Interdependent Risks Research Articles

Australia’s national climate risk Assessment: Identifying climate risk interdependencies within the infrastructure and built environment system for effective climate adaptation

Australia’s national climate risk Assessment: Identifying climate risk interdependencies within the infrastructure and built environment system for effective climate adaptation

Building cybersecurity resilience: integrating defense and recovery investment strategies in an expected resilience framework

PurposeTo remain competitive in an unpredictable environment where the complexity and frequency of cybercrime are rapidly increasing, a cyber resiliency strategy is vital for business continuity. However, one of the barriers to improving cyber resilience is that security defense and accident recovery do not combine efficaciously, as embodied by emphasizing cyber security defense strategies, leaving firms ill-prepared to respond to attacks. The present study thus develops an expected resilience framework to assess cyber resilience, analyze cyber security defense and recovery investment strategies and balance security investment allocation strategies.Design/methodology/approachBased on the expected utility theory, this paper presents an expected resilience framework, including an expected investment resilience model and an expected profit resilience model that directly addresses the optimal joint investment decisions between defense and recovery. The effects of linear and nonlinear recovery functions, risk interdependence and cyber insurance on defense and recovery investment are also analyzed.FindingsAccording to the findings, increasing the defense investment coefficient reduces defense and recovery investment while increasing the expected resilience. The nonlinear recovery function requires a smaller defense investment and overall security investment than the linear one, reflecting the former’s advantages in lowering cybersecurity costs. Moreover, risk interdependence has positive externalities for boosting defense and recovery investment, meaning that the expected profit resilience model can reduce free-riding behavior in security investments. Insurance creates moral hazard for firms by lowering defensive investment, yet after purchasing insurance, expanded coverage and cost-effectiveness incentivize firms to increase defense and recovery spending, respectively.Originality/valueThe paper is innovative in its methodology as it offers an expected cyber resilience framework for integrating defense and recovery investment and their effects on security investment allocation, which is crucial for building cybersecurity resilience but receives little attention in cybersecurity economics. It also provides theoretical advances for cyber resilience assessment and optimum investment allocation in other fields, such as cyber-physical systems, power and water infrastructure – moving from a resilience triangle metric to an expected utility theory-based method.

Supply chain chronic losses and risk interdependencies: a Bayesian Belief Network approach

ABSTRACT Supply Chain Risk Management (SCRM) has become integral to firm governance, and risk interdependencies, despite limited attention, significantly impact risk analysis and mitigation. Recurrent risks, resulting from consistent disturbances, generate chronic losses and are critically affected by other related risks. This work enhances the assessment of chronic losses with interdependent risks, building upon an existing Bayesian Belief Network (BBN) approach for risk interdependencies in SCRM, combining expected utility theory to address the complex, interconnected nature of risks. This work advocates a change in existing approaches to properly evaluate chronic losses often underestimated due to decision makers’ expectations. The research shows that the impact of recurrent risks may be underestimated by other approaches based on their frequency, undermining confidence in mitigation strategies. This work not only presents a novel approach to risk assessment but also highlights the importance of studying causal interdependencies, and emphasises the need to consider low-probability and high-impact risks but also recurrent, seemingly minor risks in SCRM processes.

Automatic monitoring the risk coupling of foundation pits: integrated point cloud, computer vision and Bayesian networks approach

PurposeManual monitoring is a conventional method for monitoring and managing construction safety risks. However, construction sites involve risk coupling - a phenomenon in which multiple safety risk factors occur at the same time and amplify the probability of construction accidents. It is challenging to manually monitor safety risks that occur simultaneously at different times and locations, especially considering the limitations of risk manager’s expertise and human capacity.Design/methodology/approachTo address this challenge, an automatic approach that integrates point cloud, computer vision technologies, and Bayesian networks for simultaneous monitoring and evaluation of multiple on-site construction risks is proposed. This approach supports the identification of risk couplings and decision-making process through a system that combines real-time monitoring of multiple safety risks with expert knowledge. The proposed approach was applied to a foundation project, from laboratory experiments to a real-world case application.FindingsIn the laboratory experiment, the proposed approach effectively monitored and assessed the interdependent risks coupling in foundation pit construction. In the real-world case, the proposed approach shows good adaptability to the actual construction application.Originality/valueThe core contribution of this study lies in the combination of an automatic monitoring method with an expert knowledge system to quantitatively assess the impact of risk coupling. This approach offers a valuable tool for risk managers in foundation pit construction, promoting a proactive and informed risk coupling management strategy.

