Risk Propagation Research Articles

European equity markets volatility spillover: Destabilizing energy risk is the new normal

AbstractWhile energy risk is increasingly recognized as a systemic risk, there is limited comprehensive analysis of the risk propagation in regional contexts. In this study, we examine oil and natural gas price changes and shocks in relation to equity market returns and volatility for 24 European Economic Area (EEA) countries. In addition to traditional panel regressions, we also deploy the Diebold‐Yilmaz (2014) spillover index for a closed network analysis. We differentiate in the cross‐section across the core EU block, PIIGS countries, EU enlargement countries joining after 2004, and other non‐EU countries, to provide insights into the ongoing debates on the European energy market stability. While we find evidence of the manifestation of energy risk throughout the sample period, we find that until 2019 the primary sources of volatility spillover in the EEA economic network arose from economic or political uncertainty. Energy risks, measured by large crude oil and natural gas price shocks also significantly contributed to equity market volatility, with increasing volatility risk arising from natural gas, a green labelled energy source after 2019. Last, we show that CEEC equity markets are more sensitive to oil and natural gas price shocks when domestic currencies depreciate against the Euro.

Power Side Risk Assessment of Multi-Energy Microgrids Considering Risk Propagation between Interconnected Energy Networks

Traditional power systems only contain a single energy type, namely, electrical energy, and involve no interaction with other networks with different energy types, such as gas networks and heat networks. With the rapid development of the Energy Internet, the coupling between various energy types has become increasingly tight, making traditional risk assessment methods no longer suitable for multi-energy microgrids. To this end, this paper proposes a microgrid risk assessment method that considers the impact of multiple interconnected networks with different energy types. First, respectively from the equipment and system levels, a risk transfer integrated energy conversion model is built, depicting the output of equipment under risk conditions and describing the process of risk transfer using energy coupling equipment in the microgrid. Thereafter, from the perspective of the energy flow distribution and considering the microgrid grid energy flow characteristics, a microgrid energy flow distribution model is built, based on which a microgrid risk analysis model that simulates the microgrid risk propagation mechanism is established by introducing risk factors that characterize equipment risk statuses. In addition, based on the system structure and the operational characteristics, a microgrid-oriented risk assessment process is designed. Finally, a numerical simulation confirms that considering the impact of multiple different energy networks to the power side in the risk assessment is necessary.

Future research of supply chain resilience: Network perspectives and incorporation of more stakeholders

In recent years, global supply chains face great challenges driven by events such as trade wars, the Covid-19 pandemic, and the Russia-Ukraine War. Since the disruptions caused by such shocks are more systematic and the duration is harder to predict compared to natural disasters or other operational risks, we highlight two features for future supply chain research: network perspectives and incorporation of more stakeholders such as governments that can influence overarching performances of industrial and supply chains. From the network perspective, we review the literature on vulnerable nodes & links identification, risk propagation modeling, network resilience measures, and disruption mitigation strategies. We suggest promising research directions such as deriving non-probabilistic resilience measures that can incorporate dynamics, extending existing methods for bottleneck detection to the network setting, balancing efficiency and resilience in a more sophisticated way, etc. In terms of incorporating more stakeholders’ roles in supply chain network decisions, we review the relevant literature and discuss research opportunities on governments’ roles in global supply chains (i.e., an external circulation perspective) and in incentivizing firms’ R&D and stimulating domestic demand (i.e., an internal circulation perspective). We suggest topics including global supply chain resilience evaluation using trade networks, technology networks, and firm ownership networks, the impact of trade policies with network perspectives, design of tariff and non-tariff measures under trade agreements, domestic industry relocation, evaluating the effects of government subsidies on firms’ long-term innovation, and design of subsidy strategies for supply chain stability and resilience. We believe research on supply chain resilience will thrive in the years ahead, better supporting firms’ and governments’ decisions.

