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This study examines the quantile connectedness among grain futures markets in BRICS and international markets, with a particular focus on the ongoing and escalating impacts of the Russia-Ukraine conflict. The findings reveal significant heterogeneity in spillover effects across different quantiles and market conditions. Specifically, the time-varying total connectedness index (TCI) consistently fluctuated around 95\% under both extreme bearish and bullish market conditions, markedly higher than in normal market conditions. Moreover, across all quantile levels, the TCI was higher during the pre-outbreak period than in the post-outbreak period. This systemic risk has notably decreased following the onset of the Russia-Ukraine conflict and the subsequent changes to the Black Sea Grain Initiative. Apart from rice, U.S. grain futures maintained a dominant position as benchmarks for international grain prices, exerting substantial influence over the grain futures markets in BRICS throughout most of the period. Finally, the study highlights that the influence of grain type and regional proximity strengthens pairwise connectedness among futures markets, with short-term spillovers being dominant and the spillover effect generally symmetric across quantiles., Comment: 42 pages, 31 figures

Resilience serves to assess the ability of financial markets to resist external shocks. The intensity and duration, used to indicate resilience, are calculated for China's financial markets in this paper, focusing on the performance of each financial market during and after several crises. Given that climate issues have been recognized as an important source of risk by financial markets, we also investigate the spillover effects and mechanism of China's climate policy uncertainty on its financial markets resilience. We have found that the two resilience indicators of each market have a relatively consistent trend, but spillovers among markets have different sensitivities to the both. In addition, China's climate policy uncertainty shocks its financial markets resilience by increasing the investor sentiment index and the non-performing loan ratio of commercial banks and by reducing the capital and financial account balance. It is further found that China's financial markets' consensus on the unswerving implementation of climate policy, which provides the reference for other countries on how to balance climate policies introduction and financial markets development.

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., Comment: 30 pages, 21 figures

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., Comment: 12 pages, 6 figures

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.

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.

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.

The current international landscape is turbulent and unstable, with frequent outbreaks of geopolitical conflicts worldwide. Geopolitical risk has emerged as a significant threat to regional and global peace, stability, and economic prosperity, causing serious disruptions to the global food system and food security. Focusing on the international food market, this paper builds different dimensions of geopolitical risk measures based on the random matrix theory and constructs single- and two-factor GJR-GARCH-MIDAS models with fixed time span and rolling window, respectively, to investigate the impact of geopolitical risk on food market volatility. The findings indicate that modeling based on rolling window performs better in describing the overall volatility of the wheat, maize, soybean, and rice markets, and the two-factor models generally exhibit stronger explanatory power in most cases. In terms of short-term fluctuations, all four staple food markets demonstrate obvious volatility clustering and high volatility persistence, without significant asymmetry. Regarding long-term volatility, the realized volatility of wheat, maize, and soybean significantly exacerbates their long-run market volatility. Additionally, geopolitical risks of different dimensions show varying directions and degrees of effects in explaining the long-term market volatility of the four staple food commodities. This study contributes to the understanding of the macro-drivers of food market fluctuations, provides useful information for investment using agricultural futures, and offers valuable insights into maintaining the stable operation of food markets and safeguarding global food security., Comment: 38 pages, 3 figures, 11 tables

The Russia-Ukraine conflict is a growing concern worldwide and poses serious threats to regional and global food security. Using monthly trade data for maize, rice, and wheat from 2016/1 to 2022/12, this paper constructs three international crop trade networks (iCTNs) and an aggregate international food trade network (iFTN). We aim to examine the structural changes following the occurrence of the Russia-Ukraine conflict. We find significant shifts in the number of edges, average degree, density, efficiency, and natural connectivity in the third quarter of 2022, particularly in the international wheat trade network. Additionally, we have shown that political reasons have caused more pronounced changes in the trade connections between the economies of the North Atlantic Treaty Organization and Russia than with Ukraine. This paper could provide insights into the negative impact of geopolitical conflicts on the global food system and encourage a series of effective strategies to mitigate the negative impact of the conflict on global food trade.