Nonlinear network connectedness: Assessing financial risk transmission in MENA and influence of external financial conditions

This study investigates the influence of global financial market conditions on financial risk connectedness and transmission among the Middle East and North Africa (MENA) economies. Utilizing weekly realized stock market volatilities as a measure of risk and employing a smooth transition threshold vector autoregressive (STVAR) model to analyze risk transmission under varying levels of financial stress, the authors also examine the impact of external macroeconomic conditions on the risk connectedness of MENA economies. The results indicate that the overall connectedness, based on a standard VAR model, is moderate at 48.34%. However, in the low financial stress regime, overall connectedness increases to 52.79%, and in the high financial stress regime, it rises to 72.94%, indicating stronger risk interdependency among MENA countries during times of high stress. In the high financial stress regime, Kuwait, Oman, Qatar, Saudi Arabia, Turkey, and the United Arab Emirates are identified as net risk transmitters among MENA countries. The study also reveals that risk transmission across MENA is more pronounced in the regime-dependent model compared to the overall mean-based VAR model.

Cross-border sovereign risk transmission in BRICIT Nations: Unveiling asymmetries and the role of country risk premiums

This research highlights the asymmetric interdependence structure among the sovereign risks of Brazil, Russia, India, China, Indonesia, and Türkiye (BRICIT) nations, challenging the traditional view that bilateral trade is the main channel for cross-border spillover effects. Despite their dependence on Russian crude oil imports for energy, the BRICIT nations show no significant sovereign risk interdependence with Russia. The study finds that Indonesia's credit default swap (CDS) has the highest level of interdependence with other nations. Further exploration of alternative transmission channels necessitates an examination of the country risk premium, revealing Türkiye as the most vulnerable nation due to its negative association with the CDS of other countries, particularly India. By contrast, India is identified as a preferred investment destination, thanks to its lower uncertainty and strong GDP growth over the past decade. Additionally, the research underscores China's economic influence, demonstrated by its positive association with all other BRICIT nations. This suggests that despite the high sovereign risk associated with China, investors do not view other emerging markets as viable, lower-risk alternatives. The perceived risk related to China appears to extend beyond its borders, impacting the sovereign risk profiles of other emerging economies.

On the efficiency of insurance institutions under interdependent risks

AbstractWe theoretically investigate the equilibria and efficiencies of public and market insurance institutions within a framework where loss probabilities are interdependently influenced by the efforts of individuals and institutions (firms). We highlight the multilateral nature of interdependency, which exists within individuals, within firms, and between them. Our analysis reveals that both public and market institutions fall short of achieving first‐best efficiency, and that the relative efficiencies between the two are indeterminate, due to the externalities. Regarding effort levels, we find that under the public institution, individual efforts are lower while institutional efforts are higher compared with the social optimum and the market institution. We also find that individual and/or institutional efforts are lower under the market institution than the social optimum, with both efforts lower under severe externalities.

A multi-objective model for selecting response strategies of primary and secondary project risks under interval-valued fuzzy uncertainty

Risks are uncertain events that can affect criteria such as project cost, quality, and completion time and ultimately lead to project failure. That is why the project risk management process, which is one of the most fundamental parts of project management, must be done properly. In this paper, a new multi-objective fuzzy model is proposed to respond appropriately to the primary and secondary project risks. The contributions, such as risk interdependency, considering the project with multi-mode activities, resource considerations, as well as attention to interval-valued fuzzy uncertainties are regarded simultaneously for the first time. The purpose of the proposed multi-objective mathematical model is to minimize cost, quality reduction, and project completion time. Then, a new two-stage uncertain solution approach is introduced in this paper. In this solution approach, using an equivalent method in the first step and then using an extended multi-choice goal programming method in the second step, for both lower and upper limits of the membership functions for interval-valued fuzzy numbers, the computational processes are performed. To show the application and effectiveness of the proposed model, a case study adapted from the literature is given. Finally, sensitivity analysis is conducted to analyze the behavior of the developed mixed-integer programming model. The analysis of obtained results indicates that the utilization of the proposed model leads to better and more appropriate solutions.

