Level Of Systemic Risk Research Articles

Impact of economic policy uncertainty on systemic risk of real enterprises: Evidence from China

This work examines the impact of economic policy uncertainty on the systemic risk of Chinese real firms from the perspectives of financing constraints and external guarantees. The findings suggest that economic policy uncertainty significantly enhances the systemic risk of real firms. The mechanism test shows that economic policy uncertainty will enhance the level of systemic risk of enterprises by exacerbating their financing constraints and expanding the scale of external guarantees. Heterogeneity analysis shows that the negative impact of economic policy uncertainty on systemic risk is more obvious in enterprises with investment and financing maturity mismatches and poorer corporate governance.

Sexual behavior is linked to changes in gut microbiome and systemic inflammation that lead to HIV-1 infection in men who have sex with men

Pathogenic changes in gut microbial composition precede the onset of HIV-1 infection in men who have sex with men (MSM). This process is associated with increased levels of systemic inflammatory biomarkers and risk for AIDS development. Using mediation analysis framework, in this report we link the effects of unprotected receptive intercourse among MSM prior to primary HIV-1 infection to higher levels of proinflammatory cytokines sCD14 and sCD163 in plasma and a significant decrease in the abundance of A. muciniphila, B. caccae, B. fragilis, B. uniformis, Bacteroides spp., Butyricimonas spp., and Odoribacter spp., and a potential increase in the abundance of Dehalobacterium spp. and Methanobrevibacter spp. in stools of MSM with the highest number of sexual partners. These differences in microbiota, together with a reduction in the pairwise correlations among commensal and short-chain fatty acid-producing bacteria with a number of sexual partners, support an increase in gut dysbiosis with the number of sexual partners. These results demonstrate the interconnectedness of sexual behavior, immune response, and microbiota composition, notably among MSM participating in high-risk sexual behaviors.

Climate risk and the systemic risk of banks: A global perspective

This paper explores the impact of climate risk on the systemic risk of banks around the world and examines its influence channels. Our findings indicate that a country’s exposure to climate risk can significantly increase the systemic risk level of its banks. Moreover, we find that the increased bank systemic risk due to higher climate risk is mostly driven by worsened credit quality rather than the depreciation of the bank’s investment portfolio. The adverse impact of climate risk is mitigated when banks have higher profitability or capital adequacy. Cross-sectionally, this effect is particularly significant for banks with extensive branch networks, high importance in the domestic credit market, a lack of dividend payments, and those classified as commercial banks. We also find that banks located in countries with higher loan interest rates, worse regulatory quality, and higher carbon emission intensity are more impacted by climate risk.

Research on Coupling Effect Measurement and Coupling Risk Simulation of Green Building Construction Safety Risk Factors

The construction of green buildings is an important direction for the transformation and development of the construction industry, but it is beset with problems such as a lack of construction experience, immature new technologies, and unstable material properties; these issues bring risks to the construction stage of green buildings, and the coupling of uncertain risk factors in the construction process of green buildings may lead to unfavorable results. The purpose of this study is to explore the coupling degree of green building construction safety risk factors and the changing trend in their coupling combinations at the system risk level. First, the risk factor index system was defined by reading the literature and gathering expert opinions, and the coupling degree between risk factors was measured using an improved coupling degree model. Then, a system dynamics model was established to simulate and analyze the coupling effects among the risk factors and determine the combinations with the greatest influence. The results show that the risk probability is proportional to the risk coupling value, the human–environment coupling value is the largest, and the material equipment–management coupling value is the smallest. The human–environment system simulation shows that reducing the coupling value of system factors will promote a decrease in the total level of system risk. According to the research conclusions, measures to prevent risk coupling are proposed, which offer theoretical references for green building practitioners carrying out risk management; these measures hold a certain guiding significance for the risk control and future development of green buildings.

FPSO/LNG hawser system lifetime assessment by Gaidai multivariate risk assessment method