Agricultural products play a critical role in human development. With economic globalization and the financialization of agricultural products continuing to advance, the interconnections between different agricultural futures have become closer. We utilize a TVP-VAR-DY model combined with the quantile method to measure the risk spillover between 11 agricultural futures on the futures exchanges of US and China from July 9,2014, to December 31,2022. This study yielded several significant findings. Firstly, CBOT corn, soybean, and wheat were identified as the primary risk transmitters, with DCE corn and soybean as the main risk receivers. Secondly, sudden events or increased economic uncertainty can increase the overall risk spillovers. Thirdly, there is an aggregation of risk spillovers amongst agricultural futures based on the dynamic directional spillover results. Lastly, the central agricultural futures under the conditional mean are CBOT corn and soybean, while CZCE hard wheat and long-grained rice are the two risk spillover centers in extreme cases, as per the results of the spillover network and minimum spanning tree. Based on these results, decision-makers are advised to safeguard against the price risk of agricultural futures under sudden economic events, and investors can utilize the results to construct a superior investment portfolio by taking different agricultural product futures as risk-leading indicators according to various situations.

The ongoing Russia-Ukraine conflict between two major agricultural powers has posed significant threats and challenges to the global food system and world food security. Focusing on the impact of the conflict on the global agricultural market, we propose a new analytical framework for tail dependence, and combine the Copula-CoVaR method with the ARMA-GARCH-skewed Student-t model to examine the tail dependence structure and extreme risk spillover between agricultural futures and spots over the pre- and post-outbreak periods. Our results indicate that the tail dependence structures in the futures-spot markets of soybean, maize, wheat, and rice have all reacted to the Russia-Ukraine conflict. Furthermore, the outbreak of the conflict has intensified risks of the four agricultural markets in varying degrees, with the wheat market being affected the most. Additionally, all the agricultural futures markets exhibit significant downside and upside risk spillovers to their corresponding spot markets before and after the outbreak of the conflict, whereas the strengths of these extreme risk spillover effects demonstrate significant asymmetries at the directional (downside versus upside) and temporal (pre-outbreak versus post-outbreak) levels., Comment: 35 pages, 2 figures

This paper adopts the random matrix theory (RMT) to analyze the correlation structure of the global agricultural futures market from 2000 to 2020. It is found that the distribution of correlation coefficients is asymmetric and right skewed, and many eigenvalues of the correlation matrix deviate from the RMT prediction. The largest eigenvalue reflects a collective market effect common to all agricultural futures, the other largest deviating eigenvalues can be implemented to identify futures groups, and there are modular structures based on regional properties or agricultural commodities among the significant participants of their corresponding eigenvectors. Except for the smallest eigenvalue, other smallest deviating eigenvalues represent the agricultural futures pairs with highest correlations. This paper can be of reference and significance for using agricultural futures to manage risk and optimize asset allocation., Comment: 19 pages, 7 figures

Grains account for more than 50% of the calories consumed by people worldwide, and military conflicts, pandemics, climate change, and soaring grain prices all have vital impacts on food security. However, the complex price behavior of the global grain spot markets has not been well understood. A recent study performed multifractal moving average analysis (MF-DMA) of the Grains & Oilseeds Index (GOI) and its sub-indices of wheat, maize, soyabeans, rice, and barley and found that only the maize and barley sub-indices exhibit an intrinsic multifractal nature with convincing evidence. Here, we utilize multifractal fluctuation analysis (MF-DFA) to investigate the same problem. Extensive statistical tests confirm the presence of intrinsic multifractality in the maize and barley sub-indices and the absence of intrinsic multifractality in the wheat and rice sub-indices. Different from the MF-DMA results, the MF-DFA results suggest that there is also intrinsic multifractality in the GOI and soyabeans sub-indices. Our comparative analysis does not provide conclusive information about the GOI and soyabeans and highlights the high complexity of the global grain spot markets., Comment: 23 pages including 14 figures

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.

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., Comment: 16 pages, 9 figures

This paper combines the Copula-CoVaR approach with the ARMA-GARCH-skewed Student-t model to investigate the tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets, taking four main agricultural commodities, namely soybean, maize, wheat, and rice as examples. The empirical results indicate that the tail dependence structures for the four futures-spot pairs are quite different, and each of them exhibits a certain degree of asymmetry. In addition, the futures market for each agricultural commodity has significant and robust extreme downside and upside risk spillover effects on the spot market, and the downside risk spillover effects for both soybeans and maize are significantly stronger than their corresponding upside risk spillover effects, while there is no significant strength difference between the two risk spillover effects for wheat, and rice. This study provides a theoretical basis for strengthening global food cooperation and maintaining global food security, and has practical significance for investors to use agricultural commodities for risk management and portfolio optimization., Comment: 37 pages, 7 figures