Downside Risk in Australian and Japanese Stock Markets: Evidence Based on the Expectile Regression

The expectile-based Value at Risk (EVaR) has gained popularity as it is more sensitive to the magnitude of extreme losses than the conventional quantile-based VaR (QVaR). This paper applies the expectile regression approach to evaluate the EVaR of stock market indices of Australia and Japan. We use an expectile regression model that considers lagged returns and common risk factors to calculate the EVaR for each stock market and to evaluate the interdependence of downside risk between the two markets. Our findings suggest that both Australian and Japanese stock markets are affected by their past development and the international stock markets. Additionally, ASX 200 index has significant impact on Nikkei 225 in terms of downside tail risk, while the impact of Nikkei 225 on ASX is not significant.

Global shocks and local connectedness: evidence from dynamic tail risk interdependences among Chinese regional carbon markets

ABSTRACT This paper analyzes tail risk interdependence among four regional carbon markets in China to investigate the effect of exogenous shocks on local connectedness. We utilize a time-varying copulas approach and find that: (i) the Shenzhen carbon market has the highest level of tail risk due to the lower liquidity and weaker resilience. (ii) Carbon markets where prices are more responsive to information have higher levels of tail risk dependence. (iii) Compared to the China – US trade dispute and the armed conflict in Europe, the shocks from the extreme weather and the COVID-19 outbreak can prominently strengthen tail risk.

Study on Resilience Factors and Enhancement Strategies in Prefabricated Building Supply Chains

Prefabricated building holds promise for quality, efficiency, and sustainability when compared to traditional techniques. However, realizing prefabricated building work hinges on strengthening supply chain resilience. This research assesses interdependent risks undermining prefab network continuity during disruption. Questionnaire data from industry experts informed a structural equation model quantifying pathways between component production, construction, information, and other uncertainties. Findings confirm that project delays can be traced to manufacturing and on-site risks, with information gaps broadly propagating impacts. Meanwhile, organizational risks have an insignificant influence, suggesting partnership networks readily reconfigure around operational contingencies. Robust information infrastructures and coordination, therefore, offer crucial leverage. Accordingly, a multidimensional resilience enhancement strategy is formulated, prioritizing supply chain transparency, digital integration, inventory buffering, contingencies planning, and transportation flexibility. Our mixed-methods approach advances the construction literature by demonstrating the applicability of structural equation modeling for diagnostic resilience analytics. Industry leaders also gain actionable, evidence-based guidance on strategic investments to stabilize project flows. This dual theoretical and practical contribution underscores the versatility of tailored statistical assessments in furthering construction innovation objectives within complex, uncertain environments.

An optimization-based risk management framework with risk interdependence for effective disaster risk reduction

Effective risk management is crucial in the field of disaster management to mitigate the impact of disasters. It involves proactive planning, resource allocation, and the implementation of response measures. By identifying and assessing risks, stakeholders can take preventive measures and establish contingency plans, fostering resilience, coordination, and efficient disaster response and recovery processes. In the context of globalization, organizations face numerous uncertainties and risks, necessitating the implementation of efficient risk management plans. While the conventional risk response analysis assumes independence among risks, in reality, they are interconnected. Therefore, evaluating risk interactions becomes vital in comprehensive risk management evaluations. This study proposes an innovative framework for disaster risk management based on a review of relevant literature, global project management standards, and the insights gained from previous studies dealing with disaster management projects. To identify appropriate risk response strategies, an optimization model is developed that incorporates a maximization utility function and directly captures potential risk interdependence. An exact optimization method and two metaheuristics are proposed to solve the developed optimization model for the selection of risk response strategies. A detailed assessment of the considered solution algorithms in terms of various performance indicators and sensitivity analysis of model parameters are conducted through a comparative study. This analysis provides valuable insights into the effectiveness and practical applicability of the proposed methodology. The outcomes of this study will benefit practitioners and policymakers involved in disaster management by improving their ability to make informed decisions and allocate resources effectively.

