the foreign exchange market as a major transmitter of spillovers, whereas the stock and bulk commodity markets primarily act as net recipients of spillovers. Moreover, we analyze the impacts of five China-related uncertainties on financial market resilience. Overall, geopolitical risks, economic and trade policy uncertainty, and U.S.-China tensions exert significant negative impacts on total market resilience, while the effect of climate policy uncertainty remains less pronounced. Importantly, the impacts of different uncertainties exhibit heterogeneity across resilience of sub-markets. Our findings not only enrich the resilience measurement of financial market but also provide new evidence to inform targeted risk management and policy design aimed at strengthening financial system's resilience.

q-fin.RM

Moment connectedness and driving factors in the energy-food nexus: A time-frequency perspective

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.

econ.GN

Quantifying the dynamic structural resilience of international staple food trade networks: An entropy-based approach

Establishing a resilient food trade system is an international consensus on safeguarding food security amid growing disruptions. However, a unified resilience framework has yet to be established, leading to the proliferation of diverse measures. Here, we conceptualize resilience as a trade-off between efficiency and redundancy and 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 internal components to resilience dynamics. 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. Following the leave-one-out approach, we furthermore identify critical economies and trade relationships that disproportionately affect the overall resilience, some of which are less well-focused in previous studies. Moreover, we highlight that overconcentration of flows along core trade relationships may undermine both efficiency and resilience, whereas peripheral trade networks may play strategic alternative roles in sustaining resilience, underscoring the importance of concentrating on developing economies and promoting broader trade links. These findings not only provide new insights for assessing the resilience of international food trade systems but also propose directions for strengthening resilience through both regional cooperation and more inclusive trade relations.

physics.soc-ph

Russia-Ukraine conflict and the quantile return connectedness of grain futures in the BRICS and international markets

This study investigates quantile-based connectedness among BRICS and international grain futures around the Russia-Ukraine conflict and milestones of the Black Sea Grain Initiative. Using a dynamic quantile VAR combined with a frequency-domain decomposition, we trace spillovers across market states and horizons. Spillovers are heterogeneous across quantiles, as the time-varying total connectedness index hovers near 95% in the tails, remains well above the median, and is higher before the outbreak than after. Furthermore, grain type and regional proximity strengthen pairwise connectedness. South African grain futures are persistent net receivers, whereas Argentine grain futures, U.S. soybean, and Ukrainian wheat are key transmitters. In the frequency domain, short-term components dominate total spillovers. In portfolio applications, the minimum connectedness portfolio delivers a positive Sharpe ratio under both normal and lower tail conditions. Overall, the results inform asset allocation and risk management in grain futures markets under geopolitical instability and support policy formulation.

q-fin.RM

Dynamic spillovers and investment strategies across artificial intelligence ETFs, artificial intelligence tokens, and green markets

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.

q-fin.RM

Structural robustness of the international food supply network under external shocks and its determinants

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.

econ.GN

Spillover effects between climate policy uncertainty, energy markets, and food markets: A time-frequency analysis

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 Barun{í}k and K{ř}hl{í}k (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.

econ.GN

The impact of external uncertainties on the extreme return connectedness between food, fossil energy, and clean energy markets

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% for the right tail and 61.47% for the left tail) is significantly higher than at the median (23.02%). Further-more, dynamic analysis reveals that connectedness fluctuates 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.

econ.GN

Multiscale risk spillovers and external driving factors: Evidence from the global futures and spot markets of staple foods

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.

econ.EM

Risk spillovers between the BRICS and the U.S. staple grain futures markets

This study examines contemporaneous and lagged spillover effects in BRICS staple grain futures markets and their linkages with U.S. markets. The results show that contemporaneous spillovers dominate, while net spillovers are driven by lagged connectedness. Systemic risk is lower in intra-BRICS markets compared to those including the U.S., highlighting the U.S. grain market's significant influence. Brazilian and U.S. grains are key net spillover contributors, excluding U.S. rice, while South African staple grains act as major net receivers. Particularly, the spillover between soybeans is the strongest. The study also reveals heterogeneous impacts of the Russia-Ukraine conflict and Black Sea Grain Initiative on grain futures.

q-fin.RM

Contemporaneous and lagged spillovers between agriculture, crude oil, carbon emission allowance, and climate change

In this paper, we examine the dynamic spillovers among the crude oil, carbon emission allowance, climate change, and agricultural markets. Adopting a novel $R^2$ decomposed connectedness approach, our empirical analysis reveals several key findings. The overall TCI dynamics have been mainly dominated by contemporaneous dynamics rather than the lagged dynamics. We also find climate change has significant spillovers to other markets. Moreover, there are heterogeneous spillover effects among agricultural markets. Specially, corn is the biggest risk contributor to this system, while barley is the major risk receiver of shocks.

q-fin.RM

Joint multifractality in the cross-correlations between grains \& oilseeds indices and external uncertainties

