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Multidimensional systemic risk measures are constructed based on dynamic risk spillover networks to quantify the level, transmission speed, and transmission breadth. To investigate the significance, direction, magnitude, and persistence of the impacts of systemic risks on future real economic distribution, we combine the quantile regression model and Bootstrap quantile t test method. Finally, the heterogeneity of the impacts of systemic risks and SIFIs (systemically important financial institutions) risks is analysed. Our empirical results find that 1) most bank companies take the role of risk drivers in the financial system, while most security, insurance, trust, and real estate companies take the role of risk distributors and risk receivers. 2) the total spillover index, average shortest path length, and average clustering coefficient have significant impacts on the left tail and central tendency of future real economy, and the latter two measures also have significant impacts on the right tail. 3) SIFIs risks have earlier and greater impacts than Non-SIFIs (non-systemically important financial institutions) risks. Multidimensional systemic risks contain additional information to identify financial risks earlier than single institutions risks. 4) the impacts of financial risks on the real economy are persistent. The superimposition of impacts may trigger extreme risks. [ABSTRACT FROM AUTHOR]

We study the impact of unexpected climate shocks on banks' individual and systemic risks. Employing climate risk measures developed using the Billion-Dollar Weather and Climate Disasters data from the National Oceanic and Atmospheric Administration (NOAA) and Dealscan syndicated lending data, we find that climate risk exposure acquired through cross-state lending increases banks' individual and systemic risks. We also find that bank profitability helps offset some of the adverse effects of climate risk. Banks reduce lending and increase loan loss reserves after the experience of an unexpected climate shock. The loan-level analysis reveals that the effect of climate risk exposure on bank risks is more pronounced for loans granted for operating and capital expenditures. We contribute to a growing literature on the impact of climate risk on financial stability and the development of robust measures of climate risk for banks. [ABSTRACT FROM AUTHOR]

We consider a general tractable model for default contagion and systemic risk in a heterogeneous financial network subjected to an exogenous macroeconomic shock. We show that under certain regularity assumptions, the default cascade model can be transformed into a death process problem represented by a balls-and-bins model. We state various limit theorems regarding the final size of default cascades. Under appropriate assumptions on the degree and threshold distributions, we prove that the final sizes of default cascades have asymptotically Gaussian fluctuations. We next state limit theorems for different system-wide wealth aggregation functions, which enable us to provide systemic risk measures in relation to the structure and heterogeneity of the financial network. Lastly, we demonstrate how these results can be utilized by a social planner to optimally target interventions during a financial crisis given a budget constraint and under partial information of the financial network. [ABSTRACT FROM AUTHOR]

This paper aims to identify the most emission-intensive industries in the Czech economy using an environmentally extended input-output framework to determine whether decarbonization of the economy might jeopardize financial system stability. The main contribution of this paper to the existing literature lies in connecting bank industry-specific loan exposures with industry-specific emission intensities, which are used to calculate potential credit losses after the introduction of a carbon tax. The analysis shows that the Czech banking sector is not significantly concentrated in industries which are most vulnerable to decarbonization of the economy and threats to the financial stability of the green transition are currently low. [ABSTRACT FROM AUTHOR]

This paper studies the predictive power of a set of systemic risk measures for macroeconomic downside risks in China. The quantile single-index model (QSIM) is used to select and combine systemic risk measures to depict the lower tail of macroeconomy distribution. The out-of-sample results show that QSIM provides prominent and robust prediction, as it aggregates all relevant systemic risk measures information and captures parameter nonlinearity. Moreover, we uncover the risk drivers behind macroeconomic downturns using the variable selection technique. The results provide an early warning mechanism for regulators and a criterion for researchers to select systemic risk measures. [ABSTRACT FROM AUTHOR]