Energy security and energy trade are the cornerstones of global economic and social development. The structural robustness of the international oil trade network (iOTN) plays an important role in the global economy. We integrate the machine learning optimization algorithm, game theory, and utility theory for learning an oil trade decision-making model which contains the benefit endowment and cost endowment of economies in international oil trades. We have reconstructed the network degree, clustering coefficient, and closeness of the iOTN well to verify the effectiveness of the model. In the end, policy simulations based on game theory and agent-based model are carried out in a more realistic environment. We find that the export-oriented economies are more vulnerable to be affected than import-oriented economies after receiving external shocks. Moreover, the impact of the increase and decrease of trade friction costs on the international oil trade is asymmetrical and there are significant differences between international organizations., Comment: 14 pages, 5 figures

Pesticides are a kind of agricultural input, whose use can greatly reduce yield loss, regulate plant growth, effectively liberate agricultural productivity, and improve food security. The availability of pesticides in economies all over the world is ensured by pesticide redistribution through international trade and economies play different roles in this process. In this work, we measure and rank the importance of economies using nine node metrics in an evolutionary way. It is found that the clustering coefficient is correlated negatively with the other eight node metrics, while the other eight node metrics are positively correlated with each other and can be grouped into three communities (betweenness; in-degree, PageRank, authority, and in-closeness; out-degree, hub, and out-closeness). We further investigate the structural robustness of the international pesticide trade networks proxied by the giant component size under three types of shocks to economies (node removal in descending order, randomly, and in ascending order). The results show that, except for the clustering coefficient, the international pesticide trade networks are relatively robust under shocks to economies in ascending orders and randomly, but fragile under shocks to economies in descending order. In contrast, removing nodes with the clustering coefficient in ascending and descending orders gives similar robustness curves. Moreover, the structural robustness related to the giant component size evolves over time and exhibits an inverse U-shaped pattern., Comment: 17 pages, 14 figures

Ensuring food supply stability is key to food security for economies, and food imports become increasingly important to safeguard food supplies in economies with inadequate food production. Food import shocks have significant impacts on targeted economies. Using import trade data of four staple crops (maize, rice, soybean, and wheat) from 1986 to 2018, this paper identifies food import trade shocks that occurred to economies during the period of 1995--2018. We compare the temporal evolution and spatial distribution of import shocks occurring to different crops and analyze the shock intensity and shock recovery in various continents based on locally weighted polynomial regression and Cook's distance. The results reveal higher frequencies during the 2007/2008 food crisis and relatively higher shock frequencies in North America, Africa, and Asia. Meanwhile, there are regional differences in shock recovery, with the majority of shocks in Asia recovering in the short term. We also find that high import diversity and a low import dependency ratio buffer economies against import shocks, resulting in a low shock rate and a high recovery rate. These results contribute to our understanding of the external supply risks of food, placing emphasis on accessibility issues in food security.

International food trade is a growing complement to gaps in domestic food supply and demand, but it is vulnerable to disruptions due to some unforeseen shocks. This paper assembles the international crop trade networks using maize, rice, soybean, and wheat trade data sets from 1986 to 2020. We assess the importance of economies using multidimensional node importance metrics. We analyze the correlation structure of different node important metrics based on the random matrix theory and incorporate 20 metrics into a single metric. We find that some metrics have many similarities and dissimilarities, especially for metrics based on the same trade flow directions. We also find that European economies have a significant impact on the iCTNs. Additionally, economies with poor crop production play a major role in import trade, whereas economies with higher food production or smaller populations are crucial to export trade. Our findings have practical implications for identifying key economies in the international crop trade networks, preventing severe damage to the food trade system caused by trade disruptions in some economies, maintaining the stability of the food supply, and ensuring food security.