Floating Production Storage and Offloading (FPSO) unit being an offshore vessel, storing and producing crude oil, prior to crude oil being transported by accompanying shuttle tanker. Critical mooring/hawser strains during offloading operation have to be accurately predicted, in order to maintain operational safety and reliability. During certain types of offloading, excessive hawser tensions may occur, causing operational risks. Current study examines FPSO vessel’s dynamic reactions to hydrodynamic wave-induced loads, given realistic in situ environmental conditions, utilizing the AQWA software package. Current study advocates novel multi-dimensional spatiotemporal risks assessment approach, that is particularly well suited for large dataset analysis, based on numerical simulations (or measurements). Advocated multivariate reliability methodology may be useful for a variety of marine and offshore systems that must endure severe environmental stressors during their intended operational lifespan. Methodology, presented in this study provides advanced capability to efficiently, yet accurately evaluate dynamic system failure, hazard and damage risks, given representative dynamic record of multidimensional system’s inter-correlated critical components. Gaidai risk assessment method being novel dynamic multidimensional system’s lifetime assessment methodology. In order to validate and benchmark Gaidai risk assessment method, in this study it was applied to FPSO and potentially LNG (i.e., Liquid Natural Gas) vessels dynamics. Major advantage of the advocated approach is that there are no existing alternative risk assessment methods, able to tackle unlimited number of system’s dimensions. Accurate multi-dimensional risk assessment had been carried out, based on numerically simulated data, partially verified by available laboratory experiments. Confidence intervals had been given for predicted dynamic high-dimensional system risk levels.

Coupled analysis and simulation of safety risk in subway tunnel construction by shallow-buried excavation method based on system dynamics

To address the existing shortcomings in the research on the coupling of safety risk factors in subway tunnel construction using the shallow-buried excavation method, this paper conducts a coupled analysis and dynamic simulation of the safety risks associated with this construction method. Firstly, by analyzing the mechanisms and effects of risk coupling in shallow-buried excavation construction of subway tunnels, this study divides the risk system into four risk subsystems (human, material, management, and environment), establishes an evaluation index system for the coupling of safety risks, calculates the comprehensive weight values of the risk indicators using the AHP-entropy weight method, and constructs a risk coupling degree model by combining the inverse cloud model and efficacy function. Subsequently, based on the principles of system dynamics, a causal relationship diagram and a system dynamics simulation model for the coupling of “human-material” risks in construction are established using Vensim PLE software. Finally, the case study of the underground excavation section of Chengdu Metro Line 2 is employed to perform dynamic simulation using the established model. By adjusting the relevant risk coupling coefficients and simulation duration, the impact of the coupling of various risk factors on the safety risk level of the human-material coupling system is observed. The simulation results demonstrate that: 1) Heterogeneous coupling of human and material risks has a particularly significant effect on the system’s safety risks; 2) Violations by personnel and initial support structure defects are key risk coupling factors. The findings of this study provide new insights for decision-makers to assess the safety risk of shallow-buried excavation construction in subway tunnel.

Generalized coefficients of clustering in (un)directed and (un)weighted networks: An application to systemic risk quantification for cryptocoin markets

A coefficient of clustering is one of the important network parameters to describe clustering characteristics around a node in a network. It accounts for the number of cycles of length 3 (triangles) relative to the number of paths of length 2 in which this node participates as their inner node. In practice, the number of longer paths and cycles may be larger than the number of 2-paths and 3-cycles, thereby requiring its generalized version. In this paper, we aim at proposing the so-called λ-clustering coefficient to characterize the node clustering tendency at level λ≥3. We define it by counting the number of λ-cycles relative to the number of (λ−1)-paths containing a fixed inner node. In addition, we introduce the total clustering coefficient by considering the total number of all cycles relative to the total number of all paths consisting of this inner node. We formulate these generalized clustering coefficients for each of the following cases: undirected and unweighted networks, undirected but weighted networks, directed but unweighted networks, and directed and weighted networks. We then implement them for correlation- and conditional risk measure-based networks that represent conventional cryptocurrency markets and stablecoin markets. They can be viewed as new measures of systemic risk. The larger their values, the larger the tendency of risk transmission from one market to other subsequent markets forming longer cycles, and therefore the higher the systemic risk level. The systemic risk quantification is appropriate when they fulfill certain properties. In summary, the novelty of this study lies in the mathematical formulation of the generalized clustering coefficients and their computation for the cryptocoin networks with the purpose of appropriately managing systemic risk in the represented cryptocoin markets.

Use case cards: a use case reporting framework inspired by the European AI Act

Despite recent efforts by the Artificial Intelligence (AI) community to move towards standardised procedures for documenting models, methods, systems or datasets, there is currently no methodology focused on use cases aligned with the risk-based approach of the European AI Act (AI Act). In this paper, we propose a new framework for the documentation of use cases that we call use case cards, based on the use case modelling included in the Unified Markup Language (UML) standard. Unlike other documentation methodologies, we focus on the intended purpose and operational use of an AI system. It consists of two main parts: firstly, a UML-based template, tailored to allow implicitly assessing the risk level of the AI system and defining relevant requirements, and secondly, a supporting UML diagram designed to provide information about the system-user interactions and relationships. The proposed framework is the result of a co-design process involving a relevant team of EU policy experts and scientists. We have validated our proposal with 11 experts with different backgrounds and a reasonable knowledge of the AI Act as a prerequisite. We provide the 5 use case cards used in the co-design and validation process. Use case cards allows framing and contextualising use cases in an effective way, and we hope this methodology can be a useful tool for policy makers and providers for documenting use cases, assessing the risk level, adapting the different requirements and building a catalogue of existing usages of AI.