Infectious diseases are a major threat to Asian shrimp aquaculture, as they proliferate at system level rather than only the individual level. We assess the impact of various forms of cooperation among Vietnamese farmers on yield risks caused by white spot disease and acute hepatopancreatic necrosis disease. Using a stochastic simulation model, we simulate shrimp farming yield risks based on input from two expert workshops. The results provide a relative comparison of expected yield losses caused by both diseases comparing a baseline scenario (no cooperation) and three scenarios with varying degrees of synchronization and information sharing across farms. Results show lower expected yield losses in all three cooperation scenarios in comparison with the farm-based scenario, highlighting the value of synchronization and information sharing practices to mitigate yield losses. We discuss the potential this has to reduce systemic risks in aquaculture, thereby potentially incentivizing the reintroduction of risk-sharing mechanisms. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology to examine spillover effects using insurer/bank connectedness as an example. Using large standard deviation jumps in daily returns to measure market shocks, our generalized event study approach can address the issues of multiple events happening in one day, positive and negative responses, asymmetries in the connected responses and endogeneity issues. We employ a random forest residuals regression approach that offers more flexibility in the connectedness relationships than standard regression models, yielding results that are more consistent with the literature (i.e., the predominance of contagion over competitive effects in connectedness). We show that standard linear regression models do not appropriately capture these relationships because they do not account for the endogeneity of the shocks to the daily returns. We find evidence that insurers' and banks' returns move in the same direction after shocks, indicating market contagion effects, rather than in the opposite direction, which would indicate market competitive effects. We also find that the event shocks of January, April, July, and October are larger and more statistically significant than other months. The evidence shows that bank shocks are relatively more destabilizing than insurer shocks. [ABSTRACT FROM AUTHOR]

This article deeply integrates the characteristics of the China financial market under the background of the spread of the global new coronavirus pneumonia epidemic and integrates the multi-sector DSGE model including heterogeneous residents, manufacturers, banks, and macro-prudential supervision, and then numerically simulates the impact of technology and financial shocks on major economic indicators. Macroprudential capital regulation can help stabilize large economic waves, increase labour supply in the economic system, and restrain excessive transmission of systemic risks in the financial system. Meanwhile, the leverage ratio of banks will also change correspondingly with the price fluctuations of the real estate market. The relevant decision-making departments of the central government and the financial supervision departments coordinated in many ways to find the optimal internal balance point and increase the combined effect of effectively deflating systemic risks. [ABSTRACT FROM AUTHOR]

This article examines the role of derivatives in the context of the recent crisis in the UK pension system, specifically in liability-driven investment schemes. It unveils derivatives' role as instruments that multiply and propagate losses among market participants once specific events (worst-case scenarios) materialise. This article questions the role of derivatives as risk-management and risk-diversification tools, given their employment, in this case as in others before, for speculative purposes. Critically, with speculation came higher than desirable levels of leverage that these financial products elicited, causing in the process a series of systemic concerns. While much criticism has been directed at post-2008 regulation, and particularly at clearing and margin requirements, this article proposes a broader view of the problems posed by derivatives in the different contexts of their applications. More specifically, attention is drawn to legal doctrines that could be redeployed for the purpose of mitigating the speculative nature of derivatives. [ABSTRACT FROM AUTHOR]

Identifying the influential variables that provide early warning of financial network instability is challenging, in part due to the complexity of the system, uncertainty of a failure, and nonlinear, time-varying relationships between network participants. In this study, we introduce a novel methodology to select variables that, from a data-driven and statistical modeling perspective, represent these relationships and may indicate that the financial network is trending toward instability. We introduce a novel variable selection methodology that leverages Shapley values and modified Borda counts, in combination with statistical and machine learning methods, to create an explainable linear model to predict relationship value weights between network participants. We validate this new approach with data collected from the March 2023 Silicon Valley Bank Failure. The models produced using this novel method successfully identified the instability trend using only 14 input variables out of a possible 3160. The use of parsimonious linear models developed by this method has the potential to identify key financial stability indicators while also increasing the transparency of this complex system. [ABSTRACT FROM AUTHOR]

Systemic risk has certain properties that make it behave as a complex system, one of which is its nonlinearity. The objective of this paper is to empirically address the systemic risk-taking channel of monetary policy from this perspective using the U.S. case and the TVPVAR model with Structural Factor Augmented (SFA-TVP-VAR). In order to obtain robust results, I estimate this model with two different metrics of the monetary policy stance, with different parameters and priors in each case. Considering the remaining system variables as latent variables, I document evidence of a complex channel: I find that the relationship between monetary policy stance and systemic risk-taking is non-linear and adaptive to economic conditions. Specifically, the posterior estimation shows that the monetary policy maintains an inverse relationship with systemic risk-taking in the long run, indicating that a loose stance increases systemic risk; however, in the short run, in earlier periods and during a financial crisis, a restrictive shock of monetary policy, instead of reducing systemic risk-taking, increases it. [ABSTRACT FROM AUTHOR]