Pesticides are important agricultural inputs to increase agricultural productivity and improve food security. The availability of pesticides is partially achieved through international trade. However, economies involved in the international trade of pesticides are impacted by internal and external shocks from time to time, which influence the redistribution efficiency of pesticides all over the world. In this work, we adopt simulations to quantify the efficiency and robustness of the international pesticide trade networks under shocks to economies. Shocks are simulated based on nine node metrics, and three strategies are utilized based on descending, random, and ascending node removal. It is found that the efficiency and robustness of the international trade networks of pesticides increased for all the node metrics except the clustering coefficient. Moreover, the international pesticide trade networks are more fragile when import-oriented economies are affected by shocks., Comment: 12 pages, 5 figures

The importance of fertilizers to agricultural production is undeniable, and most economies rely on international trade for fertilizer use. The stability of fertilizer trade networks is fundamental to food security. We use three valid methods to measure the temporal stability of the overall network and different functional sub-networks of the three fertilizer nutrients N, P and K from 1990 to 2018. The international N, P and K trade systems all have a trend of increasing stability with the process of globalization. The large-weight sub-network has relatively high stability, but is more likely to be impacted by extreme events. The small-weight sub-network is less stable, but has a strong self-healing ability and is less affected by shocks. Overall, all the three fertilizer trade networks exhibit a stable core with restorable periphery. The overall network stability of the three fertilizers is close, but the K trade has a significantly higher stability in the core part, and the N trade is the most stable in the non-core part., Comment: 13 pages including 6 figures

The statistical properties including community structure of the international trade networks of all commodities as a whole have been studied extensively. However, the international trade networks of individual commodities often behave differently. Due to the importance of pesticides in agricultural production and food security, we investigate the evolving community structure in the international pesticide trade networks (iPTNs) of five categories from 2007 to 2018. We unveil the community structures in the undirected and directed iPTNs exhibits regional patterns. However, the regional patterns are very different for undirected and directed networks and for different categories of pesticide. Moreover, the community structure is stabler in the directed iPTNs than in the undirected iPTNs. We also extract the intrinsic community blocks for the directed international trade networks of each pesticide category. It is found that the largest intrinsic community block is the stablest that appears in every pesticide category and contains important economies (Belgium, Germany, Spain, France, United Kingdom, Italy, Netherlands, and Portugal) in Europe. Other important and stable intrinsic community blocks are Canada and the United States in North America, Argentina and Brazil in South America, and Australia and New Zealand in Oceania. These findings imply the importance of geographic distance and the complementarity of important adjacent economies in the international trade of pesticides., Comment: 31 pages including 22 figures

We derive the default cascade model and the fire-sale spillover model in a unified interdependent framework. The interactions among banks include not only direct cross-holding, but also indirect dependency by holding mutual assets outside the banking system. Using data extracted from the European Banking Authority, we present the interdependency network composed of 48 banks and 21 asset classes. For the robustness, we employ three methods, called $\textit{Anan}$, $\textit{Ha\l{}a}$ and $\textit{Maxe}$, to reconstruct the asset/liability cross-holding network. Then we combine the external portfolio holdings of each bank to compute the interdependency matrix. The interdependency network is much denser than the direct cross-holding network, showing the complex latent interaction among banks. Finally, we perform macroprudential stress tests for the European banking system, using the adverse scenario in EBA stress test as the initial shock. For different reconstructed networks, we illustrate the hierarchical cascades and show that the failure hierarchies are roughly the same except for a few banks, reflecting the overlapping portfolio holding accounts for the majority of defaults. We also calculate systemic vulnerability and individual vulnerability, which provide important information for supervision and relevant management actions., Comment: 18 pages, 4 figures, and 8 tables

We revisit the dynamic relationship between domestic economic policy uncertainty and stock markets using the symmetric thermal optimal path (TOPS) method. We observe different interaction patterns in emerging and developed markets. Economic policy uncertainty drives the stock market in China, while stock markets play a leading role in the UK and the US. Meanwhile, the lead-lag relationship of the three countries reacts significantly to extreme events. Our findings have important implications for investors and policy makers.

Using a unique data set containing about 15.06 million truck transportation records in five months, we investigate the highway freight transportation diversity of 338 Chinese cities based on the truck transportation probability $p_{ij}$ from one city to the other. The transportation probabilities are calculated from the radiation model based on the geographic distance and its cost-based version based on the driving distance as the proxy of cost. For each model, we consider both the population and the gross domestic product, and find quantitatively very similar results. We find that the transportation probabilities have nice power-law tails with the tail exponents close to 0.5 for all the models. The two transportation probabilities in each model fall around the diagonal $p_{ij}=p_{ji}$ but are often not the same. In addition, the corresponding transportation probabilities calculated from the raw radiation model and the cost-based radiation model also fluctuate around the diagonal $p_{ij}^{\rm{geo}}=p_{ij}^{\rm{cost}}$. We calculate four sets of highway truck transportation diversity according to the four sets of transportation probabilities that are found to be close to each other for each city pair. Further, it is found that the population, the gross domestic product, the in-flux, and the out-flux scale as power laws with respect to the transportation diversity in the raw and cost-based radiation models. It implies that a more developed city usually has higher diversity in highway truck transportation, which reflects the fact that a more developed city usually has a more diverse economic structure., Comment: 8 pages including 5 figures and 1 table