Financial Openness, Bank Systematic Risk, and Macroprudential Supervision

International experiences have underscored the dual implications of financial openness. Given China’s unique circumstances and its escalating level of financial openness, it is crucial to assess potential impacts on the country’s bank systemic risk. This paper uses the quarterly data of 37 listed banks in China from 2010 to 2022 to explore the relationship between financial openness and systemic risk of the banking system, the mechanism of action, and the moderating effect of macroprudential policy on the two. The findings indicate an inverted “U”-shaped correlation between financial openness and bank systemic risk. On one side of this shape, financial openness primarily exacerbates funding mismatch, thereby increasing the systemic risk of banks. Conversely, on the other side, it primarily alleviates systemic risk by optimizing capital management. Moreover, with the help of macroprudential supervision, the inverted “U”-shaped relationship between financial openness and bank systemic risk leads to a lower level of systemic risk and, at the same time, promotes the early arrival of the inverted “U”-shaped inflection point between financial openness and bank systemic risk. Notably, the impact of financial openness on the systemic risk of joint-stock commercial banks, urban commercial banks, and rural commercial banks is more significant. The above research results provide a regulatory reference for effectively preventing and resolving systemic risk while achieving high-quality openness to the outside world. In deepening financial openness, the banking industry needs to pay attention to the funding mismatch and the efficiency of capital management and implement differential risk supervision and prevention mechanisms for banks with different ownership, which is conducive to the reduction of bank systematic risk.

Do interfund network centralities affect fund systematic risk? Evidence from China open-ended funds

ABSTRACT Open-ended funds have been widely favoured by investors in China due to their high liquidity and the sufficiency of their information disclosure. We construct an interfund network to investigate the relationship between fund centralities and fund systematic risk. The results confirm the existence of a linkage effect in the fund network during the stock market crisis of 2015 while the fund network returned to a looser state after the crisis. In addition, the degree centrality and closeness centrality of funds in the network have significant positive effects on systematic risk, with a fund either playing a relatively crucial role in the network or maintaining sufficient proximity to other funds, both of which amplify the level of systematic risk. In addition, the fund shareholding ratio has a positive moderating effect on the relationship between fund centrality and systematic risk. We speculate that systematic risk can be diffused across funds through the stock market, which directly relates to fund network characteristics. Fund interconnectedness plays a more pronounced role than fund size in systematic risk, thus supporting the ‘too connected to fail’ perspective. Regulators are recommended to focus on ‘social star’ funds with high degrees or proximity centralities.

Do network characteristics affect systemic risk? Evidence from the European banking system

ABSTRACT Systemic risk is one of the main concerns for banks charged with maintaining overall financial stability. This paper adopts the minimum density method to construct an interbank lending network for European banks and examines how the network structure affects systemic risk. The result reveals that banks positioned at the core of the network exhibit higher levels of systemic risk. Moreover, we find that banks with higher network centrality can show larger systemic risk during times of distress. We demonstrate the robustness of our results by addressing potential endogeneity in the model, replacing network characteristics, and reconstructing the interbank network.

Management of the designed risk level of urban drainage system in the future: Evidence from haining city, China

The design of urban drainage infrastructure is mainly based on historical conditions. Under global warming, more intense precipitation extremes will pose severe risk to current infrastructure. The evaluation of where and by how much design standards need to change, is urgently needed to help maintain well-functioning drainage systems. In this study, we used climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and InfoWorks Integrated Catchment Modeling (ICM) to simulate urban flooding. According to the latest design standard of urban drainage infrastructure, we assess the risk of future urban flooding, and evaluate the effect and benefit of drainage infrastructure adaptation measures. The results showed that, under the shared socioeconomic pathway (SSP) 5–8.5 scenario, a 35% increase in extreme rainfall would be expected. Under a 1-in-30-year precipitation event, the maximum depth would increase by 5.59%, and the withdrawal time would rise by 2.94% in the future period, relative to the baseline level. After the enlargement of drainage infrastructure in local areas, 10% pipe enlargement has a better effect to reduce risk and higher benefits than 5% pipe enlargement. These findings provide valuable insights for policymakers in enhancing the drainage system and adapting to climate change.