This paper introduces a dynamic dependence framework to calculate various indicators of systemic sovereign default risk. Our analysis reveals a notable increase in systemic fragility among euro-area sovereigns since the onset of the Subprime Crisis, particularly during the First Greek Bailout in May 2010. Furthermore, our measures successfully capture key events within the euro area, including Mario Draghi's impactful "whatever-it-takes" speech in mid-2012 and the Cypriot Banking Crisis of 2012-2013. The incorporation of dynamic dependence into our measures provides a more comprehensive depiction of systemic risk within the euro area sovereign system, often demonstrating distinct dynamics when compared to their static counterparts. These findings carry significant policy implications and contribute to enhancing our understanding of systemic risk among euro-area sovereigns. [ABSTRACT FROM AUTHOR]

The article examines the supervision of the activities of non-banking financial institutions based on current legislation. The object of the study is the set of relationships that arise in the process of financial control over the activities of non-banking financial institutions. The subject is the laws and regulations of the National Bank of Ukraine regulating the activities of non-banking financial institutions. The methodological basis of the research is general scientific and private scientific methods of cognition. It is noted that effective control and supervision of the activities of non-banking financial institutions is an integral condition for ensuring the stability and development of the financial market, protecting the rights and legitimate interests of consumers of financial services. Supervision of non-banking financial institutions is characterized by the use of: a risk-oriented approach, which assumes that supervisory measures are carried out with different frequency, intensity and concentration on the identified risks of a non-banking financial institution depending on its risk profile. Risk-based supervision arises as a supplement to formal compliance supervision. Supervision of the activities of non-banking financial institutions includes: macro prudential supervision aimed at ensuring the safety and stability of the financial market; oversight of the judgment-based regulatory model; proportional (selective) regulation and supervision; behavioral supervision; a combination of forms of supervision based on a balanced combination of various forms of control and supervision activities of the National Bank of Ukraine. Expanding the powers of non-banking financial institutions to combine types of activities will lead to the development of the market by reducing the costs of its participants and introducing new digital technologies into the practice of non-banking financial institutions. One of the most urgent tasks facing the National Bank of Ukraine today is to overcome the approach to regulation exclusively by types of legal entities by moving to a more progressive model of regulation by types of activity. [ABSTRACT FROM AUTHOR]

Conglomerates play an important role in the functioning of capital markets. Therefore, assessing their response to external shocks is a significant risk management challenge not only for conglomerate executives but also for investors and regulators alike. In this context, a conglomerate refers to a group of companies typically operating across different industries and interconnected through both equity and debt relationships. Essentially, a conglomerate functions as a financial network whose nodes are linked by two layers of reciprocal connections. This paper introduces an algorithm to evaluate a conglomerate's response to external shocks. Additionally, it proposes a protocol based on five key metrics that collectively summarize the conglomerate's overall resilience. These metrics offer two major advantages: they facilitate comparisons between the strengths of different conglomerates and help assess the effectiveness of various strategies, such as internal capital reallocations, aimed at enhancing a conglomerate's resilience. The algorithm's usefulness, including its ability to detect cascades or "second-wave" defaults, is demonstrated through two illustrative examples. [ABSTRACT FROM AUTHOR]