This study investigates the relationships between agricultural spot markets and external uncertainties via the multifractal detrending moving-average cross-correlation analysis (MF-X-DMA). The dataset contains the Grains \& Oilseeds Index (GOI) and its five sub-indices of 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 the presence of multifractal cross-correlations between agricultural markets and uncertainties. Further, statistical tests show that maize has intrinsic joint multifractality with all the uncertainty proxies, exhibiting a high degree of sensitivity. Additionally, intrinsic multifractality among GOI-GPR, wheat-GPR and soyabeans-VIX is illustrated. However, other series have apparent multifractal cross-correlations with high possibilities. Moreover, our analysis suggests that among the three kinds of external uncertainties, geopolitical risk has a relatively stronger association with grain prices.

q-fin.ST

Motif analysis and passing behavior in football passing networks

The strategic orchestration of football matchplays profoundly influences game outcomes, motivating a surge in research aimed at uncovering tactical nuances through social network analysis. In this paper, we delve into the microscopic intricacies of cooperative player interactions by focusing on triadic motifs within passing networks. Employing a dataset compiled from 3,199 matches across 18 premier football competitions, we identify successful passing activities and construct passing networks for both home and away teams. Our findings highlight a pronounced disparity in passing efficiency, with home teams demonstrating superior performance relative to away teams. Through the identification and analysis of 3-motifs, we find that the motifs with more bidirectional links are more significant. It reveals that footballers exhibit a strong tendency towards backward passes rather than direct forward attacks. Comparing the results of games, we find that some motifs are related to the goal difference. It indicates that direct and effective forward passing significantly amplifies a team's offensive capabilities, whereas an abundance of passbacks portends an elevated risk of offensive futility. These revelations affirm the efficacy of network motif analysis as a potent analytical tool for unveiling the foundational components of passing dynamics among footballers and for decoding the complex tactical behaviors and interaction modalities that underpin team performance.

physics.soc-ph

Structural evolution of international crop trade networks

Food security is a critical issue closely linked to human being. With the increasing demand for food, international trade has become the main access to supplementing domestic food shortages, which not only alleviates local food shocks, but also exposes economies to global food crises. In this paper, we construct four temporal international crop trade networks (iCTNs) based on trade values of maize, rice, soybean and wheat, and describe the structural evolution of different iCTNs from{ {1993}} to 2018. We find that the size of all the four iCTNs expanded from{ {1993}} to 2018 with more participants and larger trade values. Our results show that the iCTNs not only become tighter according to the increasing in network density and clustering coefficient, but also get more similar. We also find that the iCTNs are not always disassortative, unlike the world cereal trade networks and other international commodity trade networks. The degree assortative coefficients depend on degree directions and crop types. The analysis about assortativity also indicates that economies with high out-degrees tend to connect with economies with low in-degrees and low out-degrees. Additionally, we compare the structure of the four iCTNs to enhance our understanding of the international food trade system. Although the overall evolutionary patterns of different iCTNs are similar, some crops exhibit idiosyncratic trade patterns. It highlights the need to consider different crop networks' idiosyncratic features while making food policies. Our findings about the dynamics of the iCTNs play an important role in understanding vulnerabilities in the global food system.

physics.soc-ph

Visibility graph analysis of the grains and oilseeds indices

The Grains and Oilseeds Index (GOI) and its sub-indices of wheat, maize, soyabeans, rice, and barley are daily price indexes reflect the price changes of the global spot markets of staple agro-food crops. In this paper, we carry out a visibility graph (VG) analysis of the GOI and its five sub-indices. Our findings reveal that the degree distributions of the VGs, except for rice, exhibit exponentially truncated power-law tails, while the rice VG conforms to a power-law tail. The average clustering coefficients of the six VGs are quite large ($>0.5$) and exhibit a nice power-law relation with respect to the average degrees of the VGs. For each VG, the clustering coefficients of nodes are inversely proportional to their degrees for large degrees and are correlated to their degrees as a power law for small degrees. All the six VGs exhibit small-world characteristics. The degree-degree correlation coefficients shows that the VGs for maize and soyabeans indices exhibit weak assortative mixing patterns, while the other four VGs are weakly disassortative. The average nearest neighbor degree functions have similar patterns, and each function shows a more complex mixing pattern which decreases for small degrees, increases for mediate degrees, and decreases again for large degrees.

econ.GN

Temporal rich club phenomenon and its formation mechanisms

The temporal rich club (TRC) phenomenon is widespread in real systems, forming a tight and continuous collection of the prominent nodes that control the system. However, there is still a lack of sufficient understanding of the mechanisms of TRC formation. Here we use the international N-nutrient trade network as an example of an in-depth identification, analysis, and modeling of its TRC phenomenon. The system exhibits a statistically significant TRC phenomenon, with eight economies forming the cornerstone club. Our analysis reveals that node degree is the most influential factor in TRC formation compared to other variables. The mathematical evolution models we constructed propose that the TRC in the N-nutrient trade network arises from the coexistence of degree-homophily and path-dependence mechanisms. By comprehending these mechanisms, we introduce a novel perspective on TRC formation. Although our analysis is limited to the international trade system, the methodology can be extended to analyze the mechanisms underlying TRC emergence in other systems.

physics.soc-ph