Identification and assessment of risk factors in offshore wind-integrated hydrogen production system

Green hydrogen (H2) has received increasing attention as a potential fuel to decarbonize hard-to-electrify industries and support renewables’ growth. Compared to steam methane reforming (SMR), water electrolysis can produce green hydrogen with substantially less emissions. Efforts are ongoing worldwide to develop offshore wind-integrated green hydrogen production plants, particularly in regions with abundant water resources and favorable wind conditions to offset the external energy requirement during H2 production through water electrolysis. However, in the current literature, limited focus has been given to identifying the associated risk factors to establish such plants. To address this issue, the current work conducts a comprehensive examination of technology, economics, safety, and security-related risk factors. A thorough investigation has been performed to trace the risk factors, unraveling their contextual interrelationships, and where possible, quantitatively analyze their impact. A total of 40 risk factors were identified and modelled using the interpretive structural modelling (ISM) technique to visualize their interrelations. Control-related risk factors are found to be the most pivotal in the context of overall project failure. This research serves as a key reference for decision-makers to establish offshore wind-integrated water electrolysis-based hydrogen production facilities, ensuring due consideration for the interdependent risk factors involved in the process.

Cooperative security against interdependent risks

Firms in interorganizational networks are exposed to interdependent risks that are transferable across partner firms, such as contamination in food supply chains or data breaches in technology networks. They can be decomposed into intrinsic risks a firm faces from its own operations and extrinsic risks transferred from its partners. Firms have access to two security strategies: either they can independently eliminate both intrinsic and extrinsic risks by securing their links with partners or, alternatively, firms can cooperate with partners to eliminate sources of intrinsic risk in the network. We develop a graph‐theoretic model of interdependent security and demonstrate that the network‐optimal security strategy can be computed in polynomial time. Then, we use cooperative game‐theoretic tools to examine, under different informational assumptions, whether firms can sustain the network‐optimal security strategy via suitable cost‐sharing mechanisms. We design a novel cost‐sharing mechanism: a restricted variant of the well‐known Shapley value, the agreeable allocation, that is easy to compute, bilaterally implementable, ensures stability, and is fair. However, the agreeable allocation need not always exist. Interestingly, we find that in networks with homogeneous cost parameters, the presence of locally dense clusters of connected firms precludes the existence of the agreeable allocation, while the absence of sufficiently dense clusters (formally, k‐cores) guarantees its existence. Finally, using the SDC Platinum database, we consider all interfirm alliances formed in the food manufacturing sector from 2006 to 2020. Then, with simulated cost parameters, we examine the practical feasibility of identifying bilaterally implementable security cost‐sharing arrangements in these alliances.

A Lower Threat than I Thought: How the Analysis of the Interdependence between Risks Influences Smallholder Farmers’ Perceptions

Smallholder farming businesses are exposed to numerous risks which significantly affect productivity, income, and farmers’ livelihoods. These risks are interdependent in nature and co-occur, which makes managing a single risk in isolation an inadequate approach. This study examined how the analysis of the interdependence between risks can influence farmers’ perceptions of risk. The study employed focus group discussions and participatory activities, which involved the use of Scored Causal Diagrams (SCDs) to analyse risks with farmers and assess changes in perception. The results showed that the analysis of the interdependence between risks influenced a change in perceptions of risk for farmers, by enabling farmers to identify the important risks, cause-and-effect relationships between risks, and the root causes of risks. This also led to changes in the farmers’ own perceived ability to manage these risks. The analysis of the interdependence between risks provides insight into the design of holistic approaches for the management of risks. It enables farmers to identify the cause-and-effect relationships between risks, identify risks which contribute substantially to hindering their farming goals, and identify entry points for managing multiple interdependent risks. A unique contribution of this study is it presents a process for eliciting and analysing farmers’ perceptions of the interdependence between risks and explores how such analysis contributes to changes in farmers’ perceptions of individual risks. These changes in perceptions of individual risks ultimately shape farmers’ decisions about whether and how to adapt risk-management behaviour.