This study investigates the ESG performance–systemic risk (SR) nexus among Indian companies. Using the beta coefficient from the Capital Asset Pricing Model (CAPM) and statistical analysis, it explores how ESG performance affects SR. The findings reveal that firms with higher ESG scores have lower SR sensitivity. Notably, there is a significant difference in risk sensitivity between high- and low-ESG-rated companies, with ESG effects being less pronounced in high-cap firms compared to low-cap firms. Conversely, large firms, older firms, and those with lower borrowing costs show a diminished effect of ESG ratings on their SR sensitivity. These results underscore the importance of firm-specific characteristics in determining the efficacy of ESG strategies in risk mitigation. This study reveals that ESG performance reduces SR, with market valuation affecting this relationship. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine information (stock return, volatility and extreme risk) spillovers and interconnectedness within dual-banking systems. Design/methodology/approach: Using multilayer information spillover networks, this paper conduct a deep analysis of contagion dynamics among 24 Islamic and 46 conventional banks from 2006 to 2022. Findings: The findings show the network's rapid response to financial shocks. Through cross-sector analysis, this paper identify information spillovers between and within Islamic and conventional banking systems. Furthermore, this research illustrates distinct roles played by Islamic and conventional banks within the multilayer network structure, contingent upon the nature of the financial shock. Practical implications: Understanding the differential roles of Islamic and conventional banks in information transmission can aid policymakers and financial institutions in devising more effective risk management strategies, thereby enhancing financial stability within dual-banking systems. Originality/value: This study contributes to the literature by emphasizing the necessity of examining contagion mechanisms beyond traditional single-layer network structures, shedding light on the shadow dynamics of information transmission in dual-banking systems. [ABSTRACT FROM AUTHOR]

As a colossal developing economy, irrational, and inefficient trades broadly exist in China's stock market and are intensified by the once-in-a-century COVID-19 pandemic. This atypical but prominent event enhances systemic risk and requires a more effective analysis tool that adapts to the investors' sentiment and behavior. Based on the behavioral asset pricing model, this paper verifies the existence of noise traders in China's stock market, measures the intensity of the noise with the NTR indicator, and examines the market noise with IANM. Furthermore, the mechanism of how COVID-19 influences the market noise through investors' behaviors is analyzed with the event study method. The findings show that, based on 92 Chinese companies, the market noise significantly exists, and the noise is associated with psychological biases including over-confidence, herding effects and regret aversion. These biases are affected to varying degrees by COVID-19-related events, leading to notable implications for market stability and investor behavior during crises. Our study provides critical insights for policymakers and investors on managing market risks and understanding behavioral impacts during unprecedented events. [ABSTRACT FROM AUTHOR]

This paper empirically examines the moderating role of country-level governance indicators (CGIs) in the relationship between macro-prudential policy instruments (MPI) and systemic risk. Results from 68 countries, during the period 2000–2017, show that CGIs in terms of corruption controls, government effectiveness, regulatory quality, and rule of law play a negative moderating role in the MPI-systemic risk nexus. Countries scoring high (low) on these CGIs experience stability benefits (instability costs) from MPIs. These findings suggest that the mere implementation of macro-prudential regulations may not perform the intended function of systemic stability. Overall, institutional development in a country's governance ecosystem is necessary; hence, a coordinated effort is required from all the stakeholders of the country. [ABSTRACT FROM AUTHOR]

In this paper, we have carried out the predictions for growth or spurt in Systemic Risk across the different categories of financial institutions in India relative to the change in the market prices. We have used Bayes Theorem along with Logistic regressions to work out the actual probabilities regarding the growth in Systemic Risk with the fall in stock prices and Wilcoxon Rank sum Test to validate the robustness of the models. In this paper, we have studied the period from July 2007 to December 2020. An important feature observed was any fall in closing prices beyond 30%, is contributing for 90% growth in systemic risk. A policy implication can follow—that it is imperative to monitor a sharp decline in market prices to the tune of 30% or more by regulators to avoid a crisis. We generally presume that state ownership of Banks particularly in India generates public confidence. Our paper has been able to support the theory of public confidence wherein the Public Sector Banks are contributing less towards the growth of Systemic Risk as compared to Private Banks and NBFCs. The NBFCs are the highest contributor of the growth in systemic risk which we have differentiated from our results. So, in coming days NBFCs are to be closely monitored by the regulators and suitable regulatory measures need to be placed. [ABSTRACT FROM AUTHOR]

