A Framework for Capacity Expansion Planning in Failure-Prone Flow-Networks via Systemic Risk Analysis

How the ownership structures cause epidemics in financial markets: A network-based simulation model

In recent years, it has become apparent that an isolated microprudential approach to capital adequacy requirements of individual institutions is insufficient. It can increase the homogeneity of the financial system and ultimately the cost to society. For this reason, the focus of the financial and mathematical literature has shifted toward the macroprudential regulation of the financial network as a whole. In particular, systemic risk measures have been discussed as a risk measurement and mitigation tool. In this spirit, we adopt a general approach of multivariate, set-valued risk measures and combine it with the notion of intrinsic risk measures. In order to define the risk of a financial position, intrinsic risk measures utilize only internal capital, which is received when part of the currently held assets are sold, instead of relying on external capital. We translate this methodology into the systemic framework and show that systemic intrinsic risk measures have desirable properties such as the set-valued equivalents of monotonicity and quasi-convexity. Furthermore, for convex acceptance sets we derive a dual representation of the systemic intrinsic risk measure. We apply our methodology to a modified Eisenberg–Noe network of banks and discuss the appeal of this approach from a regulatory perspective, as it does not require to inject external capital into the system. We show evidence that this approach allows to mitigate systemic risk by moving the network toward more stable assets.

Systemic risk refers to the risk that the financial system is susceptible to\nfailures due to the characteristics of the system itself. The tremendous cost\nof systemic risk requires the design and implementation of tools for the\nefficient macroprudential regulation of financial institutions. The current\npaper proposes a novel approach to measuring systemic risk.\n Key to our construction is a rigorous derivation of systemic risk measures\nfrom the structure of the underlying system and the objectives of a financial\nregulator. The suggested systemic risk measures express systemic risk in terms\nof capital endowments of the financial firms. Their definition requires two\ningredients: a cash flow or value model that assigns to the capital allocations\nof the entities in the system a relevant stochastic outcome; and an\nacceptability criterion, i.e. a set of random outcomes that are acceptable to a\nregulatory authority. Systemic risk is measured by the set of allocations of\nadditional capital that lead to acceptable outcomes. We explain the conceptual\nframework and the definition of systemic risk measures, provide an algorithm\nfor their computation, and illustrate their application in numerical case\nstudies.\n Many systemic risk measures in the literature can be viewed as the minimal\namount of capital that is needed to make the system acceptable after\naggregating individual risks, hence quantify the costs of a bail-out. In\ncontrast, our approach emphasizes operational systemic risk measures that\ninclude both ex post bailout costs as well as ex ante capital requirements and\nmay be used to prevent systemic crises.\n

This presentation outlines a framework of how to identify, assess, and govern systemic risks, applied to the context of urban and metropolitan settings. In contrast to routine risks, systemic risks appear on a systemic and structural level, produced, and affected by complex endogenous and exogenous interdependencies. Systemic risks are unintended by-products of current transformation processes, such as the deployment and innovation of new technologies, infrastructural changes, or socio-political dynamics, for example. Urban and metropolitan provide fertile ground for systemic risks, exhibiting a nexus of tightly coupled dynamic natural, societal, and technological systems.However, so far systemic risks lack adequate assessment, evaluation, and governance approaches, which is a barrier toward developing effective policy measures. This gap in governance mechanisms is particularly glaring in the context of increased of pluvial and fluvial floods, wildfires, storms, and other extreme weather events across European cities. Failing to take the systemic interdependencies of urban settings into account may lead to higher socio-economic losses and potential systemic breakdowns, e.g., on the energy-supply, healthcare, or infrastructure level. The framework we propose entails the qualitative identification and assessment of systemic risks, ethical and societal implications, as well as the quantitative analysis thereof. We suggest a two-step approach towards the assessment and governance of systemic risks.First, a clear identification of the systemic risk in question needs to take place. The analysis of systemic risks needs to pay attention to casual relations and feedback mechanisms between various system factors at the intra- and inter-system level, which result in transboundary cascading effects. Accordingly, delineating both the relevant systems and entangled risks requires an interdisciplinary approach, combining the quantification of risks alongside their qualitative assessment. This entails understanding, conceptualizing, and modeling vulnerabilities, scenario development, as well as the integration of stakeholders to identify potential leverage points and enable the facilitation of transformative processes. In the context of urban environments, this means delineating the various affected systems, and how they interact (e.g. healthcare, water supply, and electricity infrastructure). Second, identified systemic risk requires adequate governance. Governance of systemic risks must be concerned with the analysis of embedded systems, procedural considerations of inclusion and deliberation, as well as closure. The salient features of reflection, iteration, inclusion, transparency, and accountability have been identified as guiding principles for governance processes concerned with systemic risk. The procedural governance approach also explicitly relies on ethical considerations, tied to recognition and participatory justice. In the context of urban environments, this entails for example analyzing the relevant governance bodies (e.g. local vs. national), understanding their respective responsibilities and interdependencies, and assessing the decision-making process surrounding disaster risk management.The presentation outlines a framework to address the challenges of complexities, uncertainties, and ambiguities associated with systemic risks. The framework draws on conceptual contributions as well as empirical evidence from transdisciplinary stakeholder and public engagement processes in urban and metropolitan contexts.