Uncertainty plays an important role in the global economy. In this paper, the economic policy uncertainty (EPU) indices of the United States and China are selected as the proxy variable corresponding to the uncertainty of national economic policy. By adopting the visibility graph algorithm, the four economic policy uncertainty indices of the United States and China are mapped into complex networks, and the topological properties of the corresponding networks are studied. The Hurst exponents of all the four indices are within $\left[0.5,1\right]$, which implies that the economic policy uncertainty is persistent. The degree distributions of the EPU networks have power-law tails and are thus scale-free. The average clustering coefficients of the four EPU networks are high and close to each other, while these networks exhibit weak assortative mixing. We also find that the EPU network in United States based on daily data shows the small-world feature since the average shortest path length increases logarithmically with the network size such that $L\left(N\right)=0.626\ln N+0.405$. Our research highlights the possibility to study the EPU from the view angle of complex networks., Comment: 9 pages, 13 figures

This paper aims to examine whether the global economic policy uncertainty (GEPU) and uncertainty changes have different impacts on crude oil futures volatility. We establish single-factor and two-factor models under the GARCH-MIDAS framework to investigate the predictive power of GEPU and GEPU changes excluding and including realized volatility. The findings show that the models with rolling-window specification perform better than those with fixed-span specification. For single-factor models, the GEPU index and its changes, as well as realized volatility, are consistent effective factors in predicting the volatility of crude oil futures. Specially, GEPU changes have stronger predictive power than the GEPU index. For two-factor models, GEPU is not an effective forecast factor for the volatility of WTI crude oil futures or Brent crude oil futures. The two-factor model with GEPU changes contains more information and exhibits stronger forecasting ability for crude oil futures market volatility than the single-factor models. The GEPU changes are indeed the main source of long-term volatility of the crude oil futures., Comment: 19 pages, 16 figures

Transfer entropy measures the strength and direction of information flow between different time series. We study the information flow networks of the Chinese stock market and identify important sectors and information flow paths. This paper uses the daily closing price data of the 28 level-1 sectors from Shenyin \& Wanguo Securities ranging from 2000 to 2017 to study the information transmission between different sectors. We construct information flow networks with the sectors as the nodes and the transfer entropy between them as the corresponding edges. Then we adopt the maximum spanning arborescence (MSA) to extracting important information flows and the hierarchical structure of the networks. We find that, during the whole sample period, the \textit{composite} sector is an information source of the whole stock market, while the \textit{non-bank financial} sector is the information sink. We also find that the \textit{non-bank finance}, \textit{bank}, \textit{computer}, \textit{media}, \textit{real estate}, \textit{medical biology} and \textit{non-ferrous metals} sectors appear as high-degree root nodes in the outgoing and incoming information flow MSAs. Especially, the \textit{non-bank finance} and \textit{bank} sectors have significantly high degrees after 2008 in the outgoing information flow networks. We uncover how stock market turmoils affect the structure of the MSAs. Finally, we reveal the specificity of information source and sink sectors and make a conclusion that the root node sector as the information sink of the incoming information flow networks. Overall, our analyses show that the structure of information flow networks changes with time and the market exhibits a sector rotation phenomenon. Our work has important implications for market participants and policy makers in managing market risks and controlling the contagion of risks., Comment: 12 pages including 9 figures

Information diffusion within financial markets plays a crucial role in the process of price formation and the propagation of sentiment and risk. We perform a comparative analysis of information transfer between industry sectors of the Chinese and the USA stock markets, using daily sector indices for the period from 2000 to 2017. The information flow from one sector to another is measured by the transfer entropy of the daily returns of the two sector indices. We find that the most active sector in information exchange (i.e., the largest total information inflow and outflow) is the {\textit{non-bank financial}} sector in the Chinese market and the {\textit{technology}} sector in the USA market. This is consistent with the role of the non-bank sector in corporate financing in China and the impact of technological innovation in the USA. In each market, the most active sector is also the largest information sink that has the largest information inflow (i.e., inflow minus outflow). In contrast, we identify that the main information source is the {\textit{bank}} sector in the Chinese market and the {\textit{energy}} sector in the USA market. In the case of China, this is due to the importance of net bank lending as a signal of corporate activity and the role of energy pricing in affecting corporate profitability. There are sectors such as the {\textit{real estate}} sector that could be an information sink in one market but an information source in the other, showing the complex behavior of different markets. Overall, these findings show that stock markets are more synchronized, or ordered, during periods of turmoil than during periods of stability., Comment: 12 pages including 8 figures