This study examines market reactions to EU-wide insurance stress tests, providing the first analysis of this nature for the insurance sector. Empirical results reveal that earlier tests in 2014 had a limited market impact, while the 2018 stress test garnered slightly stronger but still limited responses. Market reactions in insurance differ from banking stress tests, potentially due to factors such as non-disclosure of individual results and superior capitalization in the insurance sector. The study highlights the potential of EU-wide insurance stress tests to reduce systemic risk, particularly when governance includes stakeholder interaction. The findings emphasize the ongoing need for research on stress test impact, proposing a balance between efficiency and transparency in future designs. The study concludes by advocating for long-term analyses to ensure the robustness of results and exploring evolving trends in EU-wide insurance stress tests. [ABSTRACT FROM AUTHOR]

Understanding the interconnectedness of cryptocurrencies based on their underlying technology is crucial for effective portfolio management and risk assessment. To establish the tail dependence structure and risk spillover between cryptocurrencies, this paper used the daily closing prices of the top eight proof-of-stake-based cryptocurrencies and the top ten proof-of-work-based cryptocurrencies from September 22, 2020 to April 7, 2023. This study applied the C-vine copulas and CoVaR measures. The outcome of the copula findings for the proof-of-stake cryptocurrencies illustrates that Ethereum exhibits strong resilience during market downturns, acting as a buffer for other proof-of-stake cryptocurrencies with pairwise tail dependence coefficients ranging from 0.45 to 0.67. Bitcoin Cash emerges as a portfolio diversifier within the proof-of-work ecosystem, absorbing 45% to 75% of volatility spillovers. However, from the proof-of-stake CoVaR analysis, ETH, DOT, and MATIC rank highest in systematic importance before April 2022, signifying their significant risk transmission role, and for the proof-of-work CoVaR analysis, Bitcoin (BTC) is the primary risk transmitter in the cryptocurrency portfolio, having a positive CoVaR of 0.15. Ethereum and Bitcoin are identified as the dominant risk transmitters within their respective groups, highlighting their potential to amplify systemic risk. This study provides valuable insights for investors and policymakers navigating the increasingly complex cryptocurrency landscape.

Systemic risk is the risk imposed by a financial institution on the entire economy, the importance of which has become clear to many policymakers and economists since the financial crisis of 2008, and its measurement has been put on the agenda of many researchers. The present study presents a combined method of systemic risk measurement, in which the shape of the communication graph and the structural characteristics of financial institutions are simultaneously considered. In the proposed method, first, the communication graph is clustered using the Markov clustering algorithm. Then the systemic risk of each financial institution is measured according to its position in the cluster and using the adjusted semi-local centrality systemic risk measure. The effectiveness of the proposed method has been investigated for banks registered with the Tehran Stock Exchange and Securities Organization from 2014 to 2018 with monthly periods. Based on the results, the linear correlation of systemic risk changes calculated based on the proposed method with systemic risk calculated through simulation (SIR) was higher than the correlation of systemic risk calculated with CoVaR and PageRank measures. Also, based on the results, Mellat, Trade and Export banks have the highest systemic risk and the lowest systemic risk related to capital, and tourism banks. [ABSTRACT FROM AUTHOR]

This study examines the influence of oil shocks on systemic risk spillover among the commodity markets. Specifically, this paper uses the DCC-GARCH approach combined with the TVP-VAR model to calculate risk connectedness and the GARCH-MIDAS model to explore how oil shocks from different sources affect the risk spillover effects among the commodity markets. The results are the following: First, there are significant risk spillovers among the commodity markets with important time-varying characteristics and with sharp changes in times of crisis. The industrial metals, agriculture, precious metals, and light energy commodity markets are risk recipients, and the energy and livestock commodity markets are risk exporters. Second, oil price shocks, particularly oil aggregate demand shocks, prominently affect the total risk connectedness among the commodity markets. In particular, the impact on the net risk spillover effect of different commodity market differs. [ABSTRACT FROM AUTHOR]