Sentiment investor, exchange rates, geopolitical risk and developing stock market: evidence of co-movements in the time-frequency domain during RussiaUkraine war

PurposeThe aim of this paper is threefold: (1) to develop a new measure of investor sentiment rational (ISR) of developing countries by applying principal component analysis (PCA), (2) to investigate co-movements between the ten developing stock markets, the sentiment investor's, exchange rates and geopolitical risk (GPR) during Russian invasion of Ukraine in 2022, (3) to explore the key factors that might affect exchange market and capital market before and mainly during Russia–Ukraine war period.Design/methodology/approachThe wavelet approach and the multivariate wavelet coherence (MWC) are applied to detect the co-movements on daily data from August 2019 to December 2022. Value-at-risk (VaR) and conditional value-at-risk (CVaR) are used to assess the systemic risks of exchange rate market and stock market return in the developing market.FindingsResults of this study reveal (1) strong interdependence between GPR, investor sentiment rational (ISR), stock market index and exchange rate in short- and long-terms in most countries, as inferred from (WTC) analysis. (2) There is evidence of strong short-term co-movements between ISR and exchange rates, with ISR leading. (3) Multivariate coherency shows strong contributions of ISR and GPR index to stock market index and exchange rate returns. The findings signal the attractiveness of the Vietnamese dong, Malaysian ringgits and Tunisian dinar as a hedge for currency portfolios against GPR. The authors detect a positive connectedness in the short term between all pairs of the variables analyzed in most countries. (4) Both foreign exchange and equity markets are exposed to higher levels of systemic risk in the period of the Russian invasion of Ukraine.Originality/valueThis study provides information that supports investors, regulators and executive managers in developing countries. The impact of sentiment investor with GPR intensified the co-movements of stocks market and exchange market during 2021–2022, which overlaps with period of the Russian invasion of Ukraine.

Failure of 24 Inches Blowdown Tower Inlet Nozzle Due to Cyclic Surge Forces

Blowdown tower is one of the equipment in closed blowdown system (CBS) in Delay Coker unit in petroleum refinery. The primary intended function of the CBS is to flush out hydrocarbon vapours from coke drum using high pressure steam into blowdown tower. The system is operating in batches and cycles, due to constant operation at various temperature, pressure, and conditions, components are susceptible to various of failures including thermal fatigue, crack, corrosion, and others. Crack incident was observed on the welding of blowdown tower nozzle that connected to closed blowdown system (CBS) piping after 11 years of operation. This paper aims to study the structural integrity and risk level of the CBS system in relation to liquid level in receiving tower (blowdown tower). Dynamic piping stress analysis and Finite Element Analysis (FEA) was conducted on this line and blowdown tower nozzle to evaluate the overall material behaviour and assessed the risk of failure. The acceptance criteria of the analysis were set to be based on ASME VIII-Division 2 part 5.2.2 (Elastic Stress Analysis Method) and ASME II-part D. Findings from FEA shows that the failure occurred on the blowdown tower nozzle was due to surge pressure impact from high pressure steam flow to obstructed high liquid elevation at the inlet nozzle. The high surge pressure that happened near the nozzle has caused the stress to exceed allowable level thus initiated the crack in cyclic/ batch service. It is recommended that petroleum refinery to continuously maintain the blowdown tower level below nozzle to prevent similar phenomenon to happen in future.

Systemic Risk in the Global Energy Sector: Structure, Determinants and Portfolio Management Implications

We examine the dynamics of tail dependence across returns of 105 global energy firms from 26 countries covering the regions of America, Asia Pacific and Europe. A partial correlation-based approach is used to quantify the dependence structure and level of systemic risk under relatively stable and extremely bearish and bullish market conditions. The dependence network of energy stock returns is constructed based on the novel triangulated maximally filtered graph (TMFG). The results reveal a high degree of tail dependence and role played by geographical proximity. The strongest links are found under extreme bearish market conditions. American and European energy firms are more interconnected and contribute more to systemic risk than Asian-Pacific companies. The dependence intensifies during periods of market turmoil, especially during the COVID-19 pandemic. A higher instability in the dependence structure is observed during extremely bearish market circumstances. A simple portfolio trading strategy based on the dependence ranking of energy firms outperforms a naïve equally-weighted buy-and-hold portfolio strategy.