Cross-Space Conduction Assessment Method of Network Attack Risk under the Strong Coupling Characteristics of Electric Power Cyber Physics

With the deep integration and wide application of advanced digital sensing, Internet of Things technology, and energy technology in power systems. Power information systems and physical systems are gradually being coupled and developed into power cyber–physical systems (CPS). A number of blackouts in recent years have shown that cyberspace cyber attacks on CPS can lead to the intensification and rapid spread of faults in the physical space of the power grid, and even system collapse. Aiming at the difficulty of analyzing the evolution of cyber–physical cross-space impacts of cyber-attacks, this paper proposes a cross-domain propagation impact assessment method that considers cyber–physical coupling risks caused by attacks. First, according to the multiple coupling relationship between the power system information space and physical space, the monitoring function model and the control function model are established. Second, under the effect of high-concealment attack, analyze the impact of the risk caused by its failure after it is transmitted to the physical space with different propagation probabilities. Finally, the experimental verification was carried out using the IEEE RTS79 standard test system. The simulation results show that the proposed method can comprehensively consider the cyber–physical energy supply coupling relationship, the risk propagation probability, and the operating characteristics of the information system, and effectively quantify and evaluate the impact of information space network attacks on the physical space entity power grid. It further reveals the objective law that information space risks can evolve and spread across domains under the condition of strong coupling of information physics.

The impact of fiscal rules on cross-border bank claims

Over the last 15 years, cross-border bank claims have become an important source of international liquidity, as well as a channel for cross-country risk propagation. Consequently, their drivers are an extensively studied theme in the literature. In this paper, I point to an important determinant of cross-border lending that has so far been missing from the literature – namely, fiscal rules. I utilize the bilateral data on cross-border bank lending to developing countries and show that fiscal rules have opposing effects on private and public sector claims, but overall they decrease foreign bank lending. This conclusion has important policy implications – it shows that the implementation of fiscal rules may limit developing countries' access to cross-border financing.

Critical risks in global supply networks: A static structure and dynamic propagation perspective

The global fragmentation of supply networks results from outsourcing and offshoring strategies. Supply networks outsource segments to specialized suppliers, thereby leveraging their enhanced adaptability and reduced costs. These practices have produced intricate interdependence patterns, facilitating the spread of disruption risk. Although risk management provides extensive toolsets for addressing critical risks at the dyadic level, there has been a lack of emphasis on evaluating the efficacy of these tools in the context of complex global supply networks. Research generally concentrates on the topological properties of global supply networks while largely ignoring the underlying risk propagation process in identifying critical risks. This paper develops a comprehensive view of the supply network through detailed subtier mapping and a mathematical formulation that overcomes the limitations of the current approaches. The proposed formulation model incorporates the doing business environment of each country and reproduces the spread of disruption risks under three disruption risk scenarios in global supply networks. This paper illustrates the proposed formulation by analyzing the critical risks of global flat panel display supply networks. The results suggest that these networks are robust to random disruption but susceptible to target disruptions. Additionally, disruptions in European and Asian countries have detrimental effects on the overall operation of supply networks.

Multilayer interbank networks and systemic risk propagation: Evidence from China

Multilayer interbank networks and systemic risk propagation: Evidence from China

Comparative study on the international coal trade network before and after COVID-19 outbreak

COVID-19 that broke out at the end of 2019 made countries to implement strict blockade measures to curb the spread of the virus. These measures have seriously disrupted the coal import and export trade, causing a significant impact on the coal trade network. This study aims to investigate the disparities between coal trade networks before and after COVID-19 outbreak. First, using complex network theory, we constructed global coal trade networks for the years 2019 and 2020. Subsequently, utilizing a cascading failure model, we developed a coal supply risk propagation framework. Through an analysis of network topology and theoretical supply risk propagation, we explored the similarities and differences of the coal trade networks during these 2 years. In terms of network topology structure, our findings reveal a decrease in trade tightness in the coal trade network of 2020 compared to 2019. Moreover, the Netherlands no longer retains its significance as an important trade hub country in 2020. Looking at the theoretical perspective of supply risk transmission, we observed that in 2020, COVID-19 resulted in a higher number of countries experiencing crisis outbreaks compared to 2019. However, it is noteworthy that the core countries within the coal trade network have remained consistent, and their roles as either net importers or net exporters have not changed. This indicates that the position of coal core countries is very stable. Therefore, no matter what happens, the country should pay timely attention to the dynamics of core countries. Interestingly, in 2020, the risk associated with coal supply did not conform to the spatial patterns typically seen in trade. For instance, the Russian Federation, identified as a country facing a crisis outbreak, transmits supply risks to geographically distant regions in West Asia, including countries like Ghana and Western Sahara.