Since the financial crisis, financial networks, such as interbank lending networks, have been highly concerned by regulators in terms of their vulnerability. To study the risk contagion power of financial networks deeply, this paper integrates short-term and long-term lending data among commercial banks and stress tests the Chinese interbank lending network by using the extended distress and default contagion model. The results show that the reconstructed short-term lending network utilizing information on bank assets exhibits a core–periphery structure with systemically important banks at its core. Stress test results suggest that an interbank lending network that does not consider long-term lending data underestimates the network's systemic risk when market conditions are poor. In addition, we find that the Chinese interbank lending network has gradually become more risk-resistant over time, and the vulnerability of the network after the outbreak of COVID-19 is lower than that before. Finally, we suggest that regulators can make full use of ancillary data, such as long-term interbank lending data, to stress test financial networks, thereby accurately capturing the network's structure and preparing for ex-ante interventions before a crisis strikes to mitigate network systemic risk. [ABSTRACT FROM AUTHOR]

We study systemic risk drivers of FinTech and traditional financial institutions under normal and extreme market conditions. We use machine learning (ML) techniques (i.e. random forest and gradient boosted regression trees) to evaluate the role of macroeconomic variables, firm characteristics, and network topologies as systemic risk drivers and perform the ML-based interpretation by Shapley individual and interaction values. We find that (i) the feature importance in driving systemic risk depends on market conditions; namely, market volatility (MVOL), individual stock volatility (IVOL), and market capitalization (MC) are positive drivers of systemic risk under extreme (downside and upside) market conditions, while under normal market conditions, institutions with high price-earnings ratio, large MC, and low IVOL play an essential role in stabilizing markets; (ii) macroeconomic variables are the most important extreme systemic risk drivers, while firm characteristics are more important under normal market conditions; and (iii) the interaction between IVOL and MC or MVOL is the significant source of extreme systemic risk, and MC is the most crucial interaction attribute under normal market conditions. The interactions between macroeconomic variables are the most prominent in systemic risk under different market conditions. [ABSTRACT FROM AUTHOR]

Networking helps entrepreneurs in opportunity identification, resource acquisition, overcoming barriers and ensuring sustainability and growth. However, agri-entrepreneurs are locally embedded, and networking can provide limited options to respond to challenges. In a developing economy, agriculture has various systemic risks such as disasters, policy changes, market fluctuations and government policies. Given the large employment and emerging concerns in agriculture, managing such systemic challenges becomes significant. While the literature broadly indicates that the networking behaviour of entrepreneurs helps, little is known if such behaviour helps in mitigating challenges. This study responds to the literature gap by identifying the association between networking behaviour and systemic challenges faced by one of the less developed regions of India. The study considered 14 systematic challenges from the PESTEL framework and 15 levels of networking from the literature. Entrepreneurs from the agriculture sector (N = 402) were contacted for their responses. Findings suggest that networking behaviour is significantly associated with various systematic challenges faced by entrepreneurs in the agriculture sector. The networking behaviour also varies with the demography of the entrepreneurs. The study contributes to social networking theory, structuration and risk management theory. Networking happens under a social structure, and social networking helps in managing systematic risk. Entrepreneurs, policymakers and educators can understand and facilitate such networking. [ABSTRACT FROM AUTHOR]

Macroprudential policy is still a relatively new policy area ("more art than science"). Unlike monetary policy, it still does not have any tested and robust strategies to follow. Using a novel strategy index, we explore and critically review macroprudential strategy documents published in 30 countries in 2014–2023. The overall results point to fairly high quality of strategies, with low level of heterogeneity in the total score. Most strategy documents were published by central banks responsible for macroprudential policy. The score for strategies in European Union (EU) countries is higher and more homogenous than in non-EU countries. Elements least frequently included in the strategies include challenges for macroprudential policy (like cyber or climate risks) and its interactions with other policy areas. Macroprudential strategies were mostly issued after the credit boom. The results provide relevant policy implications on future revisions of this kind of document. [ABSTRACT FROM AUTHOR]