As a vital strategic resource, oil has an essential influence on the world economy, diplomacy and military development. Using oil trade data to dynamically monitor and warn about international trade risks is an urgent need. Based on the UN Comtrade data from 1988 to 2017, we construct unweighted and weighted global oil trade networks (OTNs). Complex network theories have some advantages in analyzing global oil trade as a system with numerous economies and complicated relationships. This paper establishes a trading-based network model for global oil trade to study the evolving efficiency, criticality and robustness of economies and the relationships between oil trade partners. The results show that for unweighted OTNs, the efficiency of oil flows gradually increases with growing complexity of the OTNs, and the weighted efficiency indicators are more capable of highlighting the impact of major events on the OTNs. The identified critical economies and trade relationships have more important strategic significance in the real market. The simulated deliberate attacks corresponding to national bankruptcy, trade blockade, and economic sanctions have a more significant impact on the robustness than random attacks. When the economies are promoting high-quality economic development, and continuously enhancing positions in the OTN, more attention needs be paid to the identified critical economies and trade relationships. To conclude, some suggestions for application are given according to the results., Comment: 19 pages, 9 figures

Predicting the occurrence of tail events is of great importance in financial risk management. By employing the method of peak-over-threshold (POT) to identify the financial extremes, we perform a recurrence interval analysis (RIA) on these extremes. We find that the waiting time between consecutive extremes (recurrence interval) follow a $q$-exponential distribution and the sizes of extremes above the thresholds (exceeding size) conform to a generalized Pareto distribution. We also find that there is a significant correlation between recurrence intervals and exceeding sizes. We thus model the joint distribution of recurrence intervals and exceeding sizes through connecting the two corresponding marginal distributions with the Frank and AMH copula functions, and apply this joint distribution to estimate the hazard probability to observe another extreme in $\Delta t$ time since the last extreme happened $t$ time ago. Furthermore, an extreme predicting model based on RIA-EVT-Copula is proposed by applying a decision-making algorithm on the hazard probability. Both in-sample and out-of-sample tests reveal that this new extreme forecasting framework has better performance in prediction comparing with the forecasting model based on the hazard probability only estimated from the distribution of recurrence intervals. Our results not only shed a new light on understanding the occurring pattern of extremes in financial markets, but also improve the accuracy to predict financial extremes for risk management., Comment: 14 pages, 5 figures, and 4 tables

Uncovering the risk transmitting path within economic sectors in China is crucial for understanding the stability of the Chinese economic system, especially under the current situation of the China-US trade conflicts. In this paper, we try to uncover the risk spreading channels by means of volatility spillovers within the Chinese sectors using stock market data. By applying the generalized variance decomposition framework based on the VAR model and the rolling window approach, a set of connectedness matrices is obtained to reveal the overall and dynamic spillovers within sectors. It is found that 17 sectors (mechanical equipment, electrical equipment, utilities, and so on) are risk transmitters and 11 sectors (national defence, bank, non-bank finance, and so on) are risk takers during the whole period. During the periods with the extreme risk events (the global financial crisis, the Chinese interbank liquidity crisis, the Chinese stock market plunge, and the China-US trade war), we observe that the connectedness measures significantly increase and the financial sectors play a buffer role in stabilizing the economic system. The robust tests suggest that our results are not sensitive to the changes of model parameters. Our results not only uncover the spillover effects within the Chinese sectors, but also highlight the deep understanding of the risk contagion patterns in the Chinese stock markets., Comment: 17 pages, 7 figures, and 3 tables