Bayesian belief network-based risk likelihood assessment for sustainable product design decision making

Numerous methods can be used during the design stage to ensure that new products are developed to optimize performance with respect to economic, environmental, and societal objectives. When an optimally chosen design is launched, many uncertainties are likely to affect its performance over different lifecycle stages. Such uncertainties give rise to various risks that can potentially lead to deviations between the expected and actual economic, environmental, and societal implications of the product designed. Therefore, it is imperative that sustainable product design alternatives are also assessed to evaluate their robustness against such risks, should they occur, before design decisions are finalized. However, comprehensive quantitative methods for use during the product design stage to evaluate the influence of the likelihood of different risks on expected sustainability performance are lacking. The purpose of this paper is to address these gaps by developing a methodology for risk likelihood assessment. The sequence of risk identification, risk analysis, and risk evaluation for systematic risk assessment, recommended in the ISO 31000 Risk Management Guidelines, is followed to develop a methodology to quantify risks and their effects. For risk identification, a comprehensive taxonomy is used to identify risks that can influence total lifecycle product sustainability performance. For risk analysis, Bayesian Belief Network (BBN)-based approach is used to develop a method to assess the influence of risks on total lifecycle product performance through predictive and diagnostic inference. A new metric, the Operational Risk Index (ORI), is developed to better communicate influence of risk likelihoods. For risk evaluation, methods to evaluate risk mitigation strategies are presented. An industrial case study is used to demonstrate the application of the methods. The proposed BBN-based decision support tool enables assessing and visualizing the effect of risks on product design performance, identifying critical risks, and risk propagation paths. The tools can enable product designers to make risk-informed choices when identifying product designs that are more robust and ensure improved total lifecycle sustainability performance. The new ORI metric is helpful to compare multiple designs with respect to total lifecycle risk assessment. The methods proposed in this research present a comprehensive methodology for total lifecycle risk likelihood assessment in sustainable product design decision making.

Evaluating Information Risk Propagation in Complex Public Opinion Environments Based on the Improved Grey Relational Analysis—Decision Making Trial and Evaluation Laboratory Method

The propagation of information risk in complex public opinion environments not only leads to severe direct reputational losses for companies but also results in significant economic damages. Therefore, during the nascent stage of information risk, identifying potential propagation pathways, determining key dissemination channels, and taking timely measures become crucial. To address this issue, this paper proposes a multi-criteria decision-making method for evaluating information risk propagation in complex public opinion environments. In this method, this paper utilizes probabilistic hesitant fuzzy sets to express the evaluation information, and provide several distance and similarity measurement methods for probabilistic hesitant fuzzy elements. To ensure the rationality of the evaluation indicator weights, this study first applies these distance measurement methods to improve the Grey Relational Analysis—Decision Making Trial and Evaluation Laboratory (GRA-DEMATEL) method for determining the objective weights of evaluation indicators. Next, this paper uses the Delphi method to establish the subjective weights of each evaluation indicator. Finally, by employing a weight synthesis operator, this paper combines the subjective and objective weights to obtain the final indicator weights. Additionally, this paper utilizes the similarity measurement methods for probabilistic hesitant fuzzy elements to improve the combined compromise solution (CoCoSo) method in evaluating and ranking potential information risk propagation pathways. Furthermore, this paper incorporates the “Probability Splitting Algorithm” to handle probabilistic hesitant fuzzy elements, enabling their application in these methodologies. Finally, based on a case study of information risk propagation in the catering industry, we conducted a sensitivity analysis and effectiveness verification of the proposed approach. The results demonstrate the effectiveness of the method and its ability to address real-world issues.

Exploring the risk propagation mechanisms of supply chain for prefabricated building projects

Prefabricated building (PB) is an innovative construction method with significant advantages in environmental protection, energy saving, productivity increase, and quality improvement. The complexity of the prefabricated building project supply chain (PBPSC) also induces risks leading to the failure of PBP. Existing researches consider risks isolated and ignores the interactions between stakeholders of PBPSC that can lead to risk propagation and management failures. This study integrated epidemic models with complex networks to explore risk propagation mechanisms in PBPSC. The hierarchical structure of PBPSC was transited into a complex network for analyzing the features of the risk propagation process and the risk propagation capacity of initial infection nodes. The analysis results indicated that risk propagation and risk recovery were medium and often overlooked, which caused risk propagation in PBPSC. When major PBP contractors and construction enterprises were affected by risks, risk propagation was more likely to occur in PBPSC. This study was a meaningful attempt to analyze the risk propagation mechanisms from the perspective of complex networks in PBPSC. Based on the analysis results, this study revealed the critical risks and propagation characteristics of PBPSC for exploring the risk propagation mechanisms and response strategies to improve the risk response capacity of PBPSC.