The article is devoted to the study of trends in the development of non-bank financial intermediation. The scale of the non-banking segment of the financial market has increased significantly, and it is believed that at the beginning of 2022 it accounted for about half of global financial assets, which may affect the financial stability not only of individual states, but also of the entire global economy. In this regard, the analysis of risks emanating from non-bank financial intermediation institutions is an urgent task of national financial regulatory authorities. The present study is aimed at solving this problem. The purpose of the study is to identify the impact of non-bank financial intermediation on the banking sector in order to determine the prospects for its anti-crisis regulation and develop approaches to the formation of strategies for managing systemic risks that may be caused by the activities of such institutions. The study is based on data from the Financial Stability Board, the International Monetary Fund, and the Bank of Russia. Methods of analyzing regulatory documents and comparative economic analysis are used. The paper systematizes possible channels for the implementation of risk factors and develops new approaches for the diagnosis of systemic risks due to the influence of non-bank financial institutions. There are suggestions made regarding the formulation of systemic strategies for risk management: strengthen regulation and supervision of NBFP institutions; provide conditions for providing liquidity in case of stress in the NBFP sector; ensure coordination between the Central Bank and sectoral regulators in order to manage crisis situations. Possible tools for setting up macroprudential policy to control risk factors of certain groups of non-banking financial institutions in order to ensure the stability of financial markets are presented: limitations of interrelationships with the banking system; indicators of sensitivity to customer panics; improving the quality of risk assessment; prohibition of secondary and tertiary securitizations of assets. It is concluded that there is a need for national authorities to apply 4 main approaches to regulation, primarily aimed at reducing liquidity risks, financial leverage, currency gaps and interconnectedness.

The purpose of this article was to investigate the relationship between the diversification of banking resources and expenses with systemic risk in the country's banking system. In this regard, the accepted banks in the Tehran Stock Exchange were evaluated. In order to estimate the systemic risk, the criterion of conditional value at risk was used. Also, in order to estimate the impact of diversification of banking resources and expenses on systemic risk, panel data and quantile regression were used for a selection of banks in the country in the period of 2010-2022. The results obtained from this study indicated that the diversification of banking resources and expenses, because it leads to more dependence between financial institutions, as well as a different combination of financial assets, leads to an increase in systemic risk and contagion. It goes to other financial institutions. In the estimated model, the coefficient of influence of bank diversification index on systemic risk in panel data regression model is equal to 1.45 and in quantile regression it is equal to 1.33. In addition, according to the results of the quantile regression, it can be stated that the effect of diversification index of sources and expenses in different banking groups has been different according to the level of systemic risk. The high level of systemic risk in Iran shows that despite the existence of supervision of some financial institutions, the systemic risk of financial activities is excessive, and this perhaps suggests the need for an effective supervisory authority.

The paper examines the impact of the war in Ukraine on the financial condition and stability of banks in Central and Eastern European nations directly bordering on the countries involved in the military conflict, i.e., Ukraine, Russia, and Belarus. The research sample, consisting of 93 banks and 45 analysed variables, was selected based on the systematic literature review presented. The empirical study uses the system generalised method of moments (SGMM). To capture the studied impact, both binary and interactive variables were used. The results are discussed in relation to banking variables and those related to the macroeconomic situation. The study allowed us to conclude that there has been no significant deterioration in the capital position of banks. However, it showed a significant negative impact of several variables on the condition and stability of the studied banks, as captured by other indicators. Additionally, the significance of 22 interactive variables indicates that the war in Ukraine has had a real impact on banks in the examined countries. Moreover, the results suggest that banks in countries with higher debt or budget deficits may be more affected by the war.

This research aims to measure systemic risk, especially in the banking system, between 2018 and 2022. In this research, the measurement method used is SRISK, which aims to measure the potential capital shortage of a bank when there is pressure on the market. The lack of capital from a bank, which is an individual risk for a bank, also poses the potential for a contagious threat which could end up becoming a systemic risk in the banking system in Indonesia. This research shows that several banks, through SRISK calculations, have the potential for capital shortages when market pressure occurs, especially from 2019 to 2022. In this research, bank size and non-interest income are essential factors in systemic risk in the banking system. Meanwhile, business complexity does not affect systemic risk in Indonesia.