Risk Interdependency Network Model for the Cost and Time of Pile Installation in Saudi Arabia, Using Partial Least Squares Structural Equation Modeling

Pile construction projects cause significant time and expense overruns. The pile installation activity is the primary reason for project underperformance and uncertainties. Additionally, the risks associated with pile installation are mostly considered independent in the overall risk management process, leading to inadequate risk assessment and response. However, few studies have evaluated the risks associated with pile installation. Thus, this study aims to establish the risks of the time and cost of pile installation, using an interdependency network model with a particular emphasis on sand and rocky terrain conditions. In addition, this study introduces a new method for establishing a model that considers the interrelationships among risks via a partial least squares structural equation model (PLS-SEM). The research methodology involves assessing the probability and impact of 53 risk factors of pile installation time and cost. Twelve pile construction experts participated in this assessment. Then, a Monte Carlo Simulation was utilized before the data were integrated into the PLS-SEM. The research findings reveal that the site and economic risks indirectly affect the cost of installing pile in sand through construction risks. Also, the risk group comprising site and equipment risks indirectly affects the cost of installing pile in rock through design risks. This study’s findings will help construction organizations to improve time and cost risk assessments for pile installation projects.

The Legal Regime of Anti-Crisis Regulation in the Healthcare

Introduction. The understanding of crisis situations in many of their varieties, including those affecting the health of the population, is shifting from temporary and exceptional one-time events to long-term processes that pose serious challenges to the rule of law and require an adequate legal response. Under such circumstances, the usual health regulators, designed for the normal state of social relations, turn out to be non-functional. In this regard, the problem of scientific development of a special legal mechanism for anti-crisis management aimed at preventing, ensuring preparedness, effective response and liquidation of the consequences of crisis situations in the healthcare sector is being actualized.
 Materials and methods. The methodological basу of the study included general scientific (dialectical, logical, systemic, historical, sociological, statistical), and private scientific methods of legal science (formal legal, historical legal and comparative legal). The empirical base of the study was domestic and foreign legal acts, law enforcement practice, and legal doctrine.
 Results. There were identified and characterized,main features of the legal mechanism of anti-crisis management in the healthcare sector including the interdependence of risks in the healthcare sector and the need for a comprehensive regulatory impact on them, the normalization of anti-crisis regulation of the health of citizens, some dejuridization of the industry, a change in the ratio of individual and public interests, acceleration digitalization and intensification of legal experiments in healthcare.
 Limitations. Due to the objective novelty of the problem under consideration the study is somewhat limited in terms of reviewing the reports on the relevant topic.
 Conclusion. In modern conditions of permanent instability, there is a tendency to form the so-called anti-crisis standard for protecting the health of citizens as a special comprehensive intersectoral perpetual legal regime adapted to exceptional threats to the life and health of citizens.

Sectoral risk contagion and quantile network connectedness on Chinese stock sectors after the COVID-19 outbreak

PurposeThis study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.Design/methodology/approachThe authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.FindingsThe empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.Originality/valueThe results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.

Network effects on risk co-movements: A network quantile autoregression-based analysis

Interconnections and volatilities among financial institutions, notably during periods of extreme risk events, are fundamental factors in studies of financial markets. Utilizing tail-event-driven networks, we depict the risk interdependencies among financial institutions and introduce a novel ranking strategy for institutional influence based on network effects and quantile regression techniques. This interdependence is examined through the prism of joint extreme movements, utilizing bivariate extreme value theory and classification analysis. To validate their effectiveness and applicability in the real world, we deploy these methodologies on Systemically Important Financial Institutions (SIFIs). Additionally, we provide a ranking of the importance of eight U.S. institutions based on tail risks, offering regulators valuable insights and references.

We made it to Germany … and now? Interdependent risks of vulnerability for refugees in a high-income country

ABSTRACT Refugees are perceived as a category of people that are ‘vulnerable’ per se. However, once they have arrived in (high-income) hosting countries and are supported by a welfare state, vulnerability needs to be rethought, as they face new challenges and potential sources of inequality. In this paper, we have measured vulnerability as the probability of experiencing jointly three interdependent risks: social isolation, financial worries and poor health. For this purpose, we estimated a trivariate logit model to evaluate how individual and household characteristics are associated with vulnerability and also made inferences regarding the residual association between pairs of risks, conditionally to a set of selected covariates. Using the 2016 wave of the IAB-BAMF-SOEP survey of refugees in Germany, we demonstrated the significant correlations between the risks experienced by refugees and assessed a ‘propagation effect’ that affects vulnerability when the three risks come together. Taking this result into account may be useful in preventing vulnerability among refugees when they are faced with even one of these risks.