This study aims to measure systemic risk in conventional commercial banks in Indonesia with the Conditional Value at Risk (CoVaR) model developed by Adrian and Brunnermeier (2009). The financial crises of 1998 and 2008 have become valuable lessons for Indonesian banks to always maintain financial system stability because the impact caused by systemic risks is very large. Systemic risk is the possibility of instability due to contagion in some or all of the financial system. This study uses a sample of 6 conventional commercial banks in Indonesia from January 2012 to December 2018. The results obtained from this study is the systemic risk is not related to the size of banks. Each bank has a negative externality so that it can cause a bank rush and systemic impact.

Systemic Risk in the Insurance Sector- General Issues and a First Assessment of Large Commercial (Re-)Insurers in Bermuda

This study investigates the link between systemic risk in the South African insurance sector real economic activity in South Africa. To this end, we use six systemic risk measures, the Conditional Value at Risk (CoVaR), the Marginal Conditional Value at Risk (ΔCoVaR), the Comovement and Interconnectedness of the South African insurance sector (Eigen), the Dynamic Mixture Copula Marginal Expected Shortfall (DMC-MES), the Average Conditional Volatility (Ave-vol), and the South African Volatility Index (SAVI). We first evaluate the significance of each measure by assessing its ability to forecast future economic downturns in South Africa. We find that only two systemic risk measures possess the ability to predict future economic downturns in South Africa. We then use principal component quantile regression analysis to aggregate these measures into a composite stress index of systemic risk for the South African insurance sector and assess the ability of the proposed index to predict future economic downturns in South Africa. Our results reveal that the proposed index is a good predictor of future economic downturns in South Africa. Thus, our results suggest that regulators and risk managers must develop an analysis of systemic risk in the insurance sector with particular attention to its effects on real economic activity. In addition, our index can potentially be used as an instrument to monitor and mitigate systemic risk in the insurance sector in order to ensure the stability of the financial system and the economy in South Africa.

Financial systemic risk measurement based on causal network connectedness analysis

Growth and Inflation Implications of Systemic Risk in the Euro Area Banking Industry: Lessons from the Financial Crisis

Climate change causes precipitation to be increasingly unstable both spatially and temporally and, thus, is a major factor contributing the risk of a water deficit in Taiwan. Because many planned water shortage mitigation projects lack extensive risk evaluation of this problem, this risk might strongly affect the stability of regional water supply. Therefore, the purpose of this study was to consider climate change in a systematic quantitative risk analysis of water shortage mitigation projects. Accordingly, this paper proposes a process for integrating the method for downscaling global climate models, generalized watershed loading functions rainfall-runoff model, system dynamics approach, and Monte Carlo simulation to evaluate water projects practicably and quan- titatively. The proposed methodology was verified using a practical case study of Northern Taiwan and can be used to make decisions regarding project implementation priorities and alternatives. While every alternative of 11 proposed projects can reduce at least 9 % of the shortage risks for both Taoyuan and Banchia-Hsintien regions, five alternatives can reduce the risk to less than 1 % for the Banchia-Hsintien region. And simulation results indicate that expanding water plant is the first priority for the study area. Furthermore, the proposed concepts and procedures are applicable to other areas.

This article outlines the methodology of the IT system risk analysis and management, including various categories of risk factors significant from the point of view of the sensitive data processing and completeness of the procedure for determining the IT system risk level. The presented methodology is divided into the IT system risk analysis and the risk management method. The IT system risk level assessed by the risk analysis method described in this article constitutes an input value for the risk management method outlined in the further part hereof, referring to the IT systems used for the processing of documents at different levels of sensitivity.

Assessing systemic risk presents a significant challenge in finance and insurance, where conditional risk measures are essential for capturing contagion effects. This paper introduces two novel systemic risk measures – conditional interval value-at-risk (CoIVaR) and conditional interval expected shortfall (CoIES) – which extend traditional metrics by incorporating interval-based uncertainty. A formal theoretical framework is developed for both measures, offering a detailed characterization of their key properties and risk contributions. We then propose a comprehensive comparison methodology for systemic risk assessment, leveraging stochastic orders, dependence structures, and marginal distributions to establish conditions for ranking risk vectors. Finally, through numerical experiments and real-world stock market applications, we demonstrate the practical utility of CoIVaR and CoIES in quantifying systemic risk under uncertainty. The findings provide valuable insights into systemic risk propagation and establish a robust foundation for risk management in interconnected financial systems.

The purpose of this paper is to measure systemic risk of US financial institutions during and following the period of the subprime crisis. So, we estimated the systemic risk of a sample composed by 90 US financial institutions during the period from 2 January 2007 to 31 December 2014. We employ the SRISK as a measure of systemic risk. We estimate the systemic risk for each year. Based on the SRISK estimated, we try to present a classification of US financial institutions and we present the decomposition of systemic risk. The empirical results found that the total systemic risk supported by the US financial institutions is very high. In addition, the contribution of each institution in the risk of the financial system in the USA is very important. After the decomposition of systemic risk, we show that the institutions that take on more debt, contribute positively and highly to systemic risk.

PurposeBefore providing an overview of the conference with the above title and this Special Issue, this paper aims to present a view of the meaning of systemic risk, factors that affect systemic risk and measures of systemic risk. Thereafter, the conference presentations and the papers in this issue are summarized.Design/methodology/approachCharacteristics and measures of systemic risk are reviewed. Conference papers and presentations are summarized.FindingsWhile some aspects of systemic risk of a financial institution can be measured, an important aspect associated with contagion through markets is not easily captured by simple measures.Originality/valueThe conference and the papers in this issue contribute to the policy debate about sources and characteristics of systemic risk.

Optimal Scenario-Dependent Multivariate Shortfall Risk Measure and its Application in Capital Allocation