Based on the perspective of the firm life cycle, this study constructs a complex bank-firm network system model under the influence of four technology financial policies (special funds, tax incentives, loan subsidies, and risk sharing) by depicting the behaviours of scientific innovation firms and banks. It examines the impact of the technology financial policies on the systemic risk of the bank-firm network system. Furthermore, this study examines the implementation effects of different technology financial policies at the three stages of start-up, growth, and maturity of scientific innovation firms. Based on the study, loan subsidies are the most effective of all four technology financial policies; however, the strength of the loan subsidies should be controlled in order to avoid excessive bank-firm credit exposure. Moreover, the study finds that policy support for scientific innovation firms is most effective during the start-up stage. [ABSTRACT FROM AUTHOR]

Drawing on a case study from Ternate Island, a densely populated volcanic island in Eastern Indonesia, this research illustrates how multi-hazards and extreme weather events are likely to compound and cascade, with serious consequences for sustainable development in small island context. At the heart of Ternate Island sits the active Gamalama volcano, posing a constant eruption threat. Its location within the Ring of Fire further exposes the island to the risks of tsunamis and earthquakes. Additionally, the island's physical features make it highly susceptible to flooding, landslides, and windstorms. Rapid urbanization has led to significant coastal alterations, increasing exposure to hazards. Ternate's small-island characteristics include limited resources, few evacuation options, vulnerable infrastructure, and inadequate resilience planning. Combining GIS multi-hazard mapping with a structured survey in 60 villages in Ternate, this case study investigates the multi-hazard exposure faced by the local population and land coverage. The findings suggest significant gaps between village chiefs' perceptions of the types of hazards and the multi-hazard assessment in each village. Out of 60 village chiefs surveyed, 42 (70%) are aware of earthquake risks, 17 (28%) recognize tsunami threats, and 39 see volcanoes as a danger. GIS assessments show that earthquakes could impact all villages, tsunamis could affect 46 villages (77%), and volcanoes could threaten 39 villages. The hazard map indicates that 32 villages are at risk of flash floods and 37 are at risk of landslides, and extreme weather could affect all villages. Additionally, 42 coastal villages on Ternate Island face potential extreme wave and abrasion disasters, but only 18 chiefs acknowledge extreme weather as a threat. The paper argues that addressing the cognitive biases reflected in the perceptions of community leaders requires transdisciplinary dialogue and engagement. [ABSTRACT FROM AUTHOR]

This study investigated a network of financial institutions in Korea using the Korea Consumer Credit Panel (KCCP). It is assumed that each consumer borrows from multiple institutions, so those institutions share risks from the same consumers regardless of the quality or type of loan. We constructed a financial network comprising those institutions and computed a contagion index based on their interconnections using a weight of probability of default for the individual borrowers. We found strong connection between banking institutions and credit card firms because of the convenience of making small loans using credit cards. However, with a weighted probability of default to the linkages among institutions, the connections between banking institutions and savings banks, non-credit card finance corporations and merchant banks are stronger than others, while banking institutions hold a central position and are exposed to the largest amount of loans individually. The contagion index hit a peak in 2013Q1 and then fell rapidly, before fluctuating at a relatively low level from 2016 to 2017Q2. The results enabled authorities to monitor systemic risk from the level of consumer credit, while accounting for specific types of consumers and their probability of default. This paper's main contribution is its construction of a network of financial institutions from the level of consumer credit. [ABSTRACT FROM AUTHOR]

Systemic risk is the risk that a company- or industry-level risk could trigger a huge collapse of another or even the whole institution. Various systemic risk measures have been proposed in the literature to quantify the domino and (relative) spillover effects induced by systemic risks such as the well-known CoVaR, CoES, MES and CoD risk measures, and associated contribution measures. This paper proposes another new type of systemic risk measure, called the joint marginal expected shortfall (JMES), to measure whether the MES of one entity's risk-taking adds to another one or the overall risk conditioned on the event that the entity is already in some specified distress level. We further introduce two useful systemic risk contribution measures based on the difference function or relative ratio function of the JMES and the conventional ES, respectively. Some basic properties of these proposed measures are studied such as monotonicity, comonotonic additivity, non-identifiability and non-elicitability. For both risk measures and two different vectors of bivariate risks, we establish sufficient conditions imposed on copula structure, stress levels, and stochastic orders to compare these new measures. We further provide some numerical examples to illustrate our main findings. A real application in analyzing the risk contagion among several stock market indices is implemented to show the performances of our proposed measures compared with other commonly used measures including CoVaR, CoES, MES, and their associated contribution measures. [ABSTRACT FROM AUTHOR]