Location-Aware Measurement for Cyber Mimic Defense: You Cannot Improve What You Cannot Measure

Cyber mimic defense is designed to ensure endogenous security, effectively countering unknown vulnerabilities and backdoors, thereby addressing a significant challenge in cyberspace. However, the immense scale of real-world networks and their intricate topology pose challenges for measuring the efficacy of cyber mimic defense. To capture and quantify defense performance within specific segments of these expansive networks, we embrace a partitioning approach that subdivides large networks into smaller regions. Metrics are then established within an objective space constructed on these smaller regions. This approach enables the establishment of several fine-grained metrics that offer a more nuanced measurement of cyber mimic defense deployed in complex networks. For example, the common-mode index is introduced to highlight shared vulnerabilities among diverse nodes, the transfer probability computes the likelihood of risk propagation among nodes, and the failure risk assesses the likelihood of cyber mimic defense technology failure within individual nodes or entire communities. Furthermore, we provide proof of the convergence of the transfer probability. A multitude of simulations are conducted to validate the reliability and applicability of the proposed metrics.

Study on the Evolution of Risk Contagion in Urban River Ecological Management Projects Based on SEIRS

The risk transmission mechanisms of urban river ecological management engineering projects are examined in this study. Using the Susceptible Exposed Infectious Recovered Susceptible (SEIRS) model for risk transmission, a model of risk propagation delay for urban river ecological management engineering projects on scale-free networks is developed, which takes into account the effects of risk propagation and delay. We conducted a steady-state analysis of the model and obtained the basic reproduction number R. When R > 1, the equilibrium point of risk outbreak is stable, and when R < 1, the equilibrium point of risk disappearance is stable. Numerical simulations of the model were conducted using the MATLAB2022b to reveal the dynamic propagation patterns of risk in urban river ecological management engineering projects. The research results show that the steady-state density of the infected nodes in the network increases with the increase in the effective propagation rate and the propagation delay time; the propagation delay reduces the risk propagation threshold in the network and accelerates the occurrence of the equilibrium state of risk outbreak. There is a correlation between the transmission rate of latent nodes and the transmission rate of infected nodes, and the effective transmission rate of latent nodes has a greater influence on risk propagation. The spread of risk in the network can be effectively controlled and mitigated with targeted immunity for susceptible nodes. This article, based on the theory of complex networks and the mean-field theory, takes into account the propagation delay and spreading of latent nodes. Building a D-SEIRS model for risk propagation broadens the research perspective on urban river ecological management risk propagation.

Green policies and transition risk propagation in production networks

This paper introduces a general equilibrium sectoral model including two types of energy sectors (green and brown) to study the short-run impact of different emission taxes schemes in production networks. The model is calibrated to the Spanish economy at the industry level using the input–output matrix and captures the effective cost of emissions associated to the use of energy in each industry. We show that for an increase in the price of emission allowances similar to that observed in recent years (from approximately €25 per tonne of CO2 in 2019 to almost €100 per tonne in 2022) the model predicts a cumulative decline in Spanish GDP after three years of 0.37%. The loss in value added is very heterogeneous among industries, depending on the exposure to the ETS and to their network ETS exposure, with values ranging from losses of 4% in the most affected industries to no impact in others.

Risk Propagation Mechanism and Prediction Model for the Highway Merging Area

The merging area is one of the most accident-prone areas on highways. After an accident occurs, the risk will propagate along the main road over a certain range and time. Therefore, the study of the propagation mechanism of accident risk will help to quantify the driving risk in this region. An effective risk prediction model is important for improving traffic control measures in this specific area. In this study, simulation experiments were conducted in SUMO (Simulation of Urban Mobility) to obtain the accident and risk propagation data in merging areas. Firstly, the Gaussian plume model was optimized for the merging area situation to determine and divide the impact range of the accidents. Then, different accident scenarios in the merging area and downstream were simulated with different input flow rates to study the time and speed of risk propagation in the three-level affected areas. Finally, LSTM (long short-term memory) and RNN (recurrent neural network) models were built to predict the accident risk in the merging area. The results showed that the LSTM model had higher accuracy. This study provides an innovative insight into the propagation process of merging area accidents. It is of benefit to the development of post-accident control measures.