This paper focuses on the horse race of weekly idiosyncratic momentum (IMOM) with respect to various idiosyncratic risk metrics. Using the A-share individual stocks in the Chinese market from January 1997 to December 2017, we first evaluate the performance of the weekly momentum based on raw returns and idiosyncratic returns, respectively. After that the univariate portfolio analysis is conducted to investigate the return predictability with respect to various idiosyncratic risk metrics. Further, we perform a comparative study on the performance of the IMOM portfolios with respect to various risk metrics. At last, we explore the possible explanations to IMOM as well as risk based IMOM portfolios. We find that 1) there are prevailing contrarian effect and IMOM effect for the whole sample; 2) the negative relations exist between most of the idiosyncratic risk metrics and the cross-sectional stock returns, and better performance is linked to idiosyncratic volatility (IVol) and maximum drawdowns (IMDs); 3) additionally, the IVol-based and IMD-based IMOM portfolios exhibit better explanatory power to the IMOM portfolios with respect to other risk metrics; 4) finally, higher profitability of IMOM as well as IVol-based and IMD-based IMOM portfolios is found to be related to upside market states, high levels of liquidity and high levels of investor sentiment., Comment: 36 pages including 15 tables

This paper constructs a global economic policy uncertainty index through the principal component analysis of the economic policy uncertainty indices for twenty primary economies around the world. We find that the PCA-based global economic policy uncertainty index is a good proxy for the economic policy uncertainty on a global scale, which is quite consistent with the GDP-weighted global economic policy uncertainty index. The PCA-based economic policy uncertainty index is found to be positively related with the volatility and correlation of the global financial market, which indicates that the stocks are more volatile and correlated when the global economic policy uncertainty is higher. The PCA-based global economic policy uncertainty index performs slightly better because the relationship between the PCA-based uncertainty and market volatility and correlation is more significant., Comment: 5 pages, 6 figures

Empirical investor networks (EIN) proposed by \cite{Ozsoylev-Walden-Yavuz-Bildik-2014-RFS} are assumed to capture the information spreading path among investors. Here, we perform a comparative analysis between the EIN and the cellphone communication networks (CN) to test whether EIN is an information exchanging network from the perspective of the layer structures of ego networks. We employ two clustering algorithms ($k$-means algorithm and $H/T$ break algorithm) to detect the layer structures for each node in both networks. We find that the nodes in both networks can be clustered into two groups, one that has a layer structure similar to the theoretical Dunbar Circle corresponding to that the alters in ego networks exhibit a four-layer hierarchical structure with the cumulative number of 5, 15, 50 and 150 from the inner layer to the outer layer, and the other one having an additional inner layer with about 2 alters compared with the Dunbar Circle. We also find that the scale ratios, which are estimated based on the unique parameters in the theoretical model of layer structures \citep{Tamarit-Cuesta-Dunbar-Sanchez-2018-PNAS}, conform to a log-normal distribution for both networks. Our results not only deepen our understanding on the topological structures of EIN, but also provide empirical evidence of the channels of information diffusion among investors., Comment: 9 pages, 9 figues, 3 tables

This paper investigates the effect of cross-shareholding on stock price synchronicity, as a measure of price informativeness, of the listed firms in the Chinese stock market. We gauge firms' levels of cross-shareholdings in terms of centrality in the cross-shareholding network. It is confirmed that it is through a noise-reducing process that cross-shareholding promotes price synchronicity and reduces price delay. More importantly, this effect on price informativeness is pronounced for large firms and in the periods of market downturns. Overall, our analyses provide insights into the relation between the ownership structure and price informativeness., Comment: 24 pages including 12 tables

User activity fluctuations reflect the performance of online society. We investigate the statistical properties of 1-min user activity time series of simultaneously online users inhabited in 95 independent virtual worlds. The number of online users exhibits clear intraday and weekly patterns due to human's circadian rhythms and week cycles. Statistical analysis shows that the distribution of absolute activity fluctuations has a power-law tail for 44 virtual worlds with an average tail exponent close to 2.15. The partition function approach unveils that the absolute activity fluctuations possess multifractal features for all the 95 virtual worlds. For the sample of 44 virtual worlds with power-law tailed distributions of the absolute activity fluctuations, the width of singularity $\Delta\alpha$ is negatively correlated with the maximum activity ($p$-value=0.070) and the time to the maximum activity ($p$-value=0.010). The negative correlations are not observed for neither the other 51 virtual worlds nor the whole sample of the 95 virtual worlds. In addition, numerical experiments indicate that both temporal structure and large fluctuations have influence on the multifractal spectrum. We also find that the temporal structure has stronger impact on the singularity width than large fluctuations., Comment: 9 pages including 8 figures