Quantifying systemic risk in the cryptocurrency market: A sectoral analysis

Lack of regulations and malicious activities exacerbate systemic risk exposure in the cryptocurrency market. Considering this, we explore Bitcoin, Ethereum, and thirteen cryptocurrency sectors to analyze the systemic risk level of the cryptocurrency market. We also examine the causality network among the sectors under extreme downside market conditions. Results show that smart contracts, along with Bitcoin and Ethereum, are sources of systemic risk. The extent of connectedness is 40 % during extreme loss periods. Our findings are useful for investors and risk managers who wish to take appropriate measures against the sectors that destabilize the market.

Systemic Risk Spillover Effects among China’s Financial Institutions: Evidence from the Spatial Econometric Model

This study investigates the spillover and contagion effects of systemic risk among Chinese financial institutions in terms of the conditional Value-at-Risk method and spatial econometric techniques. We construct different representative spatial weight matrices to demonstrate various risk connective categories and contagion channels. The spatial autoregression model is built to reveal the different magnitudes of systemic risk contagion effects and extended as the spatial quantile regression model to measure the change in spillovers across quantiles. The results highlight that the spatial agglomeration pattern of institution-level systemic risk is highly concentrated within the same sector but highly disparate between the different sectors. The closeness of the asset price channel and the information channel would enhance the systemic risk spillover effects among institutions. The higher the single institution’s systemic risk level is, the stronger its spillovers among all of them within the same financial department, yet contrarily, the spillovers are larger at lower quantiles between different sectors due to the disparate spatial tendency of systemic risk. Besides, the spillover effects across quantiles indicate the higher tail contagion of systemic risk spatial spillovers, especially during 2015 A-share market crash and 2020 COVID-19 outbreak.

A FINANCIAL CRISIS PROBABILITY MEASUREMENT MODELINTHECONTEXTOFDIGITALMODEL IN THE CONTEXT OF DIGITAL TRANSFORMATIONTRANSFORMATION

In recent decades, the structure of internaƟ onal exchange has undergone signifi cant changes, and the role of various services is increasing, the range of which is expanding under the infl uence of the scienƟ fi c and technological revoluƟ on. At the same Ɵ me, the regimes of internaƟ onal trade in services, as well as approaches to its naƟ onal and mulƟ lateral regulaƟ on, are becoming more complex. DigitalisaƟ on has become one of the main characterisƟ cs of development. The digital transformaƟ on of internaƟ onal trade is shaping the new "digital reali-ty" of the industry. The staƟ sƟ cs of the offi cial regulators of internaƟ onal trade confi rm these changes. The paper analyses the dynamics of changes in internaƟ onal trade in services in the context of the global crisis and the impact of digitalisaƟ on on it. The interdependence of countries in the condiƟ ons of increased economy and new models of internaƟ onal exchange of services are shown. The paper presents a quanƟ taƟ ve assessment model of the level of systemic risk causing the fi nancial crisis.

A new way of measuring effects of financial crisis on contagion in currency markets

Contagion is an extremely important topic in finance. Contagion is at the core of most major financial crises, in particular the global financial crisis that started in 2007. Although various approaches to quantifying contagion have been proposed, many of them lack a causal interpretation. We will present a new measure for contagion among individual currencies within the Foreign exchange market and show how the paths of contagion work within the Forex market using causal inference. This approach will allow us to pinpoint sources of contagion and to find which currencies offer good options for diversification and which are more susceptible to systemic risk, ultimately resulting in feedback on the level of global systemic risk. In particular, we will focus on the effects of the Covid-19 global pandemic.

A dynamic community gas risk-prediction method based on temporal knowledge graphs

This paper presents a community gas-safety risk-prediction method for solving the complex and continuous community gas-safety factors based on the temporal knowledge graphs. First, a community gas-safety risk-assessment index system is constructed based on the risk sources and factors influencing gas accidents. Entity and relationship features are extracted from the index system to construct a temporal knowledge graph of the community gas system. The community gas-safety risk-assessment index system is constructed based on the risk sources and risk-influencing factors of gas accidents. Furthermore, entity and relationship features are extracted in the index system to construct temporal knowledge graphs for a community gas system. Then, a gas-system risk-prediction method based on the temporal knowledge graphs is proposed, which predicts the risk level of the gas system in a certain period in the future. Finally, by applying the method in a specific community, the accuracy of the proposed prediction method is 74.87%, and the mean reciprocal rank is 87.44. The proposed gas-safety risk-prediction method based on a temporal knowledge graph network can assist managers in effectively managing community gas and has a positive auxiliary effect on sensing and controlling gas-safety situations.