Systemic risk propagation in the Eurozone: A multilayer network approach

In this paper, we study systemic risk propagation by exploring the dynamic mechanism of financial contagion among Eurozone countries. Using a multilayer information spillover network framework, we can consider the sovereign, banking and equity sectors’ risk spillover between countries simultaneously. This specification helps us to better identify the systemically important countries, i.e., the Eurozone financial stability. The findings emphasise the prominence of considering systemic risk propagation in a multilayer network structure. In fact, our empirical analysis suggests that considering intra-layer and inter-layer propagation is essential to gain a deeper insight into Eurozone systemic risk mechanisms.

Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation

Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.

A networked risk perspective for analysing debris flow losses factors considering mitigation measures

ABSTRACT As debris flow disasters are becoming increasingly severe worldwide, it is necessary to deepen the research on disaster losses, especially the impact and role of disaster mitigation measures. Based on the regional disaster system theory, this paper sets an influencing factor system of debris flow losses. Then, from the perspective of networked risk, the risk propagation network is established through the Delphi method. Finally, the social network analysis method is introduced to discuss the risk propagation relationship between these factors from two aspects of the overall network analysis and node centrality analysis. The results reveal the chain-like influence characteristics of factors and clarify the main points relevant departments should emphasise in disaster risk reduction. Environmental and engineering mitigation measures strongly influence other factors, thus dominating the risk propagation in forming debris flow losses. The peril of debris flow, residents’ escape ability, and emergency rescue can be easily influenced by other factors, so increasing the resistance of these sensitive factors to the source of influence would support loss reduction. The research results can be applied to risk assessment and loss reduction of debris flow disasters.

Supply risk propagation of global copper industry chain based on multi-layer complex network

Copper is an essential strategic mineral resource. The copper industry chain starts with the extraction of raw materials, after processing, and finally sells the final product. The uniqueness of copper and the distribution characteristics of geographical space make the relationship between copper trade and supply more complicated. The imbalance between supply and demand can easily lead to resource supply security problems. The supply shortage will affect the stable development of the industrial chain. Based on the industrial chain perspective, this study calculates the national competitiveness index at different stages and analyzes the degree of competition among different countries. The study constructs a copper multi-layer network risk propagation model (CMNRP) to simulate the impact and propagation path of supply shocks in Peru, Chile, China, and the USA on the entire industry chain. The study found that: (1) The competitiveness of countries trading along the industrial chain has gradually increased. (2) As the supply shock intensifies, the impact of the crisis gradually shifts downstream, and the number of affected countries gradually increases. (3) Peru, China, and the USA are essential nodes for risk propagation in the copper industry chain. The critical factor in ensuring the stability of the industrial chain is the stable supply of raw materials. (4) Indirect infection occupies a large proportion in different stages of the industrial chain. Influenced countries and indirect influences in the middle and lower reaches of the industrial chain are higher than those upstream. Research can provide multi-dimensional policy recommendations for China to respond to extreme disturbances, build a more resilient trade system, and improve the resilience of trade networks.

FUNCTION DRIVEN ASSESSMENT OF MANUFACTURING RISKS IN CONCEPT GENERATION STAGES

AbstractDecisions made in the concept generation phase have a significant effect on the product. While product- related risks typically can be considered in the early stages of design, risks such as supply chain and manufacturing methods are rarely easy to account for in early phases. This is because the currently available methods require mature data, which may not be available during concept generation. In this paper, we propose an approach to address this. First, the product and the non-product (manufacturing and/or supply chain) attributes are modelled using the enhanced function means (EF-M) modelling method. The EF-M method provides the opportunity to model alternative solutions-set for functions. Dependencies are then mapped within the product and the manufacturing models, and also in between them. An automatic combinatorial method of concept generation is employed where each generated instance is a design concept-manufacturing method pair. A risk propagation algorithm is then used to assess the risks of all the generated alternatives.